Researchers at University of the Witwatersrand in Johannesburg, South Africa used an EMOTIV device to link a brain to the internet similar to IoT.
Dubbed Brainternet, the BCI project aims to "simplify a person's understanding of their own brain and the brains of others" through machine learning and interactivity.
Dubbed Brainternet, the BCI project aims to "simplify a person's understanding of their own brain and the brains of others" through machine learning and interactivity.
Futurism
Researchers Have Linked a Human Brain to the Internet for the First Time Ever
Future developments could allow for data transfer in both directions.
Anthropic’s has a $5B, 4-year plan to take on OpenAI.
It plans to build a “frontier model” called “Claude-Next” 10 times more capable than GPT, but will require billions in spending over the next 18 months.
Google owns a 10% stake in the startup.
It plans to build a “frontier model” called “Claude-Next” 10 times more capable than GPT, but will require billions in spending over the next 18 months.
Google owns a 10% stake in the startup.
TechCrunch
Anthropic's $5B, 4-year plan to take on OpenAI | TechCrunch
AI research startup Anthropic aims to raise as much as $5 billion over the next two years to take on rival OpenAI.
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MetaMask announced the launch of fiat currency purchase cryptocurrency function in its Portfolio Dapp, users can use debit or credit card, PayPal, Bank Transfer, or Instant ACH to buy crypto.
metamask.io
MetaMask: The Leading Crypto Wallet Platform, Blockchain Wallet
Set up your crypto wallet and access all of Web3 and enjoy total control over your data, assets, and digital self. The go-to web3 wallet for 100+ million users.
LLMs just hit a major milestone with the release of the new "Generative agents" paper.
By using LLMs, generative agents were able to simulate human-like behavior in an interactive sandbox inspired by The Sims.
The agent architecture extends Language Models to store a complete record of the agent's experiences using natural language, synthesize those memories over time into higher-level reflections, and retrieve them dynamically to plan behavior.
There are three components to it:
1. The memory stream, which records a comprehensive list of the agent's experiences
2. Reflection, which synthesizes memories into higher-level inferences over time
3. Planning, which translates those conclusions and the current environment into high-level action plans.
Paper: https://arxiv.org/abs/2304.03442
Demo: https://reverie.herokuapp.com/arXiv_Demo/
By using LLMs, generative agents were able to simulate human-like behavior in an interactive sandbox inspired by The Sims.
The agent architecture extends Language Models to store a complete record of the agent's experiences using natural language, synthesize those memories over time into higher-level reflections, and retrieve them dynamically to plan behavior.
There are three components to it:
1. The memory stream, which records a comprehensive list of the agent's experiences
2. Reflection, which synthesizes memories into higher-level inferences over time
3. Planning, which translates those conclusions and the current environment into high-level action plans.
Paper: https://arxiv.org/abs/2304.03442
Demo: https://reverie.herokuapp.com/arXiv_Demo/
OpenAGI: When LLM Meets Domain Experts
OpenAGI is an open-source AGI research platform designed to offer complex, multi-step tasks.
OpenAGI formulates complex tasks as natural language queries serving as input to the Large Language Models.
The LLM subsequently selects, synthesizes, and executes models provided by Open AGI to address the task.
Paper https://arxiv.org/abs/2304.04370
GitHub https://github.com/agiresearch/OpenAGI
OpenAGI is an open-source AGI research platform designed to offer complex, multi-step tasks.
OpenAGI formulates complex tasks as natural language queries serving as input to the Large Language Models.
The LLM subsequently selects, synthesizes, and executes models provided by Open AGI to address the task.
Paper https://arxiv.org/abs/2304.04370
GitHub https://github.com/agiresearch/OpenAGI
GitHub
GitHub - agiresearch/OpenAGI: OpenAGI: When LLM Meets Domain Experts
OpenAGI: When LLM Meets Domain Experts. Contribute to agiresearch/OpenAGI development by creating an account on GitHub.
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State-of-Crypto.pdf
13.2 MB
A16Z crypto today realized the 2023 State of Crypto Report and introduce the State of Crypto Index.
One of the key insights that you’ll find from the report is that blockchains have more active users, and more ways to engage than ever before.
More monthly active addresses – unique addresses sending on-chain transactions each month – than ever.
Last month 15 million addresses, more than twice as many as two years ago when prices were still elevated.
One explanation: there are increasingly more ways to engage with blockchains and web3 applications.
From DeFi to web3 games – more than 700 of which launched last year – a variety of new applications create addresses for their users to interact with, without having to download or connect a wallet.
One of the key insights that you’ll find from the report is that blockchains have more active users, and more ways to engage than ever before.
More monthly active addresses – unique addresses sending on-chain transactions each month – than ever.
Last month 15 million addresses, more than twice as many as two years ago when prices were still elevated.
One explanation: there are increasingly more ways to engage with blockchains and web3 applications.
From DeFi to web3 games – more than 700 of which launched last year – a variety of new applications create addresses for their users to interact with, without having to download or connect a wallet.
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AutoGPT might be the next big step in AI
Here's why Karpathy, OpenAI, recently said "AutoGPT is the next frontier of prompt engineering".
AutoGPT is the equivalent of giving GPT-based models a memory and a body. You can now give a task to an AI agent and have it autonomously come up with a plan, execute on it, browse the web, and use new data to revise the strategy until the task is completed.
It can analyze the market and come up with a trading strategy, customer service, marketing, finance, or other tasks that requires continuous updates.
There are three components to it:
1. Architecture: It leverages GPT-4 and GPT-3.5 via API.
2. Autonomous Iterations: AutoGPT can refine its outputs by self-critical review, building on its previous work and integrating prompt history for more accurate results.
3. Memory Management: Integration with Pinecone allows for long-term memory storage, enabling context preservation and improved decision-making.
4. Multi-functionality: Capabilities include file manipulation, web browsing, and data retrieval, distinguishing AutoGPT from previous AI advancements by broadening its application scope.
Here's why Karpathy, OpenAI, recently said "AutoGPT is the next frontier of prompt engineering".
AutoGPT is the equivalent of giving GPT-based models a memory and a body. You can now give a task to an AI agent and have it autonomously come up with a plan, execute on it, browse the web, and use new data to revise the strategy until the task is completed.
It can analyze the market and come up with a trading strategy, customer service, marketing, finance, or other tasks that requires continuous updates.
There are three components to it:
1. Architecture: It leverages GPT-4 and GPT-3.5 via API.
2. Autonomous Iterations: AutoGPT can refine its outputs by self-critical review, building on its previous work and integrating prompt history for more accurate results.
3. Memory Management: Integration with Pinecone allows for long-term memory storage, enabling context preservation and improved decision-making.
4. Multi-functionality: Capabilities include file manipulation, web browsing, and data retrieval, distinguishing AutoGPT from previous AI advancements by broadening its application scope.
GitHub
GitHub - Significant-Gravitas/AutoGPT: AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission…
AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters. - Significant-Gravitas/AutoGPT
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Baby-AGI is taking the world by storm
Here's an implementation within the LangChain framework, allowing you to easily substitute in other vectorstores and other LLMs.
Here's an implementation within the LangChain framework, allowing you to easily substitute in other vectorstores and other LLMs.
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DeepSpeed Chat. Impressive open-source effort by Microsoft!
DeepSpeed Chat offers an end-to-end RLHF pipeline to train ChatGPT-like models. This is the missing piece from other efforts like Alpaca and Vicuna. The RLHF pipeline is replicated from the InstructGPT paper.
The other challenges are cost and efficiency. DeepSpeed Chat aims to make this process more accessible and affordable through a unified hybrid engine (DeepSpeed-HE) for RLHF. For example, DeepSpeed-HE can train an OPT-66B model in 2.1 days for $1620.
With further scalability (e.g., using multi-node multi-GPU systems), DeepSpeed-HE can train an OPT-13B model in 1.25 hours for $320 and an OPT-175B model in under a day for $5120. That's a big deal!
DeepSpeed Chat offers an end-to-end RLHF pipeline to train ChatGPT-like models. This is the missing piece from other efforts like Alpaca and Vicuna. The RLHF pipeline is replicated from the InstructGPT paper.
The other challenges are cost and efficiency. DeepSpeed Chat aims to make this process more accessible and affordable through a unified hybrid engine (DeepSpeed-HE) for RLHF. For example, DeepSpeed-HE can train an OPT-66B model in 2.1 days for $1620.
With further scalability (e.g., using multi-node multi-GPU systems), DeepSpeed-HE can train an OPT-13B model in 1.25 hours for $320 and an OPT-175B model in under a day for $5120. That's a big deal!
GitHub
DeepSpeed/blogs/deepspeed-chat at master · microsoft/DeepSpeed
DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective. - microsoft/DeepSpeed
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Elon Musk Makes Big Investment in Generative-AI Project at Twitter
He recently purchased roughly 10,000 GPUs for the platform
Two AI engineers from Alphabet's DeepMind joined Twitter last month.
He recently purchased roughly 10,000 GPUs for the platform
Two AI engineers from Alphabet's DeepMind joined Twitter last month.
Business Insider
International
What You Need To Know About The World
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Intel plans to make graphics processors (GPUs) that meet US rules for export to China, media report, adding that Intel CEO Pat Gelsinger is in China now meeting government officials.
工商時報
搶市場 英特爾推陸規晶片
中美科技戰背景下,美國半導體巨頭英特爾(Intel)CEO基辛格(Parick Gelsinger)展開其上任後首次訪中行程。11日基辛格與中國商務部長王文濤會面,雙方針對多項議題進行交流;英特爾同日證實,將在今年稍晚推出適合「不同市場」需求的資料中心晶片。基辛格12日並強調,英特爾對中國市場非常樂...
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All about AI, Web 3.0, BCI
AutoGPT might be the next big step in AI Here's why Karpathy, OpenAI, recently said "AutoGPT is the next frontier of prompt engineering". AutoGPT is the equivalent of giving GPT-based models a memory and a body. You can now give a task to an AI agent and…
Current architecture of research agent enables learning through Google Search, Pinecone, multiple agent feedback, and incremental draft improvement.
Based on babyAGI
Based on babyAGI
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Dolly is the first *commercially viable*, open source, instruction-following LLM.
Dolly 2.0 is available for commercial applications without having to pay for API access or sharing data with 3rd parties.
Dolly 2.0 is available for commercial applications without having to pay for API access or sharing data with 3rd parties.
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A year ago DeepMind released the Chinchilla paper, forever changing the direction of LLM training. Without Chinchilla, there would be no LLaMa, Alpaca, or Cerebras-GPT.
Deepmind
An empirical analysis of compute-optimal large language model training
We investigate the optimal model and dataset size for training a transformer language model under a given compute budget. We find that current large language models are significantly undertrained, a consequence of the recent focus on scaling language models…
Google AI researchers have developed a new aging clock that can predict a person's biological age with greater accuracy than previous methods.
The clock, which is based on deep learning models of retinal images, was trained on a dataset of over 800,000 people.
This is an exciting development in the field of aging research. The aging clock could have a major impact on our understanding of aging and our ability to prevent and treat age-related diseases.
The clock, which is based on deep learning models of retinal images, was trained on a dataset of over 800,000 people.
This is an exciting development in the field of aging research. The aging clock could have a major impact on our understanding of aging and our ability to prevent and treat age-related diseases.
Googleblog
Developing an aging clock using deep learning on retinal images
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The Startup Industry Map for Pre-Seed Fundraising
It’s based on US startups mostly in SF, California and New York.
Data is from January 1, 2023 to March 14, 2023 (so incredibly recent).
What are the key findings?
• Biotech companies raise the most money at the highest valuation caps - but not a lot of them actually get funded.
• SaaS is middle of the pack in valuation cap and total raised, but by far the biggest category.
• Investors seem less enthused about food startups lately.
It’s based on US startups mostly in SF, California and New York.
Data is from January 1, 2023 to March 14, 2023 (so incredibly recent).
What are the key findings?
• Biotech companies raise the most money at the highest valuation caps - but not a lot of them actually get funded.
• SaaS is middle of the pack in valuation cap and total raised, but by far the biggest category.
• Investors seem less enthused about food startups lately.
New stablecoin bill draft introduced in US House of Representatives
Members of the U.S. House of Representatives are taking another crack at creating a comprehensive regulatory framework for stablecoins, like USDC and Tether, digital assets intended for payments whose price is supposed to remain the same at all times.
The House Financial Services Committee released a new discussion draft bill with no official notice on Saturday.
Here are highlights of what the draft bill would do if it becomes law:
1. Put the Federal Reserve in charge of nonbank stablecoins
The U.S. central bank would approve and regulate non-bank companies like Circle and Tether that currently issue or want to issue their own stablecoins in the U.S. Credit unions and banks that want to issue their own stablecoins could do so with approval from the main financial regulator they fall under, the National Credit Union Administration, Federal Deposit Insurance Corp. or Office of the Comptroller of the Currency. Failure to register would be punishable by up to five years in prison and a $1 million fine. Any issuer that wants to do business in the U.S., regardless of where the company is based, would need to register.
2. A (temporary) ban on new stablecoins without fiat backing
The new draft bill includes a two-year ban on stablecoins that aren't backed by a hard asset, and it directs the Treasury Department to lead a study on the topic of such "endogenously backed" stablecoins. Tokens already in existence before the bill passes into law would be grandfathered in.
3. Allow the government to set interoperability standards
Banking regulators and the National Institute of Standards and Technology would have the ability to set standards for interoperability between stablecoins to allow for ease of use, including mandatory technical and legal specifications to allow users to clear and settle across different payment systems without buying native stablecoins for each.
4. Direct the Fed to study a digital dollar
If the bill becomes law, Congress and the president would direct the Federal Reserve to study the effects of a digital dollar issued by the central bank. The Fed has already begun studying whether to issue a digital dollar, but the bill would mandate certain areas of focus, like the potential impacts on monetary policy, financial stability and privacy for individuals.
Members of the U.S. House of Representatives are taking another crack at creating a comprehensive regulatory framework for stablecoins, like USDC and Tether, digital assets intended for payments whose price is supposed to remain the same at all times.
The House Financial Services Committee released a new discussion draft bill with no official notice on Saturday.
Here are highlights of what the draft bill would do if it becomes law:
1. Put the Federal Reserve in charge of nonbank stablecoins
The U.S. central bank would approve and regulate non-bank companies like Circle and Tether that currently issue or want to issue their own stablecoins in the U.S. Credit unions and banks that want to issue their own stablecoins could do so with approval from the main financial regulator they fall under, the National Credit Union Administration, Federal Deposit Insurance Corp. or Office of the Comptroller of the Currency. Failure to register would be punishable by up to five years in prison and a $1 million fine. Any issuer that wants to do business in the U.S., regardless of where the company is based, would need to register.
2. A (temporary) ban on new stablecoins without fiat backing
The new draft bill includes a two-year ban on stablecoins that aren't backed by a hard asset, and it directs the Treasury Department to lead a study on the topic of such "endogenously backed" stablecoins. Tokens already in existence before the bill passes into law would be grandfathered in.
3. Allow the government to set interoperability standards
Banking regulators and the National Institute of Standards and Technology would have the ability to set standards for interoperability between stablecoins to allow for ease of use, including mandatory technical and legal specifications to allow users to clear and settle across different payment systems without buying native stablecoins for each.
4. Direct the Fed to study a digital dollar
If the bill becomes law, Congress and the president would direct the Federal Reserve to study the effects of a digital dollar issued by the central bank. The Fed has already begun studying whether to issue a digital dollar, but the bill would mandate certain areas of focus, like the potential impacts on monetary policy, financial stability and privacy for individuals.
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3 CAMEL datasets released: Physics, Chemistry and Biology. Each dataset contains 20K problem-solution pairs, consisting of 25 topics, 25 subtopics and 32 problems for each "topic, subtopic" pair generated and solved by GPT4.
huggingface.co
camel-ai (CAMEL-AI.org)
Large Language Models, Cooperative AI, AI Society, Multi Agent Systems, Deep Learning, Artificial Intelligence, Natural Language Processing, Communicative AI
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