Professor Jürgen Schmidhuber has pioneered self-improving general problem solvers since 1987, and deep learning neural networks since 1991. The recurrent neural networks developed by his research groups at the Swiss AI Lab IDSIA (at the Università della Svizzera italiana and University of Applied Sciences and Arts of Italian Switzerland) and the Technical University of Munich were the first to win official international contests. They have revolutionised handwriting recognition, speech recognition, machine translation, image captioning and are now available to over a billion users through Google, Microsoft, IBM, Baidu and many other companies. DeepMind is heavily influenced by his lab's former students. His team's Deep Learners were the first to win object detection and image segmentation contests and achieved the world's first superhuman visual classification results, winning nine international competitions in machine learning and pattern recognition (more than any other team).

Ford outlined a few of the ways it's aiming to ship driverless cars by 2021, and part of the plan involves acquisitions. CEO Mark Fields revealed at a press event in Palo Alto today that the automaker acquired SAIPS, an Israeli company focusing on machine learning and computer vision. It's also partnering exclusively with Nirenberg Neuroscience, to bring more "humanlike intelligence" to machine learning components of driverless car systems. SAIPS' technology brings image and video processing algorithms, as well as deep learning tech focused on processing and classifying input signals, all key ingredients in the special sauce that makes up autonomous vehicle tech. This company's expertise should help with on-board interpretation of data captured by sensors on Ford's self-driving cars, and turning that data into usable info for the car's virtual driver system.

The latest news about Google's head of search, Amit Singhal, to leave the company he spent 15 years with, had the shocking effect on the SEO community. And what is more surprising - his successor, John Giannandrea, is the one who has worked on artificial intelligence at Google (including RankBrain - the part of search algorithm which uses AI to work with a queries search engine was not able to understand before). With this change of executives, we may be on the verge of a new era - the era of transition from the algorithm-based search to AI-based search. To power its artificial intelligence, Google uses deep learning (also known as neural networks) - one of machine learning methods, which uses a mathematical model to mimic the way as human brain neurons work. Deep learning is built on the concept of digital neurons, organized into layers.

OpenAI, a non-profit research company devoted to advancing artificial intelligence, has become one of the proud owners of a DGX-1, NVIDIA's so-called "supercomputer in a box," a server specifically designed for machine learning work. The system, which was hand-delivered to the company's headquarters in San Francisco by NVIDIA CEO Jen-Hsun Huang, will be used to run some of OpenAI's most computationally challenging applications. More generally, the DGX-1 will be used to support the company's mission, namely to "advance digital intelligence in the way that is most likely to benefit humanity as a whole, unconstrained by a need to generate financial return." The non-profit is being backed by Silicon Valley icons like Elon Musk and Peter Theil, and managed to attract more than a 1 billion worth of funding at the time it was founded in December 2015. The company only expects to spend a tiny fraction of that amount over the next few years.

Analysis Artificial intelligence and machine learning engines are underpinning many emerging applications and services, from making sense of big data for enterprises, to supporting hyper-personalized consumer content, or virtual reality gaming. The current challenge is to move AI from the supercomputer to the mobile device, supporting technologies like computer vision locally on the handset, car, camera or VR headset. Qualcomm has been a leader here, but the past weeks have seen Intel and its Chinese partner Rockchip invest in chip-level computer vision and AI capabilities, while Apple has acquired machine learning startup Turi, presumably to enhance its AI-driven personal assistant Siri. Rockchip has licensed the XM4 imaging and vision DSP (digital signal processor) design from IP provider CEVA, to enhance these aspects of its system-on-chip (SoC) products. It says it will enable advanced vision features at the low power levels required for mobile devices, supporting digital video stabilization, object detection and tracking, and 3D depth sensing, among others.

Data scientists and deep and machine learning researchers rely on frameworks and libraries such as Torch, Caffe, TensorFlow, and Theano. Studies by Colfax Research and Kyoto University have found that existing open source packages such as Torch and Theano deliver significantly faster performance through the use of Intel Scalable System Framework (Intel SSF) technologies like the Intel compiler and performance libraries for Intel Math Kernel Library (Intel MKL), Intel MPI (Message Passing Interface), and Intel Threading Building Blocks (Intel TBB), and Intel Distribution for Python (Intel Python). Andrey Vladimirov (Head of HPC Research, Colfax Research) noted that "new Intel SSF hardware and software in combination with code modernization delivered an observed 50x machine learning performance improvement in our case study". In the Colfax Research and Kyoto case studies as well as general Python scientific computing benchmarks, results run up to two orders of magnitude (100x) faster as a result of using Intel SSF technologies. Python is a powerful and popular scripting language that provides fast and fundamental tools for machine learning and scientific computing through popular packages such as scikit-learn, NumPy and SciPy.

This summer schools is aimed at graduate students and industrial engineers and researchers who already have some basic knowledge of machine learning (and possibly but not necessarily of deep learning) and wish to learn more about this rapidly growing field of research.

By happy circumstance, Santa Clara-based chip maker NVIDIA finds itself in the position of being an artificial intelligence (AI) startup king maker. The company designs and manufactures the entire computing platform for deep learning, the fastest growing field in AI, building everything from graphics processing units (GPUs) to software to systems purpose-built for deep learning. As the name might suggest, GPUs were developed to improve the computer graphics experience by offloading certain computationally intense image processing tasks from the standard central processing unit (CPU). The particular strength of a GPU is performing large numbers of parallel floating point calculations. This helps computer screens to increase in detail and complexity without sacrificing system performance, and modern gaming would not be possible without it.

If you have a few free minutes, try, for fun, filling them with Google Translate. And you need not be multilingual to enjoy it. Start with something straightforward: Enter an English phrase or sentence (idioms bring particular pleasure). Click a language, say, Spanish, and then "translate." Copy and paste the translated results over your original English phrase, reverse both languages (so that, in this example, Spanish is now where you begin and English is where you end), and again click "translate."

Scientists in Japan reportedly saved a woman's life by applying artificial intelligence to help them diagnose a rare form of cancer. Faced with a 60-year-old woman whose cancer diagnosis was unresponsive to treatment, they supplied an AI system with huge amounts of clinical cancer case data, and it diagnosed the rare leukemia that had stumped the clinicians in just ten minutes. The Watson AI system from IBM matched the patient's symptoms against 20m clinical oncology studies uploaded by a team headed by Arinobu Tojo at the University of Tokyo's Institute of Medical Science that included symptoms, treatment and response. The Memorial Sloan Kettering Cancer Center in New York has carried out similar work, where teams of clinicians and data analysts trained Watson's machine learning capabilities with oncological data in order to focus its predictive and analytic capabilities on diagnosing cancers. IBM Watson first became famous when it won the US television game show Jeopardy in 2011.