Our aim is to build autonomous agents that can solve tasks in environments like Minecraft. To do so, we used an imitation learning-based approach. We formulate our control problem as a search problem over a dataset of experts' demonstrations, where the agent copies actions from a similar demonstration trajectory of image-action pairs. We perform a proximity search over the BASALT MineRL-dataset in the latent representation of a Video PreTraining model. The agent copies the actions from the expert trajectory as long as the distance between the state representations of the agent and the selected expert trajectory from the dataset do not diverge. Then the proximity search is repeated. Our approach can effectively recover meaningful demonstration trajectories and show human-like behavior of an agent in the Minecraft environment.

ID verification software maker Mitek has released a passwordless authentication platform with multimodal biometrics. MiPass enables users to access digital accounts by taking a selfie and speaking a phrase with their phone, according to Mitek. The software can be embedded in applications via a dedicated software development kit. Mitek says use cases include simple account information updates, password resets, device rebinding and high-risk financial transactions. Chris Briggs, Mitek's head of products, says, "People are most loyal to companies that offer both convenience and security.

Self-Organizing Maps (SOMs, Kohonen networks) belong to neural network models of the unsupervised class. In this paper, we present the generalized setup for non-Euclidean SOMs. Most data analysts take it for granted to use some subregions of a flat space as their data model; however, by the assumption that the underlying geometry is non-Euclidean we obtain a new degree of freedom for the techniques that translate the similarities into spatial neighborhood relationships. We improve the traditional SOM algorithm by introducing topology-related extensions. Our proposition can be successfully applied to dimension reduction, clustering or finding similarities in big data (both hierarchical and non-hierarchical).

A check up by Khosla Ventures determined that Docbot Inc. was healthy enough for the prominent biotech investor to take the lead in a $4 million series A round. The new funds bring the artificial intelligence company to a total of $8.5 million in capital raised to date. Other participants included Bold Capital Partners, Collaborative Fund and Boutique Venture Partners. Docbot's platform, Ultivision AI, uses artificial intelligence to enhance detection of gastrointestinal (GI) disease. The Irvine, Calif.-based company is targeting identification and classification of polyps, Barrett's esophagus, and ulcerative colitis to start.

Figuring out which products are in stock and which stock is likely to run low is a never-ending battle, as shoppers spend an estimated 40 billion hours picking things off shelves. It's also error-prone -- employees regularly misplace an estimated one in 10 items, contributing to global retail revenue losses exceeding $1 trillion. But drones hold the answer to the inventory tracking problem, if you ask serial entrepreneur Richard Schwartz. So strong is he in this conviction that he cofounded Pensa Systems, which develops inventory systems equipped with computer vision algorithms that "understand" what's on store shelves. The Austin startup today announced the close of a $10 million follow-on seed funding round that brings its total raised to $17.2 million, and according to investor and Pensa advisory board member James McCann, the future is looking bright.

Biology has paved the way for AI. And, inspired scientists to replicate the complexity of the human brain in computer form. "The concept of a neural network is the first step," explains Steve Ritter, CTO, Mitek. Human brains, like current AI systems (that includes neural networks, machine learning and deep learning), have the ability to learn through experience, learn how to adapt and deal with new situations. "There's a difference between rule-based, programmed systems, and learning-based systems," says Ritter.

Emergence Capital today announced it has raised a $435 million fund to invest in companies that use machine learning to help people increase productivity at work. The fund will focus especially on companies that provide coaching powered by data and conversational AI to help people perform their jobs better. Emergence has previously made a number of similar investments, including in call center analysis company Chorus.ai; "Any domain that you and I now spend our time in every day will in the future have a coaching network company that owns that domain. That's where the world is headed, in our opinion," cofounder and general partner Gordon Ritter told VentureBeat in an interview.

At the end of 2017 if it didn't become abundantly clear that artificial intelligence is well on its way to changing everything that matters about the way we work, then you weren't paying attention. The examples were too numerous to list, but here's a few: In 2017 AI systems beat human doctors at detecting irregular heartbeats, tracked player statistics for NFL football fans, and out-bluffed the world's best poker players at Texas Hold'Em. When it comes to solving problems, and doing jobs that are inherently based on patterns, the machines have overwhelmingly won the race against the human brain. So as 2018 gets underway, it's worth asking this out loud: If AI is changing everything about our jobs, what does that say about how we as humans prepare for those jobs? I got into a conversation about this last month with Gordon Ritter, founder and general partner at Emergence Capital.

A new academic paper, Machine Learning for Trading, is the first conclusive study that shows success from having a machine learning-based trading strategy. The author, Gordon Ritter, Adjunct Professor in the Mathematics in Finance Program, New York University, constructed an artificial system which he knew would admit a profitable strategy, to see if a machine would find it. Newsweek is hosting an AI and Data Science in Capital Markets conference in NYC, Dec. 6-7. In order to train a machine learning algorithm to behave as a rational risk-averse investor required appropriate reinforcement learning, specifically a mathematical technique called Q-learning (playing some sort of game where you are trying to maximise the reward function that may occur at several periods in the future). The machine learning agent found and exploited arbitrage opportunities in the presence of transaction costs in a simulated market proof of concept.

A new academic paper, Machine Learning for Trading, is the first conclusive study that shows success in having a machine learning-based trading strategy. The author, Gordon Ritter, Adjunct Professor in the Mathematics in Finance Program, New York University, constructed an artificial system which he knew would admit a profitable strategy, to see if a machine would find it. In order to train a machine-learning algorithm to behave as a rational risk-averse investor required appropriate reinforcement learning, specifically a mathematical technique called Q-learning (playing some sort of game where you are trying to maximise the reward function that may occur at several periods in the future). The machine learning agent found and exploited arbitrage opportunities in the presence of transaction costs in a simulated market proof of concept. Ritter explained: "I was really trying to answer the question, does machine learning have any application to trading at all, or no application; sort of a binary question. Can machine learning be applied to the problem of trading? "I reasoned that in a system that I know admits a profitable trading strategy, because I constructed it that way, can the machine find it.