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Category: robotics/AI

The AI breakthrough that uses almost no power to create images

From creating art and writing code to drafting emails and designing new drugs, generative AI tools are becoming increasingly indispensable for both business and personal use. As demand increases, they will require even more computing power, memory and, therefore, energy. That’s got scientists looking for ways to reduce their energy consumption.

In a paper published in the journal Nature, Aydogan Ozcan, from the University of California Los Angeles, and his colleagues describe the development of an AI image generator that consumes almost no power.

AI image generators use a process called diffusion to generate images from text. First, they are trained on a large dataset of images and repeatedly add a statistical noise, a kind of digital static, until the image has disappeared.

Tesla Does It Again! (MASSIVE Robotaxi Expansion)

Questions to inspire discussion.

💰 Q: What gives Tesla an edge over competitors like Waymo in the robotaxi market? A: Tesla has a significant cost advantage and superior scalability compared to competitors, allowing for faster expansion and potentially lower operating costs.

🔄 Q: How does Tesla’s rapid scaling affect its competition? A: Tesla’s ability to rapidly scale its robotaxi service puts competitors like Waymo in “big trouble” due to Tesla’s expanding coverage and potential market dominance.

Market Impact.

📈 Q: Why is Tesla’s robotaxi expansion considered a “big deal”? A: The rapid scale and massive expansion of Tesla’s robotaxi service area in Austin demonstrates the company’s ability to quickly deploy and grow autonomous ride-hailing services, potentially disrupting the transportation industry.

🔮 Q: What does this expansion suggest about Tesla’s future in the robotaxi market? A: Tesla’s aggressive expansion suggests it’s positioning itself as a dominant player in the emerging robotaxi market, with the potential to outpace and outperform established competitors like Waymo.

Continue reading “Tesla Does It Again! (MASSIVE Robotaxi Expansion)” | >

New retina-inspired photodiodes could advance machine vision

Over the past decades, computer scientists have developed increasingly sophisticated sensors and machine learning algorithms that allow computer systems to process and interpret images and videos. This tech-powered capability, also referred to as machine vision, is proving to be highly advantageous for the manufacturing and production of food products, drinks, electronics, and various other goods.

Machine vision could enable the automation of various tedious steps in industry and manufacturing, such as the detection of defects, the inspection of electronics, automotive parts or other items, the verification of labels or expiration dates and the sorting of products into different categories.

While the sensors underpinning the functioning of many previously introduced machine vision systems are highly sophisticated, they typically do not process with as much detail as the human retina (i.e., a light-sensitive tissue in the eye that processes visual signals).

Experimental PromptLock ransomware uses AI to encrypt, steal data

Threat researchers discovered the first AI-powered ransomware, called PromptLock, that uses Lua scripts to steal and encrypt data on Windows, macOS, and Linux systems.

The malware uses OpenAI’s gpt-oss:20b model through the Ollama API to dynamically generate the malicious Lua scripts from hard-coded prompts.

Scientists just developed a new AI modeled on the human brain — it’s outperforming LLMs like ChatGPT at reasoning tasks

The hierarchical reasoning model (HRM) system is modeled on the way the human brain processes complex information, and it outperformed leading LLMs in a notoriously hard-to-beat benchmark.

Engineers send a wireless curveball to deliver massive amounts of data

High frequency radio waves can wirelessly carry the vast amount of data demanded by emerging technology like virtual reality, but as engineers push into the upper reaches of the radio spectrum, they are hitting walls. Literally.

Ultrahigh frequency bandwidths are easily blocked by objects, so users can lose transmissions walking between rooms or even passing a bookcase.

Now, researchers at Princeton Engineering have developed a machine-learning system that could allow ultrahigh frequency transmissions to dodge those obstacles. In an article in Nature Communications, the researchers unveiled a system that shapes transmissions to avoid obstacles coupled with a neural network that can rapidly adjust to a complex and dynamic environment.

AI prescribes new electrolyte additive combinations for enhanced battery performance

Batteries, like humans, require medicine to function at their best. In battery technology, this medicine comes in the form of electrolyte additives, which enhance performance by forming stable interfaces, lowering resistance and boosting energy capacity, resulting in improved efficiency and longevity.

Finding the right electrolyte for a battery is much like prescribing the right medicine. With hundreds of possibilities to consider, identifying the best additive for each battery is a challenge due to the vast number of possibilities and the time-consuming nature of traditional experimental methods.

Researchers at the U.S. Department of Energy’s (DOE) Argonne National Laboratory are using models to analyze known electrolyte additives and predict combinations that could improve battery performance. They trained models to forecast key battery metrics, like resistance and energy capacity, and applied these models to suggest new additive combinations for testing.

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