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Category: robotics/AI – Page 15

Merging Humans and AI: The Rise of Biological Computers

It’s no secret that tech companies are racing to build “artificial general intelligence,” or AI that can match a human brain without needing a lifeline. But our brains already do the same heavy lifting with just a fraction of the resources. Whether it’s energy, water, land, components, or, you know… money… human brains are just way cheaper. Right now, you can either buy a human brain cell-based computer… or rent time on a remote one. Yep, even brainpower’s got a subscription plan these days. So what can these living computers actually do? How do they work? And, most importantly, should we be freaking out a little bit?

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Video script and citations:
https://undecided.tech/how-living-com… my achieve energy security with solar guide: https://undecided.link/solar-guide Follow-up podcast: Video version — / @stilltbd Audio version — https://undecided.link/stilltbd-podcast Join the Undecided Discord server: https://undecided.link/discord 👋 Support Undecided on Patreon! / mattferrell ⚙️ Gear & Products I Like https://undecided.tech/shop/ Visit my Energysage Portal (US): Research solar panels, heat pumps, and more to get quotes for free! https://undecided.link/energysage For a curated solar buying experience (Canada) EnergyPal’s free personalized quotes: https://undecided.link/energypal 👉 Follow Me Mastodon https://mastodon.social/@mattferrell Instagram / undecidedtech Website https://undecided.tech Some music provided by Epidemic Sound https://undecided.link/epidemic I may earn a small commission for my endorsement or recommendation to products or services linked above, but I wouldn’t put them here if I didn’t like them. Your purchase helps support the channel and the videos I produce. Thank you. Chapters 00:00 — Intro 01:54 — Why? 05:29 — How? 09:17 — What? 15:59 — The Bigger Questions 17:28 — When?

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Follow-up podcast:
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Stanford Economist: The AI Risk Almost Nobody Is Talking About | Erik Brynjolfsson

Stanford economist Erik Brynjolfsson explains why the greatest danger of artificial intelligence may not be mass unemployment itself, but the concentration of wealth, power, and decision-making in the hands of a small group of companies or individuals.

In this conversation, he discusses the “Turing Trap,” the disappearance and creation of jobs, universal basic income, the future of economic growth, and why businesses should use AI to amplify human abilities rather than simply replace workers. He also explains why AI could become more transformative than the Industrial Revolution, why its impact is still largely invisible in productivity statistics, and which human skills may become increasingly valuable.

00:00 – Introduction.
01:05 – Why companies focus on eliminating jobs.
03:41 – The Turing Trap.
06:51 – Which tasks and jobs should AI replace?
08:35 – Millions of jobs will disappear.
09:25 – Why stopping technological change will fail.
10:48 – Entrepreneurship, security and the jobs of the future.
12:41 – AI, universal basic income and concentrated power.
15:29 – Why AI should complement humans.
17:41 – An economy that no longer needs human consumers.
20:05 – Is the younger generation doomed?
22:38 – How AI could help less-experienced workers.
25:10 – The most valuable human skill in the AI era.
27:24 – Access to AI and the falling price of intelligence.
32:31 – Is the AI investment boom a bubble?
33:44 – Bigger than the Industrial Revolution.
34:36 – Why AI is not yet visible in productivity statistics.
39:29 – Could AI produce explosive economic growth?
41:51 – How Erik Brynjolfsson uses AI in his own work.
45:34 – Will AI replace economists and scientists?
49:53 – Is AI destroying the traditional learning process?
54:46 – Shared prosperity or unprecedented inequality?
56:27 – Could AI replace the free market?

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Boston Dynamics’ AI-powered humanoid robot is learning to work in a factory

For decades, engineers have been trying to create robots that look and act human. Now, rapid advances in artificial intelligence are taking humanoids from the lab to the factory floor. As fears grow that AI will displace workers, a global race is underway to develop human-like robots able to do human jobs. Competitors include Tesla, startups backed by Amazon and Nvidia, and state-supported Chinese companies. Boston Dynamics is a frontrunner. The Massachusetts company, valued at more than a billion dollars, is hard at work on a humanoid it calls Atlas. South Korean carmaker Hyundai holds an 90% stake in the robot maker. As we first told you in January, we were invited to see the first real-world test of Atlas at Hyundai’s new factory near Savannah, Georgia. There, we got a glimpse of a humanoid future that’s coming faster than you might think.

Hyundai’s sprawling auto plant is about as cutting-edge as it gets. More than 1,000 robots work alongside almost 1,500 humans, hoisting, stamping and welding in robotic unison. This may look like the factory of the future, but we found the future of the future in the parts warehouse, tucked away in the back corner, getting ready for work.

Meet Atlas: A 5’9, 200 pound, AI-powered humanoid created by Boston Dynamics. The rise of the robots is science fiction no more.

Dell CEO Michael Dell makes one of largest public university donations in US history, ‘gifts’ $750 million to the University of …

Dell CEO Michael Dell has donated $750 million to the University of Texas at Austin, marking one of the largest donations ever made to a public university in the United States. The gift will help fund a new healthcare and research campus, including what the university describes as the country’s first artificial intelligence-native hospital.

Google Just Revealed What Comes After AGI And It’s Shocking

Google DeepMind just dropped a massive paper called From AGI to ASI, and the message is bigger than another AI release. The paper argues that AGI may not be the finish line everyone is waiting for. It may be the moment the real race begins. Once human-level AI can be copied, sped up, connected into agent teams, and used to build better AI, the jump after AGI could matter even more than AGI itself.

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📌 What You’ll See:
Google DeepMind’s new From AGI to ASI paper.
SOURCE: https://deepmind.google/research/publ… 2026 framework for tracking progress toward AGI SOURCE: https://blog.google/innovation-and-ai… DeepMind’s approach to AGI safety and security SOURCE: https://deepmind.google/blog/taking-a… Demis Hassabis on AI agents and the road to AGI SOURCE: https://www.axios.com/2026/05/26/deep… The Legg and Hutter paper behind formal machine intelligence SOURCE: https://arxiv.org/abs/cs/0605024 🚨 Why It Matters This is bigger than another AI paper. Google DeepMind is already talking about what happens after AGI. If human-level AI can be copied, sped up, connected, and used to build better AI, then intelligence itself could become an industrial process. #ai #agi #deepmind.
The full technical paper on arXiv.
SOURCE: https://arxiv.org/abs/2606.12683
DeepMind’s earlier framework for measuring AGI progress.
SOURCE: https://deepmind.google/research/publ…
Google’s 2026 framework for tracking progress toward AGI
SOURCE: https://blog.google/innovation-and-ai…
DeepMind’s approach to AGI safety and security.
SOURCE: https://deepmind.google/blog/taking-a…
Demis Hassabis on AI agents and the road to AGI
SOURCE: https://www.axios.com/2026/05/26/deep…
The Legg and Hutter paper behind formal machine intelligence.
SOURCE: https://arxiv.org/abs/cs/0605024

🚨 Why It Matters.
This is bigger than another AI paper. Google DeepMind is already talking about what happens after AGI. If human-level AI can be copied, sped up, connected, and used to build better AI, then intelligence itself could become an industrial process.

#ai #agi #deepmind

Liquid cooling technology for semiconductor chips is 10 times more efficient than previous record

AI data centers are power-hungry. Not only do artificial intelligence computations consume enormous amounts of electricity, but a significant amount of energy is also required to cool the semiconductor chips that heat up during operation. As AI chips continue to deliver higher performance, the amount of heat they generate increases rapidly. As a result, conventional air cooling and external copper heat spreaders are approaching their practical limits. To address this challenge, a KAIST research team has developed an ultra-high-efficiency liquid-cooling technology that cools semiconductor chips from within.

A joint research team led by Professor Sung Jin Kim of the Department of Mechanical Engineering and Professor Ikjin Lee of the School of AI and Computing has developed a highly efficient liquid-cooling technology that directly cools high-heat-flux semiconductor chips using room-temperature water. The researchers achieved this by embedding liquid-cooling channels, thinner than a human hair, directly inside a silicon semiconductor chip. The paper is published in the journal Energy Conversion and Management.

The team successfully maintained the chip temperature below 100° C (212° F) even under extreme heat-generation conditions exceeding 2,000 watts per square centimeter (W/cm2).

Beyond frozen snapshots, protein ‘breathing’ comes into view with combined imaging methods

Advances in structural biology have allowed scientists to determine molecular structures with atomic-level detail, sometimes yielding static snapshots that do not reflect the dynamism of proteins. However, these motions are often crucial for biological function. Researchers from the Institute of Science and Technology Austria (ISTA), together with international collaborators, have now combined several methods to shed light on how proteins “breathe” and how some experimental techniques freeze their motion. The findings—which could boost protein design approaches and improve AI-based structural prediction tools—are published in Nature Chemistry.

Despite serving as structural biology’s central pillar for more than half a century, protein crystallography has yielded static molecular structures—like still frames from a video—far from the buzzing life inside cells.

“How much can these ‘frozen snapshots’ of protein structures really tell us about their true biological functions and bustling molecular environments?” asks Lea Becker, the study’s first author and a Ph.D. student in Professor Paul Schanda’s group at the Institute of Science and Technology Austria (ISTA).

Tiny chip could help cameras spot hidden details

A tiny new chip could give cameras and sensing systems a far sharper view of the world, helping them detect subtle differences in materials and environments that standard color imaging systems cannot see.

In research led by Zhejiang University in collaboration with RMIT University, scientists have demonstrated a new way to build light-analysis capability directly into imaging hardware.

Cameras are highly effective at capturing images, but applications such as machine vision, automated inspection and environmental monitoring depend on understanding different colors and wavelengths of light, not just what something looks like. That information can reveal differences in materials, surface conditions or environmental changes that appear identical to the human eye.

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