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Need a hand with solid dispensing? This is PowderBot, our first paper in Digital Discovery; project led by PhD robot wizard Ying Jiang University of Liverpool MIF A I Cooper group Hatem Fakhruldeen Ai He Tianwei Gabriella 🤖 Louis Longley Rob Clowes Nicola Rankin

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Andy Cooper

9,766 subscribers

33,118 Aufrufe • vor 3 Jahren •via X (Twitter)

Wissenschaft & Technologie
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Profilbild von Andy Cooper
Andy Coopervor 3 Jahren

Also tagging @ABBRobotics @ABBRoboticsUSA @ABBRobotics_UK

Profilbild von ⌬Nessa Carson
⌬Nessa Carsonvor 3 Jahren

@digital_rsc @LivUni @MIF_UoL @AICooperGroup @HatemFakhrulde1 @Annia_Hebbe @gabriellapizz @LouisLongley @robclow11 @nici_rankin He is beautiful! Is he in Liverpool? Can I come and visit?!!

Profilbild von Sumanta Chowdhury
Sumanta Chowdhuryvor 3 Jahren

@digital_rsc @LivUni @MIF_UoL @AICooperGroup @HatemFakhrulde1 @Annia_Hebbe @gabriellapizz @LouisLongley @robclow11 @nici_rankin Wow! This robot is impressively accurate at weighing chemicals. Now, I can't help but wonder, does it also lend a hand in washing glasswares? 😄

Profilbild von Dr Nicola Bell
Dr Nicola Bellvor 3 Jahren

@DrMichaelJHall @digital_rsc @LivUni @MIF_UoL @AICooperGroup @HatemFakhrulde1 @Annia_Hebbe @gabriellapizz @LouisLongley @robclow11 @nici_rankin This is SO COOL 😎 🤓🤖

Profilbild von Prof Niamh Nic Daeid
Prof Niamh Nic Daeidvor 3 Jahren

@digital_rsc @LivUni @MIF_UoL @AICooperGroup @HatemFakhrulde1 @Annia_Hebbe @gabriellapizz @LouisLongley @robclow11 @nici_rankin Super cool - well done Andy and the team 👍😎

Profilbild von Andy Cooper
Andy Coopervor 3 Jahren

@digital_rsc @LivUni @MIF_UoL @AICooperGroup @HatemFakhrulde1 @Annia_Hebbe @gabriellapizz @LouisLongley @robclow11 @nici_rankin Thanks!

Profilbild von Joshua Schrier
Joshua Schriervor 3 Jahren

@digital_rsc @LivUni @MIF_UoL @AICooperGroup @HatemFakhrulde1 @Annia_Hebbe @gabriellapizz @LouisLongley @robclow11 @nici_rankin Digging the use of old school fuzzy logic control....

Profilbild von Andy Cooper
Andy Coopervor 3 Jahren

@digital_rsc @LivUni @MIF_UoL @AICooperGroup @HatemFakhrulde1 @Annia_Hebbe @gabriellapizz @LouisLongley @robclow11 @nici_rankin Not everything needs to be done with reinforcement learning

Profilbild von Andy Cooper
Andy Coopervor 3 Jahren

Full link to paper:

Profilbild von Franziska Emmerling
Franziska Emmerlingvor 3 Jahren

@digital_rsc @LivUni @MIF_UoL @AICooperGroup @HatemFakhrulde1 @Annia_Hebbe @gabriellapizz @LouisLongley @robclow11 @nici_rankin Impressive!

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Xpeng unveiled its next-generation IRON humanoid robot at the 2025 Xpeng Tech Day. This version emphasizes a "born from within" bionic design philosophy, featuring a human-like spine structure, full flexible skin, and customizable physique, including the option for male and female forms. CEO He Xiaopeng confirmed this is the final pre-research version, with the mass-production model standing no taller than 170cm. IRON’s technical specs are aggressive: 🦾 Hardware: 82 total Degrees of Freedom (DoF), far above the average. Its hand boasts 22 DoF with industry-smallest harmonic joints for 1:1 human hand scale, enabling delicate tasks (grabbing eggs, unscrewing caps). 🧠 Intelligence: Runs on three Turing AI chips with 2,250 TOPS of effective computing power, currently the highest in humanoids. It debuts Xpeng's first-gen Physical World Large Model (VLT + VLA + VLM) for high-level dialogue and control. 🔋 Solid-State Power: IRON is the first to use full solid-state batteries, boosting power by 30% and reducing weight by 30%. Xpeng aims for this safety-critical sector to drive SS battery mass production. 🚀 Commercialization Plan: Xpeng will prioritize commercial scenarios like guidance, sales assistance, and inspection (jointly developing complex applications with partners like Baosteel Group). CEO He Xiaopeng plans aggressive scaled mass production of advanced humanoids by the end of 2026. The robot team is over 1,000 people.

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#HUMAINChat is LIVE! Today marks a moment of pride - not only for but for all of Saudi Arabia with the launch of HUMAIN Chat, powered by ALLAM34B, the most advanced Arabic-first LLM built in the Arab world fluent in our culture, our values, and our heritage. It was deeply important to me to deliver this message to you, the Saudi people, in your native language. I recognize that my Arabic isn’t perfect, but just like me, HUMAIN Chat is still improving. And this is where I need your support. Your conversations and interactions with HUMAIN Chat will directly help improve the LLM powering it. This is your opportunity to shape a Saudi-born, Arabic-first digital product into the best of its kind before we roll it out to the world. Download HUMAIN Chat now and make it your go-to AI companion: #HUMAINAI #TheEndOfLimits
0:57

#HUMAINChat is LIVE! Today marks a moment of pride - not only for but for all of Saudi Arabia with the launch of HUMAIN Chat, powered by ALLAM34B, the most advanced Arabic-first LLM built in the Arab world fluent in our culture, our values, and our heritage. It was deeply important to me to deliver this message to you, the Saudi people, in your native language. I recognize that my Arabic isn’t perfect, but just like me, HUMAIN Chat is still improving. And this is where I need your support. Your conversations and interactions with HUMAIN Chat will directly help improve the LLM powering it. This is your opportunity to shape a Saudi-born, Arabic-first digital product into the best of its kind before we roll it out to the world. Download HUMAIN Chat now and make it your go-to AI companion: #HUMAINAI #TheEndOfLimits

Tareq Amin

52,845 Aufrufe • vor 11 Monaten

"When I had [Altman] in here, it's like talking to a politician. Like everything he says is vetted by a focus group. You don't get a real human response. All bullshit. And they're gonna leave here and keep creating this fucking monster that's gonna destroy the human race." He also agreed with Dr. Roman Yampolskiy that Sam Altman's plan to merge with AI is "extinction with extra steps". For context, Altman wrote on his blog in 2017: "I think a merge [with AI] is probably our best-case scenario. If two different species both want the same thing and only one can have it - to be the dominant species on the planet and beyond - they are going to have conflict." "We will be the first species ever to design our own descendants.” h/t AI Breakfast for the clip!
0:36

"When I had [Altman] in here, it's like talking to a politician. Like everything he says is vetted by a focus group. You don't get a real human response. All bullshit. And they're gonna leave here and keep creating this fucking monster that's gonna destroy the human race." He also agreed with Dr. Roman Yampolskiy that Sam Altman's plan to merge with AI is "extinction with extra steps". For context, Altman wrote on his blog in 2017: "I think a merge [with AI] is probably our best-case scenario. If two different species both want the same thing and only one can have it - to be the dominant species on the planet and beyond - they are going to have conflict." "We will be the first species ever to design our own descendants.” h/t AI Breakfast for the clip!

AI Notkilleveryoneism Memes ⏸️

430,715 Aufrufe • vor 1 Jahr

Synthetic data will provide the next trillion tokens to fuel our hungry models. I'm excited to announce MimicGen: massively scaling up data pipeline for robot learning! We multiply high-quality human data in simulation with digital twins. Using 50,000 training episodes across 18 tasks, multiple simulators, and even in the real-world! The idea is simple: 1. Humans tele-operate the robot to complete a task. It is extremely high-quality but also very slow and expensive. 2. We create a digital twin of the robot and the scene in high-fidelity, GPU-accelerated simulation. 3. We can now move objects around, replace with new assets, and even change the robot hand - basically augment the training data with procedural generation. 4. Export the successful episodes, and feed that to a neural network! You now have an near-infinite stream of data. One of the key reasons that robotics lags far behind other AI fields is the lack of data: you cannot scrape control signals from the internet. They simply don't exist in-the-wild. MimicGen shows the power of synthetic data and simulation to keep our scaling laws alive. I believe this principle apply beyond robotics. We are quickly exhausting the high-quality, real tokens from the web. Artificial intelligence from artificial data will be the way forward. We are big fans of the OSS community. As usual, we open-source everything, including the generated dataset! - Website: - Paper: - Dataset is hosted on HuggingFace (thanks AK!!): - Code: MimicGen is led by Ajay Mandlekar, deep dive in the thread:
0:46

Synthetic data will provide the next trillion tokens to fuel our hungry models. I'm excited to announce MimicGen: massively scaling up data pipeline for robot learning! We multiply high-quality human data in simulation with digital twins. Using 50,000 training episodes across 18 tasks, multiple simulators, and even in the real-world! The idea is simple: 1. Humans tele-operate the robot to complete a task. It is extremely high-quality but also very slow and expensive. 2. We create a digital twin of the robot and the scene in high-fidelity, GPU-accelerated simulation. 3. We can now move objects around, replace with new assets, and even change the robot hand - basically augment the training data with procedural generation. 4. Export the successful episodes, and feed that to a neural network! You now have an near-infinite stream of data. One of the key reasons that robotics lags far behind other AI fields is the lack of data: you cannot scrape control signals from the internet. They simply don't exist in-the-wild. MimicGen shows the power of synthetic data and simulation to keep our scaling laws alive. I believe this principle apply beyond robotics. We are quickly exhausting the high-quality, real tokens from the web. Artificial intelligence from artificial data will be the way forward. We are big fans of the OSS community. As usual, we open-source everything, including the generated dataset! - Website: - Paper: - Dataset is hosted on HuggingFace (thanks AK!!): - Code: MimicGen is led by Ajay Mandlekar, deep dive in the thread:

Jim Fan

332,238 Aufrufe • vor 2 Jahren

Today, we’re announcing the first major discovery made by our AI Scientist with the lab in the loop: a promising new treatment for dry AMD, a major cause of blindness. Our agents generated the hypotheses, designed the experiments, analyzed the data, iterated, even made figures for the paper. The resulting manuscript is a first-of-a-kind in the natural sciences, in which everything that needed to be done to write the paper was done by AI agents, apart from actually conducting the physical experiments in the lab and writing the final manuscript. We are also introducing Robin, the first multi-agent system that fully automates the in-silico components of scientific discovery, which made this discovery. This is the first time that we are aware of that hypothesis generation, experimentation, and data analysis have been joined up in closed loop, and is the beginning of a massive acceleration in the pace of scientific discovery that will be driven by these agents. We will be open-sourcing the code and data next week. Robin is a multi-agent system that uses Crow, Falcon, and Finch, the agents on our platform, to generate novel hypotheses, plan experiments, and analyze data. We asked Robin to find a new treatment for dry age-related macular degeneration. Robin considered the disease mechanisms associated with dry AMD, proposed a specific experimental assay that could be used to evaluate hypotheses in the wet lab, and proposed specific molecules we could test in that assay. We tested the molecules and gave it the resulting data, which it analyzed before proposing more experiments. In the end, it identified Ripasudil, a Rho Kinase inhibitor (ROCK inhibitor) that is approved in Japan for several other diseases, which seems very promising as potential treatment for dry AMD. It also identified specific molecular mechanisms that might underlie the effects of Ripasudil in RPE cells, from an RNA sequencing experiment it proposed. To be clear, no one has proposed using ROCK inhibitors to treat dry AMD in the literature before, as far as we can find, and I think it would have been very difficult for us to come up with this hypothesis without the agents. We have also run the proposed treatment by several experts in AMD, who confirm that it is interesting and novel. Moreover, this project was fast: with Robin in hand, the entire project took about 10 weeks, which is way shorter than it would have taken if we had been doing all of the in-silico components ourselves. Important caveats: We are real biologists at FutureHouse, so I want to be clear that although the discovery here is exciting, we are not claiming that we have cured dry AMD. Fully validating this hypothesis as a treatment for dry AMD will take human trials, which will take much longer. Also, this discovery is cool, but it is not yet a "move 37"-style discovery. At the current rate of progress, I'm sure we will get to that level soon. Congratulations to the team. Congratulations in particular to Robin, which generated the hypotheses, proposed the experiments, analyzed the data and generated the figures. And major congratulations also to the human team, which built Robin: Michaela Hinks, Ali Ghareeb, Benjamin Chang, Ludovico Mitchener, Mo Razzak, Kiki Szostkiewicz, and Angela Yiu.
5:57

Today, we’re announcing the first major discovery made by our AI Scientist with the lab in the loop: a promising new treatment for dry AMD, a major cause of blindness. Our agents generated the hypotheses, designed the experiments, analyzed the data, iterated, even made figures for the paper. The resulting manuscript is a first-of-a-kind in the natural sciences, in which everything that needed to be done to write the paper was done by AI agents, apart from actually conducting the physical experiments in the lab and writing the final manuscript. We are also introducing Robin, the first multi-agent system that fully automates the in-silico components of scientific discovery, which made this discovery. This is the first time that we are aware of that hypothesis generation, experimentation, and data analysis have been joined up in closed loop, and is the beginning of a massive acceleration in the pace of scientific discovery that will be driven by these agents. We will be open-sourcing the code and data next week. Robin is a multi-agent system that uses Crow, Falcon, and Finch, the agents on our platform, to generate novel hypotheses, plan experiments, and analyze data. We asked Robin to find a new treatment for dry age-related macular degeneration. Robin considered the disease mechanisms associated with dry AMD, proposed a specific experimental assay that could be used to evaluate hypotheses in the wet lab, and proposed specific molecules we could test in that assay. We tested the molecules and gave it the resulting data, which it analyzed before proposing more experiments. In the end, it identified Ripasudil, a Rho Kinase inhibitor (ROCK inhibitor) that is approved in Japan for several other diseases, which seems very promising as potential treatment for dry AMD. It also identified specific molecular mechanisms that might underlie the effects of Ripasudil in RPE cells, from an RNA sequencing experiment it proposed. To be clear, no one has proposed using ROCK inhibitors to treat dry AMD in the literature before, as far as we can find, and I think it would have been very difficult for us to come up with this hypothesis without the agents. We have also run the proposed treatment by several experts in AMD, who confirm that it is interesting and novel. Moreover, this project was fast: with Robin in hand, the entire project took about 10 weeks, which is way shorter than it would have taken if we had been doing all of the in-silico components ourselves. Important caveats: We are real biologists at FutureHouse, so I want to be clear that although the discovery here is exciting, we are not claiming that we have cured dry AMD. Fully validating this hypothesis as a treatment for dry AMD will take human trials, which will take much longer. Also, this discovery is cool, but it is not yet a "move 37"-style discovery. At the current rate of progress, I'm sure we will get to that level soon. Congratulations to the team. Congratulations in particular to Robin, which generated the hypotheses, proposed the experiments, analyzed the data and generated the figures. And major congratulations also to the human team, which built Robin: Michaela Hinks, Ali Ghareeb, Benjamin Chang, Ludovico Mitchener, Mo Razzak, Kiki Szostkiewicz, and Angela Yiu.

Sam Rodriques

1,107,457 Aufrufe • vor 1 Jahr