Artificial intelligence (AI) is no longer the stuff of science fiction. The majority of businesses today are using AI in some form, and those that aren't have plans to in the near future. Deep learning, a technique that's largely responsible for the widespread adoption of AI, has gained particular momentum as of late, with leading companies like Google, Microsoft and Amazon introducing deep learning across their services and replacing their existing machine learning systems with deep learning-based models. A recent O'Reilly study on how businesses are putting AI to work through deep learning, found that 54 percent of businesses predict deep learning will play a large or essential role in their future projects. Another 38 percent expect to use some amount of deep learning, and only eight percent of businesses said deep learning wouldn't play a role in their future projects.

The UK government launched a £1bn artificial intelligence fund with industry at Imperial College London today. The deal, announced by Business Secretary Greg Clark and Digital Secretary Matt Hancock, will fund 1,000 new government AI PhDs to keep the UK at the forefront of innovation and build the UK's status as an AI research hotspot. More than 50 leading technology companies and organisations have contributed to the development of an AI Sector Deal – which includes more than £300 million of newly allocated government funding for AI research to make the UK a global leader in this technology. Imperial is a world-leading centre in AI and machine learning with more than 600 people working with and developing artificial intelligence. These include Professor Maja Pantic, who is developing a robot which can improve learning and emotional understanding in children with autism, and Professor Aldo Faisal, who is developing software which can understand a person's intentions and direct a wheelchair by detecting their eye movement.

Given how India's public sector is showing a growing interest in artificial intelligence, can legal tech startups keep up and help transform India's judiciary system? Though India has made rapid progress in terms of technology, companies and researchers are yet to utilise the full potential of AI. In fact, a PwC report emphasises that how instead of waiting for technology to reach a level where regulatory intervention becomes necessary, India could be a frontrunner by establishing a legal infrastructure in advance. A slew of Indian legal tech startups are building NLP-based applications and introducing next-gen legal research platforms that help law firms go beyond simple, keyword-based research, thereby making it less time-consuming. Many legal startups are fast rising in AI research capabilities, some of who have their own AI research labs.

Ashley Sams is a consultant at PR 20/20. She joined the agency in 2017 with a background in marketing, specifically for higher education and social media. Ashley is a 2015 graduate of The University of Mount Union where she earned a degree in marketing.

Dogs may be our best friends, but they're also our hard-working colleagues -- tasked with everything from guarding our homes to guiding visually impaired people to sniffing out bombs. And now researchers have enlisted the help of an Alaskan Malamute named Kelp to develop an artificial intelligence system that thinks just like a dog, in hopes of creating canine-like robots. To build a database of dog behavior, a team of scientists led by Kiana Ehsani, a Ph.D. student at the University of Washington, attached sensors to Kelp's paws, torso, and tail to capture her movements for a couple of hours a day while eating, playing fetch, and walking around in various indoor and outdoor environments. A camera affixed to Kelp's head recorded what she saw as she went about her everyday activities. Over the course of several weeks, the researchers amassed more than 24,000 video frames -- all associated with particular body movements.

The International Institute of Information Technology, Hyderabad (IIIT-H) today announced expansion of its programme offered in association with TalentSprint in Artificial Intelligence (AI) and Machine Learning (ML) for technology professionals. IIIT-H said the first cycle of its Foundations of Artificial Intelligence and Machine Learning (AI/ML) conducted by its Machine Learning Lab in partnership with TalentSprint has been a success and the first cohort of 400 software professionals from 127 tech companies will graduate next month. The institute also announced that its"ongoing partnership with TalentSprint is now ready for scale-up. "The executive education programme is being expanded to multiple cities to create 10,000 certified AI/ML professionals over the next four years," it said. IIIT-H, Director, P J Narayanan, said "AI/ML is a deep and disruptive technology and we are making it accessible and digestible to industry professionals from diverse technology backgrounds.

R. Edward Freeman is a professor of strategy, ethics, and entrepreneurship at the Darden School of Business at the University of Virginia. James R. Freeland is the Sponsors Professor of Business Administration in the technology and operations area at Darden School of Business. None of us know how our technological future will unfold. Just within the last few months, we learned that Amazon Go will be opening more cashier-less, no-salesperson stores and that a burger chain has "employed" a robot to flip its burgers. At the same time, Uber has put on hold its use of self-driving vehicles after a fatal accident in Arizona.

"[…] we believe that the idea of replacing core components of a data management system through learned models has far reaching implications for future systems designs and that this work just provides a glimpse of what might be possible." Indeed the results presented by the team of Google and MIT researchers includes findings that could signal new competition for the most venerable stalwarts in the world of indexing: the B-Tree and the Hash Map. The engineering community is ever abuzz about the future of machine learning; as such the research paper has made its rounds on Hacker News, Reddit, and through the halls of engineering communities worldwide. New research is an excellent opportunity to reexamine the fundamentals of a field; and it's not often that something as fundamental (and well studied) as indexing experiences a breakthrough. This article serves as an introduction to hash tables, an abbreviated examination of what makes them fast and slow, and an intuitive view of the machine learning concepts that are being applied to indexing in the paper. In response to the findings of the Google/MIT collaboration, Peter Bailis and a team of Stanford researchers went back to the basics and warned us not to throw out our algorithms book just yet. Bailis' and his team at Stanford recreated the learned index strategy, and were able to achieve similar results without any machine learning by using a classic hash table strategy called Cuckoo Hashing. In a separate response to the Google/MIT collaboration, Thomas Neumann describes another way to achieve performance similar to the learned index strategy without abandoning the well tested and well understood B-Tree.

Mechanical engineering students at Rice University have designed a robotic horse that can mimic the movements of the real thing. The device, dubbed Stewie, is geared towards individuals requiring equine-assisted therapy, also known as hippotherapy, who may not be able to travel to or afford a facility that offers it. Hippotherapy is believed to be able to help patients develop coordination, balance and posture while also fostering a relaxed state during which other beneficial therapies can be administered. And Stewie could provide a way for more people to benefit from hippotherapy. The students have been working on the project for a while and they collected accelerometer data from real horses at a ranch in Texas.

Deep neural network (DNN) has achieved remarkable success in classification tasks such as image classification [1]. The network output can mimic the posterior probabilities of target classes for the input observation when the nonlinear activation function in the output layer is defined as a soft-max function [2]. The learning objective is to minimize the difference between the predicted distribution and the true datagenerating distribution. In information theory, the cross entropy between two probability distributions over a common event set of events measures the average number of bits needed to identify an event if coding follows a learned probability distribution rather than the true but unknow distribution [3]. Therefore, cross entropy is a reasonable loss function for the DNN-based classification. However, in practice the true data-generating probability distribution is unknown and replaced by the empirical probability distribution over a training set where each sample is drawn independently and identically distributed (i.i.d.) from the data space [4]. Under assumptions of uniform distributions of feature and label spaces, minimizing cross-entropy is equivalent to maximum likelihood, i.e., the learning problem aims to maximize likelihood of correct class for each of training samples [2]. Maximum likelihood is a generative training criterion by which the model learns the likelihood of correct class for the observation. The model makes predictions by using Bayes rules to calculate posterior probabilities of target classes for the observation and then select the most likely class.