"The field of Machine Learning seeks to answer these questions: How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?"
– from The Discipline of Machine Learning by Tom Mitchell. CMU-ML-06-108, 2006.
Until recently, artificial intelligence has struggled to gain a foothold on Wall Street. In the last few years, large investment banks like Goldman Sachs and JP Morgan have hired artificial intelligence specialists away from academia and put them in charge of their internal AI divisions. Financial technology start-ups have begun using machine-learning algorithms to model credit ratings and detect fraud. And hedge funds and high-frequency traders are using AI to make investment decisions. Politicians are starting to take notice.
DeepMind says it has created the first artificial intelligence to reach the top league of one of the most popular esport video games. It says Starcraft 2 had posed a tougher AI challenge than chess and other board games, in part because opponents' pieces were often hidden from view. Publication in the peer-reviewed journal Nature allows the London-based lab to claim a new milestone. But some pro-gamers have mixed feelings about it claiming Grandmaster status. DeepMind - which is owned by Google's parent company Alphabet - said the development of AlphaStar would help it develop other AI tools which should ultimately benefit humanity.
Google has its own perfume--or at least one team of the company's researchers does. Crafted under the guidance of expert French perfumers, the mixture has notes of vanilla, jasmine, melon, and strawberries. "It wasn't half bad," says Alex Wiltschko, who keeps a vial of the perfume in his kitchen. Google's not marketing that scent anytime soon, but it is sticking its nose into yet another aspect of our lives: smell. On Thursday, researchers at Google Brain released a paper on the preprint site Arxiv showing how they trained a set of machine-learning algorithms to predict molecules' smell based on their structures.
SAN FRANCISCO, Oct. 24, 2019 (GLOBE NEWSWIRE) -- MICRON INSIGHT -- Micron Technology, Inc. (Nasdaq: MU), today announced a powerful new set of high-performance hardware and software tools for deep learning applications with the acquisition of FWDNXT, a software and hardware startup. When combined with advanced Micron memory, FWDNXT's (pronounced "forward next") artificial intelligence (AI) hardware and software technology enables Micron to explore deep learning solutions required for data analytics, particularly in IoT and edge computing. With this acquisition, Micron is integrating compute, memory, tools and software into a comprehensive AI development platform. This platform in turn provides the key building blocks required to explore innovative memory optimized for AI workloads. "FWDNXT is an architecture designed to create fast-time-to-market edge AI solutions through an extremely easy to use software framework with broad modeling support and flexibility," said Micron Executive Vice President and Chief Business Officer Sumit Sadana.
What would you do if you had the super-power to accurately answer, in a few milliseconds, a multiple-choice question with a billion choices? Would you design the next generation of Web search engines, which could predict which of the billions of documents might be relevant to a given query? Would you build the next generation of retail recommender systems that have things delivered to your doorstep just as you need them? Or would you try and predict the next word about to be uttered by U.S. President Donald Trump? The objective in extreme classification, a new research area in machine learning, is to develop algorithms with such capabilities.
Yale University researchers have developed a way to leverage neural networks to reveal patterns of activity of individual cells from multiple individuals. Researchers at Yale University have developed a method of leveraging artificial intelligence (AI) neural networks to reveal larger patterns of activity of individual cells that come from several individuals. The AI neural network, called SAUCIE (Sparse Autoencoder for Clustering, Imputation, and Embedding), can reveal minute cellular differences within individuals, as well as broader patterns that describe how the body functions. The new method will allow researchers to identify larger clusters of cellular activity that could shed light on the basis of a host's pathogens. For example, the team used SAUCIE to analyze 20 million cells from 60 patients and identify rare Gamma-Delta T cell types that regulate how the body responds to the virus that causes Dengue fever.
Fueled by improvements in speech recognition, machine learning, better algorithms, cloud processing, and more powerful computing devices, the quality of machine translations is improving. Learning another language has never been a simple proposition. It can take months of study to absorb the basics and years to become fluent. Of course, there's the added headache that learning a language doesn't help if a person encounters one of the world's other 7,000 or so languages. "There has always been a need for human translators and interpreters," says Andrew Ochoa, CEO of translation technology firm Waverly Labs.
Whether it's dubious viral memes, gaffe-prone presidential debates, or surreal TikTok remixes, you could spend the rest of your life trying to watch all the video footage posted on YouTube in a single day. Researchers want to let artificial intelligence algorithms watch and make sense of it instead. A group from MIT and IBM developed an algorithm capable of accurately recognizing actions in videos while consuming a small fraction of the processing power previously required, potentially changing the economics of applying AI to large amounts of video. The method adapts an AI approach used to process still images to give it a crude concept of passing time. The work is a step towards having AI recognize what's happening in video, perhaps helping to tame the vast amounts now being generated.
"This is a key first step in being able to shed light on serial hijackers' behavior," says MIT Ph.D. candidate Cecilia Testart. Hijacking IP addresses is an increasingly popular form of cyber-attack. This is done for a range of reasons, from sending spam and malware to stealing Bitcoin. It's estimated that in 2017 alone, routing incidents such as IP hijacks affected more than 10 percent of all the world's routing domains. There have been major incidents at Amazon and Google and even in nation-states -- a study last year suggested that a Chinese telecom company used the approach to gather intelligence on western countries by rerouting their Internet traffic through China.
A self-driving car approaches a stop sign, but instead of slowing down, it accelerates into the busy intersection. An accident report later reveals that four small rectangles had been stuck to the face of the sign. These fooled the car's onboard artificial intelligence (AI) into misreading the word'stop' as'speed limit 45'. Such an event hasn't actually happened, but the potential for sabotaging AI is very real. Researchers have already demonstrated how to fool an AI system into misreading a stop sign, by carefully positioning stickers on it1. They have deceived facial-recognition systems by sticking a printed pattern on glasses or hats. And they have tricked speech-recognition systems into hearing phantom phrases by inserting patterns of white noise in the audio.