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The competition to build the iPhone of artificial intelligence is heating up.

On Tuesday, the technology startup Rabbit unveiled its contender: a small, orange, walkie-talkie style device that, according to the company, can use AI agents to carry out tasks on behalf of the user.

In a pre-recorded keynote address shown at the Consumer Electronics Show in Las Vegas, Rabbits founder Jesse Lyu asks the device to plan him a vacation to London; the keynote shows the device designing him an itinerary and booking his trip. He orders a pizza, books an Uber, and teaches the device how to generate an image using Midjourney.

The gadget, called the Rabbit r1, is just the latest in an increasingly active new hardware category: portable AI-first devices that can interact with users in natural language, eschewing screens and app-based operating systems. Retailing at $199, the r1 is a cheaper competitor to the Humane Ai Pin, a $699 wearable device unveiled in November that offers a similar suite of capabilities, and the $299 Meta and Rayban smart-glasses, which have an AI-powered assistant. Prominent tech investors are betting that the new advances in AI, like large language models (LLMs), will open up new vistas of personalized computing. OpenAIs CEO Sam Altman is an investor in Humane. Altman and Softbanks Masayoshi Son are reportedly in talks to design a separate AI hardware product with iPhone designer Jony Ive. Rabbit is funded to the tune of $30 million led by the billionaire Vinod Khoslas venture capital firm Khosla Ventures. Whoever can design the appropriate hardware form factor, these billionaires line of thinking goes, will win big in the AI era.

Read More: Humane Wants Its New Ai Pin to Liberate You From Your Phone Screen

Rabbits r1 is based on a new type of AI system called a large action model, Lyu said during his keynote unveiling the device. The problem with large language models, the technology that tools like ChatGPT are based on, he said, are that they struggle to take actions in the real world. Instead, Rabbits large action model is trained on graphical user interfaces like websites and apps, which means it can navigate interfaces designed for humans and take actions on their behalf. Things like ChatGPT are extremely good at understanding your intentions, but could be better at taking actions, Lyu said. The large language model understands what you say, but the large action model gets things done.

To give r1 the ability to do things like book vacations, order pizza, and call an Uber, users will need to sign into their various accounts via Rabbits web portal. Rabbits AI agents (which it calls rabbits), running on an external server rather than on the device itself, will then use those accounts to execute their actions. Rabbit says each user is assigned a dedicated and isolated environment on its secure servers, and that it does not store user passwords. Rabbits will ask for permission and clarification during the execution of any tasks, especially those involving sensitive actions such as payments, the company says on its website.

The company says on its website that it works with the best industry partners in natural language intelligence to understand your intentions, but does not disclose who those partners are. Its privacy policy retains the right to send user data to third parties for purposes including data processing. Rabbit did not immediately respond to a request for comment.

Whether a new piece of hardware is even necessary for users to interact with AI agents is an open question. Only those who have lost touch with the way consumers use tech believe these products can succeed, Francisco Jeronimo, a vice president for device data and analytics at the market intelligence firm IDC, wrote on X, referring to both Rabbit and Humanes new products. Although the ideas have merit on their own, the reality is that consumers don't need these kinds of devices, they need intelligent phones!

Altman has publicly expressed a desire to build increasingly agential capabilities into OpenAIs own software, which could obviate the need for new AI-first devices. Eventually, youll just ask a computer for what you need, and it will do all of these tasks for you, Altman said at an OpenAI developer conference in November.

But the trend toward companies empowering AIs to take actions in the real world has left some experts worried. AI devices like the Rabbit r1 have limited levers they can pull to act upon the world, but increasingly powerful agential AIs could pose many risks, according to a paper published in October by the Center for AI Safety. AI agents can be given goals such as winning games, making profits on the stock market, or driving a car to a destination, the paper says. AI agents therefore pose a unique risk: people could build AIs that pursue dangerous goals. A society that becomes dependent on a complex network of different interacting AI agents would, the paper argues, be vulnerable to problems like inescapable feedback loops or agents goals drifting in ways that could be harmful to humanity.

Altman suggested in November that safety concerns were a reason OpenAI was only taking small steps toward giving its AI tools the power to take actions in the real world. We think its especially important to move carefully towards this future of agents, he said. Its going to require a lot of thoughtful consideration by society.

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Startups Are Racing to Create the iPhone of AI - TIME

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Musicians Set to Begin Contract Negotiations With Studios On AI, Streaming Priorities  Hollywood Reporter

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Musicians Set to Begin Contract Negotiations With Studios On AI, Streaming Priorities - Hollywood Reporter

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Casa Batll introduces Sofia Crespos structures of being

Casa Batll, the UNESCO World Heritage Site designed by renowned architect Antoni Gaud, introduces Sofia Crespo as its upcoming artist in residence, who will transform the iconic landmark with her Structures of Being projection mapping artwork. This immersive audiovisual event, blending artificial intelligence with the organic inspirations that fueled Gauds creations, is scheduled for January 27-28, 2024.

Following in the footsteps of Refik Anadol, who animated the buildings facade with his 2023 Living Architecture event, Crespo is the second artist to participate in Casa Batlls The Heritage of Tomorrow residency. The program is dedicated to propelling Gauds legacy into the future by inviting contemporary artists to interact with his work, through commissions, restorations, and research projects.

Structures of Being by Sofia Crespo blends organic forms and artificial intelligence | all images courtesy of Casa Batll

Visual artist Sofia Crespo (find more here) is described as a pioneer in the exploration of organic life and its evolution through artificial intelligence. Her work, Structures of Being, draws inspiration from nature, an elemental theme in Gauds legacy. The show invites viewers on a journey through Crespos personal microscope, facilitating contemplation of lifes evolution through immersive encounters with the diverse materials, beings, and natural phenomena found within Casa Batll. The artwork is accompanied by Robert M. Thomas music, featuring local performers such as organist Juan de la Rubia and the string quartet Cosmos Quartet. Sofia Crespo and Robert M. Thomas have closely collaborated to offer a comprehensive work, guiding their creations with their own algorithms, but with a common goal: to connect us with the present through nature.

Casa Batll has a unique richness per square meter, and its capacity to inspire is endless for any artist, especially the facade, the public part of the House. Sofias creative universe is perfect for capturing it because it stems from a deep observation of nature, its essence, structures, and beauty, offering a personal view that combines art and innovation, just like Gaud did in his time, shares Gary Gautier, General Manager of Casa Batll.

the mapping draws inspiration from nature, a fundamental element in Gauds work

the show is a journey through Sofia Crespos personal microscope

viewers are encouraged to contemplate the evolution of life by immersing in materials, and natural phenomena present in Casa Batll

portrait of Sofia Crespo by Filipa Aurlio

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name: Structures of Being artist: Sofia Crespo location: Casa Batll

myrto katsikopoulou I designboom

jan 12, 2024

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sofia crespo to animate casa batll facade with bio-inspired AI projection mapping artwork - Designboom

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A sleek $199 AI assistant sold out in a day but more are on the way  Business Insider

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A sleek $199 AI assistant sold out in a day but more are on the way - Business Insider

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Computing has already accelerated scientific discovery. Now scientists say a combination of advanced AI with next-generation cloud computing is turbocharging the pace of discovery to speeds unimaginable just a few years ago.

Microsoft and the Pacific Northwest National Laboratory (PNNL) in Richland, Washington, are collaborating to demonstrate how this acceleration can benefit chemistry and materials science two scientific fields pivotal to finding energy solutions that the world needs.

Scientists at PNNL are testing a new battery material that was found in a matter of weeks, not years, as part of the collaboration with Microsoft to use to advanced AI and high-performance computing (HPC), a type of cloud-based computing that combines large numbers of computers to solve complex scientific and mathematical tasks.

As part of this effort, the Microsoft Quantum team used AI to identify around 500,000 stable materials in the space of a few days.

The new battery material came out of a collaboration using Microsofts Azure Quantum Elements to winnow 32 million potential inorganic materials to 18 promising candidates that could be used in battery development in just 80 hours. Most importantly, this work breaks ground for a new way of speeding up solutions for urgent sustainability, pharmaceutical and other challenges while giving a glimpse of the advances that will become possible with quantum computing.

We think theres an opportunity to do this across a number of scientific fields, says Brian Abrahamson, the chief digital officer at PNNL. Recent technology advancements have opened up the opportunity to accelerate scientific discovery.

PNNL is a U.S. Department of Energy laboratory doing research in several areas, including chemistry and materials science, and its objectives include energy security and sustainability. That made it the ideal collaborator with Microsoft to leverage advanced AI models to discover new battery material candidates.

The development of novel batteries is an incredibly important global challenge, Abrahamson says. It has been a labor-intensive process. Synthesizing and testing materials at a human scale is fundamentally limiting.

The traditional first step of materials synthesis is to read all the published studies of other materials and hypothesize how different approaches might work out. But one of the main challenges is that people publish their success stories, not their failure stories, says Vijay Murugesan, materials sciences group lead at PNNL. That means scientists rarely benefit from learning from each others failures.

The next traditional scientific step is testing the hypotheses, typically a long, iterative process. If its a failure, we go back to the drawing board again, Murugesan says. One of his previous projects at PNNL, a vanadium redox flow battery technology, required several years to solve a problem and design a new material.

The traditional method requires looking at how to improve on what has been done in the past. Another approach would be to take all the possibilities and, through elimination, find something new. Designing new materials requires a lot of calculations, and chemistry is likely to be among the first applications of quantum computing. Azure Quantum Elements offers a cloud computing system designed for chemistry and materials science research with an eye toward eventual quantum computing, and is already working on these kinds of models, tools and workflows. These models will be improved for future quantum computers, but they are already proving useful for advancing scientific discovery using traditional computers.

To evaluate its progress in the real world, the Microsoft Quantum team focused on something ubiquitous in our lives materials for batteries.

Microsoft first trained different AI systems to do sophisticated evaluations of all the workable elements and to suggest combinations. The algorithm proposed 32 million candidates like finding a needle in a haystack. Next, the AI system found all the materials that were stable. Another AI tool filtered out candidate molecules based on their reactivity, and another based on their potential to conduct energy.

The idea isnt to find every single possible needle in the hypothetical haystack, but to find most of the good ones. Microsofts AI technology whittled the 32 million candidates down to about 500,000 mostly new stable materials, then down to 800.

At every step of the simulation where I had to run a quantum chemistry calculation, instead Im calling the machine learning model. So I still get the insight and the detailed observations that come from running the simulation, but the simulation can be up to half a million times faster, says Nathan Baker, Product Leader for Azure Quantum Elements.

AI may be fast, but it isnt perfectly accurate. The next set of filters used HPC, which provides high accuracy but uses a lot of computing power. That makes it a good tool for a smaller set of candidate materials. The first HPC verification used density functional theory to calculate the energy of each material relative to all the other states it could be in. Then came molecular dynamics simulations that combined AI and HPC to analyze the movements of atoms and molecules inside each material.

This process culled the list to 150 candidates. Finally, Microsoft scientists used HPC to evaluate the practicality of each material availability, cost and such to trim the list to 23 five of which were already known.

Thanks to this AI-HPC combination, discovering the most promising material candidates took just 80 hours.

The HPC portion accounted for 10 percent of the time spent computing and that was on an already-targeted set of molecules. This intense computing is the bottleneck, even at universities and research institutions that have supercomputers, which not only are not tailored to a specific domain but also are shared, so researchers may have to wait their turn. Microsofts cloud-based AI tools relieve this situation.

Microsoft scientists used AI to do the vast majority of the winnowing, accounting for about 90 percent of the computational time spent. PNNL materials scientists then vetted the short list down to half a dozen candidate materials. Because Microsofts AI tools are trained for chemistry, not just battery systems, they can be used for any kind of materials research, and the cloud is always accessible.

We think the cloud is a tremendous resource in improving the accessibility to research communities, Abrahamson says.

Today, Microsoft supports a chemistry-specific copilot and AI tools that together act like a magnet that pulls possible needles out of the haystack, trimming the number of candidates for further exploration so scientists know where to focus. The vision we are working toward is generative materials where I can ask for list of new battery compounds with my desired attributes, Baker says.

The hands-on stage is where the project stands now. The material has been successfully synthesized and turned into prototype batteries that are functional and will undergo multiple tests in the lab. Making the material at this point, before its commercialized, is artisanal. One of the first steps is to take solid precursors of the materials and to grind them by hand with a mortar and pestle, explains Shannon Lee, a PNNL materials scientist. She then uses a hydraulic press to compact the material into a dime-shaped pellet. It goes into a vacuum tube and is heated to 450 to 650 degrees Celsius (842 to 1202 degrees Fahrenheit), transferred to a box to keep it away from oxygen or water, and then ground into a powder for analysis.

For this material, the 10-or-more-hour process is relatively quick, Lee says. Sometimes it takes a week or two weeks to make a single material.

Then hundreds of working batteries must be tested, over thousands of different charging cycles and other conditions, and later different battery shapes and sizes to realize commercial use. Murugesan dreams of the development of a digital twin for chemistry or materials, so you dont need to go to a lab and put this material together and make a battery and test it. You can say, this is my anode and this is my cathode and thats the electrolyte and this is how much voltage Im going to apply, and then it can predict how everything will work together. Even details like, after 10,000 cycles and five years of usage, the material performance will be like this.

Microsoft is already working on digital tools to speed up the other parts of the scientific process.

The lengthy traditional process is illustrated by lithium-ion batteries. Lithium got attention as a battery component in the early 1900s, but rechargeable lithium-ion batteries didnt hit the market until the 1990s.

Today, lithium-ion batteries increasingly run our world, from phones to medical devices to electric vehicles to satellites. Lithium demand is expected to rise five to ten times by 2030, according to the U.S. Department of Energy. Lithium is already relatively scarce, and thus expensive. Mining it is environmentally and geopolitically problematic. Traditional lithium-ion batteries also pose safety issues, with the potential to catch fire or explode.

Many researchers are looking for alternatives, both for lithium and for the materials used as electrolytes. Solid-state electrolytes show promise for their stability and safety.

The newly discovered material PNNL scientists are currently testing uses both lithium and sodium, as well as some other elements, thus reducing the lithium content considerably possibly by as much as 70 percent. It is still early in the process the exact chemistry is subject to optimization and might not work out when tested at larger scale, Abrahamson cautions. He points out that the story here is not about this particular battery material, but rather the speed at which a material was identified. The scientists say the exercise itself is immensely valuable, and it has revealed some surprises.

The AI-derived material is a solid-state electrolyte. Ions shuttle back and forth through the electrolyte, between the cathode and the anode, ideally with minimal resistance.

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How AI and high-performance computing are speeding up scientific discovery - Microsoft

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