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Tesla Full Self-Driving (Supervised) 13.2.5 completes a 30 minute zero intervention drive through Los Angeles.
Raw 1x speed footage:
Category: transportation
Boom Supersonic’s XB-1 test vehicle is scheduled to lift off today (Jan. 28) at 11 a.m. EST (1600 GMT).
A research team from DGIST’s (President Kunwoo Lee) Division of Energy & Environmental Technology, led by Principal Researcher Kim Jae-hyun, has developed a lithium metal battery using a “triple-layer solid polymer electrolyte” that offers greatly enhanced fire safety and an extended lifespan. This research holds promise for diverse applications, including in electric vehicles and large-scale energy storage systems.
Conventional solid polymer electrolyte batteries perform poorly due to structural limitations which hinder an optimal electrode contact.
This could not eliminate the issue of “dendrites” either, where lithium grows in tree-like structures during repeated charging and discharging cycles.
We report the use of a multiagent generative artificial intelligence framework, the X-LoRA-Gemma large language model (LLM), to analyze, design and test molecular design. The X-LoRA-Gemma model, inspired by biological principles and featuring ~7 billion parameters, dynamically reconfigures its structure through a dual-pass inference strategy to enhance its problem-solving abilities across diverse scientific domains. The model is used to first identify molecular engineering targets through a systematic human-AI and AI-AI self-driving multi-agent approach to elucidate key targets for molecular optimization to improve interactions between molecules. Next, a multi-agent generative design process is used that includes rational steps, reasoning and autonomous knowledge extraction. Target properties of the molecule are identified either using a Principal Component Analysis (PCA) of key molecular properties or sampling from the distribution of known molecular properties. The model is then used to generate a large set of candidate molecules, which are analyzed via their molecular structure, charge distribution, and other features. We validate that as predicted, increased dipole moment and polarizability is indeed achieved in the designed molecules. We anticipate an increasing integration of these techniques into the molecular engineering workflow, ultimately enabling the development of innovative solutions to address a wide range of societal challenges. We conclude with a critical discussion of challenges and opportunities of the use of multi-agent generative AI for molecular engineering, analysis and design.
In today’s AI news, Mark Zuckerberg announced a huge leap in Meta Platforms’s capital spending this year to between $60 billion to $65 billion, an increase driven by artificial intelligence and a massive new data center.
Zuckerberg plans to increase the company’s capital expenditures by as much as roughly 70% over 2024.
In other advancements, Hugging Face has achieved a remarkable breakthrough in AI, introducing vision-language models that run on devices as small as smartphones while outperforming their predecessors that require massive data centers. The company’s new SmolVLM-256M model, requiring less than one gigabyte of GPU memory, surpasses the performance of its Idefics 80B model from just 17 months ago — a system 300 times larger.
And, Anthropic has launched a new feature for its “Claude” family of AI models, one that enables the models to cite and link back to sources when answering questions about uploaded documents. The new feature, appropriately dubbed “Citations,” is now available for developers through Anthropic’s API.
Meanwhile, can AI agents reliably click on all images showing motorcycles or traffic lights for us? It might be too early to tell, considering that a robot will essentially have to tell a website that it is not a robot. However, it looks like at least one of OpenAI’s Operator users was able to have the AI agent beat CAPTCHAs for him.
It’s time to recalibrate the navigation systems on ships, airplanes, as the position of the magnetic North Pole is officially being changed, continuing its shift away from Canada and towards Siberia.
Experts from the US National Oceanic and Atmospheric Administration (NOAA) and the British Geological Survey (BGS) have joined forces – as they do every five years – to produce a new, more accurate World Magnetic Model (WMM).
While the geographical North Pole stays fixed in place (at the very summit of the Earth’s rotational axis), the WMM pinpoints the magnetic North Pole – where Earth’s magnetic field points straight down, a perfectly vertical magnetic field.
The chameleon, a lizard known for its color-changing skin, is the inspiration behind a new electromagnetic material that could someday make vehicles and aircraft “invisible” to radar.
As reported today in the journal Science Advances, a team of UC Berkeley engineers has developed a tunable metamaterial microwave absorber that can switch between absorbing, transmitting or reflecting microwaves on demand by mimicking the chameleon’s color-changing mechanism.
“A key discovery was the ability to achieve both broadband absorption and high transmission in a single structure, offering adaptability in dynamic environments,” said Grace Gu, principal investigator of the study and assistant professor of mechanical engineering. “This flexibility has wide-ranging applications, from stealth technology to advanced communication systems and energy harvesting.”
Summary: A study reveals that London taxi drivers prioritize complex and distant junctions during their initial “offline thinking” phase when planning routes, rather than sequentially considering streets. This efficient, intuitive strategy leverages spatial awareness and contrasts with AI algorithms, which typically follow step-by-step approaches.
The findings highlight the unique planning abilities of expert human navigators, influenced by their deep memory of London’s intricate street network. Researchers suggest that studying human expert intuition could improve AI algorithms, especially for tasks involving flexible planning and human-AI collaboration.
You’re running late at the airport and need to urgently access your account, only to be greeted by one of those frustrating tests—” Select all images with traffic lights” or “Type the letters you see in this box.” You squint, you guess, but somehow you’re wrong. You complete another test but still the site isn’t satisfied.
“Your flight is boarding now,” the tannoy announces as the website gives you yet another puzzle. You swear at the screen, close your laptop and rush towards the gate.
Now, here’s a thought to cheer you up: Bots are now solving these puzzles in milliseconds using artificial intelligence (AI). How ironic. The tools designed to prove we’re human are now obstructing us more than the machines they’re supposed to be keeping at bay.