Facial recognition using deep learning technology is being introduced by Panasonic. The new high-precision face recognition software can identify faces including at an angle, those partially hidden by sunglasses and those that are difficult to recognize with conventional technologies, according to Panasonic. The software features the iA (intelligent auto) mode, which automatically adjusts to shoot optimal images for face recognition and the best shots are then sent to the server for recognition. With conventional facial recognition systems, all the images are sent to the server, where the recognition occurs.

Abstract: Unsupervised learning can be a challenging task due to the absence of an outcome variable. When on top it concerns outlier detection, the scarcity of this type of observations makes the problem even more challenging. This talk will be about applying the isolation forest algorithm in a financial context in order to detect unusual customer behavior.

Entrepreneur and CEO of Tesla and SpaceX, Elon Musk may have a little more time on his hands (maybe), as he's departing his spot on the board of the artificial-intelligence safety group OpenAI, according to a blog post. The departure is likely the result of Tesla's move into the realm of A.I., which he said in 2017 would be the "best in the world" and would even be able to "predict your destination." Musk will continue to "donate and advise the organization," OpenAI said in a blog post Feb. 20, adding that "As Tesla continues to become more focused on AI, this will eliminate a potential future conflict for Elon." Musk and Y Combinator CEO Sam Altman co-founded the nonprofit venture in December 2015, with backing from the likes of Peter Thiel (an early backer of Facebook), Reid Hoffman (who co-founded LinkedIn), Jessica Livingston (founding partner of Y Combinator), Greg Brockman and computer scientist Ilya Sutskever, according to the OpenAI website. OpenAI's mission is to develop safe AGI (artificial general intelligence) and ensure those developments are made public; its 60 or so researchers are tasked with long-term research, according to the company.

Each new technological breakthrough comes seemingly prepackaged with a new way for hackers to kill us all: self-driving cars, space-based weapons, and even nuclear security systems are vulnerable to someone with the right knowledge and a bit of code. Now, deep-learning artificial intelligence looks like the next big threat, and not because it will gain sentience to murder us with robots (as Elon Musk has warned): a group of computer scientists from the US and China recently published a paper proposing the first-ever trojan for a neural network. Neural networks are the primary tool used in AI to accomplish "deep learning," which has allowed AIs to master complex tasks like playing chess and Go. Neural networks function similar to a human brain, which is how they got the name. Information passes through layers of neuron-like connections, which then analyze the information and spit out a response.

Such attacks fall into three categories of violations: digital, physical and political, according to the report. AI will allow the automation of tasks involved in digital cyberattacks that will make those offensives easier to carry out, larger and more efficient. They authors expect new varieties of attacks using speech synthesis for impersonation and automated hacking too. In the physical ream, using AI to automate tasks involved in drone and autonomous weapon attacks "may expand the threats associated with these attacks," the report says. Further, the report predicts new attacks that "subvert" the signals to autonomous vehicles, causing them to crash.

Game theory is a field of mathematics that is used to analyse the strategies used by decision makers in competitive situations. It can apply to humans, animals, and computers in various situations but is commonly used in AI research to study "multi-agent" environments where there is more than one system, for example several household robots cooperating to clean the house. Traditionally, the evolutionary dynamics of multi-agent systems have been analysed using simple, symmetric games, such as the classic Prisoner's Dilemma, where each player has access to the same set of actions. Although these games can provide useful insights into how multi-agent systems work and tell us how to achieve a desirable outcome for all players - known as the Nash equilibrium - they cannot model all situations. Our new technique allows us to quickly and easily identify the strategies used to find the Nash equilibrium in more complex asymmetric games - characterised as games where each player has different strategies, goals and rewards.

Mathematicians at the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) have developed a new approach to machine learning aimed at experimental imaging data. Rather than relying on the tens or hundreds of thousands of images used by typical machine learning methods, this new approach "learns" much more quickly and requires far fewer images. Daniël Pelt and James Sethian of Berkeley Lab's Center for Advanced Mathematics for Energy Research Applications (CAMERA) turned the usual machine learning perspective on its head by developing what they call a "Mixed-Scale Dense Convolution Neural Network (MS-D)" that requires far fewer parameters than traditional methods, converges quickly, and has the ability to "learn" from a remarkably small training set. Their approach is already being used to extract biological structure from cell images, and is poised to provide a major new computational tool to analyze data across a wide range of research areas. As experimental facilities generate higher resolution images at higher speeds, scientists can struggle to manage and analyze the resulting data, which is often done painstakingly by hand.

This is an illustration of a multi-compartment neural network model for deep learning. The tree-like form separates "roots," where bottoms of cortical neurons are located just where they need to be to receive signals about sensory input, from "branches" at the top, which are well placed to receive feedback error signals. Right: Illustration of simplified pyramidal neuron models. Deep-learning researchers have found that certain neurons in the brain have shape and electrical properties that appear to be well-suited for "deep learning" -- the kind of machine-intelligence used in beating humans at Go and Chess. Canadian Institute For Advanced Research (CIFAR) Fellow Blake Richards and his colleagues -- Jordan Guerguiev at the University of Toronto, Scarborough, and Timothy Lillicrap at Google DeepMind -- developed an algorithm that simulates how a deep-learning network could work in our brains.

Some deep learning systems have many different layers, and each layer can be composed of different algorithms. Analytics is the process used to find meaningful results or patterns from data. Analytics may use machine learning, but may also rely on general mathematical techniques, and usually involves statistics. When analyzing data, practitioners use feedback loops to test their results, and most of our statistical tools rely on these feedback loops to generate meaningful information. In my view, if a process doesn't include statistics and a feedback loop, then it really isn't using analytics. Analytics is a good umbrella term for the set of tools we use to solve problems and find meaning in our data, including the use of machine learning.

Despite significant advances in artificial intelligence (AI) for computer vision, its application in medical imaging has been limited by the burden and limits of expert-generated labels. We used images from optical coherence tomography angiography (OCTA), a relatively new imaging modality that measures perfusion of the retinal vasculature, to train an AI algorithm to generate vasculature maps from standard structural optical coherence tomography (OCT) images of the same retinae, both exceeding the ability and bypassing the need for expert labeling. Deep learning was able to infer perfusion of microvasculature from structural OCT images with similar fidelity to OCTA and significantly better than expert clinicians (P < 0.00001). OCTA suffers from need of specialized hardware, laborious acquisition protocols, and motion artifacts; whereas our model works directly from standard OCT which are ubiquitous and quick to obtain, and allows unlocking of large volumes of previously collected standard OCT data both in existing clinical trials and clinical practice. This finding demonstrates a novel application of AI to medical imaging, whereby subtle regularities between different modalities are used to image the same body part and AI is used to generate detailed and accurate inferences of tissue function from structure imaging.