Lifeboat Foundation

AI needs to develop more solid assumptions, falsifiable hypotheses, and rigorous experimentation.

The landscape of artificial intelligence (AI) applications has traditionally been dominated by the use of resource-intensive servers centralized in industrialized nations. However, recent years have witnessed the emergence of small, energy-efficient devices for AI applications, a concept known as tiny machine learning (TinyML).

We’re most familiar with consumer-facing applications such as Siri, Alexa, and Google Assistant, but the limited cost and small size of such devices allow them to be deployed in the field. For example, the technology has been used to detect mosquito wingbeats and so help prevent the spread of malaria. It’s also been part of the development of low-power animal collars to support conservation efforts.

Small size, big impact Distinguished by their small size and low cost, TinyML devices operate within constraints reminiscent of the dawn of the personal-computer era—memory is measured in kilobytes and hardware can be had for as little as US$1. This is possible because TinyML doesn’t require a laptop computer or even a mobile phone. Instead, it can instead run on simple microcontrollers that power standard electronic components worldwide. In fact, given that there are already 250 billion microcontrollers deployed globally, devices that support TinyML are already available at scale.

Gemini 1.5 Pro includes a breakthrough in long-context understanding, handling up to 1 million tokens. It can also decipher the content of videos and describe what is happening in a scene.

In the world of large language models (LLMs) like ChatGPT, so-called “tokens” are the fundamental units of text that these models process, akin to words, punctuation, or parts of words in human language.

Interacting many-body physical systems ranging from neural networks in the brain to folding proteins to self-modifying electrical circuits can learn to perform diverse tasks. This learning, both in nature and in engineered systems, can occur through evolutionary selection or through dynamical rules that drive active learning from experience. Here, we show that learning in linear physical networks with weak input signals leaves architectural imprints on the Hessian of a physical system. Compared to a generic organization of the system components, (a) the effective physical dimension of the response to inputs decreases, (b) the response of physical degrees of freedom to random perturbations (or system “susceptibility’‘) increases, and © the low-eigenvalue eigenvectors of the Hessian align with the task.

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A robotic spacecraft is expected to touch down on the moon’s surface Thursday night, in what will mark the United States’s first uncrewed commercial moon landing.

“We create the lowest power performance technology,” said Dipti Vachani, senior VP and general manager for Arm’s automotive business, in an interview, “so that Nuro can then take advantage of all that AI software.”

Nuro was founded in 2016 by Dave Ferguson and Jiajun Zhu, two veterans of the Google self-driving car project that would go on to become Waymo. It is one of the few companies operating fully driverless vehicles — that is, vehicles without safety drivers behind the wheel — on public roads today.

Called it. already impacting. not even a week later.

I just used AI in two films that are going to be announced soon. That kept me out of makeup for hours. In post and on set, I was able to use this AI technology to avoid ever having to sit through hours of aging makeup.

How are you thinking about approaching the threat that AI poses to certain job categories at your studio and on your productions?

Continue reading “Tyler Perry Puts $800M Studio Expansion on Hold After Seeing OpenAI’s Sora: ‘Jobs Are Going to Be Lost’” »

Two insect-like robots, a mini-bug and a water strider, developed at Washington State University, are the smallest, lightest and fastest fully functional micro-robots ever known to be created.

Such miniature robots could someday be used for work in areas such as artificial pollination, search and rescue, environmental monitoring, micro-fabrication, or robotic-assisted surgery. Reporting on their work in the proceedings of the IEEE Robotics and Automation Society’s International Conference on Intelligent Robots and Systems, the mini-bug weighs in at eight milligrams while the water strider weighs 55 milligrams. Both can move at about six millimeters a second.

Khan Academy has come up with a safe and accurate ChatGPT tutor. It’s also the best model we have for how to develop and implement AI for the public good.