added an update
Where AI Is Today, and Where It’s Going in the Future
Goal: To track AI. How AI can be used more effectively for humankind.Better to say Artificial Intelligence in business.
List of next business ideas using AI concentrating these criteria :
******not human shape
********To get some services that are risky or impossible for human
******To get some services that can save human lives
Creative something (special)
Artificial Intelligence technology. - Markets for AI hardware, software and services. - Current and future AI applications. - AI and Autonomous Vehicles. - AI technology enablers. - Companies supplying products and services that use AI in their offerings.
Suppliers of AI products and services. - Companies incorporating AI in their product offerings. - Product managers. - Business development managers. - Company strategists. - Mergers and acquisitions specialists. - Market analysts, investment bankers and consultants.
Key Topics :
2. Artificial Intelligence Technology
3. Artifical Intelligence Market
4. AI Applications
5. Artificial Intelligence And Autonomous Vehicles
6. AI Technology Enablers
Company Profiles :
AIBrain - Accenture plc - Adobe Systems Incorporated - Agralogics - Agrible - Agrilyst - Alphabet Inc. - Amazon.com - Anki - Apple Inc. - Arria NLG plc - Automated Insights - Baidu Inc - Banjo - CloudMinds - Cyberlytic - Darktrace - DeePhi - Deep Instinct - Drive.ai - Entefy - Facebook Inc. - FireEye, Inc. - Fortscale - Gamaya - Google - Hitachi, Ltd - HubSpot, Inc. - International Business Machines Corporation - Jask - Jibo - Microsoft Corporation - NEC Corporation - Narrative Science - Nervana Systems - Next IT Corporation - Orbital Insight - Palantir Technologies, Inc. - Palo Alto Networks, Inc. - PatternEx - Prisma - Qualcomm Incorporated - ReSnap - S4 - SoftBank Group Corp - Status Today - Strider - Twitter - Vectra Networks - ViSenze - Wave Computing - X.ai - Yseop - Zendesk, Inc. - harvest.ai - iCarbonX - salesforce.com, inc.
For more information about this report visit http://www.researchandmarkets.com/research/2qpk93/commercial
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artificial intelligence” can mean a whole range of things. There are three main ways in which AI can be incorporated into a business.
Assisted intelligence is all about improving what people and organizations are already doing, and is relatively common today. Think of how Gmail automatically sorts your email into “primary,” “social,” and “promotion” tabs.
augmented intelligence, which is beginning to emerge now. Augmented intelligence will enable companies to do things they couldn’t otherwise do. A good example is Netflix’s content suggestions, in which machine learning enables the company to offer a superior service to anything that was possible before.
Third is autonomous intelligence, which is still being developed, and is all about creating and deploying machines that will act on their own. Self-driving cars and automatic language-translation services are a couple of examples.
Online virtual lab in various disciplines of Science and Engineering: This can help enthuse students to conduct experiments at undergraduate level, post graduate level as well as to research scholars, provide various tools for learning, additional web-resources, video-lectures, animated demonstrations and self evaluation, share costly equipment , resources. Primarily targeted labs: 1.Nanotechnology lab(virtual) 2.Electronics and communication lab(virtual) 3.Power system lab(virtual) 4.Satellite and Image processing lab(virtual) 5.Robotics lab(virtual) 6 AI lab(virtual) https://virtuallab416365713.wordpress.com/
Personal health virtual assistant
Advanced analytics and research
Personal life coach
Learning is seen to be the quintessential characteristic of an intelligent being. Consequently, one of the driving ambitions of AI has been to develop computers that can learn from experience. The resulting developments in the AI sub-field of machine learning have resulted in a set of techniques which have the potential to alter the way in which knowledge is created.
All scientists are familiar with the statistical approach to data analysis. Given a particular hypothesis, statistical tests are applied to data to see if any relationships can be found between different parameters. Machine learning systems can go much further. They look at raw data and then attempt to hypothesise relationships within the data, and newer learning systems are able to produce quite complex characterisations of those relationships. In other words they attempt to discover humanly understandable concepts.
Learning techniques include neural networks, but encompass a large variety of other methods as well, each with their own particular characteristic benefits and difficulties. For example, some systems are able to learn decision trees from examples taken from data (Quinlan, 1986). These trees look much like the classification hierarchies discussed in Chapter 10, and can be used to help in diagnosis.
Medicine has formed a rich test-bed for machine learning experiments in the past, allowing scientists to develop complex and powerful learning systems. While there has been much practical use of expert systems in routine clinical settings, at present machine learning systems still seem to be used in a more experimental way. There are, however, many situations in which they can make a significant contribution.
- Machine learning systems can be used to develop the knowledge bases used by expert systems. Given a set of clinical cases that act as examples, a machine learning system can produce a systematic description of those clinical features that uniquely characterise the clinical conditions. This knowledge can be expressed in the form of simple rules, or often as a decision tree. A classic example of this type of system is KARDIO, which was developed to interpret ECGs (Bratko et al., 1989).
- This approach can be extended to explore poorly understood areas of medicine, and people now talk of the process of 'data mining' and of 'knowledge discovery' systems. For example, it is possible, using patient data, to automatically construct pathophysiological models that describe the functional relationships between the various measurements. For example, Hau and Coiera (1997) describe a learning system that takes real-time patient data obtained during cardiac bypass surgery, and then creates models of normal and abnormal cardiac physiology. These models might be used to look for changes in a patient's condition if used at the time they are created. Alternatively, if used in a research setting, these models can serve as initial hypotheses that can drive further experimentation.
- One particularly exciting development has been the use of learning systems to discover new drugs. The learning system is given examples of one or more drugs that weakly exhibit a particular activity, and based upon a description of the chemical structure of those compounds, the learning system suggests which of the chemical attributes are necessary for that pharmacological activity. Based upon the new characterisation of chemical structure produced by the learning system, drug designers can try to design a new compound that has those characteristics. Currently, drug designers synthesis a number of analogues of the drug they wish to improve upon, and experiment with these to determine which exhibits the desired activity. By boot-strapping the process using the machine learning approach, the development of new drugs can be speeded up, and the costs significantly reduced. At present statistical analyses of activity are used to assist with analogue development, and machine learning techniques have been shown to at least equal if not outperform them, as well as having the benefit of generating knowledge in a form that is more easily understood by chemists (King et al., 1992). Since such learning experiments are still in their infancy, significant developments can be expected here in the next few years.
- Machine learning has a potential role to play in the development of clinical guidelines. It is often the case that there are several alternate treatments for a given condition, with slightly different outcomes. It may not be clear however, what features of one particular treatment method are responsible for the better results. If databases are kept of the outcomes of competing treatments, then machine learning systems can be used to identify features that are responsible for different outcomes.
Conference Paper Surgical Robotics—Past, Present and Future
The military gets a lot of benefits from AI machines. *****They can help reducing the risk of life loss in wars. ****they can be more efficient than regular soldiers. ***Also, they are less in cost about ten times than the cost of human soldiers.
Unmanned aerial vehicle
“An unmanned aerial vehicle (UAV), also known as a unmanned aircraft system (UAS), is a machine which functions either by the remote control of a navigator or pilot or autonomously, that is, as a self-directing entity.”[Wikipedia.org, 2011]
There many different types of UAVs and they have different characteristics depends on their making purpose.
There are some that must controlled from the ground, but others have complex dynamic systems for automation. They can be used also for attacks, beside the exploration. There are some civil applications like fire fighting, and security works. In general, they used in boring, dirty, or dangerous works.
AI in mobile and web development :
AI in medicine
Robotics and AI: