Last Friday, Ng gave us a peek, a fleeting glimpse really, at what his next move will be, with these few words on Twitter: “Launching my new project! Hope will help many of you: deeplearning.ai More announcements soon #deeplearniNgAI.”
Kai-Fu Lee, chairman and chief executive of Sinovation Ventures, tackles the ‘automation’ problem, discussing how it is different this time around. He discusses how unlike the Industrial Revolution and the computer revolution, the AI revolution is not taking certain jobs and replacing them with other jobs. Instead, it is poised to bring about a wide-scale decimation of jobs — mostly lower-paying jobs, but some higher-paying ones, too.
He presents the impending problem: 1) enormous wealth will be concentrated in relatively few hands (those who are creating the AI) and 2) enormous numbers of people out of work. The solution to the problem of mass unemployment, he argues, will involve “service jobs of love.” These are jobs that AI cannot do, that society needs and that give people a sense of purpose (an article in Aeon has a similar line of reasoning). He also proposes a form of UBI to ameliorate the wealth disparity.
Following an earlier Accenture Research study into the impact of AI on 12 developed economies, they assessed the effect of AI within 16 industries. Their research shows AI has the potential to boost rates of profitability by an average of 38 percentage points and could lead to an economic boost of US$14 trillion in additional gross value added (GVA) by 2035.
This pilot project collects problems and metrics/datasets from the AI research literature and tracks progress on them. The project has charted the progress of AI research on taxonomy, game playing, vision modelling, NLP, security, and much more. Charts and source code abound…definitely worth checking out.
At EFF, they’re ultimately most interested in how this data can influence our understanding of the likely implications of AI. To begin with, they’re focused on gathering it.
Stefan Seltz-Axmacher, Starsky’s co-founder and CEO is attempting something that’s both more modest and, potentially, more disruptive to US employment than your average autonomous vehicle startup.
His company has designed an AI system for big-rig trucks that makes them mostly self-sufficient on highways, and then, when it’s time to exit onto local roads, allows them to be taken over and driven from a remote operations center. The plan is to eventually employ dozens of drivers, each of whom will keep an eye on a few
trucks at once, sitting before arrays of monitors livestreaming views of windshields and mirrors. The company’s name is a reference to a CB radio slang term for when drivers work in teams—that is, like the title characters of the 1970s TV series Starsky & Hutch.
In this research paper, the authors propose a new system for generating art. The system generates art by looking at art and learning about style and becomes creative by increasing the arousal potential of the generated art by deviating from the learned styles. They build over Generative Adversarial Networks (GAN), which have shown the ability to learn to generate novel images simulating a given distribution.
They argue that such networks are limited in their
ability to generate creative products in their original design. They propose modifications to its objective to make it capable of generating creative art by maximizing deviation from established styles and minimizing deviation from art distribution. The results show that human subjects could not distinguish art generated by the proposed system from art generated by contemporary artists and shown in top art fairs.
In a 1999 paper, Erik Demaine — now an MIT professor of electrical engineering and computer science, but then an 18-year-old PhD student at the University of Waterloo, in Canada — described an algorithm that could determine how to fold a piece of paper into any conceivable 3-D shape.
At the Symposium on Computational Geometry in July, Demaine and Tomohiro Tachi of the University of Tokyo will announce the completion of a quest that began with that 1999 paper: a universal algorithm for folding origami shapes that guarantees a minimum number of seams. Demaine and Tachi are also working to implement the algorithm in a new version of Origamizer, the free software for generating origami crease patterns whose first version Tachi released in 2008.
In May last year, engineers back at NASA installed AI software on the rover’s main flight computer that allowed it to recognize inspection-worthy features on the Martian surface and correct the aim of its rock-zapping lasers. The humans behind the Curiosity mission are still calling the shots in most of the rover’s activities. But the software allows the rover to actively contribute to scientific observations without much human input, making the leap from automation to autonomy.
The software, known as Autonomous Exploration for Gathering Increased Science, or AEGIS, selected inspection-worthy rocks and soil targets with 93 percent accuracy between last May and this April, according to a study from its developers published this week in the journal Science Robotics.
Propaganda on social media is being used to manipulate public opinion around the world, a new set of studies from the University of Oxford has revealed. From Russia, where around 45% of highly active Twitter accounts are bots, to Taiwan, accounts sharing Chinese mainland propaganda, the studies show that social media is an international battleground for dirty politics.
The reports, part of the Oxford Internet Institute’s Computational Propaganda Research Project, cover nine nations also including Brazil, Canada, China, and the United States. They found “the lies, the junk, the misinformation” of traditional propaganda is widespread online and “supported by Facebook or Twitter’s algorithms” according to Philip Howard, Professor of Internet Studies at Oxford.