Lifeboat Foundation

Herbert Ong Brighter with Herbert.

Artificial intelligence excels at sorting through information and detecting patterns or trends. But these machine learning algorithms need to be trained with large amounts of data first.

As researchers explore potential applications for AI, they have found scenarios where AI could be really useful—such as analyzing X-ray image data to look for evidence of rare conditions or detecting a rare fish species caught on a commercial fishing boat—but there’s not enough data to accurately train the algorithms.

Jenq-Neng Hwang, University of Washington professor of electrical and computer and engineering, specializes in these issues. For example, Hwang and his team developed a method that teaches AI to monitor how many distinct poses a baby can achieve throughout the day. There are limited training datasets of babies, which meant the researchers had to create a unique pipeline to make their algorithm accurate and useful.

The effort to give robots AI brains is revealing big practical challenges—and bigger ethical concerns.

By David Berreby

Artificial intelligence startup Symbolica AI launched today with an original approach to building generative AI models.

The company is aiming to tackle the expensive mechanisms behind training and deploying large language models such as OpenAI’s ChatGPT that are based on Transformer architecture.

Alongside that news, it also revealed today that it has raised $33 million in total funding combined from a Series A and seed funding round led by Khosla Ventures. Other investors included Day One Ventures, Abstract Ventures Buckley Ventures and General Catalyst.

Fascinating vision/plan by the one and only Sam Altman of how to update our economic systems to benefit everyone in the context of rapidly accelerating technological change.

My work at OpenAI reminds me every day about the magnitude of the socioeconomic change that is coming sooner than most people believe. Software that can think and learn will do more and more of the work that people now do. Even more power will shift from labor to capital. If public policy doesn’t adapt accordingly, most people will end up worse off than they are today.

We need to design a system that embraces this technological future and taxes the assets that will make up most of the value in that world–companies and land–in order to fairly distribute some of the coming wealth. Doing so can make the society of the future much less divisive and enable everyone to participate in its gains.

Continue reading “Moore’s Law for Everything” »

When it comes to achieving success, Huang knows more than most. In 1993, he co-founded computer chip company Nvidia, where he’s served as CEO for more than three decades. The company’s success turned Huang into a billionaire. Now, with Nvidia’s chips in high demand for building AI software, it’s become one of the world’s most valuable companies with a valuation north of $2 trillion.

Huang himself is one of the world’s wealthiest individuals, with an estimated net worth of $77.6 billion, according to Bloomberg.

Continue reading “Success requires ‘ample doses of pain,’ billionaire Nvidia CEO tells Stanford students: ‘I hope suffering happens to you’” »

From opennlplab, shanghai #AI lab, & MIT CSAIL

HGRN2

Gated Linear RNNs with State Expansion.

Continue reading “HGRN2: Gated Linear RNNs with State Expansion” »

Neuroprosthetics, a technology that allows the brain to control external devices such as robotic limbs, is beginning to emerge as a viable option for patients disabled by amputation or neurological conditions such as stroke.

META, a leading technology company at the forefront of innovation, has unveiled its next-generation custom silicon chip.