From deciphering burnt Roman scrolls to reading crumbling cuneiform tablets, neural networks could give researchers more data than they’ve had in centuries.
Category: robotics/AI – Page 735
Researchers Use Videos to Teach Robot Surgeons Human-Like Moves
Engineers and doctors at Johns Hopkins University and Stanford University have achieved significant advances in training robotic surgeons to have similar skill levels to human doctors.
Common Challenges in Machine Learning and How to Tackle Them
Author(s): Towards AI Team Introduction Continue reading on Towards AI » Published via Towards AI.
Insane Clown Posse’s Shaggy 2 Dope Confronts AI Version of Himself
Insane Clown Posse’s Joseph Utsler, better known by his stage persona Shaggy 2 Dope, has an AI clone — and apparently, he’s a fan.
Hosted on the Google-backed Character. AI service — which, as a sidebar, Futurism has investigated extensively, repeatedly finding horrible things — the ICP cofounder’s digital doppelganger sounds like a slightly robotic version of the real thing.
The effect is so uncanny that Mr. Dope himself decided to hit the AI up and bring it onto his livestreamed vlog, “The Shaggy Show.”
Scientists develop tech for real-time, emotion-driven robot faces
Researchers developed a “waveform movement” technology enabling robots to express emotions through natural facial gestures.
Top 5 AI Predictions From Experts In 2025
While no one has a crystal ball to predict the future — the future of AI in 2025 looks bright.
Ex OpenAI Researcher REVEALS What AGI Will Be Like, New Humanoids And More
Join my AI Academy — https://www.skool.com/postagiprepardness.
🐤 Follow Me on Twitter https://twitter.com/TheAiGrid.
🌐 Checkout My website — https://theaigrid.com/
00:01 New Chinese Humanoid Robot (LimX Dynamics)
01:47 Artificial Superintelligence (ASI) Discussion.
03:57 Sam Altman’s 2025 Predictions.
06:53 Geoffrey Hinton Supports Elon Musk’s Lawsuit Against OpenAI
09:11 O1 Model Surpasses Doctors in Diagnoses.
12:09 DeepSeek V3: A Cost-Effective Alternative to GPT-4
14:53 “Reproduce” Paper: Recreating OpenAI’s Reasoning.
15:41 Meta’s Large Concept Models (LCMs)
17:42 AGI Release Insights from OpenAI Employee.
19:45 Google CEO Gears Up for a Big 2025
23:28 Alibaba’s 70B Model.
24:30 OpenAI’s AGI Definition: $100 Billion in Profits.
25:24 Matrix One Humanoid Robot.
Links From Todays Video:
Chinese LimX humanoid robot CL2 reminds me of the new Atlas model
byu/torb insingularity
OpenAI’s Sébastien Bubeck says GPT-4 already outperformed human doctors at diagnosis, but there are cases in which o1 is nearly twice as accurate as humans pic.twitter.com/zhg0Xuop5t
— Tsarathustra (@tsarnick) December 27, 2024
https://gizmodo.com/godfather-of-ai-throws-support-behind-el…2000544349
https://www.cnbc.com/2024/12/27/google-ceo-pichai-tells-empl…-2025.html.
will AGI be announced publically?
OpenAI Ex-Researcher Daniel Kokotajlo predicts 3 important scenarios:
- before we see robots like plumbers, AGI research will already be automated for over a year
- superintelligent systems in data centers will surpass human abilities in every… pic.twitter.com/qbj77p0jYR
— Haider. (@slow_developer) December 31, 2024
Welcome to my channel where i bring you the latest breakthroughs in AI. From deep learning to robotics, i cover it all. My videos offer valuable insights and perspectives that will expand your knowledge and understanding of this rapidly evolving field. Be sure to subscribe and stay updated on my latest videos.
Was there anything i missed?
The Rise Of Unpredictable AI: Will AI Test Human Control In 2025?
This behavior highlights a critical issue: even systems designed for seemingly harmless tasks can produce unforeseen outcomes when granted enough autonomy.
The challenges posed by AI today are reminiscent of automated trading systems in financial markets. Algorithms designed to optimize trades have triggered flash crashes —sudden, extreme market volatility occurring within seconds, too fast for human intervention to correct.
Similarly, modern AI systems are built to optimize tasks at extraordinary speeds. Without robust controls, their growing complexity and autonomy could unleash consequences no one anticipated—just as automated trading once disrupted financial markets.
Data Sheet: Nvidia’s $700 million open source surprise
Plus: Bytedance’s $7 billion loophole, AI-enabled robo-surgeons, the U.S. Treasury hack, and an IBM antitrust probe—in the latest edition of Fortune’s flagship tech newsletter.
Scouts Astronautics Activity Badge
M
The human brain is the central control organ of our body. It processes information received through the senses and enables us, among other things, to form thoughts, make decisions and store knowledge. Given everything our brain is capable of, it seems almost paradoxical how little we actually still know about it.
Among those who are on the trail of the most complex and complicated organ are Jonas Thiele and Dr. Kirsten Hilger, head of the “Networks of Behavior and Cognition” working group at the Department of Psychology I at the Julius Maximilian University of Würzburg (JMU). Their latest study was recently published in the journal PNAS Nexus: “Choosing explanation over performance: Insights from machine learning-based prediction of human intelligence from brain connectivity.”
To do this, the researchers used data sets from a large-scale data-sharing project in the USA — the Human Connectome Project. Using fMRI — an imaging method that measures changes in brain activity — over 800 people were examined, both at rest and while they were performing various tasks.
The team led by Würzburg researchers looked at various connections that reflect the strength of communication between brain regions and made predictions about the intelligence of the test subjects based on these observations.
“There are already many such predictive studies and they achieve quite good prediction results,” says Kirsten Hilger. However, the psychologist questions their deeper meaning, since the predictions would never be as accurate as the results of an intelligence test. “We therefore wanted to move away from pure predictions and instead better understand the basic processes in the brain. We hope that this will give us a better understanding of the neural code of individual differences in intelligence.”
Kirsten Hilger hopes that colleagues will follow suit and that more studies will be designed in the future that will improve the conceptual understanding of human cognition with a focus on interpretability.







