Carnegie Mellon University professor Russ Salakhutdinov has been hired by Apple to lead a team focused on artificial intelligence, according to a tweet Salakhutdinov sent out this morning. He will continue to teach at Carnegie Mellon, but will also serve as "Director of AI Research" at Apple. In his tweet, Salakhutdinov says he is seeking additional research scientists with machine learning expertise to join his team. An included job posting asks that candidates have experience with Deep Learning, Computer Vision, Machine Learning, Reinforcement Learning, Optimization, and/or Data Mining. Salakhutdinov specializes in statistical machine learning and has authored many papers on neural networks, deep kernel learning, reinforcement learning, and other related topics.

In this post, we'll show how easy it is to use MLDB to build your own real time image classification service. We will use different brand of cars in this example, but you can adapt what we show to train a model on any image dataset you want. We will be using a TensorFlow deep convolutional neural network, transfer learning, and everything will run off MLDB. At a high level, transfer learning allows us to take a model that was trained on one task and use its learned knowledge on another task. We use the Inception- v3 model, a deep convolutional neural network, that was trained on the ImageNet Large Visual Recognition Challenge dataset.

Artificial intelligence (AI) continues to play an expanding role in the future of high-performance computing (HPC). As machines increasingly become able to learn and even reason in ways similar to humans, we're getting closer to solving the tremendously complex social problems that have always been beyond the realm of compute. Deep learning, a branch of machine learning, uses multi-layer artificial neural networks and data-intensive training techniques to refine algorithms as they are exposed to more data. This process emulates the decision-making abilities of the human brain, which until recently was the only network that could learn and adapt based on prior experiences.

Making computers more similar to the human brain is most probably one of the most major challenges facing us in the 21st century. We expect computers to begin talking, comprehend and provide solutions to problems of all kinds. There is now a rising demand for computers to be able to see and identify images. After being blind for too long, now our smartest computers can finally begin to see their outside world. Deep learning is making this truly revolutionary advance very possible indeed.

When the movie The Terminator was released in 1984, the notion of computers becoming self-aware seemed so futuristic that it was almost difficult to fathom. But just 22 years later, computers are rapidly gaining the ability to autonomously learn, predict, and adapt through the analysis of massive datasets. And luckily for us, the result is not a nuclear holocaust as the movie predicted, but new levels of data-driven innovation and opportunities for competitive advantage for a variety of enterprises and industries. Artificial intelligence (AI) continues to play an expanding role in the future of high-performance computing (HPC). As machines increasingly become able to learn and even reason in ways similar to humans, we're getting closer to solving the tremendously complex social problems that have always been beyond the realm of compute.

Cohort 3 research Orange Gao (left in image below) attended the Women in Machine Intelligence in Healthcare Dinner. RE.WORK, an all-female company which is a strong advocate for supporting female entrepreneurs and women working towards advancing technology and science, organized an evening dinner event of discussions & networking around the progress and application of machine intelligence within healthcare on Wednesday 12th October 2016 in London. Dinner and presentations commenced at 7pm and finished at approximated 10pm. RE.WORK invited 50 attendees from leading academics, industry experts and entrepreneurs. During the dinner, speakers gave wonderful and insightful presentations about the new trends and ideas around the Machine Learning in health.

In the movie Transcendence, Johnny Depp plays Dr Will Caster, a researcher in artificial intelligence at Berkeley trying to build a sentient computer. Stuart Russell is Will Caster's real life equivalent. He works on artificial intelligence at the University of California at Berkeley, and is co-author of the definitive textbook on AI. He has also been very vocal about the risks of research in AI succeeding. Earlier this year, Google's DeepMind taught a computer program to play a wide variety of Atari video games at a superhuman level in a matter of hours.

Artificial intelligence, or "AI," is the current direction of technological innovation, but if the U.S. does not step up its game, another world power may lead the future. The number of journal articles mentioning "deep learning" or "deep neural networks" produced by China…

OpenAI wants to build the technology that will finally create a computer that can converse in a way that is indistinguishable humans. The nonprofit, backed by Tesla CEO Elon Musk and his PayPal co-founder Peter Thiel, brought on NVIDIA's supercomputer DGX-1, which has 170 teraflops of computing power, to help hone machine learning systems to create algorithms that can comprehend language and teach robots to respond appropriately. That should solve one of the biggest hindrances to making AI systems that can learn complex interactions: the slowness of current computers. "The speed of our computers is in some sense the lifeblood of deep learning," OpenAI research director Ilya Sutskever in an NVIDIA video. The goal of this project is to allow a robot to become smart enough to not only recognize speech, but to also use the data it gathers to formulate appropriate responses on its own--and to do that, computers need to digest data more quickly than they are currently capable of. The DGX-1, which is optimized for an arm of machine learning called deep learning, can feed copious amounts of natural language data into OpenAI's network much quicker than ever before.

Technology company Nauto has entered into agreements with BMW i Ventures and Toyota Research Institute, as well as with Allianz Ventures, part of the leading global financial service provider and insurance company Allianz Group. Nauto developed deep learning capabilities that run both in the cloud and on retrofit devices that can be mounted in any vehicle. Nauto is already deployed into commercial passenger, logistics and delivery fleets and enables these fleets to manage vehicle and driver safety and operate more efficiently. Nauto detects driver attention, coaches drivers and warns of collisions, keeps fleet managers in touch with their drivers and helps them optimize vehicle deployment. In fact, Nauto claims, its algorithms provide 5X more risk differentiation between the best and the worst drivers.