Neural Information Processing Systems http://nips.cc/

These algorithms have been shown to outperform Oja's algorithm, empirically, in terms of the iteration complexity, and to have runtime comparable with Oja's.

Scientists have announced a first-of-its-kind diagnostic test for autism spectrum disorder (ASD) that only requires a single strand of hair. LinusBio, based in New Jersey, launched its Clearstrand-ASD Thursday to help physicians rule out the condition in children between one and 36 months of age when autism is a concern. It does not provide a diagnosis. 'The test is intended for infants and toddlers who are at an elevated risk of autism, such as those who were born preterm, who have a sibling with autism, or who have demonstrated characteristics associated with autism,' researchers said. While Clearstrand-ASD only rules out autism, doctors currently rely on observational signs that leave parents waiting for answers.

Recent years have witnessed rapid advances in the use of neural networks to solve combinatorial optimization problems. Nevertheless, designing the "right" neural model that can effectively handle a given optimization problem can be challenging, and often there is no theoretical understanding or justification of the resulting neural model. In this paper, we focus on the rectilinear Steiner minimum tree (RSMT) problem, which is of critical importance in IC layout design and as a result has attracted numerous heuristic approaches in the VLSI literature. Our contributions are two-fold. On the methodology front, we propose NN-Steiner, which is a novel mixed neural-algorithmic framework for computing RSMTs that leverages the celebrated PTAS algorithmic framework of Arora to solve this problem (and other geometric optimization problems). Our NN-Steiner replaces key algorithmic components within Arora's PTAS by suitable neural components. In particular, NN-Steiner only needs four neural network (NN) components that are called repeatedly within an algorithmic framework. Crucially, each of the four NN components is only of bounded size independent of input size, and thus easy to train. Furthermore, as the NN component is learning a generic algorithmic step, once learned, the resulting mixed neural-algorithmic framework generalizes to much larger instances not seen in training. Our NN-Steiner, to our best knowledge, is the first neural architecture of bounded size that has capacity to approximately solve RSMT (and variants). On the empirical front, we show how NN-Steiner can be implemented and demonstrate the effectiveness of our resulting approach, especially in terms of generalization, by comparing with state-of-the-art methods (both neural and non-neural based).

Gather AI, a startup using drones to inventory items in warehouses, today announced that it raised $10 million in a Series A round led by Tribeca Venture Partners with participation from Xplorer Capital, Dundee Venture Capital, Expa, Bling Capital, XRC Labs and 99 Tartans. The proceeds bring the company's total raised to $17 million, which CEO Sankalp Arora says is being put toward expanding Gather's deployment capacity and go-to-market plans as well as hiring new machine learning engineers. Arora co-founded Gather AI in 2019 with Daniel Maturana and Geetesh Dubey, graduate students at Carnegie Mellon's Robotics Institute. The trio had the idea to use drones to gather data -- specifically data in warehouses, such as the number of items on a shelf and the locations of particular pallets. Over the course of several years, they designed a prototype of an inventory monitoring system that used off-the-shelf autonomous drones, which became Gather's core product.

Alexa, Siri and Google Home might be making children less intelligent and socially stunted, it was claimed today. The voice-controlled devices -- popular in homes across the world -- allow users to ask questions and receive answers. But this may impede youngster's learning skills, critical thinking and empathy, says Dr Anmol Arora, a researcher at Cambridge University. Dr Anmol Arora, a researcher at Cambridge University, says this is down to the tech only offering short and concise answers to questions, inappropriate responses and being unable to give feedback on their social skills. Alexa, Siri and Google Home might be making children less intelligent and socially stunted, according to an artificial intelligence expert.

From reminding potty-training toddlers to go to the loo to telling bedtime stories and being used as a "conversation partner", voice-activated smart devices are being used to help rear children almost from the day they are born. But the rapid rise in voice assistants, including Google Home, Amazon Alexa and Apple's Siri could, researchers suggest, have a long-term impact on children's social and cognitive development, specifically their empathy, compassion and critical thinking skills. "The multiple impacts on children include inappropriate responses, impeding social development and hindering learning opportunities," said Anmol Arora, co-author of an article published in the journal Archives of Disease in Childhood. A key concern is that children attribute human characteristics and behaviour to devices that are, said Arora, "essentially a list of trained words and sounds mashed together to make a sentence." The children anthropomorphise and then emulate the devices, copying their failure to alter their tone, volume, emphasis or intonation.

From reminding potty-training toddlers to go to the loo to telling bedtime stories and being used as a "conversation partner", voice-activated smart devices are being used to help rear children almost from the day they are born. But the rapid rise in voice assistants, including Google Home, Amazon Alexa and Apple's Siri could, new research suggests, have a long-term impact on children's social and cognitive development, specifically their empathy, compassion and critical thinking skills. "The multiple impacts on children include inappropriate responses, impeding social development and hindering learning opportunities," said Anmol Arora, co-author of research published in the journal Archives of Disease in Childhood. A key concern is that children attribute human characteristics and behaviour to devices that are, said Arora, "essentially a list of trained words and sounds mashed together to make a sentence." The children anthropomorphise and then emulate the devices, copying their failure to alter their tone, volume, emphasis or intonation.

A few years ago, when Tata Sampann was working on a recast and new visual brand identity, it experimented with a new way of figuring out what would work and what would not. Rather than the usual focus group discussions and surveys, it opted for neuro research. "The thought was to use the technique and understand elements that capture attention, high points of emotional engagement and trigger memory retention to create packaging for the brand to make it stand out from the rest," says Richa Arora, president, packaged foods, India, Tata Consumer Products. From eye tracking to virtual reality-based tests, Tata Sampann used novel techniques to gauge the subconscious feelings of consumers about the design and how they reacted to it. As a result, says Arora, it could come up with clutter-breaking appeal in its final packaging and visual identity.

New augmented intelligence and no-code data management capabilities in ThoughtSpot 6.2 aim to make the BI tool easier and faster for users to explore data. ThoughtSpot, a BI vendor founded in 2012 and based in Sunnyvale, Calif., unveiled its latest platform update on Wednesday with 10 new features now generally available. ThoughtSpot 6.2 includes Answer Explorer 2, a search tool that utilizes AI and machine learning to not only help customers run queries but guide users to questions they didn't think to ask on their own. The feature is able to recommend additional searches based on users' previous activity, and over time continuously improves as it learns more about users' needs. In addition, DataFlow improves the data management capabilities of ThoughtSpot's platform by enabling customers to simply point and click to load their data into Falcon, the vendor's in-memory database.