Would you buy a 43-inch TV that works in vertical mode? Why didn't you buy Anki's cute toy robots? When are you going to try that meatless Burger King Whopper? Cozmo and Vector couldn't save it.Anki is closing the doors on its toy-robot business Anki, the startup responsible for adorable robotics, is closing its doors and will terminate nearly 200 employees Wednesday. Recode reported CEO Boris Sofman broke the news to staff Monday.

The Xbox Adaptive Controller might just become an important tool for some US military veterans. Microsoft and the Department of Veterans Affairs have formed a partnership that will donate controllers, consoles, games and adaptive gear to 22 Veterans Affairs rehabilitation centers across the US. The accessible gamepads will help with rehab and therapy activities focused on hand-eye coordination and muscle activation, and should help veterans both have fun and socialize. VA staff will provide feedback to Microsoft both on the usefulness of the Adaptive Controller for therapy as well as the overall experience. Microsoft says the 22 centers represent "initial contributions," and that you can expect more.

It has been more than a decade since President George W. Bush set out to get electronic health records for every American. In the 15 years since his pronouncement, there's been significant implementation of EHRs across the country, propagating an incomprehensible amount of data. In many cases, that data sits dormant and untapped of its potential. Some healthcare organizations contend that it's financial constraints that provide limitations. Lack of dollars makes it difficult for all but the most advanced and lucrative healthcare organizations to put machine learning or artificial intelligence in place to make the most of the data.

At George Mason University in Virginia, a fleet of several dozen autonomous robots deliver food to students on campus. At George Mason University in Virginia, a fleet of several dozen autonomous robots deliver food to students on campus. George Mason University looks like any other big college campus with its tall buildings, student housing, and manicured green lawns – except for the robots. This Northern Virginia university recently set up several dozen meal delivery robots from Starship Technologies to make it easier for students to access food. Multiple colleges across the country have deployed delivery robots – including University of the Pacific in Stockton, Calif., and Northern Arizona University – but George Mason University is the first college in the United States to incorporate robots into its student dining plan. The school is partnering with food service provider Sodexo for the program.

The past decade has seen artificial intelligence develop from a mere fantasy to a fully integrated part of a marketing strategy, for brands that look to differentiate and improve their customer experiences and online strategies. Take Farfetch for example, which utilized RFID-enabled clothing racks and digital mirrors to allow its customers the choice of size and colour before directly checking out online. This particular use of AI shows the seamless integration of online and offline experiences, and proves that this technology has no end to the benefits and creativity it can bring for a brands engagement efforts. Found at the core of AI technology is data and analytics, allowing brands to streamline digital ads and offer a personalized customer service. This can result in a significant lift to brands engagement efforts and empowers them to fully engage with customer at every stage of the purchase lifecycle.

Akshaya Asokan works as a Technology Journalist at Analytics India Magazine. She has previously worked with IDG Media and The New Indian Express. When not writing, she can be seen either reading or staring at a flower.

Given that artificial intelligence (AI) is the new buzzword for high-growth industries in recent years, I thought it would be interesting to dig deeper to discover what's in store in the coming years in terms of innovations and trends. Interestingly, I discovered that there are actually three categories of AI, and the world is currently dealing with just the first one. For those who feel that AI is pretty advanced now, the good news is that there is still a significant runway for the technology to improve further. It is still early days in terms of recognising the potential for AI, and with further advancements in the years to come, all of us can look forward to more amazing inventions and contraptions. There is, of course, also the risk of AI becoming "too smart for its own good."

Google shares slumped on Monday after the company failed to beat analyst predictions, following a year of internal turmoil, privacy concerns, and several international fines. Stock for Alphabet, Google's parent company, was down 7% in after-hours trading after the company reported first quarter revenue of $36.34bn, lower than the $37.33bn revenue forecast by analysts. The quarter one earnings represent a 17% increase from the same time last year, in which it reported $31.15bn in revenue. In a call with investors on Monday, Google's CEO, Sundar Pichai, said the company would continue to invest more in algorithms on YouTube, following recent incidents that saw the platform offering misinformation, hate speech, and disturbing content targeting children. He also promised to continue to address user privacy concerns.

Reinforcement learning has seen great advancements in the past five years. The successful introduction of deep learning in place of more traditional methods allowed reinforcement learning to scale to very complex domains achieving super-human performance in environments like the game of Go or numerous video games. Despite great successes in multiple domains, these new methods suffer from their own issues that make them often inapplicable to the real world problems. Extreme lack of data efficiency, together with huge variance and difficulty in enforcing safety constraints, is one of the three most prominent issues in the field. Usually, millions of data points sampled from the environment are necessary for these algorithms to converge to acceptable policies. This thesis proposes novel Generative Adversarial Imaginative Reinforcement Learning algorithm. It takes advantage of the recent introduction of highly effective generative adversarial models, and Markov property that underpins reinforcement learning setting, to model dynamics of the real environment within the internal imagination module. Rollouts from the imagination are then used to artificially simulate the real environment in a standard reinforcement learning process to avoid, often expensive and dangerous, trial and error in the real environment. Experimental results show that the proposed algorithm more economically utilises experience from the real environment than the current state-of-the-art Rainbow DQN algorithm, and thus makes an important step towards sample efficient deep reinforcement learning.

Archiving large sets of medical or cell images in digital libraries may require ordering randomly scattered sets of image data according to specific criteria, such as the spatial extent of a specific local color or contrast content that reveals different meaningful states of a physiological structure, tissue, or cell in a certain order, indicating progression or recession of a pathology, or the progressive response of a cell structure to treatment. Here we used a Self Organized Map (SOM)-based, fully automatic and unsupervised, classification procedure described in our earlier work and applied it to sets of minimally processed grayscale and/or color processed Scanning Electron Microscopy (SEM) images of CD4+ T-lymphocytes (so-called helper cells) with varying extent of HIV virion infection. It is shown that the quantization error in the SOM output after training permits to scale the spatial magnitude and the direction of change (+ or -) in local pixel contrast or color across images of a series with a reliability that exceeds that of any human expert. The procedure is easily implemented and fast, and represents a promising step towards low-cost automatic digital image archiving with minimal intervention of a human operator.