Artificial intelligence could spot the early signs of dementia from a simple brain scan long before major symptoms appear – and in some cases before any symptoms appear – say Cambridge researchers. Dementias are characterized by the build-up of different types of protein in the brain, which damages brain tissue and leads to cognitive decline. In the case of Alzheimer's disease, these proteins include beta-amyloid, which forms'plaques', clumping together between neurons and affecting their function, and tau, which accumulates inside neurons. Molecular and cellular changes to the brain usually begin many years before any symptoms occur. Diagnosing dementia can take many months or even years.

Dementias are characterized by the build-up of different types of protein in the brain, which damages brain tissue and leads to cognitive decline. In the case of Alzheimer's disease, these proteins include beta-amyloid, which forms'plaques," clumping together between neurons and affecting their function, and tau, which accumulates inside neurons. Molecular and cellular changes to the brain usually begin many years before any symptoms occur. Diagnosing dementia can take many months or even years. It typically requires two or three hospital visits and can involve a range of CT, PET and MRI scans as well as invasive lumber punctures. A team led by Professor Zoe Kourtzi at the University of Cambridge and The Alan Turing Institute has developed machine learning tools that can detect dementia in patients at a very early stage. Using brain scans from patients who went on to develop Alzheimer's, their machine learning algorithm learnt to spot structural changes in the brain. When combined with the results from standard memory tests, the algorithm was able to provide a prognostic score--that is, the likelihood of the individual having Alzheimer's disease. For those patients presenting with mild cognitive impairment--signs of memory loss or problems with language or visual/spatial perception--the algorithm was higher than 80% accurate in predicting those individuals who went on to develop Alzheimer's disease. It was also able to predict how fast their cognition will decline over time. Professor Kourtzi, from Cambridge's Department of Psychology, said: "We have trained machine learning algorithms to spot very early signs of dementia just by looking for patterns of gray matter loss--essentially, wearing away--in the brain.

Listen to today's podcast featuring CTO, Dr. Danny Rittman: "What Epsilon will have that all of these guys will not have ... Epsilon will have Avant! in it," Rittman said in a recent interview with Investorideas.com. "It will do something very simple that currently no other programs do. Avant! will equip Epsilon with a phenomenon that we call'an attention to details'. The neural network will actually pay attention as the design is going forward. GBT announced its intention to implement its Avant! "We identified the EDA field, a modern domain used to design integrated circuits (ICs), that we believe can significantly benefit from our AI technology.

Today's podcast features recent interviews with [two] experts in top AI management positions discussing recent developments within their companies and the overall sector: Dr. Danny Rittman, CTO of GBT Technologies Inc. (OTC PINK:GTCH), and Mr. Babak Hodjat, VP of Evolutionary AI, Cognizant Technology Solutions Corporation (NasdaqGS:CTSH). Listen to the podcast interview with Dr. Danny Rittman, CTO of GBT Technologies Inc. (OTC PINK:GTCH) discussing the company's recently announced implementation and development of recurrent relational reasoning (RRN) in its AI, and its applications in the medical field. In a recently published press release, GBT Technologies CTO, Dr. Danny Rittman explained the company's rationale for incorporating recurrent relational reasoning (RRN) into its Avant! "Our goal is to implement a fundamental part of human intelligence called relational reasoning, which is planned to enable Avant! to acquire expertise on its own by understanding object's relations. Avant! will include an advanced artificial neural network (ANN) capable of pattern recognition and reasoning about those patterns which is very similar to the human brain."

GBT Technologies announced this week commencement of its new neuromorphic microchip project called the CLEVER chip. The microchip is targeted to become a hardware support for its AI software technology. The CLEVER chip is intended to be a neuromorphic AI chip, which is defined as an advanced integrated circuit that is built based on a model of the human brain, electronically modelling its neurons and synapses architecture. It is GBT's intent to design the microchip to process vast data such as media (images, sound) and to respond to changes in that data in ways not specifically programmed but concluded by the chip's algorithms. The goal of the CLEVER Chip is to accelerate GBT's AI technology, which is based on deep machine learning and automated analytical model building, and in turn enabling real-time response for interaction with humans.

All Mark Rittman wanted was a cup of tea. Little did he know he would have to spend 11 hours waiting for his new hi-tech kettle to boil the water. Rittman, a data specialist who lives in Hove, England, set about trying to make a cup of tea around 9am. But thanks to his Wi-Fi enabled kettle it wasn't long before he ran into trouble. Still haven't had a first cup of tea this morning, debugging the kettle and now iWifi base-station has reset. Three hours later the kettle was still having problems.