As facial recognition systems become increasingly accurate, more governments and law enforcement organizations are tapping them to verify people's identities, nab criminals and keep transactions secure. In recent months, France's government announced a nationwide facial recognition ID program, a UK court ruled that live facial recognition doesn't violate privacy rights and research revealed that the US Immigration and Customs Enforcement (ICE) agency and the FBI are using facial recognition to apprehend undocumented immigrants. Most of this activity is undertaken in the name of safety and security, but it is also raising major red flags among privacy advocates. They argue that the technology--which can scan and identify faces without consent in crowded streets, retail stores and sports stadiums--is predatory and invasive. Among consumers, the jury is still out.

One of the most difficult of tasks on the modern battlefield is predicting where the enemy will attack next. Although the Army has plenty of ways to find the enemy, figuring out his intentions are something else entirely. Now the U.S. Army plans to use drones, target recognition, artificial intelligence, and machine learning to tell the colonels and generals where an attack appears imminent. The Army's Aided Threat Recognition from Mobile Cooperative and Autonomous Sensors (ATR-MCAS) program aims to operate autonomous air and ground drones throughout the battle zone, keeping a continuous watch on the enemy. The drones identify the enemy weapons systems, such as tanks or infantry fighting vehicles, then pass on the sightings to the AI.

The two that have struck me the most are from Google and Amazon, each taking a different tack to sell you on their respective intelligent assistants. Amazon got Ellen DeGeneres and Portia de Rossi leading off the joke about what life was like before Alexa, then people asking other people throughout history things they'd normally ask Alexa. I was with it until there was a joke about refusing to erase the Nixon tapes which maybe was a little too on the nose given this past year's voice-recording scandals. In any case, the motive is clear: make Alexa seem like an integral part of our lives now, like electricity or running water. But do that in a way that makes it seem lighthearted and fun, not oppressive.

On Wednesday, Microsoft Corp. announced AI for Health, a new $40 million, five-year program and part of the AI for Good initiative, that will leverage artificial intelligence (AI) technology to empower researchers and organizations addressing some of the world's toughest challenges in health. "Artificial intelligence has the potential to solve some of humanity's greatest challenges, like improving the health of communities around the world," said Brad Smith, president, Microsoft. "We know that putting this powerful technology into the hands of experts tackling this problem can accelerate new solutions and improve access for underserved populations. That's why we created AI for Health." In a new era of tech intensity, in which technology is reshaping every organization and becoming embedded in the fabric of every aspect of our lives, digital advances will continue to reshape our world in profound ways.

Artificial intelligence (AI) will transform the pharmaceutical cold chain -- not in the distant, hypothetical future, but in the next few years. As the president of a company that has been actively involved in the creation of an application that will utilize machine learning to generate predictive data on environmental hazards in the biopharmaceutical cold chain cycle, I've seen firsthand the promise of this technology. When coupled with machine learning and predictive analytics, the AI transformation goes much deeper than smarter search functions. It holds the potential to address some of the biggest challenges in pharmaceutical cold chain management. By aggregating and analyzing data from multiple sources -- a drug order and weather data along a delivery route, for example -- AI-based systems can provide complete visibility with predictive data throughout the cold chain.

Researchers at McGill University showed that analysis of blood samples using artificial intelligence (AI) could predict and provide a more comprehensive explanation for the progression of neurodegenerative diseases. The findings were published in the journal Brain. The results were gathered from analyzing the blood-brain samples of over 1,900 patients with the presence of late-onset Alzheimer's and Huntington's disease. Researchers used a novel gene expression contrastive trajectory inference (GE-cTI) method able to unveil enriched temporal patterns, while also predicting neuropathological severity among affected participants. Spanning decades, the machine learning algorithm identified how the patients' genes expressed themselves uniquely, a first study of which revealed how molecular changes underlies neurodegeneration.

Deep learning has a growing history of successes, but heavy algorithms running on large graphical processing units are far from ideal. A relatively new family of deep learning methods called quantized neural networks have appeared in answer to this discrepancy. In Leapmind R&D, we are working on quantization methods, among others, for enabling efficient high-performance deep learning computation on small devices. Neural networks are composed of multiple layers of parameters, each layer transforms the input image, separating and contracting [0] the feature space, resulting in the separation of input images to their various classes. Perhaps the most notable of deep learning problems are image classification, object detection, and segmentation.

Run a Google search on "bias in AI" and you'll find all kinds of stories about what can -- and does -- happen when systems become automated and the human element is removed. Of course, today, AI and machine learning are embedded into myriad different technologies, and while it no doubt plays a positive role, biased data is often problematic. As AI applications become more prevalent, channel firms can play a role in helping customers mitigate algorithm bias. Biased AI ranked the second biggest AI-related ethical concern associated with AI in Deloitte's 2018 "State of AI in the Enterprise" study, behind AI's power to help create and spread false information. "Today, algorithms are commonly used to help make many important decisions, such as granting credit, detecting crime, and assigning punishment," the report notes.

This is the SECOND in a series of posts on applying Tim Ferriss' accelerated learning framework to Data Science. My goal is to become a world-class (top 5%) Data Scientist in 6 months, while open-sourcing everything I find and learn along the way. And if you stick around until the end, you're in for a special treat. A simple Google search of "how to learn Data Science" returns thousands of learning plans, degree programs, tutorials, and bootcamps. It's never been more difficult for a beginner to find signal in the noise. Everyone seems to have a different opinion, and the only common approach appears to be dumping a long list of courses to take and books to read, all the while providing little to no context into how these concepts fit into the bigger picture.

Over the last few decades, machine learning has revolutionized many sectors of society, with machines learning to drive cars, identify tumors and play chess--often surpassing their human counterparts. Now, a team of scientists based at the Okinawa Institute of Science and Technology Graduate University (OIST), the University of Munich and the CNRS at the University of Bordeaux have shown that machines can also beat theoretical physicists at their own game, solving complex problems just as accurately as scientists, but considerably faster. In the study, recently published in Physical Review B, a machine learned to identify unusual magnetic phases in a model of pyrochlore--a naturally-occurring mineral with a tetrahedral lattice structure. Remarkably, when using the machine, solving the problem took only a few weeks, whereas previously the OIST scientists needed six years. "This feels like a really significant step," said Professor Nic Shannon, who leads the Theory of Quantum Matter (TQM) Unit at OIST. "Computers are now able to carry out science in a very meaningful way and tackle problems that have long frustrated scientists."