working on live music and some tech projects
Joined September 2014
- Tweets154
- Following500
- Followers235
- Likes584
johnvanliere retweeted
AI Chaos is a ladder...for $TAO
i just posted this note to openai:
Hi All–
Mira has been instrumental to OpenAI’s progress and growth the last 6.5 years; she has been a hugely significant factor in our development from an unknown research lab to an important company.
When Mira informed me this morning that she was leaving, I was saddened but of course support her decision. For the past year, she has been building out a strong bench of leaders that will continue our progress.
I also want to share that Bob and Barret have decided to depart OpenAI. Mira, Bob, and Barret made these decisions independently of each other and amicably, but the timing of Mira’s decision was such that it made sense to now do this all at once, so that we can work together for a smooth handover to the next generation of leadership.
I am extremely grateful to all of them for their contributions.
Being a leader at OpenAI is all-consuming. On one hand it’s a privilege to build AGI and be the fastest-growing company that gets to put our advanced research in the hands of hundreds of millions of people. On the other hand it’s relentless to lead a team through it—and they have gone above and beyond the call of duty for the company.
Mark is going to be our new SVP of Research and will now lead the research org in partnership with Jakub as Chief Scientist. This has been our long-term succession plan for Bob someday; although it’s happening sooner than we thought, I couldn’t be more excited that Mark is stepping into the role. Mark obviously has deep technical expertise, but he has also learned how to be a leader and manager in a very impressive way over the past few years.
Josh Achiam is going to take on a new role as Head of Mission Alignment, working across the company to ensure that we get all pieces (and culture) right to be in a place to succeed at the mission.
Kevin and Srinivas will continue to lead the Applied team.
Matt Knight will be our Chief Information Security Officer having already served in this capacity for a long time. This has been our plan for quite some time.
Mark, Jakub, Kevin, Srinivas, Matt, and Josh will report to me. I have over the past year or so spent most of my time on the non-technical parts of our organization; I am now looking forward to spending most of my time on the technical and product parts of the company.
...
Leadership changes are a natural part of companies, especially companies that grow so quickly and are so demanding. I obviously won’t pretend it’s natural for this one to be so abrupt, but we are not a normal company, and I think the reasons Mira explained to me (there is never a good time, anything not abrupt would have leaked, and she wanted to do this while OpenAI was in an upswing) make sense. We can both talk about this more tomorrow during all-hands.
Thank you for all of your hard work and dedication.
Sam
johnvanliere retweeted
ReadyAI is building a decentralized Scale AI
Instead of human annotators, we are using distributed LLMs
In just 3 months, our top miners are now exceeding the MTurk benchmark by 71% and GPT 4o by 37%
Previous studies saw only a 25% increase over the MTurk bench for ChatGPT.
Read more 👇
readyai.ai/p/readyai_benchma…
Summary of Findings:
This study evaluates the performance of miners using their own fine-tuned models in comparison to the Mechanical Turk (MTurk) benchmark, which is used as the gold standard for comparing LLMs for annotation tasks. Previous studies, such as Gilardi, Fabrizio et al. (2023), "ChatGPT Outperforms Crowd Workers for Text-Annotation Tasks," showed that ChatGPT outperformed the Mechanical Turk benchmark by 25% on average.
Miners on subnet 33 are currently outperforming the MTurk benchmark by 71% and GPT 4o with no further optimizations by 37%. SN33's top miner performance has improved 75% over the three months since the subnet's launch.
Two other key considerations in LLMs vs. MTurk performance are cost and time to complete tasks. For time to complete a task, the average for MTurk was 3 minutes and 57 seconds per task. For costs, MTurk averages $0.12 per task. SN33 miners are producing these annotations at a cost of 660x less. Additionally, recent research from the Swiss Federal Institute of Technology suggests that a substantial portion of crowd workers may be leveraging Large Language Models (LLMs) to complete their tasks. Specifically, Veselovsky et al. (2023) estimate that between 33% and 46% of crowd workers utilized LLMs during task completion. Enterprises are paying human costs for LLM work that is 660x cheaper to produce using SN33.
Structured data is the foundation of successful AI models and applications, and it requires high-quality, trusted data labeling pipelines. Annotations are critical in transforming raw information into high-quality, organized datasets that fuel AI development and performance. The data annotation process for AI development faces significant challenges of cost inefficiency, temporal constraints, limited scalability, and annotation inconsistency.
The findings of this study provide compelling evidence for the efficacy of ReadyAI's innovative, cost-efficient structured data pipeline. Our novel approach demonstrates significant innovation in addressing the challenges in traditional data annotation processes.
$TAO @opentensor
johnvanliere retweeted
ReadyAI exceeding MTurk benchmarks by 71%
Improving model performance over 75% since launch
Just getting started over here 🫡
ReadyAI is building a decentralized Scale AI
Instead of human annotators, we are using distributed LLMs
In just 3 months, our top miners are now exceeding the MTurk benchmark by 71% and GPT 4o by 37%
Previous studies saw only a 25% increase over the MTurk bench for ChatGPT.
Read more 👇
readyai.ai/p/readyai_benchma…
Summary of Findings:
This study evaluates the performance of miners using their own fine-tuned models in comparison to the Mechanical Turk (MTurk) benchmark, which is used as the gold standard for comparing LLMs for annotation tasks. Previous studies, such as Gilardi, Fabrizio et al. (2023), "ChatGPT Outperforms Crowd Workers for Text-Annotation Tasks," showed that ChatGPT outperformed the Mechanical Turk benchmark by 25% on average.
Miners on subnet 33 are currently outperforming the MTurk benchmark by 71% and GPT 4o with no further optimizations by 37%. SN33's top miner performance has improved 75% over the three months since the subnet's launch.
Two other key considerations in LLMs vs. MTurk performance are cost and time to complete tasks. For time to complete a task, the average for MTurk was 3 minutes and 57 seconds per task. For costs, MTurk averages $0.12 per task. SN33 miners are producing these annotations at a cost of 660x less. Additionally, recent research from the Swiss Federal Institute of Technology suggests that a substantial portion of crowd workers may be leveraging Large Language Models (LLMs) to complete their tasks. Specifically, Veselovsky et al. (2023) estimate that between 33% and 46% of crowd workers utilized LLMs during task completion. Enterprises are paying human costs for LLM work that is 660x cheaper to produce using SN33.
Structured data is the foundation of successful AI models and applications, and it requires high-quality, trusted data labeling pipelines. Annotations are critical in transforming raw information into high-quality, organized datasets that fuel AI development and performance. The data annotation process for AI development faces significant challenges of cost inefficiency, temporal constraints, limited scalability, and annotation inconsistency.
The findings of this study provide compelling evidence for the efficacy of ReadyAI's innovative, cost-efficient structured data pipeline. Our novel approach demonstrates significant innovation in addressing the challenges in traditional data annotation processes.
$TAO @opentensor
johnvanliere retweeted
Founder @DavFields presenting the vision for @afterparty and @ReadyAI_ at Token2049 DePIN Founders Day
Come join!
lu.ma/qx7l81vd
Excited to speak alongside some of my favorite DePIN founders during Token 2049 at DePIN Founders Day
Join us! 🫡
lu.ma/qx7l81vd
johnvanliere retweeted
Love it! Here is what ReadyAI (SN33) has been cooking up. Appreciate all voter consideration 🙏
Studies have shown ChatGPT outperforms human annotators for Structured Data by about 25% and costs 30x less. 1
In just 2 months, miners on SN33 running ChatGPT without optimization can’t survive.
Today we announce SN33 is now @ReadyAI_ to fully align with our mission 👇
SN33 is building a more performant and significantly cheaper alternative to Scale AI
Today structured data is performed primarily by human annotation services like Amazon’s Mechanical Turk and Scale AI
It is now more important than ever for every business and individual to make their data AI Ready. However, taking unstructured data and making it Structured Data using today’s tools is extremely costly.
SN33 revolutionizes this process, unlocking immense opportunities for commercialization. We lay out the vision for it in this detailed blog post: readyai.ai/p/readyai_llmoil
Validators TODAY can monetize access to this structured data pipeline independently, but we’re streamlining this process, launching a frontend soon that any validator can opt into to provide bandwidth.
We've received great feedback from the community, recognizing that what we're building goes far beyond Conversational AI. Building the world's largest annotated conversational dataset (which we've already accomplished) is just one of countless real-world applications for SN33's Structured Data pipeline.
We're building a decentralized Scale AI, offering a full suite of Structured Data commodities—from text metadata tagging (available today) to fully customizable queries for company-specific data annotation use cases and image metadata tagging coming soon 👀.
Thanks for all the feedback! It has been invaluable so keep bringing it to us! 🙏$TAO @opentensor
1 “ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks” shows “The zero-shot accuracy of ChatGPT exceeds that of crowd-workers by about 25 percentage points on average [...] Moreover, the per-annotation cost of ChatGPT is less than $0.003—about thirty times cheaper than MTurk”
johnvanliere retweeted
ReadyAI is here 🤝
Studies have shown ChatGPT outperforms human annotators for Structured Data by about 25% and costs 30x less. 1
In just 2 months, miners on SN33 running ChatGPT without optimization can’t survive.
Today we announce SN33 is now @ReadyAI_ to fully align with our mission 👇
SN33 is building a more performant and significantly cheaper alternative to Scale AI
Today structured data is performed primarily by human annotation services like Amazon’s Mechanical Turk and Scale AI
It is now more important than ever for every business and individual to make their data AI Ready. However, taking unstructured data and making it Structured Data using today’s tools is extremely costly.
SN33 revolutionizes this process, unlocking immense opportunities for commercialization. We lay out the vision for it in this detailed blog post: readyai.ai/p/readyai_llmoil
Validators TODAY can monetize access to this structured data pipeline independently, but we’re streamlining this process, launching a frontend soon that any validator can opt into to provide bandwidth.
We've received great feedback from the community, recognizing that what we're building goes far beyond Conversational AI. Building the world's largest annotated conversational dataset (which we've already accomplished) is just one of countless real-world applications for SN33's Structured Data pipeline.
We're building a decentralized Scale AI, offering a full suite of Structured Data commodities—from text metadata tagging (available today) to fully customizable queries for company-specific data annotation use cases and image metadata tagging coming soon 👀.
Thanks for all the feedback! It has been invaluable so keep bringing it to us! 🙏$TAO @opentensor
1 “ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks” shows “The zero-shot accuracy of ChatGPT exceeds that of crowd-workers by about 25 percentage points on average [...] Moreover, the per-annotation cost of ChatGPT is less than $0.003—about thirty times cheaper than MTurk”
johnvanliere retweeted
Studies have shown ChatGPT outperforms human annotators for Structured Data by about 25% and costs 30x less. 1
In just 2 months, miners on SN33 running ChatGPT without optimization can’t survive.
Today we announce SN33 is now @ReadyAI_ to fully align with our mission 👇
SN33 is building a more performant and significantly cheaper alternative to Scale AI
Today structured data is performed primarily by human annotation services like Amazon’s Mechanical Turk and Scale AI
It is now more important than ever for every business and individual to make their data AI Ready. However, taking unstructured data and making it Structured Data using today’s tools is extremely costly.
SN33 revolutionizes this process, unlocking immense opportunities for commercialization. We lay out the vision for it in this detailed blog post: readyai.ai/p/readyai_llmoil
Validators TODAY can monetize access to this structured data pipeline independently, but we’re streamlining this process, launching a frontend soon that any validator can opt into to provide bandwidth.
We've received great feedback from the community, recognizing that what we're building goes far beyond Conversational AI. Building the world's largest annotated conversational dataset (which we've already accomplished) is just one of countless real-world applications for SN33's Structured Data pipeline.
We're building a decentralized Scale AI, offering a full suite of Structured Data commodities—from text metadata tagging (available today) to fully customizable queries for company-specific data annotation use cases and image metadata tagging coming soon 👀.
Thanks for all the feedback! It has been invaluable so keep bringing it to us! 🙏$TAO @opentensor
1 “ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks” shows “The zero-shot accuracy of ChatGPT exceeds that of crowd-workers by about 25 percentage points on average [...] Moreover, the per-annotation cost of ChatGPT is less than $0.003—about thirty times cheaper than MTurk”
johnvanliere retweeted
Missed Novelty Search today? Chat with SN33 AI to get a summary of the call or the details on the restitution effort and new technical upgrades coming to $TAO like Ledger
conversations.xyz/p/sn33_sup…
For $TAO community members who missed Novelty Search
Our SN33 AI now has the latest one (July 25) in its database.
Chat with the AI to get a summary of the call or details on the restitution effort, latest technical updates (Ledger, Child keys, etc) or @const_reborn chat with @chamath
🤖: conversations.xyz/p/sn33_sup…
johnvanliere retweeted
For $TAO community members who missed Novelty Search
Our SN33 AI now has the latest one (July 25) in its database.
Chat with the AI to get a summary of the call or details on the restitution effort, latest technical updates (Ledger, Child keys, etc) or @const_reborn chat with @chamath
🤖: conversations.xyz/p/sn33_sup…
johnvanliere retweeted
What if you could make a 'Super' version of yourself?
⌛ On 24/7
🌐 All of the information you want to share with the world
💼 About you or your business.
Just launched one of me for @ConversationGen
🤖: conversations.xyz/p/sn33_sup…
The power of SN33’s structured data...how it works 👇
SN33 provides a cost-efficient pipeline for the “digital commodity” of structured data, the key ingredient for high quality, fine-tuned models and RAG solutions.
Validators have the ability to process any conversational or transcript data through SN33 and get valuable structured & vectorized data to use for fine tuning AI models and RAG databases. Structured data is the key ingredient to making conversational models more life-like and accurate.
We continue to focus on developing the best enterprise grade applications on top of SN33 data to showcase structured data’s value and enable the selling of this digital commodity through our soon-to-be-completed frontend for validators.
Launching Our First “Super AI” Utilizing SN33 Data
Up-to-date information about SN33 at your fingertips instantly. You will immediately notice the difference in accuracy and completeness of the information vs. other RAG solutions.
FOR MINERS: you can walk through all your initial questions to get up and running with a Runpod instance, and get help with common errors (see video demo as well)
FOR VALIDATORS: you can ask about the validation mechanism and the latest details on the SN33 roadmap.
FOR NEWBIES: you have an easy place to start and get all your questions answered.
We are planning to launch more of these Super AIs for the Bittensor ecosystem more broadly and make available this data pipeline for other companies and applications to deliver accurate and up-to-date information in the voice of a trusted leader like a founder, creator or senior executive that makes the experience feel super human.
If you are interested in one for your business DMs are open 🚀
$TAO
johnvanliere retweeted
10 days of the chain halted gave us a natural experiment. What effect do emissions have on "sell pressure". Our co-founder, @DavFields, shares some data.
Fast-growing technology companies don’t buy back stock; they invest in the future, even though they know some of those bets will fail.
So why is Bittensor buying back its stock $TAO by recycling emissions?
Argument goes it reduces "sell pressure" from miners.
With the chain paused for 10 days, we had a natural experiment: What happens when there is 10 days' worth of bottled-up "sell pressure"?
Since the chain reopened, the price is up 22%, compared to down 37% since the push to recycle emissions in May. Let's take a look.
Over the last 2 months, subnets, miners, and validators have received less emissions and more is getting recycled. In theory “less sell pressure” from miners and subnet owners selling TAO.
Over that period, though, $TAO has fallen 37%, compared to $ETH, which has fallen 9%, and $SOL, which has fallen 15%.
With the pause of the TAO chain for 10 days, we get an experiment. How does 10 days of sell pressure affect the price?
Two months ago, on May 22, 2024, emissions were first shifted to the root network. Today, 1 out of every 4 TAO is not being given to miners, validators, or subnet owners but is being recycled.
The thought was that this would lower sell pressure. At the $300 TAO price, there are a total of $2.16 million in emissions a day. 41% goes to validators, and most of that goes to those staking so that money is not moving.
That leaves $1.275 million that could add to sell pressure.
$TAO has averaged around $80 million in daily sales volume, so the total sell pressure is only 1.6% of total volume. The market can easily absorb this. Diverting 26% of daily emissions to the root network decreases sell pressure by $330k a day, only 0.41% of the total daily trading volume.
The German government recently aimed to sell 50k BTC, which was ~10% of daily volumes. This led to a 14% decrease in price over a short-term period.
Well, from July 2 to July 12, the Bittensor chain was in safe mode. So subnets were still accruing emissions but could not transfer and, therefore, could not sell $TAO.
That created 10 days of emissions overhang to sell into the market, 15.9% of daily volumes.
Did this lead to a big drop in price? NO.
From before the hack to today, $TAO is up 11%
From the reopening of the chain to today $TAO is up 22%.
It is very clear that the supply impact from emissions sales is NOT negatively impacting price.
Emissions should be distributed, not recycled.
For the good of the network, validators should assign weight to performant subnets with long-term vision.
Subnets such as SN18, SN19, SN8, and SN33 will serve as the best possible marketing for Bittensor's long-term value.
The most important thing for Bittensor is to continue to attract the best teams to the ecosystem.
HIGH emissions are $TAO’s competitive advantage.
Higher incentives attract the best teams. Subsequently, the validators (and eventually the DTAO community) distribute those emissions to the most deserving recipients.
Let’s get back to driving the future growth of $TAO. Yes, some growth spending is wasted even by the best companies, but TAO is a competitive marketplace that can adjust to changing conditions at the speed of markets.
johnvanliere retweeted
Fast-growing technology companies don’t buy back stock; they invest in the future, even though they know some of those bets will fail.
So why is Bittensor buying back its stock $TAO by recycling emissions?
Argument goes it reduces "sell pressure" from miners.
With the chain paused for 10 days, we had a natural experiment: What happens when there is 10 days' worth of bottled-up "sell pressure"?
Since the chain reopened, the price is up 22%, compared to down 37% since the push to recycle emissions in May. Let's take a look.
Over the last 2 months, subnets, miners, and validators have received less emissions and more is getting recycled. In theory “less sell pressure” from miners and subnet owners selling TAO.
Over that period, though, $TAO has fallen 37%, compared to $ETH, which has fallen 9%, and $SOL, which has fallen 15%.
With the pause of the TAO chain for 10 days, we get an experiment. How does 10 days of sell pressure affect the price?
Two months ago, on May 22, 2024, emissions were first shifted to the root network. Today, 1 out of every 4 TAO is not being given to miners, validators, or subnet owners but is being recycled.
The thought was that this would lower sell pressure. At the $300 TAO price, there are a total of $2.16 million in emissions a day. 41% goes to validators, and most of that goes to those staking so that money is not moving.
That leaves $1.275 million that could add to sell pressure.
$TAO has averaged around $80 million in daily sales volume, so the total sell pressure is only 1.6% of total volume. The market can easily absorb this. Diverting 26% of daily emissions to the root network decreases sell pressure by $330k a day, only 0.41% of the total daily trading volume.
The German government recently aimed to sell 50k BTC, which was ~10% of daily volumes. This led to a 14% decrease in price over a short-term period.
Well, from July 2 to July 12, the Bittensor chain was in safe mode. So subnets were still accruing emissions but could not transfer and, therefore, could not sell $TAO.
That created 10 days of emissions overhang to sell into the market, 15.9% of daily volumes.
Did this lead to a big drop in price? NO.
From before the hack to today, $TAO is up 11%
From the reopening of the chain to today $TAO is up 22%.
It is very clear that the supply impact from emissions sales is NOT negatively impacting price.
Emissions should be distributed, not recycled.
For the good of the network, validators should assign weight to performant subnets with long-term vision.
Subnets such as SN18, SN19, SN8, and SN33 will serve as the best possible marketing for Bittensor's long-term value.
The most important thing for Bittensor is to continue to attract the best teams to the ecosystem.
HIGH emissions are $TAO’s competitive advantage.
Higher incentives attract the best teams. Subsequently, the validators (and eventually the DTAO community) distribute those emissions to the most deserving recipients.
Let’s get back to driving the future growth of $TAO. Yes, some growth spending is wasted even by the best companies, but TAO is a competitive marketplace that can adjust to changing conditions at the speed of markets.
johnvanliere retweeted
AI Chat currently lacks the nuance of human conversation
AI today is focused only on facts – the best human interactions engage you w/ opinions & emotional connection
@ConversationGen is indexing the world's conversations to bring the nuance of human connection to AI 👇
$TAO
johnvanliere retweeted
With @ConversationGen we are building a decentralized Scale AI
Bittensor gives validators access to a subnet commodity based on TAO stake
SN 3️⃣3️⃣'s commodity is structured data: the critical ingredient for LLMs
Today, we enabled validators to process ANY text data thru SN33
New Release Live - CGP 1.4.3
Validator Custom Conversation Server
• Provides example code and documentation for Validators to launch a custom Conversation Data Server github.com/afterpartyai/bitt…
This is a crucial step for the subnet, both in terms in decentralization and monetization, as it allows for Validators to facilitate processing of custom and/or proprietary data. Validators are free to continue using the CGP-maintained Conversations API, or configure their own data for processing.
johnvanliere retweeted
SN 3️⃣3️⃣ 🌐 has been live for 1 ½ weeks, processing over 10 million lines of conversation data!
Since launch, we have released a few updates including support for mining and validating with GPT, Groq and Anthropic models
Here is our Near-Term Roadmap 🚀
Personas API - launching next week
• Thousands of personas created from the SN33 dataset, which can be used for character/roleplaying chat experiences, in-game character assets, e-commerce chatbots, and more. Available via API and Firebase.
• The Personas API will ultimately enable any validator to sell access to users to process text or audio data through the CGP and develop their own Personas. Some use cases: Persona of a loved one (grandfather, mother, etc.)Persona of a long lost friend or ex you have had text conversations with and want to get answers from.
• Enterprise Personas API: This will also enable enterprise customers to process annotated data and create chatbots for their businesses (think restaurants, small businesses) and e-commerce sites.
Largest Annotated Conversational Dataset(!)
• We have already processed over 10 million lines of data, which exceeds the largest dataset of its kind (PersonalDialog). We will publish the dataset to Huggingface shortly and continually update it.
• This is very valuable for the open-source community to use for fine-tuning conversational AI models. We are working with several AI researchers on this dataset and will publish the results of those models.
Dynamic RAG
• The CGP’s vector database will ultimately allow an LLM to dynamically change its personality as it speaks to a user and understands their personality, mood, and interests.
Other improvements coming to SN33:
• Launch local model support
• Expand tagging categories to include personality traits, opinions, interests, topics, and more.
• Optimize the data processing pipeline for performance
• Continuously expanding and refining the dataset based on community feedback and evolving conversational AI research needs
• Partnering with applications to power finetuned persona AIs utilizing the dataset
johnvanliere retweeted
In 3 days @ConversationGen , we have already processed 2 million lines of conversation data.
The largest open-source structured conversation dataset is only 8 million lines. We will exceed the largest dataset of its kind in less than 2 weeks.
This is the power of @bittensor_
johnvanliere retweeted
🚀🧠 Subnet 33 @ConversationGen on Bittensor: The Next Big Thing in AI $TAO 🧠🚀
Hey, $TAO community,
There’s some seriously exciting stuff happening in the Bittensor ecosystem right now. Subnet 33, also known as the Conversation Genome Project (CGP), is making big waves by leveraging Bittensor’s decentralized infrastructure to completely change the game in AI data processing.
This project is building a global index of human conversations, which is going to spark major breakthroughs in conversational AI.
So what’s the big deal anyway? I'll tell you why this matters:
The CGP is tackling one of the biggest challenges in AI – the double-spend problem – using crypto-economic systems.
This means they’re eliminating the need for costly human oversight and reinforcement learning with human feedback (RLHF).
Instead, they’re combining low-cost inference models with a strong verification layer, making data processing both efficient and affordable.
This approach opens up access to high-quality AI data, speeding up development across the board.
🔑 Key Points:
Global Indexing: CGP is set to provide a detailed, open-source dataset that captures the nuances of human conversations, making AI interactions more natural and engaging.
Decentralized Verification: By incentivizing a global network of miners and validators, CGP ensures high-quality data processing without the hefty price tag of constant human oversight.
Cost-Efficient: The combination of low-cost models with a robust verification system democratizes access to top-tier AI data, pushing development forward at a rapid pace.
Why This Matters
CGP is addressing fundamental challenges in conversational AI, particularly the lack of personalization and high-quality annotated data. This is a huge deal for Bittensor and $TAO, as it sets the wave for creating more engaging, context-aware, and empathetic AI systems. It’s projects like these that pave the way for the future of AI 💪
Resources:
CGP Oveview
conversations.xyz/p/cgp_over…
CGP Github github.com/afterpartyai/bitt…
CGP on X @ConversationGen xitter.cf/ConversationGen
Stay tuned for more updates as we watch this exciting development!
Youtube Link for Sharing:
youtu.be/AU7Pjplivq0?si=ChWy…
Crypto AI has a double spend problem. Verification requires additional cost
@conversationgen on @bittensor_ is uniquely designed to solve this for structured data. We remove expensive RLHF and human oversight from AI data processing reducing costs of decentralized AI
Read more: conversations.xyz/p/cgpblog5…
johnvanliere retweeted
Introducing the future of conversational AI: @conversationgen 🌐
Bittensor Guru Episode 30 - @afterparty Subnet w/ @Davfields. Afterparty.com is launching the Conversation Genome Project. One small step for Bittensor, one giant leap for open source AI. conversations.xyz 👨🚀
johnvanliere retweeted
Bittensor Guru Episode 30 - @afterparty Subnet w/ @Davfields. Afterparty.com is launching the Conversation Genome Project. One small step for Bittensor, one giant leap for open source AI. conversations.xyz 👨🚀
just released the @ConversationGen subnet on @bittensorcom testnet with the @afterparty team.
Excited to be building the largest open-source conversational dataset in history on the incredible bittensor protocol. Checkout our Github at the link below:
github.com/afterpartyai/bitt…