What are the current challenges and solutions to minimize the level of pollution and exhaustion of fossil fuel supplies impacting the climate change?

for example, Natural Intelligence and Research in Neurodegenerative diseases.

While we are still at an early stage in answering some key questions about Natural Intelligence [NI] [such as what algorithms the mind uses] the rapidly advancing Artificial Intelligence [AI] has already begun to change our Daily Lives. Machine learning has brought to light remarkable potential in healthcare, facilitating speech recognition, clinical image analysis, and medical diagnosis. For example, there is a growing need for automation of medical imaging, as it takes a lot of time and resources to train an Expert Human Radiologist. Deep learning AI architectures have been developed to analyze medical images of the brain, lungs, heart, breast, liver, skeletal muscle, some of which have already been used in clinics to aid in disease diagnosis.

AI in the healthcare sector is a broad term that refers to the use of machine learning algorithms to imitate human cognition to analyze, understand and present complex medical data.

Invest in high value AI use cases in healthcare

It is important to use machine learning in areas where it can provide effective solutions.

The first 5G radio networks are now live. This generation of telecommunication system aims to deliver

The purpose is to be faster, more reliable and manage the scale of devices predicted for the Mobile Internet of Things (MIoT). Enabling the digital transformation of our society, business processes and manufacturing. To enable this, 5G will deliver multi-network slicing, multi-level of services and multi-connectivity network capabilities. To allow the required flexibility, agility and economies of scale these technologies will be delivered via virtual and containerised environments. This is a revolutionary way of working for the industry. 5G has designed in security controls to address many of the threats faced in today’s 4G/3G/2G networks. These controls include new mutual authentication capabilities, enhanced subscriber identity protection, and additional security mechanisms. 5G offers the mobile industry an unprecedented opportunity to uplift network and service security levels. 5G provides preventative measures to limit the impact to known threats, but the adoption of new network technologies introduces potential new threats for the industry to manage.

Agriculture is an important sector of the U.S. economy. The crops, livestock, and seafood produced  in the United States contribute more than $300 billion to the economy each year. When food-service and other agriculture-related industries are included, the agricultural and food sectors contribute more than $750 billion to the gross domestic product. Agriculture and fisheries are highly dependent on the climate. Increases in temperature and carbon dioxide (CO2) can increase some crop yields in some places. But to realize these benefits, nutrient levels, soil moisture, water availability, and other conditions must also be met. Changes in the frequency and severity of droughts and floods could pose challenges for farmers and ranchers and threaten food safety.

The U.S. Department of Commerce’s National Institute of Standards and Technology (NIST) has chosen the first group of encryption tools that are designed to withstand the assault of a future quantum computer, which could potentially crack the security used to protect privacy in the digital systems we rely on every day — such as online banking and email software. The four selected encryption algorithms will become part of NIST’s post-quantum cryptographic standard, expected to be finalized in about two years.

Image segmentation has often been defined as the problem of localizing regions of an image relative to content (e.g., image homogeneity). However, recent image segmentation approaches have provided interactive methods that implicitly define the segmentation problem relative to a particular task of content localization.

The random walker algorithm determines the segmentation of an image from a set of markers labeling several phases (2 or more). An anisotropic diffusion equation is solved with tracers initiated at the markers’ position. The local diffusivity coefficient is greater if neighboring pixels have similar values, so that diffusion is difficult across high gradients. The label of each unknown pixel is attributed to the label of the known marker that has the highest probability to be reached first during this diffusion process. In this example, two phases are clearly visible, but the data are too noisy to perform the segmentation from the histogram only. We determine markers of the two phases from the extreme tails of the histogram of gray values, and use the random walker for the segmentation.

source:

Random Walks for Image Segmentation by Leo Grady