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Explore the latest questions and answers in Deep Learning, and find Deep Learning experts.
Questions related to Deep Learning
  • asked a question related to Deep Learning
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Less training data, Less model performance. Is it inevitable that pre-training + few-shot learning will not be as good as sufficient data in a specific field?
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the process of data argumentation seeks to vary the nature of already existing train data so as to help increase the effectiveness of your model or algorithm
  • asked a question related to Deep Learning
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I am looking for research focused on classifying Arabic text into ( Verb / Noun/ Letter )
most of what I found is stemming, deep learning stuff but not word classification.
Any help please ?!
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There is a lot of work being done, check out the survey by Meshrif Alruily "Classification or Arabic tweets a review"
  • asked a question related to Deep Learning
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In your opinion, does it make sense to create a new generation of something similar to ChatGPT, which will use databases built solely on the basis of continuously updated data, information, objectively verified knowledge resources taken from online scientific knowledge bases, online scientific portals and online indexing databases of scientific publications?
I'm curious to know what you think about this? This kind of solution based on an intelligent publication search system and an intelligent content analysis system of retrieved publications on an online scientific portal could be of great help to researchers and scientists. In my opinion, the creation of a new generation of something similar to ChatGPT, which will use databases built solely on the basis of online scientific knowledge bases, online scientific portals and online scientific publication indexing databases makes sense if basic issues of copyright respect are met and such tools use continuously updated and objectively and scientifically verified knowledge, data and information resources. With such a solution, researchers and scientists conducting research on a specific topic would have the opportunity to review the literature within the millions of scientific publications collected on specific online scientific portals and scientific publication indexing databases. Besides, what is particularly important, the mentioned partially automated literature review would probably be realized in a relatively short time. Thus, an intelligent system for searching and analyzing the content of scientific publications would, in a short period of time, from among the millions of texts archived in specific scientific publication indexing databases, select those publications in which other researchers and scientists have described analogous, similar, related, correlated, related, etc. issues, results of scientific research conducted, selected publications within the same scientific discipline, the same topic or in the interdisciplinary field. Besides, an intelligent system for searching and analyzing the content of scientific publications could also categorize the retrieved publications into those in which other researchers and scientists confirmed analogous conclusions of conducted similar research, polemicized with the results of other researchers' research on a specific topic, obtained other results from conducted research, suggested other practical applications of obtained research results realized on the same or similar topic, etc. However, for ethical reasons and properly conducted research, i.e., respecting the research results of other researchers and scientists, it would be unacceptable for this kind of intelligent system for searching and analyzing the content of many publications available on specific databases for indexing scientific publications to enable plagiarism, i.e., to provide research results, provide retrieved content on specific issues and topics, etc., without accurately providing the source of the data, description of the source data, names of the authors of the publications, etc., and some unreliable researchers would take advantage of this opportunity. This kind of intelligent system for searching and analyzing the content of scientific publications should give for all searched publications full bibliographic descriptions, source descriptions, footnotes containing all the data that are necessary to develop full source footnotes for possible citation of specific studies, research results, theses, data, etc. contained in other publications written by other researchers and scientists. So, building this kind of intelligent tool would make sense if ChatGPT-type tools were properly improved and the system of laws for their use appropriately supplemented so that the use of ChatGPT-type tools does not violate copyrights and that these tools are used in accordance with ethics and do not generate misinformation. Improving these tools so that they do not generate disinformation, do not create "fictitious facts" in the form of descriptions, essays, photos, videos, etc. containing nicely described, presented never and nowhere seemingly facts is to keep Big Data systems updated, update data sets and information, based on which they create answers to questions, create descriptions, photos, companies and so on. This is important because current online tools like ChatGPT often create "nicely described fictitious facts," which is used to generate fake news and misinformation in online social media. When all that I have written above would be corrected and the use completed, and not only in some parts of the world but on a global scale, then the creation of a new generation of something similar to ChatGPT, which will use databases built solely on the basis of online scientific knowledge bases, online scientific portals and online indexing databases of scientific publications would make sense and could prove helpful to people, including researchers and scientists. Besides, the current online ChatGPT-type tools are not perfect, as they draw data not directly in real-time online from specific databases and knowledge contained in selected websites and portals, but draw information, knowledge, data from an offline database created some time ago. For example, currently the most popular ChatGPT still relies on a database of data, information, etc. contained in many publication texts downloaded from selected websites and web portals but not today or yesterday downloaded only in 2021! So these are data and information already outdated on many issues. Hence the absurdities, inconsistencies with the facts, creation of "fictitious facts" by ChatGPT in a significant part of the answers generated by this system to questions asked by Internet users. In view of the above, in a number of issues, both technological, organizational, formal, normative, etc., such intelligent systems should be improved so that they can be used in open access in the applications I wrote about above.
In view of the above, I address the following question to the esteemed community of scientists and researchers:
In your opinion, does it make sense to create a new generation of something similar to ChatGPT, which will use databases built solely on the basis of continuously updated data, information, objectively verified knowledge resources taken from online scientific knowledge bases, online scientific portals and online indexing databases of scientific publications?
What do you think about creating a new generation of something similar to ChatGPT, which will use exclusively online scientific knowledge resources?
And what is your opinion about it?
What is your opinion on this topic?
Please answer,
I invite everyone to join the discussion,
Thank you very much,
Warm regards,
Dariusz Prokopowicz
Counting on your opinions, on getting to know your personal opinion, on a fair approach to the discussion of scientific issues, I deliberately used the phrase "in your opinion" in the question.
The above text is entirely my own work written by me on the basis of my research.
Copyright by Dariusz Prokopowicz
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IHMO this is the only sane way to use Large Language Models (LLMs) like ChatGPT. The idea that computer programs like ChatGPT can replace inter-human communication must be fought by all means, if necessary, through legislations.
Lately a guy in France killed himself, because ChatGPT advised him that suicide was the best way to reduce his carbon footprint .... do we need more of this ? IMHO: NO!
Remember that Artificial Intelligence has nothing to do with "intelligence" - it is a computer program which can repeat, interpolate and extrapolate known information.
AI is incapable of ANY kind of "thinking" including any kind of critical thinking.
Since LLMs only know what they are told, it is very easy to make them biased, by only teaching the information that the owner of the LLM wants repeated to the public.
Information from the current generation of LLMs comes without any kind of source information and is hard to verify. In many situations the generalizations made by the LLM are outright wrong. The lack of ability to know or verify the sources of information makes LLMs useless in most kinds of research. Even if the LLM is supplied with all relevant information (text) by the user, and asked to generalize that information, the result will often be wrong. Using LLMs is like having a pocket calculator, but you still need to check all the calculations by hand, because some of the results of the calculator can be wrong...... <- actually a good analogy.
  • asked a question related to Deep Learning
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Recently,I know that there are many deep learning in atmospheric science field,I am interested in how to use the deep learning in prediction of drought and how could I start to learn machine learning
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Predicting drought using deep learning involves utilizing neural networks to analyze various environmental and meteorological data, such as precipitation, temperature, soil moisture, and vegetation indices. These models employ time series analysis, convolutional neural networks (CNNs), or recurrent neural networks (RNNs) to capture complex patterns and dependencies in the data. By training on historical drought records and associated variables, deep learning models can forecast drought conditions, providing valuable insights for early warning systems and resource management. Regular model updates and integration with real-time data sources enhance accuracy, aiding in proactive drought mitigation and resource allocation efforts.
  • asked a question related to Deep Learning
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I would like to add some questions that made me stuck for sometimes. So i have an issue in imbalance segmentation data for landslide modelling using U-Net (my landslide data is way less than my non-landslide data). So my questions are:
1. Should i try to find a proper loss function for the imbalance problem? or should i focus on balancing data to improve my model?
2. Some suggest to use SMOTE (oversampling), but since my data are images (3D) i have found out that it is not suitable to use SMOTE for my data. So, any other suggestions?
Thank you,
Your suggestions will be appreciated.
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Solving class imbalance in segmentation data for a deep learning model is essential to ensure that the model does not bias its predictions toward the majority class. Imbalanced data can lead to poor segmentation performance, where the model may struggle to identify and classify the minority class correctly. Here are several strategies to address class imbalance in segmentation data:
1. **Data Augmentation**:
- Augment the minority class samples by applying random transformations such as rotations, translations, scaling, and flipping. This can help increase the diversity of the minority class data.
2. **Resampling Techniques**:
- **Oversampling**: Increase the number of samples in the minority class by duplicating existing samples or generating synthetic samples. Techniques like SMOTE (Synthetic Minority Over-sampling Technique) can be used to create synthetic samples that are similar to the minority class.
- **Undersampling**: Reduce the number of samples in the majority class to balance the class distribution. However, be cautious with undersampling, as it can lead to loss of important information.
3. **Weighted Loss Function**:
- Modify the loss function of your deep learning model to assign higher weights to the minority class. This gives more importance to correctly classifying the minority class during training.
4. **Patch-Based Training**:
- Instead of training on entire images, divide the images into smaller patches and balance the class distribution within each patch. This can help the model learn the minority class better.
5. **Transfer Learning**:
- Utilize pre-trained models on large datasets (e.g., ImageNet) and fine-tune them on your segmentation task. Transfer learning can help your model learn useful features even with limited data.
6. **Use Multiple Models**:
- Train multiple models with different initializations or architectures and combine their predictions. This can help in reducing the bias towards the majority class.
7. **Data Collection**:
- If possible, collect more data for the minority class. A larger and balanced dataset can often alleviate class imbalance issues.
8. **Change Evaluation Metrics**:
- Consider using evaluation metrics that are less sensitive to class imbalance, such as the Intersection over Union (IoU) or Dice coefficient, instead of accuracy.
9. **Post-processing**:
- After segmentation, post-process the results to further refine the predictions. Morphological operations like erosion, dilation, and connected component analysis can help clean up the segmentation masks.
10. **Ensemble Methods**:
- Combine predictions from multiple models, which may have been trained with different strategies, to improve overall segmentation accuracy.
It's essential to choose the most appropriate strategy based on the specifics of your dataset and the problem at hand. Experiment with different approaches and evaluate the performance of your deep learning model using appropriate validation techniques to ensure that the class imbalance is effectively addressed without introducing other issues.
  • asked a question related to Deep Learning
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All these terms are so confusing! I want to understand these terminologies in a very crisp and simplified manner. Can someone help me out with this confusion explaining their differences and real life examples? Any authorized books of reputed publishers? Thanks in advance.
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Dear Vivek Bhandarkar
Robotics, , Industry 4.0, AI, ML, DL, CPS, CPPS, IoT, IIoT.
It is little bit tough to explain all the above theologies in a precise way. Smart manufacturing, Smart factory are the applications of all the above technologies you mentioned. In my view there is a wider range of other applications also there .
industry 4.0 contains a set of domains like AI,IoT,Cloud,Big data.....so on ,these technologies are having significant impact on current industry.
AI,ML,DL we can keep in a backset and rest of the things are other.
I hope this will help you.
  • asked a question related to Deep Learning
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The Unbearable Shallow Understanding ofDeep Learning
AlessioPlebe · GiorgioGrasso
Vol.:(0123456789)
Minds and Machines (2019) 29:515–553
Thank you.
Francisco Sercovich
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Thanks for your answer!
Regards,
Francisco
  • asked a question related to Deep Learning
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1 answer
I am trying to implement Deep Learning based approach for Dynamic Spectrum Sensing in 5G network. Facing some issue in "How to get the value of Probability of detection" and "Probability of False alarm". Kindly help me. If some one can share any Python code I will be very grateful. I am using RadionML2016.10b dataset.
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  • To calculate PD, you need to determine how well your model detects the presence of a signal when it is actually present. PD is calculated as: PD = True Positives / (True Positives + False Negatives)
  • To calculate PFA, you need to determine how often your model incorrectly detects a signal when the spectrum is actually idle. PFA is calculated as: PFA = False Positives / (False Positives + True Negatives)
  • asked a question related to Deep Learning
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Im trying to create an image classification model that classifies plants from an image dataset made up of 33 classes, the total amount of images is 41,808, the images are unbalanced but that is something me and my thesis team will work on using Kfold; but going back to the main problem.
The VGG16 model itself is from a pre-trained model from keras
My source code should be attached in this question (paste_1292099)
The results of a 15-epoch run is also attached as well
what I have done so far is changing the optimizers from SGD to Adam, but the results are generally the same.
Am I doing something wrong or is there anything I can do to improve on this model to get it to atleast be in a "working" state, regardless if its overfitting or the like as that can be fixed later.
This is also the link to our dataset:
It is specifically a dataset consisting of Medicinal Plants and Herbs in our region with their augmentations. The are not yet resized and normalized in the dataset.
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To enhance the performance of your VGG16 model during training and validation, you can start by applying data augmentation techniques to increase dataset diversity and reduce overfitting. It's crucial to ensure the dataset's cleanliness, correct labeling, and appropriate division into training, validation, and test subsets. Experiment with different learning rates and optimizers, and consider using learning rate schedulers if necessary. Employ regularization methods like dropout and L2 regularization to tackle overfitting issues. Keep a close eye on the training process, implement early stopping, and adjust the batch size as needed. You might also want to explore alternative model architectures or smaller models that could better suit your dataset. Lastly, make sure your hardware resources are utilized effectively, and explore ensemble methods to potentially enhance model performance. These strategies should help you overcome the low accuracy challenge with your VGG16 model.
  • asked a question related to Deep Learning
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If ChatGPT is merged into search engines developed by internet technology companies, will search results be shaped by algorithms to a greater extent than before, and what risks might be involved?
Leading Internet technology companies that also have and are developing search engines in their range of Internet information services are working on developing technological solutions to implement ChatGPT-type artificial intelligence into these search engines. Currently, there are discussions and considerations about the social and ethical implications of such a potential combination of these technologies and offering this solution in open access on the Internet. The considerations relate to the possible level of risk of manipulation of the information message in the new media, the potential disinformation resulting from a specific algorithm model, the disinformation affecting the overall social consciousness of globalised societies of citizens, the possibility of a planned shaping of public opinion, etc. This raises another issue for consideration concerning the legitimacy of creating a control institution that will carry out ongoing monitoring of the level of objectivity, independence, ethics, etc. of the algorithms used as part of the technological solutions involving the implementation of artificial intelligence of the ChatGPT type in Internet search engines, including those search engines that top the rankings of Internet users' use of online tools that facilitate increasingly precise and efficient searches for specific information on the Internet. Therefore, if, however, such a system of institutional control on the part of the state is not established, if this kind of control system involving companies developing such technological solutions on the Internet does not function effectively and/or does not keep up with the technological progress that is taking place, there may be serious negative consequences in the form of an increase in the scale of disinformation realised in the new Internet media. How important this may be in the future is evident from what is currently happening in terms of the social media portal TikTok. On the one hand, it has been the fastest growing new social medium in recent months, with more than 1 billion users worldwide. On the other hand, an increasing number of countries are imposing restrictions or bans on the use of TikTok on computers, laptops, smartphones etc. used for professional purposes by employees of public institutions and/or commercial entities. It cannot be ruled out that new types of social media will emerge in the future, in which the above-mentioned technological solutions involving the implementation of ChatGPT-type artificial intelligence into online search engines will find application. Search engines that may be designed to be operated by Internet users on the basis of intuitive feedback and correlation on the basis of automated profiling of the search engine to a specific user or on the basis of multi-option, multi-criteria search controlled by the Internet user for specific, precisely searched information and/or data. New opportunities may arise when the artificial intelligence implemented in a search engine is applied to multi-criteria search for specific content, publications, persons, companies, institutions, etc. on social media sites and/or on web-based multi-publication indexing sites, web-based knowledge bases.
In view of the above, I address the following question to the esteemed community of scientists and researchers:
If ChatGPT is merged into search engines developed by online technology companies, will search results be shaped by algorithms to a greater extent than before, and what risks might be associated with this?
What is your opinion on the subject?
What do you think about this topic?
Please respond,
I invite you all to discuss,
Thank you very much,
Best wishes,
Dariusz Prokopowicz
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If tools such as ChatGPT, after the necessary update and adaptation to current Internet technologies, are combined with search engines developed by Internet technology companies, search results can be shaped by certain complex algorithms, by generative artificial intelligence learned to use and improve complex models for advanced intelligent search of precisely defined topics, intelligent search systems based on artificial neural networks and deep learning. If such solutions are created, it may involve the risk of deliberate shaping of algorithms of advanced Internet search systems, which may generate the risk of interference and influence of Internet search engine technology companies on search results and thus shaping the general social awareness of citizens on specific topics.
What is your opinion on this issue?
Please answer,
I invite everyone to join the discussion,
Thank you very much,
Best regards,
Dariusz Prokopowicz
  • asked a question related to Deep Learning
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How to create a system of digital, universal tagging of various kinds of works, texts, photos, publications, graphics, videos, etc. made by artificial intelligence and not by humans?
How to create a system of digital, universal labelling of different types of works, texts, texts, photos, publications, graphics, videos, innovations, patents, etc. performed by artificial intelligence and not by humans, i.e. works whose legal, ethical, moral, business, security qualification .... etc. should be different for what is the product of artificial intelligence?
Two days earlier, in an earlier post, I started a discussion on the question of the necessity of improving the security of the development of artificial intelligence technology and asked the following questions: how should the system of institutional control of the development of advanced artificial intelligence models and algorithms be structured, so that this development does not get out of control and lead to negative consequences that are currently difficult to foresee? Should the development of artificial intelligence be subject to control? And if so, who should exercise this control? How should an institutional system for controlling the development of artificial intelligence applications be built? Why are the creators of leading technology companies developing ICT, Internet technologies, Industry 4.0, including those developing artificial intelligence technologies, etc. now calling for the development of this technology to be periodically, deliberately slowed down, so that the development of artificial intelligence technology is fully under control and does not get out of hand? On the other hand, while continuing my reflections on the indispensability of improving the security of the development of artificial intelligence technology, analysing the potential risks of the dynamic and uncontrolled development of this technology, I hereby propose to continue my deliberations on this issue and invite you to participate in a discussion aimed at identifying the key determinants of building an institutional control system for the development of artificial intelligence, including the development of advanced models composed of algorithms similar or more advanced to the ChatGPT 4.0 system developed by the OpenAI company and available on the Internet. It is necessary to normatively regulate a number of issues related to artificial intelligence, both the issue of developing advanced models composed of algorithms that form artificial intelligence systems; posting these technological solutions in open access on the Internet; enabling these systems to carry out the process of self-improvement through automated learning of new content, knowledge, information, abilities, etc.; building an institutional system of control over the development of artificial intelligence technology and current and future applications of this technology in various fields of activity of people, companies, enterprises, institutions, etc. operating in different sectors of the economy. Recently, realistic-looking photos of well-known, highly recognisable people, including politicians, presidents of states in unusual situations, which were created by artificial intelligence, have appeared on the Internet on online social media sites. What has already appeared on the Internet as a kind of 'free creativity' of artificial intelligence, creativity both in terms of the creation of 'fictitious facts' in descriptions of events that never happened, in descriptions created as an answer to a question posed for the ChatGPT system, and in terms of photographs of 'fictitious events', already indicates the potentially enormous scale of disinformation currently developing on the Internet, and this is thanks to the artificial intelligence systems whose products of 'free creativity' find their way onto the Internet. With the help of artificial intelligence, in addition to texts containing descriptions of 'fictitious facts', photographs depicting 'fictitious events', it is also possible to create films depicting 'fictitious events' in cinematic terms. All of these creations of 'free creation' by artificial intelligence can be posted on social media and, in the formula of viral marketing, can spread rapidly on the Internet and can thus be a source of serious disinformation realised potentially on a large scale. Dangerous opportunities have therefore arisen for the use of technology to generate disinformation about, for example, a competitor company, enterprise, institution, organisation or individual. Within the framework of building an institutional control system for the development of artificial intelligence technology, it is necessary to take into account the issue of creating a digital, universal marking system for the various types of works, texts, photos, publications, graphics, films, innovations, patents, etc. performed by artificial intelligence and not by humans, i.e. works whose legal, ethical, moral, business, security qualification ..., should be different for what is the product of artificial intelligence. It is therefore necessary to create a system of digital, universal labelling of the various types of works, texts, photos, publications, graphics, videos, etc., made by artificial intelligence and not by humans. The only issue for discussion is therefore how this should be done.
In view of the above, I address the following question to the esteemed community of scientists and researchers:
How to create a system for the digital, universal marking of different types of works, texts, photos, publications, graphics, videos, innovations, patents, etc. made by artificial intelligence and not by humans, i.e. works whose legal, ethical, moral, business, security qualification .... etc. should be different for what is the product of artificial intelligence?
How to create a system of digital, universal labelling of different types of works, texts, photos, publications, graphics, videos, etc. made by artificial intelligence and not by humans?
What do you think about this topic?
What is your opinion on this subject?
Please respond,
I invite you all to discuss,
Thank you very much,
Best regards,
Dariusz Prokopowicz
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Some technological companies offering various Internet services have already announced the creation of a system for digital marking of creations, works, works, studies, etc. created by artificial intelligence. Probably, these companies have already noticed that in this field it is possible to create certain standards for digital marking of creations, works, elaborations, etc. created by artificial intelligence, and this can be another factor of competition and market advantage.
And what is your opinion about it?
Please answer,
I invite everyone to join the discussion,
Thank you very much,
Warm regards,
Dariusz Prokopowicz
  • asked a question related to Deep Learning
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What has been missing from the open-source availability of ChatGPT-type artificial intelligence on the Internet? What is missing in order to make it possible to comply with the norms of text publishing law, tax law, copyright law, property law, intellectual value law, to make it fully ethical, practical and effective, and to make it safe and not generate misinformation for Internet users to use this type of technology?
How should an automated system for verifying the authorship of texts and other works be structured and made openly available on the Internet in order to verify whether phrases, fragments of text, phrases, wording, etc. are present in a specific text submitted to the editors of journals or publishers of books and other text-based publications? If so, to what extent and from which source texts did the artificial intelligence extract specific phrases, fragments of text, thus giving a detailed description of the source texts, providing footnotes to sources, bibliographic descriptions of sources, etc., i.e. also as is done by efficient and effective computerised anti-plagiarism systems?
The recent appeal by the creators of ChatGPT-type artificial intelligence technology, the appeal by businessmen and founders and co-founders of start-ups developing artificial intelligence technology about the need to halt the development of this type of technology for at least six months confirms the thesis that something was not thought of when OpenAI made ChatGPT openly available on the Internet, that something was forgotten, that something was missing from the openly available ChatGPT-type artificial intelligence system on the Internet. I have already written about the issue of the potential massive generation of disinformation in my earlier posts and comments on previously formulated questions about ChatGPT technology and posted on my discussion profile of this Research Gate portal. On the other hand, to the issue of information security, the potential development of disinformation in the public space of the Internet, we should also add the issue of the lack of a structured system for the digital marking of "works" created by artificial intelligence, including texts, publications, photographs, films, innovative solutions, patents, artistic works, etc., in order to ensure the security of information. In this regard, it is also necessary to improve the systems for verifying the authorship of texts sent to journal editors, so as to verify that the text has been written in full compliance with copyright law, intellectual property law, the rules of ethics and good journalistic practice, the rules for writing texts as works of intellectual value, the rules for writing and publishing professional, popular science, scientific and other articles. It is necessary to improve the processes of verifying the authorship of texts sent to the editorial offices of magazines and publishing houses of various text publications, including the improvement of the system of text verification by editors and reviewers working in the editorial offices of popular-scientific, trade, scientific, daily and monthly magazines, etc., by creating for their needs anti-plagiarism systems equipped with text analysis algorithms in order to identify which fragments of text, phrases, paragraphs were created not by a human but by an artificial intelligence of the ChatGPT type, and whose authorship these fragments are. An improved anti-plagiarism system of this kind should also include tools for the precise identification of text fragments, phrases, statements, theses, etc. of other authors, i.e. providing full information in the form of bibliographic descriptions of source publications, providing footnotes to sources. An anti-plagiarism system improved in this way should, like ChatGPT, be made available to Internet users in an open access format. In addition, it remains to be seen whether it is also necessary to legally oblige editors of journals and publishers of various types of textual and other publications to use this kind of anti-plagiarism system in verifying the authorship of texts. Arguably, the editors of journals and publishers of books and other types of textual publications will be interested in doing so in order to apply this kind of automated verification system for the resulting publication works. At the very least, those editors of journals and publishers of books and other types of textual publications that recognise themselves and are recognised as reputable will be interested in using this kind of improved system to verify the authorship of texts sent to the editors. Another issue is the identification of technological determinants, including the type of technologies with which it will be possible to appropriately improve the automated verification system for the aforementioned issue of text authorship. Paradoxically, here again, the technology of artificial intelligence comes into play, which can and should prove to be of great help in the aforementioned issue of verification of the aforementioned question of authorship of texts and other works.
In view of the above, I address the following question to the esteemed community of scientists and researchers:
How should an automated and open-access online system for verifying the authorship of texts and other works be structured in order to verify whether phrases, text fragments, phrases, wordings, etc. are present in a specific text sent to the editors of journals or publishers of books and other textual publications? If YES, to what extent and from which source texts did the artificial intelligence retrieve specific phrases, fragments of text, thus giving detailed characteristics of the source texts, providing footnotes to sources, bibliographic descriptions of sources, etc., i.e. also as implemented by efficient and effective computerised anti-plagiarism systems?
What was missing from making a ChatGPT-type artificial intelligence system available on the Internet in an open access format? What is missing in order to make it possible to comply with the norms of text publishing law, tax law, copyright law, property law, intellectual property law, to make it fully ethical, practical and effective, and to make it safe and not generate disinformation for Internet users to use this type of technology?
What do you think about this topic?
What is your opinion on this subject?
Please respond,
I invite you all to discuss,
Thank you very much,
Best wishes,
Dariusz Prokopowicz
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Answer
Much still needs to be improved systemically so that the use of this type of technology complies with the norms of text publishing law, tax law, copyright law, property law, intellectual value law, so that it is fully ethical, practical and effective, as well as safe and does not generate misinformation among Internet users. It is necessary to regulate the use of various tools based on artificial intelligence so that this use generates positive rather than negative aspects. It is necessary to increase the scale of control over the use of artificial intelligence-based tools available on the Internet so that this use does not generate disinformation, copyright violations, new categories of threats, cyber crime, etc.
What is your opinion on this issue?
Please answer,
I invite everyone to join the discussion,
Thank you very much,
Best regards,
Dariusz Prokopowicz
  • asked a question related to Deep Learning
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Small sample learning, why is it called Few-Shot Learning, not Few-Data Learning?
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Few-shot learning is called so because it focuses on the challenge of learning and generalizing from a very small number of examples or "shots." While the term "Few-Data Learning" could be used to describe a similar concept, "Few-Shot Learning" specifically emphasizes the ability of a model to make accurate predictions or classifications with a limited number of instances or "shots" of data. This terminology highlights the emphasis on the model's capacity to generalize knowledge effectively from just a few examples, which is a key characteristic of this machine-learning paradigm.
  • asked a question related to Deep Learning
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The fourth technological revolution currently underway is characterised by rapidly advancing ICT information technologies and Industry 4.0, including but not limited to machine learning, deep learning, artificial intelligence, ... what's next? Intelligent thinking autonomous robots?
The fourth technological revolution currently underway is characterised by rapidly advancing ICT information technologies and Industry 4.0, including but not limited to technologies learning machines, deep learning, artificial intelligence. Machine learning, machine learning, machine self-learning or machine learning systems are all synonymous terms relating to the field of artificial intelligence with a particular focus on algorithms that can improve themselves, improving automatically through the action of an experience factor within exposure to large data sets. Algorithms operating within the framework of machine learning build a mathematical model of data processing from sample data, called a learning set, in order to make predictions or decisions without being programmed explicitely by a human to do so. Machine learning algorithms are used in a wide variety of applications, such as spam protection, i.e. filtering internet messages for unwanted correspondence, or image recognition, where it is difficult or infeasible to develop conventional algorithms to perform the needed tasks. Deep learning is a kind of subcategory of machine learning, which involves the creation of deep neural networks, i.e. networks with multiple levels of neurons. Deep learning techniques are designed to improve, among other things, automatic speech processing, image recognition and natural language processing. The structure of deep neural networks consists of multiple layers of artificial neurons. Simple neural networks can be designed manually so that a specific layer detects specific features and performs specific data processing, while learning consists of setting appropriate weights, significance levels, value system for components of specific issues defined on the basis of processing and learning from large amounts of data. In large neural networks, the deep learning process is automated and self-contained to a certain extent. In this situation, the network is not designed to detect specific features, but detects them on the basis of the processing of appropriately labelled data sets. Both such datasets and the operation of neural networks themselves should be prepared by specialists, but the features are already detected by the programme itself. Therefore, large amounts of data can be processed and the network can automatically learn higher-level feature representations, which means that they can detect complex patterns in the input data. In view of the above, deep learning systems are built on Big Data Analytics platforms built in such a way that the deep learning process is performed on a sufficiently large amount of data. Artificial intelligence, denoted by the acronym AI (artificial intelligence), is respectively the 'intelligent', multi-criteria, advanced, automated processing of complex, large amounts of data carried out in a way that alludes to certain characteristics of human intelligence exhibited by thought processes. As such, it is the intelligence exhibited by artificial devices, including certain advanced ICT and Industry 4.0 information technology systems and devices equipped with these technological solutions. The concept of artificial intelligence is contrasted with the concept of natural intelligence, i.e. that which pertains to humans. In view of the above, artificial intelligence thus has two basic meanings. On the one hand, it is a hypothetical intelligence realised through a technical rather than a natural process. On the other hand, it is the name of a technology and a research field of computer science and cognitive science that also draws on the achievements of psychology, neurology, mathematics and philosophy. In computer science and cognitive science, artificial intelligence also refers to the creation of models and programmes that simulate at least partially intelligent behaviour. Artificial intelligence is also considered in the field of philosophy, within which a theory is developed concerning the philosophy of artificial intelligence. In addition, artificial intelligence is also a subject of interest in the social sciences. The main task of research and development work on the development of artificial intelligence technology and its new applications is the construction of machines and computer programmes capable of performing selected functions analogously to those performed by the human mind functioning with the human senses, including processes that do not lend themselves to numerical algorithmisation. Such problems are sometimes referred to as AI-difficult and include such processes as decision-making in the absence of all data, analysis and synthesis of natural languages, logical reasoning also referred to as rational reasoning, automatic proof of assertions, computer logic games e.g. chess, intelligent robots, expert and diagnostic systems, among others. Artificial intelligence can be developed and improved by integrating it with the areas of machine learning, fuzzy logic, computer vision, evolutionary computing, neural networks, robotics and artificial life. Artificial intelligence (AI) technologies have been developing rapidly in recent years, which is determined by its combination with other Industry 4.0 technologies, the use of microprocessors, digital machines and computing devices characterised by their ever-increasing capacity for multi-criteria processing of ever-increasing amounts of data, and the emergence of new fields of application. Recently, the development of artificial intelligence has become a topic of discussion in various media due to the open-access, automated and AI-enabled solution ChatGPT, with which Internet users can have a kind of conversation. The solution is based and learns from a collection of large amounts of data extracted in 2021 from specific data and information resources on the Internet. The development of artificial intelligence applications is so rapid that it is ahead of the process of adapting regulations to the situation. The new applications being developed do not always generate exclusively positive impacts. These potentially negative effects include the potential for the generation of disinformation on the Internet, information crafted using artificial intelligence, not in line with the facts and disseminated on social media sites. This raises a number of questions regarding the development of artificial intelligence and its new applications, the possibilities that will arise in the future under the next generation of artificial intelligence, the possibility of teaching artificial intelligence to think, i.e. to realise artificial thought processes in a manner analogous or similar to the thought processes realised in the human mind.
In view of the above, I address the following question to the esteemed community of scientists and researchers:
The fourth technological revolution currently taking place is characterised by rapidly advancing ICT information technologies and Industry 4.0, including but not limited to machine learning technologies, deep learning, artificial intelligence, .... what's next? Intelligent thinking autonomous robots?
What do you think about this topic?
What is your opinion on this subject?
Please respond,
I invite you all to discuss,
Thank you very much,
Best regards,
Dariusz Prokopowicz
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The drive to build autonomous, thinking, intelligent robots, androids raises many ethical controversies and potential risks. In addition to this, the drive to build artificial consciousness as a kind of continuation of the development of artificial intelligence is also controversial.
What is your opinion on this topic?
Best regards,
Dariusz Prokopowicz
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Can deep learning be harnessed to predict and prevent zero-day attacks in cloud environments, bolstering overall security posture?
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That is an excellent answer, Len. With your permission, I have copied and pasted it into my files.
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Can you explain the concept of the vanishing gradient problem in deep learning? How does it affect the training of deep neural networks, and what techniques or architectures have been developed to mitigate this issue?
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The core of any deep learning architecture is the Gradient descent algorithm and Backpropagation.
In Backpropagation, you essentially compute gradients of the Loss function with the respect to the weights starting at the output layer and going back to the input layer chaining the gradients (aka chain rule) as you go, and these gradients are in the order of few thousands a part ( about 0.001 or 0.0001) at every layer and they almost vanish when you multiply them all.
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Can ChatGPT be used in conducting market and other analyses that are helpful in managing a business entity?
What could be other applications of generative artificial intelligence, the ChatGPT language model or other similar artificial intelligence technologies in the business of companies and enterprises operating in the SME sector?
Currently and prospectively, generative artificial intelligence, a ChatGPT-type language model, is finding various applications helpful in analyses carried out for business purposes. ChatGPT can be helpful, for example, in quickly drawing up a competitive analysis against a specific company, enterprise, start-up or other type of business entity. This kind of real-time analysis can be helpful in the effective management of a business entity. However, an issue for the time being may be the outdatedness of the data and information contained in the database that ChatGPT uses to answer questions. The aforementioned database is a kind of Big Data database built from data and information collected from a selection of multiple websites, but not now only in 2021. Large corporations, including large technology companies, have the financial capacity to create research and development departments within their company structures, where they develop technological innovations, new technologies, technology standards, etc., in order to be technologically at the forefront and maintain their strong position in specific markets. Consequently, large technology companies also have an interest in ensuring that emerging new technologies and innovations are quickly incorporated into their business. This also applies to the currently developing Industry 4.0 technologies, including machine learning, deep learning and artificial intelligence. On the other hand, companies and enterprises operating in the SME sector, including above all micro-enterprises and start-ups, have much more limited financial possibilities to finance the creation of research and development departments within their organisations, where new technologies, innovations and technological standards would be created. Of course, SME companies are also interested in incorporating new technological solutions and innovations into their businesses. However, the new technologies that they implement are not mainly the result of their research activities, but are bought and then implemented as ready-made solutions, already proven technologies or patents applied in other companies. However, some technological solutions for new technologies and/or specific applied and open-access technological solutions for information Internet services are also available for use by companies and enterprises operating in the SME sector already in the early stages of business development and with relatively small financial investments. For example, these are the Internet-accessible new online media, social media and online information services offered by large Internet-based technology companies, which have created specific standards in the implementation of certain Industry 4.0 technologies and have established a strong position in the market for certain online information and other services. Consequently, the currently rapidly developing artificial intelligence technology, including specific solutions of it made available on the Internet such as ChatGPT, can be used in specific business development support applications by various types of business entities, including companies and enterprises in the SME sector. For example, ChatGPT can be used in carrying out market and other analyses that are helpful in managing a business entity also operating in the SME sector. However, the applicability of both this kind of shared technological tool, a kind of language model of generative artificial intelligence made available on the Internet under an open access formula, and other technological solutions of artificial intelligence, which will probably soon be offered by the technology companies creating them, is much greater.
In view of the above, I address the following question to the esteemed community of scientists and researchers:
What are the applications of ChatGPT in the business of companies and enterprises operating in the SME sector?
Can ChatGPT be used in conducting market and other analyses that are helpful in managing a business entity?
What could be other applications of generative artificial intelligence, the ChatGPT language model or other similar artificial intelligence technologies in the business of companies and enterprises operating in the SME sector?
What is your opinion on this topic?
What is your opinion on this subject?
Please answer,
I invite you all to discuss,
Thank you very much,
Best wishes,
Dariusz Prokopowicz
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Recently, there are many applications, plug-ins based on ChatGPT, which are dedicated to specific business applications in business entities.
Best regards,
Dariusz Prokopowicz
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In your opinion, will a thinking artificial intelligence without human feelings be a more effective 'employee' in a company or a more effective and dangerous tool employed by a competing company?
Will the development of artificial intelligence technology help in terms of the management of business entities, public institutions, financial institutions, etc., and help to achieve the strategic business goals of certain commercially operating companies and businesses and perhaps public sector institutions as well?
The development of artificial intelligence may in the future lead to the creation of digital surrogates that mimic thought processes, artificial consciousness, emotional intelligence, etc. Many technology companies developing their business on the basis of new information technologies and Industry 4.0, which are being created and implemented in business, are conducting research to create new generations of artificial intelligence, which will be enriched with thought processes, artificial consciousness, emotional intelligence, etc. Some large technology companies that are also active online are working on creating their equivalents of generative artificial intelligence built in an advanced language model as ChatGPT. Internet users, companies and institutions are currently exploring the possibilities of practical applications of ChatGPT. The generation of this tool currently available online is already the fourth ChatGPT 4.0, but this is probably not the end of the development of this technology and its applications. In addition to this, some technology companies that are at the forefront of the development of artificial intelligence technology using various machine learning and deep learning solutions in conjunction with access to large sets of information and data collected on Big Data platforms are teaching artificial intelligence to perform various activities, jobs, solving increasingly complex tasks that until now have only been performed by humans. As part of these learning processes and the continual technological advances within ICT and Industry 4.0, leading technology companies are attempting to create a highly advanced artificial intelligence that will be capable of carrying out what we know as thought processes that have so far only taken place in the human brain (and perhaps in some animals). Perhaps in the future, an artificial intelligence will be created that is capable of simulating, digitally generating what we call human emotions, emotional intelligence. Perhaps in the future, an artificial intelligence will be created equipped with digitally generated artificial consciousness. What if, in the future, autonomous robots are created that are equipped not only with artificial intelligence, but also with digitally generated reactions that symbolise human emotions, are equipped with digitally generated thought processes, are equipped with artificial consciousness and behave as if they are also equipped with artificial emotional intelligence? Perhaps this will happen in the future. But the solutions that are currently being developed and the increasing applications of artificial intelligence that are emerging are devoid of typically human characteristics, i.e. thought processes within the framework of, among other things, abstract thinking, are devoid of emotional intelligence, human emotions and feelings, consciousness and so on. Will the rapidly developing artificial intelligence technology and also the rapidly appearing new and more numerous different applications of artificial intelligence technology solve many problems or will more new problems be generated? Perhaps a kind of thinking artificial intelligence will soon emerge, but one that does not have human feelings, e.g. empathy, in addition to having no digital equivalent of emotional intelligence. New applications for this kind of enhanced artificial intelligence will probably quickly emerge. Will it then be associated with the possibility of solving a multitude of problems, or could this kind of AI development generate new risks and dangers for humans? Technology companies pursuing this kind of technological advancement and improved artificial intelligence assume that a thinking artificial intelligence without human feelings can be a more effective 'employee' in a company. On the other hand, a thinking artificial intelligence being a more efficient and effective 'employee' may perhaps also be a more dangerous tool employed by a competitive company. The developers of these technological solutions usually start from the assumption that the development of artificial intelligence technology will help in the field of management of business entities, public institutions, financial institutions, etc. and will help to achieve the strategic business objectives of certain commercially operating companies and enterprises and perhaps also public sector institutions. However, we do not know whether this will be the case, whether the technological advances taking place in the field of artificial intelligence, the emergence of new generations of these technologies will generate only safe and positive applications for people, or whether new risks and threats will also emerge.
Counting on your opinions, on getting to know your personal opinion, on an honest approach to the discussion of scientific issues and not the ready-made answers generated in ChatGPT, I deliberately used the phrase "in your opinion" in the question.
The above text is entirely my own work written by me on the basis of my research.
I have not used other sources or automatic text generation systems such as ChatGPT in writing this text.
Copyright by Dariusz Prokopowicz
In view of the above, I address the following questions to the esteemed community of scientists and researchers:
Will the development of artificial intelligence technology help in the field of management of business entities, public institutions, financial institutions, etc. and will it help in achieving the strategic business goals of certain commercially operating companies and enterprises and perhaps also public sector institutions?
In your opinion, will a thinking artificial intelligence without human feelings be a more effective 'employee' in a company or a more effective and dangerous tool employed by a competitive company?
Will a thinking artificial intelligence without human feelings solve many problems or will it generate more new problems?
What do you think about this topic?
What is your opinion on this subject?
Please respond,
I invite you all to discuss,
Thank you very much,
Best wishes,
Dariusz Prokopowicz
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In my opinion, the development of artificial intelligence technology will help in the management of business entities, public institutions, financial institutions, etc., and will help achieve the strategic business goals of some commercially operating companies and enterprises, and perhaps public sector institutions. Well, whether a thinking artificial intelligence devoid of human feelings will solve many problems or generate more new problems depends mainly on how it is applied.
What is your opinion on this issue?
Please answer,
I invite everyone to join the discussion,
Thank you very much,
Warm regards,
Dariusz Prokopowicz
  • asked a question related to Deep Learning
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Could you discuss the inherent limitations and challenges faced by deep learning algorithms, especially in terms of data requirements, interpretability, and adversarial attacks?
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Deep learning algorithms have limitations such as the need for large labeled datasets, computational resource requirements, lack of interpretability, susceptibility to overfitting and adversarial attacks, data efficiency challenges, and limited causal understanding. These factors should be considered when deciding whether deep learning is the most efficient approach for a specific problem, taking into account available resources and interpretability requirements.
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Whether Python language can act as a versatile tool to learn deep learning?
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Yes, Python is widely regarded as one of the best programming languages for deep learning. Python is a top choice for deep learning due to its rich ecosystem of libraries and frameworks like TensorFlow, PyTorch, and Keras. It is beginner-friendly, has extensive community support, integrates well with other tools, and offers flexibility and scalability. These factors make Python a popular language for developing and deploying deep learning models.
  • asked a question related to Deep Learning
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How can artificial intelligence break through the existing deep learning/neural network framework, and what are the directions?
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Well, maybe I'm not that expert yet and this is just a disquistion but idea can be that AI can select activation functions based on known information about trainning process. Another way can be to change NN structure as we know. Right now DNN is sum of multiplications which we after that put under activation function. What if we use multiplications or powers in some layers instead? How would that influence amount of layers needed? So many questions can be raised but all of them need testing.
  • asked a question related to Deep Learning
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What are the applications of machine learning, deep learning and/or artificial intelligence technologies to securities market analysis, including stock market analysis, bonds, derivatives?
ICT information technologies have already been implemented in banking and large companies operating in non-financial sectors of the economy since the beginning of the third technological revolution. Subsequently, the Internet was used to develop online and mobile banking. Perhaps in the future, virtual banking will be developed on the basis of the increasing scale of application of technologies typical of the current fourth technological revolution and the growing scale of implementation of Industry 4.0 technologies to businesses operating in both the financial and non-financial sectors of the economy. In recent years, various technologies for advanced, multi-criteria data processing have increasingly been applied to business entities in order to improve organisational management processes, risk management, customer and contractor relationship management, management of supply logistics systems, procurement, production, etc., and to improve the profitability of business processes. In order to improve the profitability of business processes, improve marketing communications, offer products and services remotely to customers, etc., such Industry 4.0 technologies as the Internet of Things, cloud computing, Big Data Analytics, Data Science, Blockchain, robotics, multi-criteria simulation models, digital twins, but also machine learning, deep learning and artificial intelligence are increasingly being used. In the field of improving the processes of equity investment management, the processes of carrying out economic and financial analyses, fundamental analyses concerning the valuation of specific categories of investment assets, including securities, i.e. improving the processes carried out in investment banking, ICT information technologies and Industry 4.0 have also been used for many years now. In this connection, there are also emerging opportunities to apply machine learning, deep learning and/or artificial intelligence technologies to the analysis of the securities market, including the analysis of the stock market, bonds, derivatives, etc., i.e. key aspects of business analytics carried out in investment banking. Improving such analytics through the use of the aforementioned technologies should, in addition to the issue of optimising investment returns, also take into account important aspects of the financial security of capital markets transactions, including issues of credit risk management, market risk management, systemic risk management, etc.
In view of the above, I would like to address the following question to the esteemed community of scientists and researchers:
What are the applications of machine learning, deep learning and/or artificial intelligence technologies for securities market analysis, including equity, bond, derivatives market analysis?
What is your opinion on the subject?
What do you think about this topic?
Please respond,
I invite you all to discuss,
Thank you very much,
Best regards,
Dariusz Prokopowicz
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  1. Financial investors are maneuvering computer systems to mechanize their stock trading processes, and
  2. The financial markets have restructured themselves, so virtually all markets right now are limited to order books.
The majority of financial transactions at the present time have become electronic and the total period it takes to execute a stock trade has been significantly reduced to nanoseconds.
The academic jury is still out on the market volatility (risk) consequences of AI trading in the stock market.
In fact, a hybrid system may be a more sustainable future for the finance industry. Thus, the direction of higher education may change towards infusion of data science (FinTech) applications where machines (AIs) and humans coexist, i.e. personal contact and human discretion will be imperative at certain stages of investing.
In this sense, dear Dariusz Prokopowicz , tech-know-logical speed cannot guarantee quality investments, i.e. the creative destruction of the finance industry depends on human macro-prudence, in terms of choosing right investment directions, where the right steps decide and not just the speed of transactions.
_______
It is precisely the necessity of making profits and avoiding losses that gives to the consumers a firm hold over the entrepreneurs and forces them to comply with the wishes of the people.
Lv Mises
  • asked a question related to Deep Learning
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This is futher to the use of machine learning and deep learning models. I wont be using Physical models for the calculations of above.
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Hi
you can use from "sgp4" propagator for leo satellites.
  • asked a question related to Deep Learning
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How can deep learning models improve anomaly detection for enhancing cloud security against sophisticated cyber threats?
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Deep learning models can significantly enhance anomaly detection for improving cloud security against sophisticated cyber threats. Anomaly detection in cloud security involves identifying unusual patterns or behaviors that deviate from established norms. Deep learning models, particularly neural networks, offer several advantages for this task:
1. Feature Learning:
Deep learning models can automatically learn relevant features from large and complex datasets. In the context of anomaly detection, this means that the models can discover subtle and non-linear relationships in the data, which may not be apparent through traditional rule-based methods.
2. Scalability:
Deep learning models can scale effectively to handle large volumes of data, making them suitable for the massive amounts of log and event data generated in cloud environments. This scalability allows for the detection of anomalies in real-time or near-real-time.
3. Non-linearity:
Deep learning models, particularly deep neural networks, are capable of modeling complex, non-linear relationships in data. Cyber threats often exhibit non-linear and evolving patterns, making deep learning well-suited for capturing these variations.
4. Temporal Analysis:
Many deep learning architectures, such as recurrent neural networks (RNNs) and Long Short-Term Memory (LSTM) networks, can effectively model sequential data. This capability is valuable for detecting anomalies that occur over time, such as advanced persistent threats (APTs) or gradual system compromise.
5. Unsupervised Learning:
Deep learning-based anomaly detection methods can operate in an unsupervised manner, meaning they don't require labeled data for training. This is crucial for identifying novel and previously unseen threats.
6. Autoencoders:
Autoencoders, a type of neural network, are commonly used for anomaly detection. They learn to reconstruct input data and are sensitive to deviations from normal patterns. Anomalies result in high reconstruction errors, making them detectable.
7. Transfer Learning:
Transfer learning allows pre-trained deep learning models, often trained on large datasets, to be fine-tuned for anomaly detection in cloud security. This can reduce the need for extensive data collection and training.
8. Ensemble Methods:
Ensemble methods combining multiple deep learning models or combining deep learning with traditional methods can improve detection accuracy and reduce false positives.
9. Real-time Detection:
Deep learning models can analyze cloud logs and network traffic in real-time, enabling rapid detection and response to threats as they occur.
10. Adaptability:
Deep learning models can adapt to evolving cyber threats. Continuous retraining of models with updated data helps them stay effective against new attack vectors.
Despite these advantages, it's essential to consider some challenges when using deep learning for anomaly detection in cloud security:
  • Data Quality: Deep learning models require high-quality, labeled, or unlabeled training data. Ensuring data accuracy and relevancy is crucial.
  • Interpretability: Deep learning models can be challenging to interpret, making it essential to develop methods for explaining model decisions.
  • Resource Requirements: Training deep learning models can be computationally intensive. Cloud resources and infrastructure are needed to support the training and deployment of these models.
  • False Positives: Reducing false positives is a challenge in anomaly detection. Fine-tuning models and adjusting detection thresholds can help mitigate this issue.
In summary, deep learning models, with their ability to learn complex patterns and adapt to evolving threats, can significantly enhance anomaly detection for cloud security against sophisticated cyber threats. However, it's essential to address challenges related to data quality, model interpretability, resource requirements, and false positives to maximize their effectiveness in protecting cloud environments. Additionally, a holistic security strategy, including complementary security measures and practices, should be in place alongside deep learning-based anomaly detection for comprehensive cloud security.
  • asked a question related to Deep Learning
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What are the optimal cloud deployment strategies to accelerate training and inference of deep learning models?
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Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy ML models quickly. By using cloud GPUs and TPUs, you can reduce the time and cost of training your models, especially for deep learning tasks that involve neural networks.
  • asked a question related to Deep Learning
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Can cloud resources mitigate resource constraints for training deep learning models on massive datasets?
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Hai, how are you. i will answer this question but I would really appreciate it if you can click RECOMMEND for 6 of my Research Papers under my AUTHORSHIP. Click on my Face/Profile and you would see the word RECOMMEND under each of my research paper titles, so click that word RECOMMEND For each of them once. Below is my answer for your question and I hope it helps.
Moving model development and training to the cloud opans up a ton of computing power that might otherwise be out of rach. Those cloud providers give you access to GPU clustars and tens/hundreds of terabytes of storge as naded for doing some serious model trianing!
No longer tied to a singal workstation or local lab sarvar. The cloud lets you trian on the whole dataset instead of having to sampla or chunk it up. Can do paralel training acrost nodas too. All in all, cloud is a good way to push past resorce bariars and raaly turn the dials on modol capabilty.
So in summury, yea cloud tek can absolutaly aliavate resorce constraints that usid to limit how big you could go with daap laerning modals bafore. Opens up possibiltiys for massive trianing!
  • asked a question related to Deep Learning
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Could you elaborate on the distinctions between supervised and unsupervised deep learning approaches, highlighting their respective use cases and advantages in various applications?
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Supervised learning relies on the use of labeled data, meaning each data input is associated with a label or desired outcome. Through this method, the model is trained to predict a specific outcome based on the input information. This approach is widely used in tasks such as image classification or time series prediction. On the other hand, unsupervised learning operates without labels, focusing on identifying underlying patterns or structures in the data. Typical applications include dimensionality reduction and data clustering. In terms of advantages, while supervised learning can offer accuracy and specificity in predictive tasks, unsupervised learning is valuable when there is no labeled data available or when the goal is to uncover non-obvious relationships in the data.
  • asked a question related to Deep Learning
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What are the trade-offs between using specialized cloud-based AI services versus building and training custom deep learning models?
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Custom Deep Learning models are easier to tailor to a specific problem. However in specific cases such as large language models they can take a lot to train, and require specific hardware for that. Cloud based pretrained models can make this process faster, but are less flexible towards tailoring to specific problems. Having that said it doesn't mean that cloud based pertained models can not be tailored to certain extent to specific problems using transfer learning. Generally it depends also on the size of the model. For smaller models i like to chose custom models, but for extremely large using pretrained models can save time frequently.
  • asked a question related to Deep Learning
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How does cloud-based distributed computing impact the speed and performance of large-scale deep learning tasks?
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Cloud-based distributed computing revolutionizes large-scale deep learning by harnessing parallel processing and scalable resources. For instance, training a deep neural network for medical image analysis often demands immense computational power and storage. Cloud platforms like AWS, Google Cloud, and Azure provide the necessary GPUs or TPUs, enabling simultaneous training across multiple nodes. This slashes training time from weeks to hours, improving research agility. Furthermore, handling colossal datasets, optimizing model configurations, and fault tolerance become feasible through cloud scalability. As a result, breakthroughs in fields like healthcare, autonomous driving, and natural language processing accelerate, powered by the fusion of cloud convenience and deep learning prowess.
  • asked a question related to Deep Learning
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What are the trade-offs between different cloud providers for cost-effective machine and deep learning model deployment?
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Cloud providers like AWS, GCP, and Azure offer trade-offs for cost-effective machine and deep learning model deployment. AWS provides extensive services but may be costlier. GCP offers competitive prices and strong ML offerings. Azure integrates with Microsoft tools. The choice depends on budget, needs, and scalability for cost-effective deployment.
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I want to develop a system based on the neural network that can accurately and fast recognize human actions in real-time, both from live webcam feeds and pre-recorded videos. My goal is to employ state-of-the-art techniques that can handle diverse actions and varying environmental conditions.
I would greatly appreciate any insights, recommendations, or research directions that experts could provide me with.
Thank you so much in advance.
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this question basically deals with classification, you need to identify the necessary python machine learning libraries, that supports that. as well the necessary functions
  • asked a question related to Deep Learning
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I was exploring differential privacy (DP) which is an excellent technique to preserve the privacy of the data. However, I am wondering what will be the performance metrics to prove this between schemes with DP and schemes without DP.
Are there any performance metrics in which a comparison can be made between scheme with DP and scheme without DP?
Thanks in advance.
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  1. Epsilon (ε): The fundamental parameter of differential privacy that quantifies the amount of privacy protection provided. Smaller values of ε indicate stronger privacy guarantees.
  2. Delta (δ): Another parameter that accounts for the probability that differential privacy might be violated. Smaller values of δ indicate lower risk of privacy breaches.
  3. Accuracy: Measures how much the output of a differentially private query deviates from the non-private query output. Lower accuracy indicates more noise added for privacy preservation.
  4. Utility: Assesses how well the data analysis task can be accomplished while maintaining differential privacy. Higher utility implies less loss of useful information.
  5. False Positive Rate: In the context of hypothesis testing, it's the probability of incorrectly identifying a sensitive individual as not being in the dataset.
  6. False Negative Rate: The probability of failing to identify a sensitive individual present in the dataset.
  7. Sensitivity: Defines the maximum impact of changing one individual's data on the query output. It influences the amount of noise introduced for privacy.
  8. Data Reconstruction Error: Measures how well an adversary can reconstruct individual data points from noisy aggregated results.
  9. Risk of Re-identification: Measures the likelihood that an attacker can associate a specific record in the released data with a real individual.
  10. Privacy Budget Depletion: Tracks how much privacy budget (ε) is consumed over multiple queries, potentially leading to eventual privacy leakage.
  11. Trade-off Between Privacy and Utility: Evaluates the balance between privacy gains and the degradation of data quality or analysis accuracy.
  12. Adversarial Attack Resistance: Assessing the effectiveness of differential privacy against adversaries attempting to violate privacy by exploiting the noise added to the data.
  • asked a question related to Deep Learning
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Currently, I am exploring federated learning (FL). FL seems going to be in trend soon because of its promising functionality. Please share your valuable opinion regarding the following concerns.
  • What are the current trends in FL?
  • What are the open challenges in FL?
  • What are the open security challenges in FL?
  • Which emerging technology can be a suitable candidate to merge with FL?
Thanks for your time.
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  1. Communication Efficiency: Federated learning involves frequent communication between devices and a central server. Optimizing communication protocols and reducing communication overhead is a challenge, especially for devices with limited bandwidth.
  2. Heterogeneous Data: Nodes in a federated learning system may have diverse and non-i.i.d (independent and identically distributed) data. Developing methods to handle data heterogeneity while preserving model performance is crucial.
  3. Model Aggregation: Combining models from different nodes without compromising model accuracy or privacy is complex. Aggregation methods need to be robust against outliers, adversarial nodes, and noisy updates.
  4. Privacy and Security: Ensuring that individual node data remains private is a central concern. New techniques for encryption, differential privacy, and secure aggregation are needed to protect sensitive information.
  5. Bias and Fairness: Federated learning can inherit biases present in node data, leading to biased models. Addressing bias and fairness issues across distributed data sources is a challenge.
  6. Imbalanced Data: Some nodes might have imbalanced datasets, leading to biased models. Developing techniques to mitigate the impact of data imbalance on model training is essential.
  7. Node Heterogeneity: Devices can have varying computation power and energy constraints. Designing federated algorithms that accommodate such heterogeneity is important for scalability and inclusivity.
  8. Stragglers: Slow or faulty nodes can slow down the training process. Techniques for dealing with stragglers and their impact on overall model performance need to be explored.
  9. Model Deployment: Transitioning federated models to production environments while maintaining security, performance, and compatibility with different devices is a challenge.
  10. Cross-Domain Learning: Extending federated learning to scenarios where nodes have different domains or tasks is an emerging area that requires novel solutions.
  11. Adversarial Attacks: Federated learning models could be vulnerable to new types of attacks, including those targeting the aggregation process or compromising the central server.
  12. Resource-efficient Algorithms: Developing algorithms that optimize for computation, memory, and energy usage while maintaining model accuracy is crucial for resource-constrained devices.
  13. Regulatory and Ethical Concerns: Ensuring compliance with data protection regulations and ethical considerations is essential in federated learning, particularly when dealing with personal or sensitive data.
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Nowdays, the machine learning techniques and deep learning techniques have been employed in tackling with various of kinds of security problems, such as malware mitigation. Is the difference of their performance obvious? Which is better?
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Yes, there are differences between machine-learning (ML) techniques and deep-learning (DL) techniques when employed to tackle security problems, although they share similarities as well. Here's an overview of their distinctions:
Machine Learning (ML) Techniques:
  1. Feature Engineering: ML often requires manual feature engineering, where relevant features need to be identified and extracted from data before training the model.
  2. Dimensionality: ML models might struggle with high-dimensional data, necessitating dimensionality reduction techniques.
  3. Performance: ML models may perform well for structured data and simpler tasks, but could struggle with complex patterns in unstructured data.
  4. Generalization: ML models might need fine-tuning for optimal generalization to new and unseen data.
  5. Interpretability: Some ML models offer better interpretability, making it easier to understand the decision-making process.
Deep Learning (DL) Techniques:
  1. Feature Learning: DL models can automatically learn relevant features from raw data, reducing the need for manual feature engineering.
  2. High Dimensionality: DL models handle high-dimensional data efficiently and can uncover intricate patterns.
  3. Complex Patterns: DL excels in capturing complex patterns in unstructured data, such as images, audio, and text.
  4. Data Abundance: DL models often require large amounts of labeled data to perform well, making them well-suited for well-labeled datasets.
  5. Black Box Nature: DL models can be considered more of a "black box" due to their complexity, making their decision-making less interpretable.
In the context of security problems:
  • ML in Security: ML techniques are effective for tasks like intrusion detection, malware classification, and spam filtering. They can identify known patterns of attacks or anomalies based on historical data.
  • DL in Security: DL techniques excel in tasks like image-based authentication, facial recognition, and natural language processing for threat analysis. Their ability to learn intricate patterns makes them suitable for complex, evolving threats.
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Could you explain the foundational principle that underlies deep learning and how it differs from traditional machine learning methods?
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The fundamental concept behind deep learning is to emulate the human brain's neural networks in order to perform tasks that require pattern recognition, feature extraction, and decision making. Deep learning is a subset of machine learning, which itself is a branch of artificial intelligence (AI). Deep learning uses artificial neural networks with many layers (hence the term "deep"), and each layer consists of interconnected nodes called neurons that process and transform input data.
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Why CNN is better than SVM for image classification and which is better for image classification machine learning or deep learning?
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CNNs (Convolutional Neural Networks) are generally preferred over SVMs (Support Vector Machines) for image classification tasks due to their ability to automatically learn hierarchical features from images. Here's why CNNs are often considered better for image classification:
  1. Hierarchical Feature Learning: CNNs are designed to mimic the visual processing in the human brain, where early layers capture low-level features like edges and textures, and deeper layers learn more complex and abstract features. This hierarchical feature learning allows CNNs to adaptively extract relevant information from images, making them highly effective for capturing intricate patterns that are crucial for image classification.
  2. Spatial Hierarchy: CNNs utilize convolutional and pooling layers to maintain the spatial hierarchy of features, which is crucial for recognizing objects regardless of their position or orientation within an image. SVMs treat all features as independent, which can result in losing spatial relationships.
  3. Translation Invariance: CNNs inherently possess translation invariance, meaning they can recognize objects regardless of their position in the image. SVMs require explicit feature engineering to achieve similar invariance.
  4. End-to-End Learning: CNNs learn features directly from raw pixel values, eliminating the need for manual feature extraction. SVMs often require careful feature engineering to achieve good results.
  5. Scale and Complexity: CNNs can handle a wide range of image scales and complexities, making them suitable for tasks with varying levels of detail and object sizes. SVMs might struggle with complex images or those with high dimensionality.
Regarding whether machine learning or deep learning is better for image classification, the answer depends on several factors:
  1. Amount of Data: Deep learning models, particularly CNNs, tend to require a large amount of labeled data to generalize effectively. If you have a limited dataset, traditional machine learning algorithms might perform better due to their ability to work well with smaller datasets.
  2. Feature Complexity: Deep learning excels at automatically learning intricate and complex features from data, which is essential for tasks like image classification. If your task involves capturing subtle visual patterns, deep learning is often the better choice.
  3. Computational Resources: Deep learning models are computationally intensive and often require significant computational resources, especially during training. If computational resources are limited, traditional machine learning algorithms might be more feasible.
  4. State-of-the-Art Performance: Deep learning, particularly CNNs, has achieved state-of-the-art performance in various image classification benchmarks. If achieving the highest accuracy is a priority and you have the necessary resources, deep learning is likely the better choice.
In summary, CNNs are generally favored over SVMs for image classification due to their ability to automatically learn relevant features. Whether to choose machine learning or deep learning for image classification depends on factors like data availability, feature complexity, computational resources, and the desired level of performance. In many cases, deep learning, especially CNNs, has demonstrated superior performance on large and complex image datasets.
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What deep learning algorithms are used for image processing and which CNN algorithm is used for image classification?
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Deep learning has revolutionized image processing, and Convolutional Neural Networks (CNNs) are the most commonly used algorithms for image classification tasks. CNNs are a class of deep neural networks specifically designed to process and analyze visual data, making them highly effective for image-related tasks. CNNs leverage the concept of convolution to automatically learn hierarchical features from images, capturing patterns and structures at various levels of abstraction.
Some popular CNN architectures used for image classification include:
  1. LeNet-5: One of the earliest CNN architectures, designed for handwritten digit recognition.
  2. AlexNet: Introduced in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) in 2012, it demonstrated the power of deep learning on large-scale image datasets.
  3. VGG (Visual Geometry Group) Networks: Known for their simplicity and uniform architecture, VGG networks have various depths, with VGG16 and VGG19 being common variants.
  4. GoogLeNet (Inception): Introduced the concept of "inception modules" that allow the network to learn features at multiple scales simultaneously.
  5. ResNet (Residual Network): Addresses the vanishing gradient problem by introducing residual connections, enabling training of extremely deep networks.
  6. DenseNet: Each layer is connected to every other layer in a feed-forward fashion, promoting feature reuse and encouraging more efficient parameter utilization.
  7. MobileNet: Designed for mobile and embedded vision applications, it uses depth-wise separable convolutions to reduce computational complexity.
  8. EfficientNet: A family of models designed to achieve better accuracy and efficiency by optimizing model scale and resolution.
  9. Xception: An extension of the Inception architecture that replaces standard convolutions with depth-wise separable convolutions.
  10. SqueezeNet: Focuses on reducing model size while maintaining accuracy, making it suitable for resource-constrained environments.
These are just a few examples, and there are many other CNN architectures tailored for different tasks, including object detection, image segmentation, and more.
For image classification specifically, the choice of CNN architecture often depends on the complexity of the problem, available computing resources, and dataset size. More recent architectures like ResNet, DenseNet, and EfficientNet have demonstrated superior performance on large-scale image classification challenges due to their ability to handle deep networks and capture intricate image features.
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Can anybody explain to me this? Is there any formula or equation to predict manually, the number of images that can be generated.
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The ImageDataGenerator facilitates diversifying your data without augmenting the image count, thereby avoiding memory overload. This technique, known as on-the-fly data augmentation, enables you to expand the variety of training samples without increasing storage demands.
For instance, if you possess 100 training images in your dataset and your batch size is 10, each epoch will process 10 batches (100/10=10). Consequently, the data generator will generate 10 augmented images in every epoch.
Let's look at the implementation:
batch_size = 4
train_data_generator = ImageDataGenerator(
rescale=1./65535,
preprocessing_function=preprocess_input,
rotation_range=10,
width_shift_range=0.1,
height_shift_range=0.1,
shear_range=0.1,
zoom_range=0.1,
horizontal_flip=True,
fill_mode='nearest'
)
train_generator = train_data_generator.flow(
x=train_images,
y=train_labels,
batch_size=batch_size,
shuffle=True,
seed=726
)
The provided code snippet performs data augmentation. Let's visualize the resulting augmented images. Please note that in this implementation, I have set the batch size to 4:
# Generate a batch of images from the training generator
batch_images = next(train_generator)[0]
# Create subplots
fig, ax = plt.subplots(nrows=1, ncols=batch_size, figsize=(15, 15))
# Iterate through the batch of images and display them
for i in range(batch_size):
image = batch_images[i]
ax[i].imshow(image, cmap='gray', vmin=0, vmax=65535) # Display using a grayscale colormap and 16-bit range
ax[i].axis('off')
The following images are the obtained results:
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The concept of Circular Economy (CE) in the Construction Industry (CI) is mainly about the R-principles: Rethink, Reduce, Reuse, Repair, and Recycle. Thus, if the design stage following an effective job site management would include consideration of the whole lifecycle of the building with further directions of the possible use of the structure elements, the waste amount could be decreased or eliminated. Analysis of the current literature has shown that CE opportunities in CI are mostly linked to materials reuse. Other top-researched areas include the development of different circularity measures, especially during the construction period.
In the last decade, AI merged as a powerful method. It solved many problems in various domains, such as object detection in visual data, automatic speech recognition, neural translation, and tumor segmentation in computer tomography scans.
Despite the broader range of works on the circular economy, AI was not widely utilized in this field. Thus, I would like to ask if you have an opinion or idea on how Artificial intelligence (AI) can be useful in developing or applying circular construction activities?
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Very welcome. :)
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Within the technological developments associated with artificial intelligence and mobile communication systems applied to healthcare, chatbots represent a trend that is increasing in popularity as an efficient mechanism that promotes interactions between application users for different sectors, since it provides personalized information and allows interactions in time and a capacity to reach millions of people at the same time. From the patient’s perspective, chatbot technologies as representation of natural language processing, along with deep learning and virtual reality, also referred as cognitive services, have been identified as healthcare drivers by their possibility for the creation of great impact applications on medical and preventive health services.
source: FROM THE EDITED VOLUME
Chatbots - The AI-Driven Front-Line Services for Customers https://www.intechopen.com/online-first/86857
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There are several key challenges that chatbot developers will need to address as AI-powered chatbots become more prevalent in the healthcare industry:
Ensuring accuracy of medical information provided - Chatbots will need extensive training and validation to provide sound medical advice aligned with guidelines from healthcare authorities. Wrong information could be dangerous.
Protecting patient privacy - Chatbot conversations will contain highly sensitive patient data that must be safeguarded to the highest standards. Data security and protocols for access will be crucial.
Handling complex health inquiries - While useful for common questions, chatbots may struggle with nuanced patient issues. Seamless handoff to human experts will be important when reaching the limits of chatbot capabilities.
Establishing trust - Patients may be hesitant to rely on chatbot recommendations. Transparency about chatbot capabilities and integration with human oversight can help build user confidence over time.
Regulatory compliance - Strict regulations govern software that dispenses medical advice. Chatbots will need to meet requirements from regulators like the FDA as medical devices/diagnostic tools.
Liability considerations - Defining liability if chatbots err will be challenging. Questions include whether liability rests with developers or the deploying healthcare organization.
Accessibility for all - Chatbots will need inclusive design to serve populations of different age, digital literacy, language proficiency and abilities.
Overall, developing safe, effective and trustworthy AI chatbots for healthcare comes with significant technology, ethical and regulatory challenges. A measured, transparent and patient-centered approach will be important as chatbots increasingly support health decision-making.
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How can machine and deep learning techniques be adapted or fine-tuned to accommodate variations in brain tumor types, locations, and patient populations for more personalized diagnosis and treatment?
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YES!! Deep learning techniques can indeed be adapted and fine-tuned to accommodate variations in brain tumor types, locations and patient populations. Deep learning models are known for their ability to learn intricate patterns and features from complex data, and this adaptability makes them promising tools for medical image analysis including brain tumour detection and classification.
Here are some tips on how this adaptations and fine – tuning can be achieved:
1. Data Collection and Annotation: gathering a diverse and representative dataset is crucial. This dataset should encompass various brain tumour types, different locations within the brain, and a range of patient demographics. The dataset should be properly labeled with accurate annotations indicating tumour type, location, and other relevant information.
2. Preprocessing: before feeding the data into a deep learning model, preprocessing steps are necessary. The steps might involve resizing the images, normalizing intensities, removing artifacts, and more, to ensure consistent and high quality input.
3. Architecture Selection: choosing an appropriate deep learning architecture is essential. Convolutional Neural Networks (CNNs) are commonly used for medical image analysis. However, more advanced architectures like 3D CNNs,
Attention mechanisms and even hybrid architectures can be explored for the better accuracy and adaptability.
4. Transfer Learning: transfer learning involves using a pre – trained deep learning model as a starting point and fine-tuning it on the specific medical imaging task. This approach is effective as pre – trained deep learning models have learned useful general features from a wide range of data. By fine – tuning, you’re allowing the model to specialize in detecting brain tumours and related features.
5. Data Augmentation: since medical imaging dataset are usually limited in size, data augmentation techniques can be employed to artificially increase the diversity of the dataset. Techniques like rotation, flipping, scaling, and adding noise can help the model become more robust to different variations.
6. Hyperparameter Tuning: the hyperparameters of the deep learning model (learning rate, batch, size, etc) should be carefully tuned to specific tasks and datasets. This can be done through experimentation and optimization techniques.
7. Regularization Techniques: regularization techniques such as drop out, batch normalization, and L2 regularization can help prevent overfitting, and help improve the model’s generalization to new cases.
8. Validation and Evaluation: the model should be thoroughly validated and evaluated on separate test datasets to ensure its performance is consistent and effective for different brain tumour types, locations, and patient populations.
9. Feedback Loop: continuously gathering new data and fine – tuning the data based on real – world feedback from medical professionals can lead to further improvements in its adaptability and accuracy.
In summary, deep learning techniques can be tailored to address the specific challenges posed by variations in brain tumour types, locations and patient populations. The success of such adaptations depends on the availability of high – quality data, thoughtful architecture choices, careful preprocessing, and iterative refinement through fine – tuning and validation.
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How can machine and deep learning models be integrated with medical imaging technologies, such as MRI, CT, and PET scans, to improve brain tumor detection and classification?
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Yes, deep learning models particularly Convolutional Neural Network( CNNs) have demonstrated superior performance in many medical image analysis tasks such as detecting brain tumor with different imaging modalities such as MRI, CT scan , PET scan etc.
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Amer Salih / Iraq
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If you have a deep learning model implemented in an Excel file and you want to use it for predictions in MATLAB, you'll need to follow a few steps to ensure proper normalization and integration into MATLAB. Here's a general guideline:
  1. nderstand Normalization: Grasp how normalization is done in the Excel file, whether it's scaling input features or normalizing target outputs.
  2. Load Excel File: Import data from the Excel file into MATLAB using functions like xlsread, readtable, or importdata.
  3. Extract Parameters: Get the normalization parameters (mean, standard deviation, etc.) used in the Excel file.
  4. Normalize Input Data: Apply the same normalization process to input data as in Excel using extracted parameters.
  5. Reimplement Model: Implement the deep learning model in MATLAB using TensorFlow, PyTorch, or MATLAB's deep learning toolbox.
  6. Make Predictions: Use the model to make predictions on the normalized input data.
  7. Reverse Normalization: If post-prediction normalization was done in Excel, reverse it in MATLAB.
  8. Validation: Thoroughly validate predictions by comparing, checking metrics, and ensuring accuracy.
Remember, understanding the specifics of normalization in your Excel file is crucial for a successful integration with MATLAB for predictions.
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What are the most commonly used machine and deep learning algorithms? Specifically, for forecasting hourly solar power generation.
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Here are some specific machine and deep learning algorithms that are commonly used for forecasting hourly solar power generation:
  • Artificial neural networks (ANNs) are a popular choice for forecasting hourly solar power generation because they can learn complex relationships between input and output data. ANNs have been shown to be effective for forecasting hourly solar power generation, even when the data is noisy or non-linear.
  • Support vector machines (SVMs) are another type of machine learning algorithm that can be used for forecasting hourly solar power generation. SVMs are good at finding patterns in data, even when the data is not well-behaved. However, SVMs can be sensitive to outliers, so it is important to carefully clean the data before training the model.
  • Random forests are a type of ensemble learning algorithm that combines multiple decision trees to make predictions. Random forests are often used for forecasting because they are able to handle noisy data and are relatively robust to overfitting. However, random forests can be computationally expensive to train, so they may not be a good choice for large datasets.
  • Long short-term memory (LSTM) networks are a type of deep learning algorithm that is specifically designed for forecasting time series data. LSTM networks are able to learn long-term dependencies in the data, which makes them well-suited for forecasting hourly solar power generation. However, LSTM networks can be difficult to train, and they may not be a good choice for small datasets.
In addition to these algorithms, there are a number of other machine and deep learning algorithms that can be used for forecasting hourly solar power generation. The best algorithm to use will depend on the specific characteristics of the data and the desired forecasting horizon.
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What differentiates PyTorch in terms of usability and flexibility compared to other deep learning frameworks?
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Dear Robert Kinzler,
Enables easy debugging with popular Python tools. Offers scalability and is well-supported on major cloud platforms. Provides a small community focused on open source. Exports learning models to the Open Neural Network Exchange (ONNX) standard format.
PyTorch optimizes performance by taking advantage of native support for asynchronous execution from Python. In TensorFlow, you'll have to manually code and fine-tune every operation to be run on a specific device to allow distributed training.
PyTorch is constructed in a way that is intuitive to understand and easy to develop machine learning projects. Easier to Learn: PyTorch is relatively easier to learn than other deep learning frameworks, as its syntax is similar to conventional programming languages like Python.
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I am exploring the application of Deep Learning in the aspect of Inventory Management in Supply Chain. What are the specific techniques, models, or algorithms being used? Are there practical cases that have demonstrated significant improvement in stock management, cost reduction, or service level enhancement?
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Joshua Depiver "Profound thanks for your insights; your synthesis of epistemological nuances and empirical methodologies has significantly enriched the discourse here."
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In your opinion, does it make sense to create a new generation of something similar to ChatGPT, which will use databases built solely on the basis of continuously updated data, information, objectively verified knowledge resources, a kind of online business advisor, using defined business websites and portals, financial and economic information portals, which will answer the questions of entrepreneurs, businessmen, managers in charge of companies and enterprises, who will ask questions about the future development of their business, their company, enterprise, corporation?
In my opinion, it makes sense to create a new generation of something similar to ChatGPT, which will use databases built solely on the basis of continuously updated data, information, objectively verified knowledge resources, a kind of online business advisor, using defined business websites and portals, financial and economic information portals, which will answer the questions of entrepreneurs, businessmen, managers in charge of companies and enterprises, who will ask questions about the future development of their business, their company, enterprise, corporation. Such intelligent systems drawing on large data and information resources, processing large sets of economic and financial information and data in real time on Big Data Analytics platforms, providing current analytical data to business intelligence systems supporting business management processes, can prove very useful as tools to facilitate organizational management processes, forecasting various scenarios of abnormal events and scenarios of developments in the business environment, diagnosing escalation of risks, supporting early warning systems, diagnosing and forecasting opportunities and threats to the development of the company or enterprise, providing warning signals for contingency and risk management systems.
In view of the above, I address the following question to the esteemed community of scientists and researchers:
In your opinion, does it make sense to create a new generation of something similar to ChatGPT, which will use databases built solely on the basis of continuously updated data, information, objectively verified knowledge resources, a kind of online business advisor, using defined business websites and portals, financial and economic information portals, which will answer the questions of entrepreneurs, businessmen, managers in charge of companies and enterprises, who will ask questions about the future development of their business, their company, enterprise, corporation?
In your opinion, does it make sense to create a new generation of something similar to ChatGPT, a kind of intelligent online business advisor?
What do you think about this topic?
What is your opinion on this issue?
Please answer,
I invite everyone to join the discussion,
Thank you very much,
Best wishes,
Dariusz Prokopowicz
Counting on your opinions, on getting to know your personal opinion, on a fair approach to the discussion of scientific issues, I deliberately used the phrase "in your opinion" in the question.
The above text is entirely my own work written by me on the basis of my research.
In writing this text I did not use other sources or automatic text generation systems.
Copyright by Dariusz Prokopowicz
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Absolutely! Just as technology evolves (📱), so do business needs. A new-gen ChatGPT as a savvy online business advisor could be a game-changer 🚀. With its AI smarts, it'll make strategizing smoother than a well-oiled machine. Embrace the future of business advice! 💼🤖
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What kind of innovative startups do you think can be created using a new generation of smart tools similar to ChatGPT and/or whose business activities would be helped by such smart tools and/or certain new business concepts would be based on such smart tools?
There is a growing body of data suggesting that innovative startups may be created using the next generation of ChatGPT-like smart tools and/or whose business activities would be helped by such smart tools and/or certain new business concepts would be based on such smart tools. On the one hand, there are already emerging Internet startups based on artificial intelligence systems specialized in specific areas of creating textual, graphic, video, etc. elaborations that are variants of something similar to ChatGPT. On the other hand, arguably, some of these kinds of solutions may in the future turn into a kind of online business advisors generating advice for entrepreneurs developing new innovative startups.
In view of the above, I address the following question to the esteemed community of scientists and researchers:
What kind of innovative startups do you think could be developed using a new generation of smart tools similar to ChatGPT and/or whose business activities would be helped by such smart tools and/or certain new business concepts would be based on such smart tools?
What kind of innovative startups can be created based on the next generation of ChatGPT-like smart tools?
What do you think about this topic?
What is your opinion on this issue?
Please answer,
I invite everyone to join the discussion,
Thank you very much,
Best wishes,
Dariusz Prokopowicz
Counting on your opinions, on getting to know your personal opinion, on a fair approach to the discussion of scientific issues, I deliberately used the phrase "in your opinion" in the question.
The above text is entirely my own work written by me on the basis of my research.
In writing this text I did not use other sources or automatic text generation systems.
Copyright by Dariusz Prokopowicz
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Innovative startups can emerge in AI-driven customer service, virtual personal assistants, content generation, and language translation. Think of a "ChatGPT Café" ☕ where AI baristas craft witty conversations, making your latte and linguistics equally frothy! 🚀🤖
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The more benefit of large language model is its big capability, not benefit of few-shot learning ability?
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The real gain of large language models is their big capability. They can perform a wide range of tasks such as text generation, summarization, translation, and question answering. Few-shot learning is one of the benefits of large language models but not the only one. Large language models can learn from a few examples and generalize to new tasks and domains.
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What are the ethical and regulatory considerations associated with the integration of machine and deep learning technologies in brain tumor identification, and how can patient privacy and data security be ensured?
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The integration of machine and deep learning technologies in brain tumor identification brings to the forefront critical ethical and regulatory considerations. As these technologies offer promising advancements in accuracy and efficiency, concerns emerge regarding patient privacy, informed consent, and the potential impact of algorithmic biases. Striking a balance between the benefits of these innovations and safeguarding patient rights requires robust regulatory frameworks that govern data usage, sharing, and patient confidentiality. Collaboration among medical experts, technologists, ethicists, and policymakers becomes essential to ensure the responsible and transparent deployment of these technologies, aligning medical progress with ethical standards and regulatory compliance.
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As a CS/SE student currently I'm finding an area for research.
Currently my idea was to investigate more about recommendation systems using ML and deep Learning, but with the timeline to complete and the project is limited.
In the above topic research gap is very limited.
Please suggest some research gaps for me to consider.
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Dear Rishini Hettiarachchi,
a very broad area of research is the use of Artificial Intelligence in the IoT. The idea of Digital Twins is fundamental to this. For details see:
As an appendix I added a small reference list about convergence of Digital Twin, IoT and Machine Learning.
Good luck and best regards
Anatol Badach
Maninder Jeet Kaur,Ved P Mishra, Piyush Maheshwar: „The Convergence of Digital Twin, IoT, and Machine Learning: Transforming Data into Action“; in book: Digital Twin Technologies and Smart Cities, Jan 2020, DOI: 10.1007/978-3-030-18732-3_1
Maninder Jeet Kaur,Ved P Mishra, Piyush Maheshwar: „The Convergence of Digital Twin, IoT, and Machine Learning: Transforming Data into Action“; in book: Digital Twin Technologies and Smart Cities, Jan 2020, DOI: 10.1007/978-3-030-18732-3_1
Muhammad Mazhar Rathore, Syed Attique Shah, Dhirendra Shukla, Elmahdi Bentafat, Spiridon Bakiras: „The Role of AI, Machine Learning, and Big Data in Digital Twinning: A Systematic Literature Review, Challenges, and Opportunities“; IEEE Access, Vol. 9, Feb 2021, DOI: 10.1109/ACCESS.2021.3060863
Xiaoming Li, Hao Liu, Weixi Wang, Ye Zheng, Haibin Lv, Zhihan Lv: Big data analysis of the Internet of Things in the digital twins of smart city based on deep learning; Future Generation Computer Systems, Vol. 128, Mar 2022, DOI: 10.1016/j.future.2021.10.006
Yueyue Dai, Ke Zhang, Sabita Maharjan, Yan Zhang: Deep Reinforcement Learning for StochasticComputation Offloading in Digital Twin Networks; IEEE Transactions on Industrial Informatics, Vol. 17, Issue: 7, Jul 2021, DOI: 10.1109/TII.2020.3016320
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I'm expecting to use stock prices from the pre-covid period up to now to build a model for stock price prediction. I doubt regarding the periods I should include for my training and test set. Do I need to consider the pre-covid period as my training set and the current covid period as my test set? or should I include the pre-covid period and a part of the current period for my training set and the rest of the current period as my test set?
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To split your data into training and test sets for predicting stock prices using pre-COVID and current COVID periods, consider using a time-based approach. Allocate a portion of data from pre-COVID for training and the subsequent COVID period for testing, ensuring temporal continuity while evaluating predictive performance.
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How can transfer learning and data augmentation techniques be leveraged to overcome data scarcity and improve the generalizability of machine and deep learning models for brain tumor analysis?
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Transfer learning employs pre-trained models on abundant data to enhance performance on smaller datasets. Data augmentation involves creating variations of limited data. Combining these techniques, pre-trained models extract useful features, while augmentation enhances dataset diversity, mitigating data scarcity and boosting model generalization for improved outcomes.
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Would you like to use a completely new generation of ChatGPT-type tool that would be based on those online databases you would choose yourself?
What do you think about such a business concept for an innovative startup: creating a new generation of something similar to ChatGPT, which will use databases built solely on the basis of continuously updated data, information, objectively verified knowledge resources, and which will use only those online databases, knowledge bases, portals and websites that individual Internet users will select themselves?
In your opinion, does it make sense to create a new generation of something similar to ChatGPT, which will use databases built exclusively on the basis of continuously updated data, information, objectively verified knowledge resources, and which will use exclusively those Internet databases, knowledge bases, portals and websites that individual Internet users themselves will select, determine, define?
In my opinion, it makes sense to create a new generation of something similar to ChatGPT, which will use databases built exclusively on the basis of continuously updated data, information, objectively verified knowledge resources, and which will use exclusively those Internet databases, knowledge bases, portals and websites that individual Internet users themselves will select, define, define. This kind of solution, which would allow personalization of the functionality of such generative artificial intelligence systems, would significantly increase its functionality for individual users, Internet users, citizens. In addition, the scale of innovative solutions for practical applications of such personalized intelligent systems for analyzing content and data contained in selected specific Internet resources would increase significantly.
In view of the above, I address the following question to the esteemed community of scientists and researchers:
In your opinion, does it make sense to create a new generation of something similar to ChatGPT, which will use databases built solely on the basis of continuously updated data, information, objectively verified knowledge resources, and which will use only those Internet databases, knowledge bases, portals and websites that individual Internet users themselves will select, specify, define?
What do you think of such a business concept for an innovative startup: the creation of a new generation of something similar to ChatGPT, which will use databases built exclusively on the basis of continuously updated data, information, objectively verified knowledge resources, and which will use exclusively those online databases, knowledge bases, portals and websites that individual Internet users will themselves select?
Would you like to use a completely new generation of ChatGPT-type tool, which would be based on those online databases that you yourself would select?
What do you think about this topic?
What is your opinion on this issue?
Please answer,
I invite everyone to join the discussion,
Thank you very much,
Best regards,
Counting on your opinions, on getting to know your personal opinion, on a fair approach to the discussion of scientific issues, I deliberately used the phrase "in your opinion" in the question.
Dariusz Prokopowicz
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Certainly, embracing a new ChatGPT generation based on curated online databases offers exciting potential. The enriched data could enhance accuracy and relevance, fostering more insightful and contextually aware interactions, ultimately delivering an elevated user experience.
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Deep Learning Frameworks for Image Recognition, Natural Language Processing (NLP), Speech Recognition, Recommender Systems, Generative Models, Time Series Analysis, Autonomous Vehicles, Healthcare, Robotics, and Financial Analysis, etc.
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Just a question of taste.
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What are the state-of-the-art machine and deep learning techniques used for brain tumor identification, and how do they compare in terms of accuracy and efficiency?
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AI-based methods, specifically deep learning algorithms, have shown great promise in brain tumor identification and segmentation from medical imaging data. Here are some of the state-of-the-art techniques used:
  1. Convolutional Neural Networks (CNNs): CNNs have been widely used for image classification tasks, including brain tumor identification. They can automatically learn features from raw pixel data, eliminating the need for manual feature extraction. One popular type of CNN used in medical imaging is U-Net, designed specifically for biomedical image segmentation.
  2. 3D Convolutional Neural Networks (3D-CNNs): These networks process data in three dimensions, making them useful for brain tumor identification in 3D imaging data, such as MRI scans.
  3. Recurrent Neural Networks (RNNs): RNNs are used in sequential data and can be applied to medical imaging by considering each slice of an image as a sequence. This approach can capture spatial dependencies in 3D imaging data.
  4. Transfer Learning: Given the limited availability of labeled medical imaging data, transfer learning is often used in this field. Pretrained models (usually trained on large-scale image datasets like ImageNet) are fine-tuned on the specific task of brain tumor identification, which can improve performance and reduce training time.
  5. Ensemble Learning: Ensemble learning methods combine several machine learning models to achieve better performance. This approach can also be applied in brain tumor identification to combine predictions from multiple models.
  6. AutoML (Automated Machine Learning) Techniques: These are being used increasingly to automate parts of the machine learning process, including hyperparameter tuning, model selection, and feature selection.
In terms of accuracy, deep learning methods (especially CNNs and their variants) have achieved state-of-the-art results on several brain tumor imaging datasets. For example, on the BraTS (Multimodal Brain Tumor Segmentation Challenge) dataset, deep learning methods consistently rank at the top.
However, these methods can be computationally expensive and often require large amounts of data to achieve high performance. Also, while they can achieve high accuracy, they can also be "black boxes," making their predictions difficult to interpret, which is a significant challenge in healthcare where interpretability is crucial.
Therefore, while deep learning methods are the most accurate for brain tumor identification, other factors, such as computational efficiency, data availability, and interpretability, also need to be considered when choosing a method. It's also worth mentioning that ongoing research continues to improve upon these techniques and develop new methods that balance accuracy, efficiency, and interpretability.
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I'm looking for opportunities in research Assistance or any kind of involvement in research in the fields of Machine Learning, Deep Learning, or NLP. I am eager to contribute my efforts and dedication to research endeavors. Please let me know if you have any openings for this kind of work.
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You can join our team.
e-mail: hilal. yagin@inonu.edu.tr
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I am interested in publishing research on deep learning-based automatic speech recognition (ASR) for a specific low-resource language. What are the key areas or research gaps that I should focus on to contribute to this field? Are there any influential papers or research works that I should read to gain a comprehensive understanding of the current state-of-the-art in this area? I would greatly appreciate any guidance or recommendations from experts in this field. Thank you in advance!
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The research gaps include data scarcity, lack of text transcriptions, challenges with out-of-vocabulary words, and handling accent and dialect variability. Additionally, code-switching and multilingualism, model adaptation, and robustness to adverse acoustic conditions are important areas for exploration.
You can focus on developing novel data augmentation techniques, effective transfer learning and multilingual modeling, and exploring unsupervised and semi-supervised learning methods. Investigate robustness to adverse acoustic conditions, devise language-specific modeling approaches, and work on active learning and data collection strategies to optimize the annotation process.
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With the development of artificial intelligence and the increase in the scale of its applications in the production of goods and the provision of services, is it the working time of people that should be reduced?
In connection with the development of artificial intelligence, which is increasingly replacing humans in the performance of various activities and professions, and in a situation of increasing labor productivity through its greater automation and objectification, should people's working time be reduced?
Accordingly, should people's working time be reduced, for example, from 5 working days per week to 4 working days?
Features of highly developed economies include high levels of productivity, innovation in the economic activities of companies and enterprises, the use of new technologies in manufacturing processes, labor productivity and income. High levels of labor productivity are largely due to the use of new technologies in the processes of producing goods and offering services. In recent years, new ICT information technologies, Industry 4.0 technologies, typical of the current fourth technological revolution, including machine learning technologies, deep learning and artificial intelligence, are being applied to the manufacturing processes of companies and enterprises operating in various sectors of the economy. these technologies are now and in the next several years will change labor markets by replacing people in certain jobs or supporting, improving the work done by people. As some of the work done by humans will be taken over by artificial intelligence then perhaps there will be an opportunity to reduce working hours for humans. When this kind of solution is applied then labor productivity should not decline, as productivity will be maintained at a certain level or will increase and employed citizens will have more time for personal activities, leisure, hobbies, for family, dal to develop personal passions, etc. and thus can be more productive and creative during work time.
In view of the above, I address the following question to the esteemed community of scientists and researchers:
In view of the development of artificial intelligence, which is increasingly replacing humans in the performance of various activities and professions, and in a situation of increasing labor productivity through its greater automation and objectification, should people's working time be reduced?
Accordingly, should people's working hours be reduced, for example, from 5 working days per week to 4 working days?
With the development of artificial intelligence and the increase in the scale of its applications in the production of goods and the provision of services, should people's working time be reduced?
Will the increase in the scale of applications of artificial intelligence make it possible to reduce people's working time?
And what is your opinion about it?
What is your opinion on this issue?
Please answer,
I invite you all to join the discussion,
Thank you very much,
Best regards,
Dariusz Prokopowicz
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The increase in the scale of artificial intelligence applications holds the potential to significantly reduce human labor hours in various sectors. AI technologies, such as automation, machine learning, and robotics, can streamline and optimize repetitive tasks, leading to increased productivity and efficiency. By handling mundane and time-consuming activities, AI allows human workers to focus on more complex and creative aspects of their jobs. Moreover, AI-driven advancements in industries like manufacturing, logistics, customer service, and data analysis can lead to streamlined processes, faster decision-making, and improved outcomes. However, the extent to which AI reduces human labor hours will depend on factors such as the rate of AI adoption, the adaptability of the workforce, and ethical considerations surrounding job displacement. Striking a balance between AI-driven automation and the preservation of meaningful work opportunities will be crucial in harnessing the full potential of AI to benefit both businesses and the workforce.