Lifeboat Foundation

OpenAI CEO Sam Altman over the weekend called for enhanced collaboration between the U.S. and China on artificial intelligence development. Without mentioning the fact that his company’s products like ChatGPT are not available in China, he argued that China should be a major player in ensuring the safety of global AI development and rollout.

“With the emergence of the increasingly powerful AI systems, the stakes for global cooperation have never been higher,” he said in the keynote address for a conference hosted by the Beijing Academy of Artificial Intelligence, sounding more like someone leading an advocacy group on responsible tech than what he is: the CEO of a company responsible for shepherding that emergence.

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“Even though recent months have seen AI labs locked in an out-of-control race to develop and deploy ever more powerful digital minds that no one—not even their creators—can understand, predict, or reliably control,” stated the letter.

The idea is that AI development should be “planned for and managed with commensurate care and resources.” However, the authors of the letter say that this level of planning is not happening. This leads to AI systems that are out of control.

Transparently communicating the limitations of the model and providing clear disclaimers when interacting with users can foster trust and accountability.

Qualcomm Technologies, Inc. unveiled the Qualcomm Video Collaboration Platform, a new suite of video collaboration solutions that allows original equipment manufacturers (OEMs) to easily design and deploy video conferencing products featuring superior video, audio and customizable on-device AI to power engaging, immersive virtual meeting experiences across enterprise, healthcare, educational, and home environments. The Qualcomm® Video Collaboration Platform is a one-stop solution that provides essential hardware and software features specifically tailored for video conferencing so that customers can quickly design and deploy a wide variety of video conferencing products, from enterprise video collaboration systems and huddle room systems to digital whiteboards, to touch controllers and personal devices for the home.

With support for Android and Linux, the three AI-rich platforms offer greater flexibility and ability to customize and deploy video conferencing products across diverse environments. Qualcomm Technologies’ industry-leading innovations in connectivity, compute, AI, audio, and video work together to deliver features that eliminate distractions, enhance productivity, and allow remote meeting callers to feel more connected to conference room participants by providing individual views of everybody in the room, creating an equal viewing experience for all participants.

With the rapid advances in generative AI, future meeting experiences will offer even more advanced video, speech, and text capabilities. Collaboration devices with dedicated hardware support for on-chip AI acceleration will be able to optimize these experiences by splitting workloads between the cloud and edge-based device.

AI isn’t programming’s future, it’s its present.

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Walmart, Meta and LinkedIn are three companies currently testing internal generative AI options for employees that are safe for the use of company data, either in the form of generative AI “playgrounds” that offer a variety of models to choose from, or in the case of Meta, its own in-house internal chatbot.

These examples stand in contrast to companies that have banned the use of public generative AI tools like ChatGPT, including Goldman Sachs, Amazon and Verizon.

COPENHAGEN, June 14 (Reuters) — Swedish electric self-driving truck company Einride expects to reduce CO₂ emissions in Norway by 2,100 tonnes over the coming three years as it partners up with Scandinavia’s leading postal service, PostNord, the company said on Wednesday.

Norway has the world’s highest number of electric vehicles per head of population and aims for all heavy vehicles to be zero-emission by 2040, potentially cutting CO₂ emissions by 4.4 million tonnes or nearly 9% of the country’s annual emissions.

“Given Norway’s pioneering work in electrifying passenger vehicles, it’s only logical that they should take a leading role in the electrification of heavy-duty freight as well,” Einride CEO Robert Falck said.

Explore how AI is revolutionizing the music industry, from creating original songs to altering how we discover and listen to music & even posing questions about copyright.

The fascinating collective behaviors of biological systems have inspired extensive studies on shape assembly of robot swarms^6,7,8,9. One class of strategies widely studied in the literature are based on goal assignment in either centralized or distributed ways^10,11,12. Once a swarm of robots are assigned unique goal locations in a desired shape, the consequent task is simply to plan collision-free trajectories for the robots to reach their goal locations¹⁰ or conduct distributed formation control based on locally sensed information^6,13,14. It is notable that centralized goal assignment is inefficient to support large-scale swarms since the computational complexity increases rapidly as the number of robots increases^15,16. Moreover, when robots fail to function normally, additional algorithms for fault-tolerant detection and goal re-assignment are required to handle such situations¹⁷. As a comparison, distributed goal assignment can support large-scale swarms by decomposing the centralized assignment into multiple local ones^11,12. It also exhibits better robustness to robot faults. However, since distributed goal assignments are based on locally sensed information, conflicts among local assignments are inevitable and must be resolved by sophisticated algorithms such as local task swapping^11,12.

Another class of strategies for shape assembly that have also attracted extensive research attention are free of goal assignment^18,19,20,21. For instance, the method proposed in ref. ¹⁸ can assemble complex shapes using thousands of homogeneous robots. An interesting feature of this method is that it does not rely on external global positioning systems. Instead, it establishes a local positioning system based on a small number of pre-localized seed robots. As a consequence of the local positioning system, the proposed edge-following control method requires that only the robots on the edge of a swarm can move while those inside must stay stationary. The method in ref. ¹⁹ can generate swarm shapes spontaneously from a reaction-diffusion network similar to embryogenesis in nature. However, this method is not able to generate user-specified shapes precisely. The method in ref. ²¹ can aggregate robots on the frontier of shapes based on saliency detection. The user-defined shape is specified by a digital light projector. An interesting feature of this method is that it does not require centralized edge detectors. Instead, edge detection is realized in a distributed manner by fusing the beliefs of a robot with its neighbors. However, since the robots cannot self-localize themselves relative to the desired shape, they make use of random walks to search for the edges, which would lead to random trajectories. Another class of methods that do not require goal assignment is based on artificial potential fields^22,23,24,25. One limitation of this class of methods is that robots may easily get trapped in local minima, making it difficult to assemble nonconvex complex shapes.

Here, we propose a strategy for shape assembly of robot swarms based on the idea of mean-shift exploration: when a robot is surrounded by neighboring robots and unoccupied locations, it would actively give up its current location by exploring the highest density of nearby unoccupied locations in the desired shape. This idea does not rely on goal assignment. It is realized by adapting the mean-shift algorithm^26,27,28, which is an optimization technique widely used in machine learning for locating the maxima of a density function. Moreover, a distributed negotiation mechanism is designed to allow robots to negotiate the final desired shape with their neighbors in a distributed manner. This negotiation mechanism enables the swarm to maneuver while maintaining a desired shape based on a small number of informed robots. The proposed strategy empowers robot swarms to assemble nonconvex complex shapes with strong adaptability and high efficiency, as verified by numerical simulation results and real-world experiments with swarms of 50 ground robots. The strategy can be adapted to generate interesting behaviors including shape regeneration, cooperative cargo transportation, and complex environment exploration.

A novel type of neural network is helping physicists with the daunting challenge of data analysis.