Lifeboat Foundation

Almost half of our jobs will vanish by 2033 due to robotics and computer automation, according to an Oxford University study. Another study commissioned by the real-estate services company CB Richard Ellis predicts that half the occupations we have now will disappear by 2025.

So who can expect pink slips during the Rise of the Machines?

Predictably, people who work on assembly lines, plantations and construction sites will be replaced by robots that don’t sleep, get sick or take smoke breaks.

Continue reading “There’s A 47% Chance A Robot Will Steal Your Job” »

Most companies working on autonomous vehicles consider lidar sensors mandatory for vehicles to safely navigate alone and distinguish objects such as pedestrians and cyclists. But the best existing sensors are bulky, extremely expensive, and in short supply as demand surges (see “Self-Driving Cars’ Spinning Laser Problem”). Alphabet and Uber have both said they were forced to invent their own, better-performing sensors from scratch to make self-driving vehicles viable. Luminar hopes to serve automakers that would rather not go to that effort.

Russell doesn’t have a college degree—he dropped out of Stanford in return for a $100,000 check under a program started by venture capitalist Peter Thiel to encourage entrepreneurship. But Russell says a (short) lifetime of tinkering and building with electronics helped him design a new lidar sensor that sees farther and in more detail than those on the market.

Artificial intelligence picks up racial and gender biases when learning language from text, researchers say. Without any supervision, a machine learning algorithm learns to associate female names more with family words than career words, and black names as being more unpleasant than white names.

For a study published today in Science, researchers tested the bias of a common AI model, and then matched the results against a well-known psychological test that measures bias in humans. The team replicated in the algorithm all the psychological biases they tested, according to study co-author Aylin Caliskan, a post-doc at Princeton University. Because machine learning algorithms are so common, influencing everything from translation to scanning names on resumes, this research shows that the biases are pervasive, too.

“Language is a bridge to ideas, and a lot of algorithms are built on language in the real world,” says Megan Garcia, the director of New America’s California branch who has written about this so-called algorithmic bias. “So unless an alg is making a decision based only on numbers, this finding is going to be important.”

Continue reading “AI picks up racial and gender biases when learning from what humans write” »

https://youtube.com/watch?v=Qb0Kzb3haK8

Tesla CEO Elon Musk revealed today, on the eve of the New York International Auto Show that the company plans to unveil its all-electric semi truck, called ‘Tesla Semi’, in September.

Tesla Semi truck unveil set for September. Team has done an amazing job. Seriously next level.

Continue reading “Tesla Semi all-electric truck to be unveiled in September and be ‘next level’, says Elon Musk” »

Seti using #AI to find ET.

Join IBM’s Graham Mackintosh, SETI Institute CEO Bill Diamond, and SETI Institute Board of Trustees member Jonathan Knowles as we talk about using machine learning to help better understand the volumes of data collected by the SETI Institute Allen Telescope Array.

Machine learning algorithms and artificial intelligence systems influence many aspects of people’s lives: news articles, movies to watch, people to spend time with, access to credit, and even the investment of capital. Algorithms have been empowered to make such decisions and take actions for the sake of efficiency and speed. Despite these gains, there are concerns about the rapid automation of jobs (even such jobs as journalism and radiology). A better understanding of attitudes toward and interactions with algorithms is essential precisely because of the aura of objectivity and infallibility cultures tend to ascribe to them. This report illustrates some of the shortcomings of algorithmic decisionmaking, identifies key themes around the problem of algorithmic errors and bias, and examines some approaches for combating these problems. This report highlights the added risks and complexities inherent in the use of algorithmic decisionmaking in public policy. The report ends with a survey of approaches for combating these problems.

If it can figure out what you’re trying to draw, that is.

No one really knows how the most advanced algorithms do what they do. That could be a problem.

In its new budget, the government of Prime Minister Justin Trudeau pledged $93 million ($125 million Canadian) to support A.I. research centers in Toronto, Montreal and Edmonton, which will be public-private collaborations.

Today’s striking advances in artificial intelligence owe a lot to research in Canada over the years. But the country has so far failed to cash in.