Israeli robotics startup Intuition Robotics has raised $36 million in a series B round of funding co-led by Sparx Group and OurCrowd, with participation from Samsung Next, Toyota AI Ventures, Bloomberg Beta, iRobot, Sompo Holdings, Union Tech Ventures, Happiness Capital, and Capital Point. Founded in 2015, Intuition Robotics is creating what it calls "social companion" robots and related technologies, with an initial focus on reducing loneliness and isolation in elderly people. The company's first product was a $1,500 robot called ElliQ that opened for preorders last January and has accumulated "over 10,000 days" in homes across the U.S., though the company hasn't revealed specific sales figures. The majority of ElliQ's users are between 80 and 90 years of age. ElliQ more closely resembles a desk lamp than a humanoid, and it sits on a small dock with a tablet screen and cameras.

There's constantly an extraordinary business opportunity to help a business in any easily overlooked detail they do. As businesses in practically all enterprises have moved here and there to digital, the opportunity to help with their procedures online has exploded. There are a significant number of ways to continually make arrangements and enhancements for business forms. That is where SaaS becomes possibly the most important factor. SaaS attempts to continually develop the manner in which we work through different new devices and arrangements that meet our advancing needs.

With retail stalwart Macy's announcing significant store closures across the United States, it's clear that the reign of the brick and mortar kings has long since passed. Every business in operation today has either embraced digital transformation, is no longer operating, or has been purchased by an e-commerce giant. From local dealerships and neighborhood service centers to the largest suppliers of automotive vehicles and products, going digital is now mandatory. In fact, "traditional stores are closing down at a faster pace than ever before, with around 12,000 stores closing by the end of 2019, and the ones remaining are struggling to understand how to adapt to the new shopping paradigms." Alternatively, "over 40% of ecommerce sales being done on a mobile device."

Law enforcement agencies around the world are enthusiastically adopting the services of Clearview AI, a tech company whose powerful software scrapes several billion open-source images for the purposes of facial recognition. As the company confronts mounting criticism over its disturbing surveillance practices, its CEO, Hoan Ton-That, is rolling out an audacious new defense: he claims that Clearview's practices are protected by the first amendment. Ton-That's upside-down views of civil liberties are, it seems, just as Orwellian as his company's surveillance apparatus. Fortunately he is dead wrong. The constitution does not shield Clearview AI from accountability.

The paper addresses the classical network tomography problem of inferring local traffic given origin-destination observations. Focussing on large complex public transportation systems, we build a scalable model that exploits input-output information to estimate the unobserved link/station loads and the users path preferences. Based on the reconstruction of the users' travel time distribution, the model is flexible enough to capture possible different path-choice strategies and correlations between users travelling on similar paths at similar times. The corresponding likelihood function is intractable for medium or large-scale networks and we propose two distinct strategies, namely the exact maximum-likelihood inference of an approximate but tractable model and the variational inference of the original intractable model. As an application of our approach, we consider the emblematic case of the London Underground network, where a tap-in/tap-out system tracks the start/exit time and location of all journeys in a day.

Bidding for the only known'Nintendo-PlayStation' prototype console - the result of a failed partnership between Nintendo and Sony in 1991 - is already up to $350,000 (£268,000). The super-rare console, which is being sold with a Sony-branded SNES controller and cartridge that enables CD-Rom functionality, only went up for bids on Thursday. Potential buyers have 21 days left to bid on the console, which is being auctioned off by Dallas-based Heritage Auctions on March 6. The collectors' item is a rare artefact from a brief collaboration between the Japanese gaming giants before they took separate paths in the video game market. The rare'SNES-PlayStation' was one of 200 prototypes made - and the rest are thought to be destroyed.

We show the existence of a Locality-Sensitive Hashing (LSH) family for the angular distance that yields an approximate Near Neighbor Search algorithm with the asymptotically optimal running time exponent. We also introduce a multiprobe version of this algorithm and conduct an experimental evaluation on real and synthetic data sets.We complement the above positive results with a fine-grained lower bound for the quality of any LSH family for angular distance. Our lower bound implies that the above LSH family exhibits a trade-off between evaluation time and quality that is close to optimal for a natural class of LSH functions. Papers published at the Neural Information Processing Systems Conference.

Electroencephalography (EEG) recordings of rhythm perception might contain enough information to distinguish different rhythm types/genres or even identify the rhythms themselves. We apply convolutional neural networks (CNNs) to analyze and classify EEG data recorded within a rhythm perception study in Kigali, Rwanda which comprises 12 East African and 12 Western rhythmic stimuli – each presented in a loop for 32 seconds to 13 participants. We investigate the impact of the data representation and the pre-processing steps for this classification tasks and compare different network structures. Using CNNs, we are able to recognize individual rhythms from the EEG with a mean classification accuracy of 24.4% (chance level 4.17%) over all subjects by looking at less than three seconds from a single channel. Aggregating predictions for multiple channels, a mean accuracy of up to 50% can be achieved for individual subjects.

We consider the problem of learning the canonical parameters specifying an undirected graphical model (Markov random field) from the mean parameters. For graphical models representing a minimal exponential family, the canonical parameters are uniquely determined by the mean parameters, so the problem is feasible in principle. The goal of this paper is to investigate the computational feasibility of this statistical task. Our main result shows that parameter estimation is in general intractable: no algorithm can learn the canonical parameters of a generic pair-wise binary graphical model from the mean parameters in time bounded by a polynomial in the number of variables (unless RP NP). Indeed, such a result has been believed to be true (see the monograph by Wainwright and Jordan) but no proof was known.

Roger Magoulas recently sat down with Edward Jezierski, reinforcement learning AI principal program manager at Microsoft, to talk about reinforcement learning (RL). They discuss why RL's role in AI is so important, challenges of applying RL in a business environment, and how to approach ethical and responsible use questions. Get a free trial today and find answers on the fly, or master something new and useful. Reinforcement learning is different than simply trying to detect something in an image or extract something from a data set, Jezierski explains-- it's about making decisions. "That entails a whole set of concepts that are about exploring the unknown," he says.