At the recently concluded ITC India conference, Mentor experts presented the two highest-attended tutorials. One tutorial was AI Chip Technologies and Its DFT Methodologies, presented by Mentor's Yu Huang, Rahul Singhal, and Lee Harrison Hardware acceleration for Artificial Intelligence (AI) is now a very competitive and rapidly evolving market. There are more than 50 startups and 25 established semiconductor companies all racing to capture a portion of the business. The ITC India tutorial covered the basics of deep learning and gave an overview of how AI chips accelerate deep learning computations. They covered the critical and special characteristics and the architecture of the most popular AI chips.

An AI team at the University of Waterloo, Canada, developed a new type of compact family of deep neural networks (AttoNets), which can even run on smartphones, tablets, and other mobile devices. The main problem with available neural networks is they require high configuration machines and difficult to run in any real-world situations. While AttoNets is faster and efficient for edge computing and can have great applications in aerospace, automotive, finance, agriculture, medical diagnostics, consumer electronics sector, etc. AttoNets uses Generative Synthesis, which was recently validated by Intel, and in a recent paper with Audi Electronics Ventures shown to accelerate the deep learning design for autonomous driving greatly.

The following is adapted from a joint release from the MIT Press and the Harvard Data Science Initiative. The MIT Press and the Harvard Data Science Initiative (HDSI) have announced the launch of the Harvard Data Science Review (HDSR). The open-access journal, published by MIT Press and hosted online via the multimedia platform PubPub, an initiative of the MIT Knowledge Futures group, will feature leading global thinkers in the burgeoning field of data science, making research, educational resources, and commentary accessible to academics, professionals, and the interested public. With demand for data scientists booming, HDSR will provide a centralized, authoritative, and peer-reviewed publishing community to service the growing profession. The first issue features articles on topics ranging from authorship attribution of John Lennon-Paul McCartney songs to machine learning models for predicting drug approvals to artificial intelligence (AI).

Alibaba is well aware of the growing demand for dedicated compute to power today's AI applications. Last year, the Hangzhou-based tech giant launched its semiconductor subsidiary Pingtouge ("Honey Badger" in Chinese) to develop embedded chip and neural network accelerators. At the time, Alibaba CTO Jeff Zhang pledged Pingtouge would produce the world's most advanced neural network chip by the middle of this year. Today, Alibaba kept its promise. At the Alibaba Cloud (Aliyun) Apsara Conference 2019, Pingtouge unveiled its first AI dedicated processor for cloud-based large-scale AI inferencing.

City to roll out'smart traffic' system Bhubaneswar: More than a year after chief minister Naveen Patnaik inaugurated the adaptive traffic signal system (ATSC) under the ambitious Smart City programme here, the commissionerate police have decided to implement the technology along the busy KIIT-Jayadev Vihar route. "We have decided to adopt the artificial intelligence (AI) technology along the KIIT-Jayadev Vihar route that often sees traffic congestions. On Thursday evening, we introduced the innovation at Damana Square on an experimental basis and received a positive feedback," deputy commissioner of police (traffic) Sagarika Nath told TOI. The ATSC is equipped with an AI system that can automatically judge the density of traffic and analyse the optimum time needed to flash the signal instead of setting a fixed time for the signal to turn green or red at traffic posts. Though ATSC was launched in July 2018, its implementation in the capital city was delayed owing to lack of proper infrastructure and logistic support.

AI comes with a promise of overcoming a majority of healthcare barriers that beleaguer India. This feature explores few AI applications that are currently in use in India and outlines challenges that need to be overcome in the coming decade. This site uses Akismet to reduce spam. Learn how your comment data is processed.

FOR TWO years reports of mass incarceration have seeped out of the remote Chinese province of Xinjiang. Over 1m people, mainly Uighurs and other Muslim minorities, have been locked up in camps. Millions more live under a police state. American officials, fearful of upending trade negotiations, have dithered over a response. On October 7th, three days ahead of the 13th round of talks, they put their foot down.

Jeff Hawkins is the founder of Redwood Center for Theoretical Neuroscience in 2002 and Numenta in 2005. In his 2004 book titled On Intelligence, and in his research before and after, he and his team have worked to reverse-engineer the neocortex and propose artificial intelligence architectures, approaches, and ideas that are inspired by the human brain. These ideas include Hierarchical Temporal Memory (HTM) from 2004 and The Thousand Brains Theory of Intelligence from 2017. This conversation is part of the Artificial Intelligence podcast. Audio podcast version is available on https://lexfridman.com/ai/

Though most are loath to admit it, retailers already make extensive, behind-the-scenes use of biometrics to track employees, nab shoplifters and improve store security. An October 2018 New York Magazine article titled, "Smile! Peter Trepp, CEO of facial recognition software company FaceFirst, told McClatchy in May 2018 that his company can "match a face against the database of 25 million people in just under a second," and the firm's website counts hundreds of big-box stores, superstores, department stores, grocery stores, pharmacies and Fortune 500s among its clients. A similar article, published by BuzzFeed News in August 2018, titled, "Thousands of Stores Will Soon Use Facial Recognition, and They Won't Need Your Consent," similarly revealed the technology's pervasiveness and delved deeper into its privacy implications. In our latest report, "Biometric Marketing 2019," we found that despite these concerns, retailers are exploring biometric technology, including behavioral tracking and facial and voice recognition, for advertising and promotional targeting.

Model-based reinforcement learning could enable sample-efficient learning by quickly acquiring rich knowledge about the world and using it to improve behaviour without additional data. Learned dynamics models can be directly used for planning actions but this has been challenging because of inaccuracies in the learned models. In this paper, we focus on planning with learned dynamics models and propose to regularize it using energy estimates of state transitions in the environment. We visually demonstrate the effectiveness of the proposed method and show that off-policy training of an energy estimator can be effectively used to regularize planning with pre-trained dynamics models. Further, we demonstrate that the proposed method enables sample-efficient learning to achieve competitive performance in challenging continuous control tasks such as Half-cheetah and Ant in just a few minutes of experience.