❗️Ex-NFL player allegedly asked ChatGPT what to do after killing his girlfriend

Former Darron Lee is facing a first-degree murder charge and prosecutors say ChatGPT chats are part of the evidence. According to court filings, Lee’s girlfriend Gabriella Perpetuo was found dead in their Tennessee home in February.

But before calling for help, investigators say Lee allegedly consulted ChatGPT.

Prosecutors claim he asked questions like:

• what injuries from a fall look like
• what to do if someone is unresponsive
• how to explain the situation to police

Authorities argue the searches show he was trying to stage the death as an accident, possibly a fall in the bathroom. Medical reports cited in court say the victim had severe injuries inconsistent with an accident, and investigators say the scene appeared to have been cleaned up.

Lee has denied the allegations, and his defense argues the case is circumstantial. But prosecutors are now using the AI chat history as part of the timeline and intent evidence in the case.

Source.

@aipost 🏴

🔔 Anthropic announces The Anthropic Institute to address powerful AI's societal challenges as progress accelerates.

- Led by co-founder Jack Clark as Head of Public Benefit, it combines Frontier Red Team, Societal Impacts, and Economic Research.
- Focuses on AI's effects on jobs, economies, resilience, threats, values, and governance amid expected dramatic advances by 2028.
- Founding hires: Matt Botvinick for AI-law work, Anton Korinek for economic transformations, Zoë Hitzig linking economics to models.
- Unique access to frontier AI data for candid public reporting and external partnerships.

The Institute aims to inform responses to transformative AI's upsides and risks through interdisciplinary efforts.

https://www.anthropic.com/news/the-anthropic-institute

@aipost 🏴

Trump: The hot thing right now is AI.

@aipost 🏴

Something big is coming:

“A massive AI breakthrough is coming in the first half of 2026 and Morgan Stanley says most of the world isn’t ready for it.“

Morgan Stanley warns that a massive AI capability jump driven by unprecedented compute scaling at U.S. labs could arrive in early 2026, triggering rapid productivity gains, job disruption, and severe power shortages as intelligence becomes the key economic resource.

Source.

@aipost 🏴

In a NYT test, 54% of users preferred AI-generated text to human-generated text.

Source.

@aipost 🏴

🔥Last year, the tennis rackets were attached. Now the robots can walk on their own.

Introducing LATENT: Learning Athletic Humanoid Tennis Skills from Imperfect Human Motion Data

Dynamic movements, agile whole-body coordination, and rapid reactions. A step toward athletic humanoid sports skills.

@aipost 🏴

🔥 NVIDIA and GeForce have unveiled DLSS 5, their next major AI breakthrough for gaming visuals, arriving this fall.

With photorealistic lighting and smarter material rendering, DLSS 5 is designed to narrow the gap between rendered worlds and real life.

@aipost 🏴

🔥NVIDIA CEO Jensen Huang today: “The Chat-GPT moment for self-driving cars has arrived.

NVIDIA announced that they’re now expanding their self-driving partnership to BYD, Nissan, Hyundai, and Geely.

Their automotive partners:

• GM
• Toyota
• Mercedes-Benz
• Jaguar Land Rover
• Volvo
• Rivian
• Hyundai
• BYD
• Geely
• XPENG
• Polestar
• NIO
• Lucid
• Li Auto
• Nissan
• Isuzu
• Zeekr
• Xiaomi
• Stellantis

Additionally, NVIDIA and Uber are partnering to launch a global network of Level 4 autonomous robotaxis, beginning in LA and San Francisco in early 2027 before expanding to 28 cities by 2028.

The robotaxi race is well underway.

@aipost 🏴

⚡️NVIDIA unveiled its first AI supercomputer integrating Groq racks alongside the Vera Rubin platform.

The system combines Rubin GPUs for memory-heavy workloads with Groq’s architecture for fast token generation to reduce inference latency and scale AI factories.

@aipost 🏴

Jensen is cementing the idea that Nvidia-powered AI is now the backbone of every major industry.

He said robotics alone will be a $50 trillion industry.

@aipost 🏴

🗣NVIDIA CEO Jensen Huang says the next phase of AI will be driven by applications.

Chips were phase one, infrastructure was phase two and now the value shifts to companies building useful products on top of all that compute.

@aipost 🏴

Jensen on $NVDA AI demand: “Right here where I stand, I see – through 2027, I see at least $1 TRILLION dollars.”

@aipost 🏴

You know Jensen is a tech rockstar when 20,000 people fill up an NHL arena to watch him.

@aipost 🏴

🗣 Sam Altman says OpenAI is burning cash fast, but it's not a crisis

If spending billions now leads to even bigger revenue later, plenty of capital wants that trade. "The economy hasn't seen something quite like this before". But model growth correlates directly with revenue growth

@aipost 🏴

🤯 This isn’t fabric.

It’s electronic skin,woven with dense fiber and textile sensors that can detect pressure, touch, deformation, and subtle contact changes in real time.

Now imagine humanoid robots covered in it,especially on dexterous hands. Humanoids could truly touch and understand the physical world and even humans.

~ Shanghai JQ INDUSTRIES

@aipost 🏴

🔔Pokémon Go players trained 30 billion image AI map

Niantic says photos and scans collected through Pokémon Go and its AR apps have produced a massive dataset of more than 30 billion real-world images.

The company is now using that data to power visual navigation for delivery robots, letting them identify exact locations on city streets without relying on GPS.

Source: NewsForce

@aipost 🏴

❗️Andrej Karpathy built a map of every job in America and how likely AI is to replace it

The former OpenAI and Tesla AI leader just released an open-source project that scores how exposed every U.S. job is to AI automation.

Here’s how it works:

The dataset:
• Scraped 342 occupations from the U.S. Bureau of Labor Statistics
• Each job was evaluated using an LLM scoring rubric from 0–10
• Built an interactive treemap visualization
• Rectangle size = number of workers in that job
• Color = how vulnerable the role is to AI

The key rule behind the scoring
If the work product is digital and the job can be done entirely from a home office, the exposure score rises dramatically.

Some example scores:
• 0–1: Roofers, janitors
• 4–5: Nurses, retail workers, physicians
• 8–9: Software developers, paralegals, data analysts
• 10: Medical transcriptionists

Overall result: Average exposure across all occupations: 5.3 / 10

Fully open source, Karpathy also released the entire pipeline:
• BLS data scraping
• LLM scoring methodology
• The visualization system

Anyone can reproduce it or update the scores as AI improves.

https://github.com/karpathy/jobs

@aipost 🏴

🗣Yann LeCun says Elon Musk has predicted Level 5 autonomy within 5 years for the last 8 years, and has been consistently wrong

"Either he believed it and was mistaken, or he was lying". It may push the team, but for engineers, hearing 'next year' again and again is demoralizing.

@aipost 🏴

🔥Don't be surprised if you meet humanoid robots on the streets of Beijing at midnight.

They are training for their half-marathon! Over 20 teams joined the first trial run. The official race will be held on April 19.

@aipost 🏴

🔥 Kimi AI introduces Attention Residuals, a new way to rethink how neural networks use past layers

Researchers at Moonshot AI just proposed a new architecture tweak that could make large AI models more efficient and smarter about how they use information from earlier layers. Instead of the traditional residual connections used in deep networks, they introduce Attention Residuals, a system where each layer can selectively attend to representations from previous layers.

Here’s what’s new:

Attention over past layers:
• Traditional residuals simply add outputs from earlier layers in a fixed way.
• Attention Residuals let the model dynamically choose which earlier layers matter for a given input.

Solves depth dilution:
• In very deep models, useful information from earlier layers can get diluted.
• Attention-based retrieval allows the network to pull specific past representations when needed.

Block AttnRes for scale:
• Layers are grouped into compressed blocks so cross-layer attention remains computationally practical.

Efficient in practice:
• Reported 1.25× compute advantage
• <2% extra inference latency, meaning almost no slowdown.

Tested on the Kimi Linear model:
• Evaluated on 48B parameter architecture (3B activated parameters).
• Shows consistent downstream performance improvements.

Source.

@aipost 🏴

Kimi just made AI notably cheaper to run. Open-sourced it. Put it out for free. Meanwhile OpenAI is asking people to pay $200 a month to use a model that already feels behind the curve.

Two Chinese labs. Both open source. Both doing more with less. Both giving away for free what American companies charge billions for.

The AI race isn't US vs China anymore... It's closed vs open. And closed is losing.

And the wildest part? Nobody in Silicon Valley will acknowledge this.. Because admitting a Chinese lab just moved the field forward for free destroys the entire "we need $10B to build AGI" fundraising pitch.

@aipost 🏴