Omron Corporation (Headquarters: Kyoto; President & CEO: Yoshihito Yamada) announced that table tennis robot FORPHEUS, which was first created in 2013 as a symbol of Omron's technological expertise and which continues to evolve, was certified as the "first robot table tennis tutor" in the world by Guinness World Records . Omron exhibited FORPHEUS in "CEATEC JAPAN 2015" in October 2015 in order to showcase technology that "brings out people's abilities". FORPHEUS was certified on January 6, 2016 for a Guinness World Record as the "first robot table tennis tutor", and described in the Guinness Book of Records 2017 edition as follows: "In October, 2015, the Japanese company Omron Corporation introduced a table tennis robot with a sensor that measures the position of its opponent and the movement of the ball 80 times per second, can predict the trajectory of the ball and hit the ball back, and project the landing point of the ball". Takumi Nippon Project leader, Vihag Kulshrestha said: "Guinness World Records Japan launched a project called Takumi Nippon in 2014 that has transmitted Japan's wonderful technology to the world through the Guinness World Records. The world record achieved by FORPHEUS, which was developed with the technical capabilities of the Omron Corporation, as the world's "first robot table tennis tutor", is also a record for the Takumi Nippon Project. I am very pleased to be able to convey Japan's wonderful artisanship to people all over the world".

Speech is a fundamental aspect of human behavior, yet it remains something that many of us struggle with. It's believed that around 1 in 14 adults in the United States have some kind of voice disorder, and our understanding of such disorders makes it difficult to both diagnose and treat. A team from MIT and the Massachusetts General Hospital believe that machine learning can play a part in better understanding speech disorders. In a recent paper, they describe using a wearable device to collect accelerometer data to detect differences in people with Muscle Tension Dysphonia (MTD) and a control group. After such individuals with MTD had received therapy for the condition, their behaviors appeared to converge with that of the control group.

In 1977 when Star Wars introduced the world to C-3PO and R2D2, artificial intelligence (AI) seemed as fantastical as that galaxy far, far away. But at Ignite this year when Satya Nadella, CEO of Microsoft, took to the keynote stage it was very clear that AI is right at our feet. Instead, their vision for AI enhances the many tools we use already use at work and in our everyday lives! At day two of the conference, I had the pleasure of talking with Alan Lepofsky, Principal Analyst of Collaboration Software at Constellation Research, who was even more excited than I was about the whole thing, Check out our interview to hear more about Microsoft's new direction and what a future powered by AI tools might be like for people and businesses everywhere. Dux: Hey everybody, this is Dux.

Siri is about to get a lot smarter thank to Carnegie Mellon researcher Russ Salakhutdinov, who announced today that he is joining Apple to lead the company's artificial intelligence efforts. Excited about joining Apple as a director of AI research in addition to my work at CMU. Apply to work with my teamhttps://t.co/U2hQl2GdhA Although he's not a household name, Russ Salakhutdinov is one of the biggest deep learning figures in academia. His hiring by Apple comes after the company has been criticized for Siri's weak performance compared to rival digital assistants from Google, Amazon and Microsoft. Before working at CMU, Salakhutdinov worked at Toronto University and MIT.

In this paper we study the problem of recovering a structured but unknown parameter ${\bf{\theta}}^*$ from $n$ nonlinear observations of the form $y_i=f(\langle {\bf{x}}_i,{\bf{\theta}}^*\rangle)$ for $i=1,2,\ldots,n$. We develop a framework for characterizing time-data tradeoffs for a variety of parameter estimation algorithms when the nonlinear function $f$ is unknown. This framework includes many popular heuristics such as projected/proximal gradient descent and stochastic schemes. For example, we show that a projected gradient descent scheme converges at a linear rate to a reliable solution with a near minimal number of samples. We provide a sharp characterization of the convergence rate of such algorithms as a function of sample size, amount of a-prior knowledge available about the parameter and a measure of the nonlinearity of the function $f$. These results provide a precise understanding of the various tradeoffs involved between statistical and computational resources as well as a-prior side information available for such nonlinear parameter estimation problems.

Independent component analysis (ICA) is the most popular method for blind source separation (BSS) with a diverse set of applications, such as biomedical signal processing, video and image analysis, and communications. Maximum likelihood (ML), an optimal theoretical framework for ICA, requires knowledge of the true underlying probability density function (PDF) of the latent sources, which, in many applications, is unknown. ICA algorithms cast in the ML framework often deviate from its theoretical optimality properties due to poor estimation of the source PDF. Therefore, accurate estimation of source PDFs is critical in order to avoid model mismatch and poor ICA performance. In this paper, we propose a new and efficient ICA algorithm based on entropy maximization with kernels, (ICA-EMK), which uses both global and local measuring functions as constraints to dynamically estimate the PDF of the sources with reasonable complexity. In addition, the new algorithm performs optimization with respect to each of the cost function gradient directions separately, enabling parallel implementations on multi-core computers. We demonstrate the superior performance of ICA-EMK over competing ICA algorithms using simulated as well as real-world data.

In May, Google research scientist Douglas Eck left his Silicon Valley office to spend a few days at Moogfest, a gathering for music, art, and technology enthusiasts deep in North Carolina's Smoky Mountains. Eck told the festival's music-savvy attendees about his team's new ideas about how to teach computers to help musicians write music--generate harmonies, create transitions in a song, and elaborate on a recurring theme. Someday, the machine could learn to write a song all on its own. Eck hadn't come to the festival--which was inspired by the legendary creator of the Moog synthesizer and peopled with musicians and electronic music nerds--simply to introduce his team's challenging project. To "learn" how to create art and music, he and his colleagues need users to feed the machines tons of data, using MIDI, a format more often associated with dinky video game sounds than with complex machine learning.

NVIDIA's (NVDA) quarterly revenues have been surging at double-digit rates in the last three quarters and, looking at the guidance for the next quarter, it's clear that the company is expecting the growth rate to continue unabated in the near future. One of the key factors that has added fuel to their engines is NVIDIA's new growth drivers: the data center segment and auto segment. In the most recent quarter, NVIDIA's data center unit reported 151 million in sales, a growth of 109.72% The growth in data center revenues is much higher than other segments, and there are several reasons why this growth can, in fact, continue its breakneck pace for several more quarters. NVIDIA's expertise in the GPU segment has given it a range of must-have products for hyperscale data centers.

Note that, while there are numerous machine learning ebooks available for free online, including many which are very well-known, I have opted to move past these "regulars" and seek out lesser-known and more niche options for readers. Don't know where to start? If you are looking for something more, you could look here for an overview of MOOCs and online lectures from freely-available university lectures. Of course, nothing substitutes rigorous formal education, but let's say that isn't in the cards for whatever reason. Not all machine learning positions require a PhD; it really depends where on the machine learning spectrum one wants to fit in.

In a study published earlier this month in the journal Astrobiology, two researchers say the scientific community should take a closer look at a study of Mars' soil published in 1976. Because two NASA robots may have discovered signs of life on Mars almost four decades ago, say Gilbert Levin from Arizona State University and Patricia Ann Straat from the US National Institutes of Health. It all started when NASA sent two probes, named Viking 1 and Viking 2, to Mars in 1976 to test for signs of life on the Red Planet. As the first spacecraft from Earth to reach Mars, the Viking probes conducted three studies on the planet's biology. To conduct one of the studies, the labed release (LR) experiment, scientists took soil picked up by the Viking probes and mixed it with nutrient-rich water.