Lifeboat Foundation

In case you missed the hype, Humane is a startup founded by ex-Apple executives that’s working on a device called the “Ai Pin” that uses projectors, cameras and AI tech to act as a sort of wearable AI assistant. Now, the company has unveiled the AI Pin in full at a Paris fashion show (Humane x Coperni) as a way to show off the device’s new form factor. “Supermodel Naomi Campbell is the first person outside of the company to wear the device in public, ahead of its full unveiling on November 9,” Humane wrote.

The company describes the device as a “screenless, standalone device and software platform built from the ground up for AI.” It’s powered by an “advanced” Qualcomm Snapdragon platform and equipped with a mini-projector that takes the place of a smartphone screen, along with a camera and speaker. It can perform functions like AI-powered optical recognition, but is also supposedly “privacy-first” thanks to qualities like no wake word and thus no “always on” listening.”

JPMorgan Chase CEO Jamie Dimon said the next generation of employees will live to 100 and work 3.5 days a week thanks to artificial intelligence.

Two decades ago, the nanotechnology revolution avoided stumbling by bringing a wide range of people to the table to chart its development. The window is closing fast on AI following suit.

It’s been a busy week for IonQ, the quantum computing start-up focused on developing trapped-ion-based systems. At the Quantum World Congress today, the company announced two new systems (Forte Enterprise and Tempo) intended to be rack-mountable and deployable in a traditional data center. Yesterday, speaking at Tabor Communications (HPCwire parent organization) HPC and AI on Wall Street conference, the company made a strong pitch for reaching quantum advantage in 2–3 years, using the new systems.

If you’ve been following quantum computing, you probably know that deploying quantum computers in the datacenter is a rare occurrence. Access to the vast majority NISQ era computers has been through web portals. The latest announcement from IonQ, along with somewhat similar announcement from neutral atom specialist QuEra in August, and increased IBM efforts (Cleveland Clinic and PINQ²) to selectively place on-premise quantum systems suggest change is coming to the market.

IonQ’s two rack-mounted solutions are designed for businesses and governments wanting to integrate quantum capabilities within their existing infrastructure. “Businesses will be able to harness the power of quantum directly from their own data centers, making the technology significantly more accessible and easy to apply to key workflows and business processes,” reported the company. IonQ is calling the new systems enterprise-grade. (see the official announcement.)

Discover how THALES collaborates with the CNRS to identify new genetic markers leading to the development of pediatric cancers, thereby contributing to the improvement of patient care.

Benchmarks are a key driver of progress in AI. But they also have many shortcomings. The new GPT-Fathom benchmark suite aims to reduce some of these pitfalls.

Benchmarks allow AI developers to measure the performance of their models on a variety of tasks. In the case of language models, for example, answering knowledge questions or solving logic tasks. Depending on its performance, the model receives a score that can then be compared with the results of other models.

These benchmarking results form the basis for further research decisions and, ultimately, investments. They also provide information about the strengths and weaknesses of individual methods.

Chipotle announces that robots will soon be assembling bowls and salads.

Robots are great specialists, but poor generalists. Typically, you have to train a model for each task, robot, and environment. Changing a single variable often requires starting from scratch. But what if we could combine the knowledge across robotics and create a way to train a general-purpose robot?

Today, we are launching a new set of resources for general-purpose robotics learning across different robot types, or embodiments. Together with partners from 33 academic labs we have pooled data from 22 different robot types to create the Open X-Embodiment dataset. We also release RT-1-X, a robotics transformer (RT) model derived from RT-1 and trained on our dataset, that shows skills transfer across many robot embodiments.

In this work, we show training a single model on data from multiple embodiments leads to significantly better performance across many robots than those trained on data from individual embodiments. We tested our RT-1-X model in five different research labs, demonstrating 50% success rate improvement on average across five different commonly used robots compared to methods developed independently and specifically for each robot. We also showed that training our visual language action model, RT-2, on data from multiple embodiments tripled its performance on real-world robotic skills.

This is a risky bet, given the limitations of the technology. Tech companies have not solved some of the persistent problems with AI language models, such as their propensity to make things up or “hallucinate.” But what concerns me the most is that they are a security and privacy disaster, as I wrote earlier this year. Tech companies are putting this deeply flawed tech in the hands of millions of people and allowing AI models access to sensitive information such as their emails, calendars, and private messages. In doing so, they are making us all vulnerable to scams, phishing, and hacks on a massive scale.

I’ve covered the significant security problems with AI language models before. Now that AI assistants have access to personal information and can simultaneously browse the web, they are particularly prone to a type of attack called indirect prompt injection. It’s ridiculously easy to execute, and there is no known fix.

In an indirect prompt injection attack, a third party “alters a website by adding hidden text that is meant to change the AI’s behavior,” as I wrote in April. “Attackers could use social media or email to direct users to websites with these secret prompts. Once that happens, the AI system could be manipulated to let the attacker try to extract people’s credit card information, for example.” With this new generation of AI models plugged into social media and emails, the opportunities for hackers are endless.