What a day! Physical Intelligence - a team of robotics and AI all-stars out to build a universal AI for machines.
$70 million in seed funding from Thrive, Khosla, Lux and Sequoia.
Physical Intelligence building foundation models that can control any robot for any application including the ones that don't even exist today.
$70 million in seed funding from Thrive, Khosla, Lux and Sequoia.
Physical Intelligence building foundation models that can control any robot for any application including the ones that don't even exist today.
Bloomberg.com
Physical Intelligence Is Building a Brain for Robots
Physical Intelligence is building software intended to power robots that can learn a wide range of tasks.
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Google presents Synth^2: Boosting Visual-Language Models with Synthetic Captions and Image Embeddings
Introduces a method using LLMs and image generation to create synthetic image-text pairs, significantly boosting VLM training efficiency
Introduces a method using LLMs and image generation to create synthetic image-text pairs, significantly boosting VLM training efficiency
arXiv.org
Synth$^2$: Boosting Visual-Language Models with Synthetic Captions...
The creation of high-quality human-labeled image-caption datasets presents a significant bottleneck in the development of Visual-Language Models (VLMs). In this work, we investigate an approach...
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WEF_Metaverse_Identity_Insights_Report_2024_1710335062.pdf
15.2 MB
The World Economic Forum released its "Metaverse Identity: Defining the Self in a Blended Reality" insight report.
Digital Identity is not us, but in a growing landscape of virtual worlds it's going to mirror us in virtual domains, extending the reach and impact of our activities, but also potentially opening to new individual and societal vulnerabilities.
Digital Identity is not us, but in a growing landscape of virtual worlds it's going to mirror us in virtual domains, extending the reach and impact of our activities, but also potentially opening to new individual and societal vulnerabilities.
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Google introducing SIMA: the first generalist AI agent to follow natural-language instructions in a broad range of 3D virtual environments and video games.
The SIMA research builds towards more general AI that can understand and safely carry out instructions in both virtual and physical settings.
Such generalizable systems will make AI-powered technology more helpful and intuitive.
The SIMA research builds towards more general AI that can understand and safely carry out instructions in both virtual and physical settings.
Such generalizable systems will make AI-powered technology more helpful and intuitive.
Google DeepMind
A generalist AI agent for 3D virtual environments
Introducing SIMA, a Scalable Instructable Multiworld Agent
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Stripe 2023 letter out - "the output of businesses that run on Stripe sums to roughly 1% of global GDP" !
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Cerebras announced Condor Galaxy 3 (CG-3)
Condor Galaxy 3 are 64 Cerebras CS-3 Systems. Each CS-3 is powered by the new 4 trillion transistor, 900,000 AI core WSE-3. Manufactured at TSMC at the 5-nanometer node, the WSE-3 delivers twice the performance at the same power and for the same price as the previous generation part.
Purpose built for training the industry’s largest AI models, WSE-3 delivers an astounding 125 petaflops of peak AI performance per chip.
Condor Galaxy has trained state-of-the art industry leading generative AI models, including Jais-30B, Med42, Crystal-Coder-7B and BTLM-3B-8K.
Jais 13B and Jais30B are the best bilingual Arabic models in the world, now available on Azure Cloud. BTLM-3B-8K is the number one leading 3B model on HuggingFace, offering 7B parameter performance in a light 3B parameter model for inference. Med42, developed with M42 and Core42, is a leading clinical LLM, trained on Condor Galaxy 1 in a weekend and surpassing MedPaLM on performance and accuracy.
Condor Galaxy 3 are 64 Cerebras CS-3 Systems. Each CS-3 is powered by the new 4 trillion transistor, 900,000 AI core WSE-3. Manufactured at TSMC at the 5-nanometer node, the WSE-3 delivers twice the performance at the same power and for the same price as the previous generation part.
Purpose built for training the industry’s largest AI models, WSE-3 delivers an astounding 125 petaflops of peak AI performance per chip.
Condor Galaxy has trained state-of-the art industry leading generative AI models, including Jais-30B, Med42, Crystal-Coder-7B and BTLM-3B-8K.
Jais 13B and Jais30B are the best bilingual Arabic models in the world, now available on Azure Cloud. BTLM-3B-8K is the number one leading 3B model on HuggingFace, offering 7B parameter performance in a light 3B parameter model for inference. Med42, developed with M42 and Core42, is a leading clinical LLM, trained on Condor Galaxy 1 in a weekend and surpassing MedPaLM on performance and accuracy.
BusinessWire
Cerebras and G42 Break Ground on Condor Galaxy 3, an 8 exaFLOPs AI Supercomputer
Cerebras Systems, the pioneer in accelerating generative AI, and G42, the Abu Dhabi-based leading technology holding group, today announced the build
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Anthropic released Claude 3 Haiku, "the fastest and most affordable model in its intelligence class"
It's available via API for devs at a million tokens for just $0.25.
World-class AI models just got 90% cheaper.
It's available via API for devs at a million tokens for just $0.25.
World-class AI models just got 90% cheaper.
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The top 100 consumer genAI apps by a16Z
Consumer AI is moving fast, with some major updates since last ranks 6 months ago.
1. The pace of progress is breakneck.
ChatGPT kept the lead, and eight of the top ten were consistent from last list.
But, the list also saw 22 new entrants out of 50 (44%!).
2. New categories have landed.
Suno (#36) debuts as the first Music co in our rankings, after launching a prompt -> song gen site in Dec.
The Productivity category debuted w/ seven cos. Six of these offer Chrome extensions for users to inject AI into their workflows.
3. Companionship grows up.
8 companion products made the web list, up from two in our last ranks - and two made the mobile list.
Companion mobile apps in particular have unusually high engagement.
The Character AI app sees nearly 300 sessions per user per month.
4. Mobile unlocks new use cases.
Thanks to the unique features of the smartphone as a platform, a few product types reign supreme:
- ChatGPT "copycats"
- Avatar generators (selfies = training data)
- Messaging keyboards
- Edtech (homework scanner, live language coach, etc.).
5. AI is a global pursuit.
The SF Bay Area took the top on both web and mobile. 30% of web products + 12% of mobile apps were developed here.
But, ~60% of the combined top 100 originated outside the U.S. - with global app studios like Codeway and HubX taking 3 spots each.
Consumer AI is moving fast, with some major updates since last ranks 6 months ago.
1. The pace of progress is breakneck.
ChatGPT kept the lead, and eight of the top ten were consistent from last list.
But, the list also saw 22 new entrants out of 50 (44%!).
2. New categories have landed.
Suno (#36) debuts as the first Music co in our rankings, after launching a prompt -> song gen site in Dec.
The Productivity category debuted w/ seven cos. Six of these offer Chrome extensions for users to inject AI into their workflows.
3. Companionship grows up.
8 companion products made the web list, up from two in our last ranks - and two made the mobile list.
Companion mobile apps in particular have unusually high engagement.
The Character AI app sees nearly 300 sessions per user per month.
4. Mobile unlocks new use cases.
Thanks to the unique features of the smartphone as a platform, a few product types reign supreme:
- ChatGPT "copycats"
- Avatar generators (selfies = training data)
- Messaging keyboards
- Edtech (homework scanner, live language coach, etc.).
5. AI is a global pursuit.
The SF Bay Area took the top on both web and mobile. 30% of web products + 12% of mobile apps were developed here.
But, ~60% of the combined top 100 originated outside the U.S. - with global app studios like Codeway and HubX taking 3 spots each.
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All about AI, Web 3.0, BCI
Cognition AI introduced Devin, the first AI software engineer. Devin is an autonomous agent that solves engineering tasks through the use of its own shell, code editor, and web browser. When evaluated on the SWE-Bench benchmark, which asks an AI to resolve…
AgentCoder is a research paper very similar to Devin
Raw LLMs like Claude Opus achieve 84.9% and GPT-4 gets 81.7% on HumanEval, a test of coding ability.
AgentCoder has:
— chain-of-thought
— terminal access
— test generation
— error feedback
and achieves 96.3% on HumanEval!
Raw LLMs like Claude Opus achieve 84.9% and GPT-4 gets 81.7% on HumanEval, a test of coding ability.
AgentCoder has:
— chain-of-thought
— terminal access
— test generation
— error feedback
and achieves 96.3% on HumanEval!
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Apple released MM1, a new LLM that competes with GPT-4 and Gemini.
Apple published a new paper unveiling MM1, a new family of multimodal AI models — with the largest at 30B parameters.
Apple published a new paper unveiling MM1, a new family of multimodal AI models — with the largest at 30B parameters.
arXiv.org
MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training
In this work, we discuss building performant Multimodal Large Language Models (MLLMs). In particular, we study the importance of various architecture components and data choices. Through careful...
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All models are wrong and yours are useless: making clinical prediction models impactful for patients
An insightful read on why machine learning models fail to help in the clinic.
The 5 key observations:
1. Success in academia ≠ success in the clinic.
Academic success = papers, grants, citations.
Clinical success = how often is your model being used in how many hospitals? how many patients does it help?
These incentives don't always work in the same direction.
2. Successful models use data available in routine practice.
Academics use rich datasets that most clinics simply don't have access to on their patients.
The academic view of an important step forward (spatial, multi-omics!), is not grounded in clinical reality.
3. Successful models are linked to actions.
Some academically interesting models are unhelpful in the clinic. Some people do better and others do worse, so what?
Doctors care about: what action should we take to help a particular patient? what drug, if any, should we give them?
4. Successful models are implemented outside of centers of excellence.
Helping research-savvy clinicians in Cambridge, Stanford, or Zurich is great. But the most impactful tools need to help the majority of doctors elsewhere too.
5. Success in the clinic is hard earned.
Hospitals are conservative, highly regulated environments. You must produce heaps of evidence before any hospital would even consider applying your academic insights.
It takes hard work to make tools from academia useful to patients.
An insightful read on why machine learning models fail to help in the clinic.
The 5 key observations:
1. Success in academia ≠ success in the clinic.
Academic success = papers, grants, citations.
Clinical success = how often is your model being used in how many hospitals? how many patients does it help?
These incentives don't always work in the same direction.
2. Successful models use data available in routine practice.
Academics use rich datasets that most clinics simply don't have access to on their patients.
The academic view of an important step forward (spatial, multi-omics!), is not grounded in clinical reality.
3. Successful models are linked to actions.
Some academically interesting models are unhelpful in the clinic. Some people do better and others do worse, so what?
Doctors care about: what action should we take to help a particular patient? what drug, if any, should we give them?
4. Successful models are implemented outside of centers of excellence.
Helping research-savvy clinicians in Cambridge, Stanford, or Zurich is great. But the most impactful tools need to help the majority of doctors elsewhere too.
5. Success in the clinic is hard earned.
Hospitals are conservative, highly regulated environments. You must produce heaps of evidence before any hospital would even consider applying your academic insights.
It takes hard work to make tools from academia useful to patients.
Nature
All models are wrong and yours are useless: making clinical prediction models impactful for patients
npj Precision Oncology - All models are wrong and yours are useless: making clinical prediction models impactful for patients
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Digital asset investment products saw record weekly inflows totalling US$2.9bn, beating the prior week’s all-time record of US$2.7bn.
This week’s inflows have pushed year-to-date inflows to US$13.2bn, smashing the full 2021 inflows of US$10.6bn. During the week global Crypto ETPs broke the US$100bn mark for the first time.
This week’s inflows have pushed year-to-date inflows to US$13.2bn, smashing the full 2021 inflows of US$10.6bn. During the week global Crypto ETPs broke the US$100bn mark for the first time.
Medium
Volume 174: Digital Asset Fund Flows Weekly Report
Another record broken, with US$2.9bn inflows
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Quilt is building AI assistants for solutions teams.
Quilt’s core products are AI-powered assistants designed to help solutions engineers with tasks like filling out requests for proposals, answering basic technical questions and prepping for demos.
The assistants can complete security and due diligence questionnaires, field questions from reps via Slack and summarize the contents of notes, calls and research ahead of customer meetings.
Quilt is uniquely able to incorporate engineers’ technical knowledge and “understand context.”
Quilt’s core products are AI-powered assistants designed to help solutions engineers with tasks like filling out requests for proposals, answering basic technical questions and prepping for demos.
The assistants can complete security and due diligence questionnaires, field questions from reps via Slack and summarize the contents of notes, calls and research ahead of customer meetings.
Quilt is uniquely able to incorporate engineers’ technical knowledge and “understand context.”
TechCrunch
Quilt is building AI assistants for solutions teams
The job of so-called “solutions professionals” — people like sales engineers, solutions architects and consultants — revolves around pitching complex enterprise tech to potential customers. It’s important work. But despite this being the case, rarely are…
Remember the GPT Store? Well, developers are afraid OpenAI's forgotten about it too.
Once promised to be OpenAI's "app store moment," the GPT Store has seen low levels of usage with no road to monetization in sight.
Once promised to be OpenAI's "app store moment," the GPT Store has seen low levels of usage with no road to monetization in sight.
The Information
OpenAI’s Chatbot App Store Is Off to a Slow Start
Last fall, OpenAI CEO Sam Altman sought to capitalize on the raging success of ChatGPT by launching an app store. Similar to the way Apple turned the iPhone into a big business for mobile app developers, OpenAI hoped developers would tap into its artificial…
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