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Researchers in artificial intelligence can stand to make a ton of money. But this week, we actually know just how much some A.I. experts are being paid — and it’s a lot, even at a nonprofit.
OpenAI, a nonprofit research lab, paid its lead A.I. expert, Ilya Sutskever, more than $1.9 million in 2016, according to a recent public tax filing. Another researcher, Ian Goodfellow, made more than $800,000 that year, even though he was only hired in March, the New York Times reported.
As the publication points out, the figures are eye-opening and offer a bit of insight on how much A.I. researchers are being paid across the globe. Normally, this kind of data isn’t readily accessible. But since OpenAI is a nonprofit organization, it’s required by law to make these figures public.
As it becomes apparent that artificial intelligence will replace ever-more jobs in the coming years, a growing number of politicians, nonprofits, and Silicon Valley entrepreneurs have started thinking about how we’ll cope with a world in which not everyone can — or needs to — work.
Basic income experiments, in which people are given a regular salary just to live, no strings attached, are popping up all over Europe, Africa, and North America.
That, he said, will lead to new forms of expression, such as music, that will be as different from today’s communication as current human expression is from that of primates.
Asked how the U.S. and other countries would pay for a basic income, given existing large deficits, Kurzweil predicted that massive deflation would make goods much cheaper.
Separately: Kurzweil debuted a new Google project called “Talk to Books,” a new free tool that allows people to use their voice to ask a question and that will go find the best answers from hundreds of thousands of books. Unlike traditional search, it is based on semantics, not keywords.
Before I started working on real-world robots, I wrote about their fictional and historical ancestors. This isn’t so far removed from what I do now. In factories, labs, and of course science fiction, imaginary robots keep fueling our imagination about artificial humans and autonomous machines.
Real-world robots remain surprisingly dysfunctional, although they are steadily infiltrating urban areas across the globe. This fourth industrial revolution driven by robots is shaping urban spaces and urban life in response to opportunities and challenges in economic, social, political, and healthcare domains. Our cities are becoming too big for humans to manage.
Good city governance enables and maintains smooth flow of things, data, and people. These include public services, traffic, and delivery services. Long queues in hospitals and banks imply poor management. Traffic congestion demonstrates that roads and traffic systems are inadequate. Goods that we increasingly order online don’t arrive fast enough. And the WiFi often fails our 24/7 digital needs. In sum, urban life, characterized by environmental pollution, speedy life, traffic congestion, connectivity and increased consumption, needs robotic solutions—or so we are led to believe.
Russia is actively implementing a lunar program through 2030, aiming to send astronauts to the moon, President Vladimir Putin said Thursday, Russia’s Cosmonautics Day.
Putin said “yes” to the question “We are going to fly to the moon, right?” when he visited the Space Pavilion at the all-Russian Center of Achievements of the National Economy, according to the Kremlin.
The industry partners will use the money to train artificially intelligent laboratory robots.
Many people assume that when robots enter the economy, they’ll snatch low-skilled jobs. But don’t let a PhD fool you — AI-powered robots will soon impact a laboratory near you.
The days of pipetting liquids around were already numbered. Companies like Transcriptic, based in Menlo Park, California, now offer automated molecular biology lab work, from routine PCR to more complicated preclinical assays. Customers can buy time on their ‘robotic cloud lab’ using any laptop and access the results in a web app.
“If you went to bed last night as an industrial company, you’re going to wake up this morning as a software and analytics company.” Jeff Immelt, former CEO of General Electric
The second wave of digitization is set to disrupt all spheres of economic life. As venture capital investor Marc Andreesen pointed out, “software is eating the world.” Yet, despite the unprecedented scope and momentum of digitization, many decision makers remain unsure how to cope, and turn to scholars for guidance on how to approach disruption.
The first thing they should know is that not all technological change is “disruptive.” It’s important to distinguish between different types of innovation, and the responses they require by firms. In a recent publication in the Journal of Product Innovation, we undertook a systematic review of 40 years (1975 to 2016) of innovation research. Using a natural language processing approach, we analyzed and organized 1,078 articles published on the topics of disruptive, architectural, breakthrough, competence-destroying, discontinuous, and radical innovation. We used a topic-modeling algorithm that attempts to determine the topics in a set of text documents. We quantitatively compared different models, which led us to select the model that best described the underlying text data. This model clustered text into 84 distinct topics. It performs best at explaining the variability of the data in assigning words to topics and topics to documents, minimizing noise in the data.
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