Google's Boston Dynamics is up for sale - and could be sold to Amazon or Toyota, it has been revealed. The firm is best known for Atlas, its 5 foot 9 humanoid robot, and spot, a four legged'dog robot'. Boston Dynamic has revealed the new wireless version of its humanoid robot in a new video showing it walk, run, and even be pushed over and get up again on its own. According to Boston Dynamics, Atlas is a'high mobility, humanoid robot designed to negotiate outdoor, rough terrain. 'Atlas can walk bipedally leaving the upper limbs free to lift, carry, and manipulate the environment.

Ride-hailing service Uber has sounded out car companies about placing a large order for self-driving cars and may have placed a giant order with Mercedes for 100,000 limousines, it has been claimed. 'They wanted autonomous cars,' a source, who declined to be named, told Reuters. Uber has sounded out car companies about placing a large order for self-driving cars and may have placed a giant order with Mercedes for 100,000 S-Class limousines, it has been claimed. Uber is already testing an early version of its system, which is being developed with Carnegie Mellon University. The firm hopes to develop a self driving taxi to take on autonomous car projects from Google, Apple and others. Loss-making Uber would make drastic savings on its biggest cost -- drivers -- if it were able to incorporate self-driving cars into its fleet.

Digital expertise will become increasingly critical in the insurance sector as digitization and machine learning leads to more highly'automatable' insurance according to management consultants McKinsey & Company. Meanwhile a separate piece of research by Accenture found that insurance companies are accelerating the shift to a radically different distribution model, where they say digital will play an increasingly important role in most interactions, and were agents' efforts are being refocused to add more value. And analysis by research outfit Ovum suggests strong investment in digital channels also. According to Ovum, " When it comes to investment, digital channels remains the top area for insurers. However, the significant majority of insurers will be increasing budgets across a broad range of functional areas with no single activity completely dominating spend. This reflects the complex set of priorities that IT groups are being asked to meet by the wider business, simultaneously addressing revenue growth, operational efficiency and regulatory compliance."

We at Venture Scanner are tracking 957 Artificial Intelligence companies across 13 categories, with a combined funding amount of 4.8 Billion. The 15 visuals below summarize the current state of Artificial Intelligence. Deep Learning/Machine Learning (General): Companies that build computer algorithms that operate based on their learnings from existing data. Examples include predictive data models and software platforms that analyze behavioral data. Deep Learning/Machine Learning (Applications): Companies that utilize computer algorithms that operate based on existing data in vertically specific use cases.

Editor's note: This is the third in a series of articles about the effects of software and automation on the economy. You can read the other stories here and here. The way Hod Lipson describes his Creative Machines Lab captures his ambitions: "We are interested in robots that create and are creative." Lipson, an engineering professor at Cornell University (this July he's moving his lab to Columbia University), is one of the world's leading experts on artificial intelligence and robotics. His research projects provide a peek into the intriguing possibilities of machines and automation, from robots that "evolve" to ones that assemble themselves out of basic building blocks. A few years ago, Lipson demonstrated an algorithm that explained experimental data by formulating new scientific laws, which were consistent with ones known to be true. He had automated scientific discovery. Lipson's vision of the future is one in which machines and software possess abilities that were unthinkable until recently.

I was lucky enough to represent ACM and ACM-W at the recent Ada Lovelace Symposium at Oxford University, celebrating her 200th birthday. I participated in a panel "Enchantress of Abstraction and Bride of Science: can women scientists escape being icons, role-models and heroines." Why do so many current organizations and events identify with and recognize Ada Lovelace? We are well into the 21st century; Ada was born 200 years ago. Why do so many women today seem to look to her as a model and icon?

Suresh Venkatasubramanian of the University of Utah presented a method for finding disparate impact in algorithms last year at the ACM Conference on Knowledge Discovery and Data Mining. If you have ever searched for hotel rooms online, you have probably had this experience: surf over to another website to read a news story and the page fills up with ads for travel sites, offering deals on hotel rooms in the city you plan to visit. Buy something on Amazon, and ads for similar products will follow you around the Web. The practice of profiling people online means companies get more value from their advertising dollars and users are more likely to see ads that interest them. The practice has a downside, though, when the profiling is based on sensitive attributes, such as race, sex, or sexual orientation.

Peter Naur, a Danish computer scientist and 2005 recipient of the ACM A.M. Turing Award for fundamental contributions to programming language design and the definition of Algol 60, to compiler design, and to the art and practice of computer programming, died January 3 after a brief illness. Born in Fredricksberg, Denmark, Naur studied astronomy at the University of Copenhagen, where he received his Ph.D. in that field before going to Kings College Cambridge in the 1950s to conduct research both into astronomy and the emerging field of computer programming. As he told Computerworld Denmark in a 2014 interview (http://bit.ly/1O13v1I), "I had the great privilege to get to Cambridge in the early 1950s. Here I discovered that calculations of planetary motion that could take several hours, could now be carried out in seconds with a computer."

In a world with more mobile phones than flush toilets, digital devices are now standard equipment among even the world's poorest and most remote people. Farmers in these areas are getting tools for their devices that help deliver water, nutrients, and medicine to plants as needed; test for crop diseases and malnourishment; and survey their soil for future planning. In some cases, these emerging apps are the biggest new technologies resource-poor farms have seen in hundreds of years. That is not very surprising to Rajiv "Raj" Khosla, professor of Precision Agriculture at the College of Agricultural Sciences of Colorado State University. "What we're finding is that many small-scale farmers in resource-poor environments are still farming in the 1500s. They're looking for leapfrog technologies," he said.

Though with progress, model learning and performing posterior inference still remains a common challenge for using deep generative models, especially for handling discrete hidden variables. This paper is mainly concerned with algorithms for learning Helmholz machines, which is characterized by pairing the generative model with an auxiliary inference model. A common drawback of previous learning algorithms is that they indirectly optimize some bounds of the targeted marginal log-likelihood. In contrast, we successfully develop a new class of algorithms, based on stochastic approximation (SA) theory of the Robbins-Monro type, to directly optimize the marginal log-likelihood and simultaneously minimize the inclusive KL-divergence. The resulting learning algorithm is thus called joint SA (JSA). Moreover, we construct an effective MCMC operator for JSA. Our results on the MNIST datasets demonstrate that the JSA's performance is consistently superior to that of competing algorithms like RWS, for learning a range of difficult models.