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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.

Researchers claim the new electronic chip can mimic human vision and memory, which could help make self-driving cars smarter.

Researchers at the Royal Melbourne Institute of Technology (RMIT) have successfully developed a tiny electronic device that, they claim, can mimic human vision and memory. This could be a promising step to one-day developing sophisticated ways to make rapid decision-making in self-driving cars.

Continue reading “Innovative human eye-mimicking chip has been developed at RMIT” »

MusicGen has been trained on 20,000 hours of music.

Meta has unveiled MusicGen, an artificial intelligence(AI) music-generating system which can be conditioned using text prompts or melodies. It’s similar to Google’s MusicLM, which can build on existing melodies, whether they are whistled, hummed, sung, or played on an instrument.

Generating music is a challenging task as it contains harmonies and melodies from different instruments, which create complex structures. Meta’s model was trained on 20,000 hours of music, reported Tech Xplore. Meta released a demo of MusicGen on Hugging Face, and Interesting Engineering decided to have a go at it.

Amazon’s cloud computing unit likes to build its services from the ground up, and AMD could be the perfect fit.

When Advanced Micro Devices (AMD) unveiled its new artificial intelligence (AI) chip during a keynote presentation in San Francisco on Tuesday, Wall Street reacted by sending its stock price down by 3.5 percent. This was attributed to the lack of a prospective buyer for the chip. It has now emerged that Amazon is considering the chip for its cloud unit, as per a Reuters.

AI models are the new buzzword in the tech industry, and Nvidia has hogged all the limelight when it comes to powering them. Once known for making great graphic cards for gaming purposes, Nvidia has recently gained popularity for its chips powering great AI models.

A first-of-its-kind robot which gives clinicians the ability to ‘feel’ patients remotely has been launched as part of a Finnish hospital pilot by deep tech robotics company Touchlab, a new tenant of the world-leading centre for robotics and artificial intelligence the National Robotarium.

Controlled by operators wearing an electronic haptic glove, the Välkky telerobot is equipped with the most advanced electronic skin (e-skin) technology ever developed to transfer a sense of touch from its robotic hand to users. E-skin is a material which is made up of single or multiple ultra-thin force sensors to transmit tactile sensations like pressure, vibration or motion from one source to another in real-time.

The 3-month pilot at Laakso Hospital in Helsinki, Finland will see a team of purpose-trained nurses explore how robotics systems can help deliver care, reduce workload and prevent the spread of infections or diseases. The pilot at Laakso Hospital is coordinated by Forum Virium Helsinki, an innovation company for the City of Helsinki. The research is part of a wider €7 billion project aimed at developing the most advanced hospital in Europe, due to be completed in 2028.