Artificial intelligence is now part of our daily lives, whether in voice recognition systems or route finding apps. But scientists are increasingly drawing on artificial intelligence to understand society, design new materials and even improve our health.
We’re releasing an analysis showing that since 2012, the amount of compute used in the largest AI training runs has been increasing exponentially with a 3.5 month-doubling time (by comparison, Moore’s Law had an 18-month doubling period). Since 2012, this metric has grown by more than 300,000x (an 18-month doubling period would yield only a 12x increase). Improvements in compute have been a key component of AI progress, so as long as this trend continues, it’s worth preparing for the implications of systems far outside today’s capabilities.
Oracle Corp. has acquired a Culver City, California-based data science platform.
DataScience.com centralizes data science tools, projects and infrastructure for enterprise operations. Data science teams use the platform to organize work, access data and computing resources, and execute end-to-end model development workflows.
The company’s clients including Amgen, Rio Tinto and Sonos use the platform to improve productivity, reduce operational costs and deploy machine learning solutions faster.
“Data science requires a comprehensive platform to simplify operations and deliver value at scale,” said DataScience.com CEO Ian Swanson in a statement. “With DataScience.com, customers leverage a robust, easy-to-use platform that removes barriers to deploying valuable machine learning models in production.”
Japan’s Preferred Networks Inc. has only one publicly available product, a whimsical application that uses artificial intelligence to automate the coloring of manga cartoons.
Yet the four-year-old firm has become Japan’s most valuable startup, with a venture capital funding that priced it at more than $2 billion, according to people familiar with the matter. Toyota Motor Corp., its biggest backer, handed over $110 million on a bet its algorithms will help them compete with Google in driverless cars. Last February, Prime Minister Shinzo Abe posed for pictures with the firm’s two young founders at his office, where they were awarded a prize for promising new ventures.
What sets Preferred Networks apart from the hundreds of other AI startups is its ties to Japan’s manufacturing might. Deep learning algorithms depend on data and the startup is plugging into some of the rarest anywhere. Its deals with Toyota and Fanuc Corp., the world’s biggest maker of industrial robots, give it access to the world’s top factories. While Google used its search engine to become an AI superpower, and Facebook Inc. mined its social network, Preferred Networks has an opportunity to analyze and potentially improve how just about everything is made.
Last year, Nature convened 16 meetings and workshops in universities across Europe and the United States to explore the state of lab health, pressures on individual groups and how best to tackle them. Scientists shared what they liked and loathed about their workplace, from navigating interpersonal relationships to enforcing and encouraging best practices. Nature’s survey grew from these discussions, in an effort to back up such anecdotes with data. It is the largest publicly reported analysis of its kind.
The encouraging news is that morale is reasonably high. For the most part, scientists around the world view their groups as healthy — calling them ‘friendly’, ‘collaborative’ and ‘supportive’. But signs of stress bubble underneath the surface: around one in five respondents in more junior positions (that is, those who don’t lead the group, such as graduate students and postdoctoral fellows) were negative about their labs, describing them as ‘stressful’, ‘tense’ and ‘toxic’ (see ‘Words matter’).
Wall Street Journal; Danny Dougherty, Brian McGill, Dante Chinni and Aaron Zitner
from
Which cities have the most drawing power among college grads? Small metros lose out to big ones in gaining graduates, but some buck the trend. Our database of 445 schools shows where the alumni of each one wind up.
The picturesque slice of Switzerland’s southern tip is home to about 60,000 people, including a computer scientist named Jürgen Schmidhuber. He’s a professor, a researcher, and the co-founder of a 25-employee AI startup called Nnaisense. (Pronounced like “nascence,” the name is proof that Silicon Valley holds no monopoly on ridiculous company names.) Schmidhuber is a pioneer who effectively figured out how to give AI systems memories. His ideas appear in one form or another in just about every smartphone, social network, and digital assistant. He’s not shy to mention these things, or to cite reams of documentation to back himself up, or to say things like, “My team plans to change the course of human history,” in between bites of salmon lasagna at a Lugano cafe.
For decades, Schmidhuber and a handful of other AI savants have pursued the quest for an AGI along similar paths, but only in the past six years has the right mix of powerful computers and plentiful data existed to start turning their theories into reality. The others—among them Geoffrey Hinton, Yoshua Bengio, Richard Sutton, and Yann LeCun—have become celebrities in the tech industry. They’re beloved as mentors, sought out by top companies, and feted at conferences as progenitors of a new age. Outside most academic circles, Schmidhuber remains largely unknown. Partly, that’s because of Lugano’s isolation in the Alps. Mostly, it seems to be because the guy’s peers don’t like him. While they shy away from commenting in public, the other AI legends describe him privately as egomaniacal, deceptive, and an overall pain.
Schmidhuber has a history of, among other things, haranguing fellow researchers in academic journals and at conferences, interrupting speeches to demand that peers admit they’ve borrowed or even stolen his ideas.
Michael Eisen was about to leave with his daughter for the Taylor Swift concert Saturday when someone flagged the offending news: Eric Lander, one of the most powerful men in American science, had toasted James Watson, a discoverer of DNA’s double helix, who has expressed racist and sexist views.
Eisen took to Twitter quickly and vociferously, declaring that Lander, the president of the Broad Institute of MIT and Harvard, was “a deceitful megalomaniac who is destroying science” and who had offered “glowing support of misogynistic, anti-Semitic racists.”
Its name may playfully give homage to a 1980s video arcade game, but the technology on board The Ohio State University’s first satellite — the CubeRRT — could be vital for Earth science missions into the future. It is scheduled for launch on May 20.
Project leader Joel Johnson, professor and chair of electrical and computer engineering (ECE) at Ohio State, said the CubeSat Radiometer Radio Frequency Interference Technology Validation mission (CubeRRT) contains advanced sensors for observing Earth’s environment from space.
A team of Waterloo researchers found that applying artificial intelligence to the right combination of data retrieved from wearable technology may detect whether your health is failing.
The study, which involved researchers from Waterloo’s Faculties of Applied Health Sciences and Engineering, found that the data from wearable sensors and artificial intelligence that assesses changes in aerobic responses could one day predict whether a person is experiencing the onset of a respiratory or cardiovascular disease.
“The onset of a lot of chronic diseases, including type 2 diabetes and chronic obstructive pulmonary disease, has a direct impact on our aerobic fitness,” said Thomas Beltrame, who led the research while at the University of Waterloo, and is now at the Institute of Computing in University of Campinas in Brazil. “In the near future, we believe it will be possible to continuously check your health, even before you realize that you need medical help.”
Today, VoiceBase, the leading provider of AI-powered speech analytics announced a partnership with the University of Sheffield, to develop the next generation of speech and language technology. Together, VoiceBase and world-renowned Professor of Speech and Audio Technology, Thomas Hain, will run a Centre for Speech & Language Technology. This Centre will be at the forefront of machine learning and artificial intelligence applications within automated speech recognition (ASR) and other language technologies.
“Professor Thomas Hain is notably one of the leading experts in the highly competitive field of speech technology,” stated Walter Bachtiger CEO VoiceBase. “VoiceBase is honored to have found tremendous synergy around our speech technology vision, in order to jointly advance the science of automated speech recognition.”
Data are increasingly being used for child welfare purposes. Across the country, caseworkers are already relying on large administrative datasets collected through various social service systems to make evidence-based decisions about their clients’ safety, risk, and service planning. The challenge now is to create data-driven tools to ensure all this new information effectively enhances and supports—but does not replace—the real-life expertise and intuition of highly-seasoned caseworkers. One set of promising tools is machine-learning-driven predictive analytics and recommender systems.
What Are Machine-Learning-Driven Predictive Analytics and Recommender Systems?
Let’s start with predictive analytics. In its simplest form, predictive analytics is about using existing information to make predictions about future outcomes, like whether a person’s job application is likely to lead to a job offer, or the chances a consumer shopping online will ultimately purchase the item they’re reading about. Predictive analytics allows researchers to tell sophisticated algorithms a lot of past information, and the algorithms can provide an educated guess about the likely outcomes for a particular future event.
The birthplace of Samsung, LG and Hyundai, South Korea is a hotbed for tech and consumer electronics innovation. The country however trails neighbors China and Japan in the artificial intelligence competition, and lags far behind global leader the United States.
Eager to kickstart its AI industry, the nation of 52 million yesterday released an ambitious national plan to invest ₩2.2 trillion (US$2 billion) by 2022 to strengthen its AI R&D capability. The program includes the establishment of six new AI research institutes, JoongAng Ilbo reports.
Beacon Labs launches from Bayes Impact as an independent nonprofit organization to focus on expansion of data-driven public policy solutions in the U.S. With a goal to better serve under-resourced communities, and with a growing need to empower policymakers and advocates with the best information, Beacon Labs is unveiling Encompass, an open-source initiative that visualizes how accessibility to basic services like health care vary across demographic groups.
Lobe is an easy-to-use visual tool that lets you build custom deep learning models, quickly train them, and ship them directly in your app without writing any code. Start by dragging in a folder of training examples from your desktop. Lobe automatically builds you a custom deep learning model and begins training. When you’re done, you can export a trained model and ship it directly in your app.
