Google buys machine vision startup focusing on'instant object recognition' It's a good time to be a machine learning startup. Two weeks after Twitter bought, has purchased . The acquisition was made for an unknown sum, and seems primarily a grab for talent. Moodstocks' engineers and researchers will move to Google's Paris R&D site, and the startup's primary commercial product -- an image recognition API for smartphones -- will be phased out. "Ever since we started Moodstocks, our Read More ... Tags: Computer systems Artificial intelligence Machine intelligence Machine learning Places: Americas North America United States Google today announced it has acquired French machine learning startup Moodstocks for an undisclosed sum. The deal is expected to close in the next few weeks and seems to be focused primarily on the talent, with the team at Moodstocks moving to Google's Paris R&D site, and its image recognition API for smartphones to be gradually phased out.

July 6, 2016, Over a third (36%) of all developers who are actively working on Big Data or advanced analytics projects now use elements of machine learning according to Evans Data's recently released Big Data and Advanced Analytics survey report. While the market for machine learning is still fragmented, those developers actively working with machine learning are most likely to be targeting financial sectors, Internet of Things, or manufacturing. The survey of over 500 developers actively working with Big Data also showed that decision trees are the most used analytical model which links in closely with artificial intelligence and machine learning development. Linear regression and logistics regression were the next most cited analytical models. Logistics, distribution, or operations were the company departments most likely to be using advanced data analytics or Big Data solutions.

"BioTime harnesses the largest collection of highest-quality gene expression data coming from scrupulously designed and controlled cell differentiation experiments we have seen to date. It was large enough to train a complex architecture of deep neural networks to work as a classifier and a predictor of the embryonic state. We recently tested Embryonic.AI using mouse data and noticed surprising results showing the capabilities of this system in cross-species analysis. Research projects using Embryonic.AI may transform our understanding of cancer and other diseases and possible developments in reinforcement learning may help navigate and control cellular differentiation states", said Alex Zhavoronkov, Ph.D., CEO of Insilico Medicine, Inc. The system utilizes a sophisticated architecture of multi-class deep neural networks (DNNs) and DNN ensembles trained on thousands of samples of carefully selected cells of multiple classes: embryonic stem cells, induced pluripotent stem cells, progenitor stem cells, adult stem cells and adult cells to recognize the class and embryonic state of the sample, achieving high accuracy in simulations.

The Obama administration wants the U.S. to be a world leader in economic and defense policy related to a new wave of automation powered by machine learning and artificial intelligence, White House Chief of Staff Denis McDonough said Tuesday. "We can return to these questions in a way that America can kind of set the space and then set the parameters for how we go about it," McDonough said during a White House conversation on the topic that he moderated. The conversation comes as the White House looks to wrap up its string of workshops on artificial intelligence Thursday. In May, the White House Office of Science and Technology Policy announced plans to explore the uses and risks of AI. Since then the office has hosted three workshops and another event on the matter.

Airborne drone delivery is still more PR than public reality, but wheeled, self-driving delivery bots could be trundling down a sidewalk near you sooner than you think. London-based Starship Technologies, which counts Skype co-founders Ahti Heinla and Janus Friis among its founding team, is launching a broad testing phase of its autonomous delivery bots in parts of the UK, Germany and Switzerland starting this month. Starship's relatively small wheeled delivery bots have been in testing in select cities in 12 countries during the last nine months already, but this expansion of the trial will mark the first time the robots are being tested in actual delivery scenarios. That means they're bringing on partners to provide the delivery inventory, including food delivery players Just Eat, and London-based Pronto.co.uk. German retailer Metro Group and parcel delivery company Hermes will also take part in the pilot, which will span five cities providing deliveries to actual paying customers.

Halfway into 2016, it is clear that we are living in a new era of innovation. Beyond Silicon Valley, corporations and startup hubs worldwide are tackling big problems like water scarcity and cancer. The concept of the "next big thing" is becoming redundant because breakthroughs have become normal. Artificial intelligence that can learn and function independent of human overlords seems like science fiction. Yet, this may become our new reality within the next 3–5 years.

Tesla on Wednesday said it has no evidence that one of its cars was in autopilot mode when it hit a guardrail and flipped on the Pennsylvania Turnpike about 100 miles east of Pittsburgh on July 1. Police in Pennsylvania said the driver of a Tesla Model X told them he'd activated autopilot before the accident, but the car maker has been unable to asses the allegations first reported by The Detroit Free Press. Tesla says it has been unable to reach the motorist, a suburban Detroit art dealer, and also hasn't received digital crash data, perhaps because the car's antenna was damaged. "We have no data at this point to indicate that Autopilot was engaged or not engaged," a company spokeswoman said in a statement. "As we do with all crash events, we immediately reached out to the customer to confirm they were ok and offer support, but were unable to reach him. We have since attempted to contact the customer three times by phone without success. It is not possible to learn more without access to the vehicle's onboard logs."

Dark energy is a phrase used by physicists to describe a mysterious'something' that is causing the universe to accelerate in its expansion. It is the'gravitational glue' that holds galaxies together and is thought to make up five sixths of the universe's mass. These substances have profound effects on the birth and lives of galaxies and stars and yet almost nothing is known about their physical nature. But now a new computer model, twenty-five times faster than other methods, will allow scientists to compute virtual universes in the search of explanations about these mysteries. The new method makes the universe models more accurate by comparing the model's properties with an'inverted' version.

Linguists and primatologists have joined forces to create the groundwork for'primate linguistics,' helping to decipher the meanings behind monkey speech. The comprehensive study examines the calls of different species, analyzing the structure and placement of these vocalizations, and explains what individual calls and sequences mean. While the language of primates may not be as complex as our own, researchers say these animals demonstrate linguistic capabilities that are both'exciting and sometimes challenging.' Linguists and primatologists have joined forces to create the groundwork for'primate linguistics,' helping to decipher the meanings behind monkey speech. The study, led by an international team of researchers, was published recently in the journals Natural Language & linguistic Theory, and builds on earlier research.

Deep clustering is a recently introduced deep learning architecture that uses discriminatively trained embeddings as the basis for clustering. It was recently applied to spectrogram segmentation, resulting in impressive results on speaker-independent multi-speaker separation. In this paper we extend the baseline system with an end-to-end signal approximation objective that greatly improves performance on a challenging speech separation. We first significantly improve upon the baseline system performance by incorporating better regularization, larger temporal context, and a deeper architecture, culminating in an overall improvement in signal to distortion ratio (SDR) of 10.3 dB compared to the baseline of 6.0 dB for two-speaker separation, as well as a 7.1 dB SDR improvement for three-speaker separation. We then extend the model to incorporate an enhancement layer to refine the signal estimates, and perform end-to-end training through both the clustering and enhancement stages to maximize signal fidelity. We evaluate the results using automatic speech recognition. The new signal approximation objective, combined with end-to-end training, produces unprecedented performance, reducing the word error rate (WER) from 89.1% down to 30.8%. This represents a major advancement towards solving the cocktail party problem.