DeepMind's New AI Tracks Serengeti Herds from Images Alone DeepMind, the U.K.-based AI research subsidiary acquired by Alphabet in 2014 for $500 million, today detailed ecological research its science team is conducting to develop AI systems that'll help study the behavior of animal species in Tanzania's Serengeti National Park. They extend the popular BERT architecture to a multi-modal two-stream model, processing both visual and textual inputs in separate streams that interact through co-attentional transformer layers.

While virtualization technologies have proven themselves in the enterprise with cost effective, scalable and reliable IT computing, High Performance Computing (HPC) however has not evolved and is still bound to dedicating physical resources to obtain explicit runtimes and maximum performance. VMWare has developed technologies to effectively share accelerators for compute and networking. It is also possible to provision multiple GPUs to a single VM, enabling maximum GPU acceleration and utilization. With the impending end to Moore's law, the spark that is fueling the current revolution in deep learning is having enough compute horsepower to train neural-network based models in a reasonable amount of time The needed compute horsepower is derived largely from GPUs, which NVIDIA began optimizing for deep learning since 2012. The latest GPU architecture from NVIDIA is Turing, available with T4 as well as the RTX 6000 and RTX 8000 GPUs, which all support virtualization. .

This story is part of What Happens Next, our complete guide to understanding the future. In March 2016, AlphaGo's deep learning algorithms ruthlessly dethroned mankind's best Go player. The whole world jittered, knowing that the same job-eating AI technology was coming soon to an office near everyone. Civilization has absorbed economic shocks driven by technology in the past, turning hundreds of millions of farmers into factory workers over the 19th and 20th centuries. However, these structural changes didn't arrive as quickly as the breakneck pace we're currently experiencing with AI.

There are more than a trillion sensors in the world today and according to some estimates there will be about 50 trillion cameras worldwide within the next 5 years, all collecting data either sporadically or around the clock. With such explosive growth of available data and computing resources, recent advances in machine learning and data analytics have yielded transformative results across diverse scientific disciplines, including image recognition, natural language processing, cognitive science, and genomics. However, in many engineering applications, quality and error-free data is not easy to obtain, e.g., for system dynamics characterized by bifurcations and instabilities, hysteresis, delayed responses, and often irreversible responses. Admittedly, as in all everyday applications, in engineering problems, the volume of data has increased substantially compared to even a decade ago but analyzing big data is expensive and time-consuming. Data-driven methods, which have been enabled in the past decade by the availability of sensors, data storage, and computational resources, are taking center stage across many disciplines (physical and information) of science.

In the midst of the AI "gold rush," how can you separate the nuggets from the fool's gold? There's no shortage of cautionary tales involving overhyped AI claims. And applying AI technologies to health care, education, and law enforcement mean that getting it wrong can have real consequences for society--not just for investors who bet on the wrong unicorn. So IEEE Spectrum asked experts to share their tips for how to identify AI hype in press releases, news articles, research papers, and IPO filings. "It can be tricky, because I think the people who are out there selling the AI hype--selling this AI snake oil--are getting more sophisticated over time," says Tim Hwang, director of the Harvard-MIT Ethics and Governance of AI Initiative.

AI Research is capital intensive & research-focused bodies aren't profit-making entities: In October 2018, the Massachusetts Institute of Technology in Cambridge announced a $1-billion investment in computer and AI research. Meanwhile, Stanford University in California and the University of Cambridge and Oxford University are also doubling down on building their own centres for AI research. Closer home, we have the Wadhwani Institute for Artificial Intelligence (AI) that is focusing more on solving India's economic and societal issues. Academic groups around the globe are investing heavily in AI research, but we don't look for profits and concrete products coming out of academia. While it's true that university budgets pale in comparison to corporate-owned AI research organisations, but heavy-duty research roadmaps demand big-ticket investments.

In this post, we will take a tour of the most popular machine learning algorithms. It is useful to tour the main algorithms in the field to get a feeling of what methods are available. There are so many algorithms that it can feel overwhelming when algorithm names are thrown around and you are expected to just know what they are and where they fit. I want to give you two ways to think about and categorize the algorithms you may come across in the field. Both approaches are useful, but we will focus in on the grouping of algorithms by similarity and go on a tour of a variety of different algorithm types.

An artificial intelligence system that generates realistic stories, poems and articles has been updated, with some claiming it is now almost as good as a human writer. The text generator, built by research firm OpenAI, was originally considered "too dangerous" to make public because of the potential for abuse. But now a new, more powerful version of the system - that could be used to create fake news or abusive spam on social media - has been released. The BBC, along with some AI experts, decided to try it out. The model, called GPT-2, was trained on a dataset of eight million web pages, and is able to adapt to the style and content of the initial text given to it.

Microsoft will apply Stasior's AI experience to its current programs such as Cognitive Toolkit, the company's deep learning framework that helps …

From driver-assisted vehicles on our city streets to self-driving vehicles on our factory floors, robotic and autonomous systems are becoming commonplace. You may even have one in your home, vacuuming the floors for you while you stay busy with more meaningful work. The truth is, these hands-off systems are just about everywhere anymore. In a sign of the growing adoption of robotic systems, the market-advisory firm ABI Research predicts that, by 2025, more than 4 million commercial robots will be on the job in over 50,000 warehouses, up from just under 4,000 robotic warehouses in 2018.1 And that's just warehouses -- that's not the "everywhere else" where these worker bees are found.