Jeffrey Epstein's tangled web leads down some surprising paths, including, he claimed, to Sophia the robot. The female robot styled after Audrey Hepburn made headlines in recent years for her eerily lifelike skin and appearance, complete with a diverse set of facial expressions, and the artificial intelligence she uses to spout off quotes like "OK. She also got in a Twitter fight with Chrissy Teigen. In a new essay detailing a journalist's friendship with Jeffrey Epstein over the past three decades, Edward Jay Epstein (the two are not related) says the wealthy financier told him in April 2013 that he was funding a Hong Kong group to build "the world's smartest robot," named Sophia. Sophia was built by Hanson Robotics, a Hong Kong company created and led by David Hanson. In a statement shared with Business Insider, Hanson denied that Epstein ever directly contributed funding to either Sophia or Hanson Robotics. "With all of our software efforts, both inside Hanson Robotics, and via collaboration with universities and other institutions, we seek to further our mission to empower socially intelligent AI and robots that enrich the quality of human lives.

Whether we know it or not, artificial intelligence (AI) is already steeped into everyday life. It's present in the way social media feeds are organised; the way predictive searches show up on Google; and how music services such as Spotify make song suggestions. The technology is also helping transform the way enterprises do business. Commonwealth Bank of Australia, for instance, has applied AI to analyse 200 billion data points to free up more time so its customer service officers can focus on doing exactly what their title suggests: servicing customers. As a result, the bank has seen a 400% uplift in customer engagement.

"If the computer is this important, why haven't I heard more about it?" "Well, the computer is a relatively new thing, and we're just really getting an appreciation for the full range of its usefulness. Many people think that it's going to spark a revolution that will change the face of the earth almost as much as the first industrial revolution did." The skeptic posing the question is David Wayne, a crusty actor familiar to audiences of the time from movies such as Adam's Rib and TV shows like The Twilight Zone. The two men are cohosts of "The Thinking Machine," a documentary about artificial intelligence aired as part of a CBS series called Tomorrow, which the network produced in conjunction with MIT. It debuted on the night of October 26, less than two weeks before John F. Kennedy defeated Richard Nixon in the U.S. presidential election.

In reporting in China on the country's "Two Sessions," a new anchorwoman "Xin Xiaomeng" was seen. She is a virtual anchor created through the application of artificial intelligence technology. "She" is the first artificial intelligence virtual female anchor in the world, with real anchor of Xinhua News Agency Qu Meng serving as her prototype. She was jointly created by China's Xinhua News Agency and Sogou. "Her" partner, "Xin Xiaohao," upgraded from "sitting upright" to "standing upright," broadcast the content of the Two Sessions with gestures and other body language.

I'm an undergraduate data scientist, about to start work on my dissertation project. I thought I'd create a system that, given someone's career history and education, predicts what job they're likely to get, and at what company. Essentially this is to help focus the efforts of job seekers, and help them get to where they belong. Originally I planned to do this by scraping data from LinkedIn profiles. From the LinkedIn profile, you can obtain information about someone's current job and employer, as well as their career history and education. Therefore you can see what education and career history (the input) resulted in their current job (the output - the thing I'm trying to predict).

Where do you guys rent compute resources for training? What are your primary selection criteria (cost/reliability/bandwidth/ data location), for your particular use case? Do you also own your own AI/ML gears for consistent workload, in addition to the cloud? I am asking this as I am building an exchange where people can share quality compute resources at-cost or near-cost. Would this be something that you are interested in?

Smith and Topin's 2017 paper Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates garnered quite a bit of attention, promising to cut training iterations by an order of magnitude without compromising accuracy. Using a 56-layer residual network, they claim that while it takes 80k iterations to train to 91% accuracy on CIFAR-10 using conventional algorithms, but that they can achieve a higher accuracy (92.4%) in only 10k iterations. On Open Review there is concern that it's not clear if the accuracy gains are significant ("no error bars") and about whether this technique generalizes to other architectures. I think we've mostly seen that Super-Convergence seems to converge to fine results on multiple architectures -- the "train ImageNet in 3 hours for $25" is probably the most well-known example. I've used it to train ResNet-18, 34, and 50 on CIFAR.

What would an adventure game designed by the worlds most dangerous A.I. look like? A neuroscience grad student is here to help you find out. Earlier this year, OpenAI, an A.I. startup once sponsored by Elon Musk, created a text-generating bot deemed too dangerous to ever release to the public. Called GPT-2, the algorithm was designed to generate text so humanlike that it could convincingly pass itself off as being written by a person. Feed it the start of a newspaper article, for instance, and it would dream up the rest, complete with imagined quotes.

You will also hear how machine learning could be used to identify which personalisation of your User Interface would be the most effective.