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AI trustworthiness depends on data quality, which is why Mercury uses what we call Canonicals Canonicals are accurate source data, linked and included in LLM responses These are system level primitives. Our LLMs only return fully-sourced data points, minimizing hallucinations

Ryan Wiggins

2,025 subscribers

15,992 次观看 • 3 个月前 •via X (Twitter)

Anya Rossi• Live Now

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Dear friends, we are constantly researching on new aesthetics and concepts by using AI. This new experiment uses our eight-year-long Machine Hallucinations research archive to create new AI Data Paintings.

Dear friends, we are constantly researching on new aesthetics and concepts by using AI. This new experiment uses our eight-year-long Machine Hallucinations research archive to create new AI Data Paintings.

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To register as a member of APC, your name, your data, and your primary data are sourced from NIMC. We are the only political party that sources primary data from NIMC. NIMC verifies data beyond what you think. - Nentawe Yilwatda, APC national chairman, speaking on the membership registration process in the party #PoliticsToday

To register as a member of APC, your name, your data, and your primary data are sourced from NIMC. We are the only political party that sources primary data from NIMC. NIMC verifies data beyond what you think. - Nentawe Yilwatda, APC national chairman, speaking on the membership registration process in the party #PoliticsToday

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56,156 次观看 • 1 个月前

AI only exists today because it is trained on real data. The better the data input, The better the AI output. We capture incredibly high-quality raw data of real humans, in 3D, from all angles. You do the Math ✍🏻 Our data can vastly improve and expand the capabilities of AI

AI only exists today because it is trained on real data. The better the data input, The better the AI output. We capture incredibly high-quality raw data of real humans, in 3D, from all angles. You do the Math ✍🏻 Our data can vastly improve and expand the capabilities of AI

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“Rafales and missiles are useless unless you have software that leverages their delivery. So we must invest in AI. The 12 models Mr Vaishnaw has created, three or four should go to the military because our data cannot be shared in the civil stream. We need exclusive LLMs. On those LLMs, put 50 solid data engineers. An additional secretary doing AI or a major general doing AI is pointless, he doesn’t understand AI. Someone with real AI expertise has to come in and be told: this is our data, these are our combat plans, now leverage them.” Lt Gen Raj Shukla (Retd) on what the Indian defence industry should focus on. Watch Full Episode Here:

“Rafales and missiles are useless unless you have software that leverages their delivery. So we must invest in AI. The 12 models Mr Vaishnaw has created, three or four should go to the military because our data cannot be shared in the civil stream. We need exclusive LLMs. On those LLMs, put 50 solid data engineers. An additional secretary doing AI or a major general doing AI is pointless, he doesn’t understand AI. Someone with real AI expertise has to come in and be told: this is our data, these are our combat plans, now leverage them.” Lt Gen Raj Shukla (Retd) on what the Indian defence industry should focus on. Watch Full Episode Here:

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Scale alone is not enough for AI data. Quality and complexity are equally critical. Excited to support all of these for LLM developers with Snorkel AI Data-as-a-Service, and to share our new leaderboard! — Our decade-plus of research and work in AI data has a simple point: scale alone is not enough. AI success is all about the quality, complexity, and distribution of data—in addition to volume. We’re excited to be powering leading LLM developers with Snorkel AI Expert Data-as-a-Service, our white glove service for custom, expert-level AI datasets—and to now preview some of what we’re building via our new Expert Data Leaderboard (🔗 in 🧵) + upcoming OSS dataset releases! Snorkel Expert Data-as-a-Service is built to meet the rapidly evolving data needs of the agentic AI world—where success is built on the quality, complexity, and distribution of datasets, in addition to size and scale. This kind of high-quality, frontier AI data can only come from a union of technology and human expertise. With Snorkel Expert Data-as-a-Service, we’re powering frontier LLM developers across agentic, expert knowledge, reasoning, coding, multi-modal, and other task types via the combination of these two key components: - (1) The Snorkel Expert Network: A global team of subject matter experts focused wholly on specialized knowledge–spanning thousands of topics in STEM/academic, vertical/professional, and consumer/lifestyle domains. - (2) Snorkel AI Data Development Platform: Our unique programmatic data curation and quality control platform, accelerating and improving expert authoring and review through principled techniques developed over the last decade of R&D. Now: we’re incredibly excited to showcase some of the power of Snorkel Expert Data-as-a-Service via the new Snorkel Leaderboard—putting frontier models to the test in complex, agentic, and reasoning settings inspired by real industry scenarios (not esoteric puzzles)! We’ll be releasing new leaderboards and accompanying expert-verified open source datasets (coming soon!) regularly. To start, we’re sharing three initial ones in preview: - SnorkelFinance: Q&A over financial documents requiring agentic tool-calling and reasoning - SnorkelUnderwrite: Agentic insurance tasks requiring industry-specific reasoning and tool use - SnorkelSequences: Mathematical tasks requiring compositional multi-step reasoning

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#WATCH | India AI Impact Summit 2026 | Delhi: Founder Chairman and CEO of Sampark Foundation & former CEO of HCL Technologies, Vineet Nayar says, "...From an employment point of view I think it is very important for us to understand that Indian companies, including Indian IT companies, are going to be profit-driven and therefore if you believe that they are going to create employment you must be dreaming. Therefore, the question is how do we create employment in this environment, and that employment comes from mass scale startups, which is what this government has already doing. So, how do we create new sets of people who are trying to solve new sets of problems not new sets of technology and if we do that we will get it right. I think we as Indians have to be very careful on who does data belong to and that is the debate we have a problem with. The LLM models which exist worldwide are far superior than the Indian models. Unfortunately, in India, we never develop products, so therefore we do not have SLMs and LLMs which are world-class. On one side, we have global LLM products which are coming to India and trading on our Indian data. Should we allowed that or should we not allowed that? But on the other side if we don't allow that then we have the data but we don't have the LLM models. So, how do we encourage technology completely to develop the LLM models. This needs radicals strategic thinking and a very important aspect otherwise we will either give up a data. So, I think it's a very critical aspect for us to think about - who does this data belong, what is the kind of incentives we are going to give to develop LLM technologies or SLM technologies fast so that we train on our data otherwise an LLM will come in with our data and we'll immediately see return and we'll celebrate and we will do all these kind of press releases but the India will lose a competitive advantage on something which is very critical for the next decade."

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Alex Ratner

49,964 次观看 • 1 年前

#WATCH | Delhi | On National Statistics Day, Minister of State for Statistics and Programme Implementation and Planning, Rao Inderjit Singh says, "The idea is that when we give credible data to our government and not only our government but also to people who are dependent on us like in our neighboring countries, third world countries, we should be able to guide them in giving credible data so that their respective countries can progress based on this data. The Prime Minister and the PMO are both very concerned that data should not be half-hearted..."

#WATCH | Delhi | On National Statistics Day, Minister of State for Statistics and Programme Implementation and Planning, Rao Inderjit Singh says, "The idea is that when we give credible data to our government and not only our government but also to people who are dependent on us like in our neighboring countries, third world countries, we should be able to guide them in giving credible data so that their respective countries can progress based on this data. The Prime Minister and the PMO are both very concerned that data should not be half-hearted..."

ANI

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LangChain

89,138 次观看 • 1 年前

Everyone is talking about AI models, but almost no one is talking about data ownership. For years, centralized systems have dominated data pipelines, opaque processes, hidden biases, and zero contributor visibility. We call it innovation…, but in reality, it’s a black box. And that’s exactly how we ended up facing model degradation risks today. Because here’s the truth: AI is only as good as the data it learns from. That’s why Sovereign AI Data matters. To me, it means control, traceability, and accountability. Perle Labs is approaching this differently: ◈ On-chain provenance → Every data point is verifiable ◈ Expert-driven input → Quality over quantity ◈ Reputation systems → Trust becomes measurable This isn’t just a technical upgrade. It’s a paradigm shift. Instead of: Anonymous contributors ➠Unverified datasets ➠Centralized ownership ➠We move toward: Transparent data flows, Credible expertise and Distributed value creation If AI is the brain, then data is the memory. And right now most systems are learning from memory, which they can’t verify. Perle is solving that. While others focus on scaling models… This is about fixing the foundation they stand on. And foundations decide everything. #PerleAI #ToPerle participating in Perle Labs community campaign

Everyone is talking about AI models, but almost no one is talking about data ownership. For years, centralized systems have dominated data pipelines, opaque processes, hidden biases, and zero contributor visibility. We call it innovation…, but in reality, it’s a black box. And that’s exactly how we ended up facing model degradation risks today. Because here’s the truth: AI is only as good as the data it learns from. That’s why Sovereign AI Data matters. To me, it means control, traceability, and accountability. Perle Labs is approaching this differently: ◈ On-chain provenance → Every data point is verifiable ◈ Expert-driven input → Quality over quantity ◈ Reputation systems → Trust becomes measurable This isn’t just a technical upgrade. It’s a paradigm shift. Instead of: Anonymous contributors ➠Unverified datasets ➠Centralized ownership ➠We move toward: Transparent data flows, Credible expertise and Distributed value creation If AI is the brain, then data is the memory. And right now most systems are learning from memory, which they can’t verify. Perle is solving that. While others focus on scaling models… This is about fixing the foundation they stand on. And foundations decide everything. #PerleAI #ToPerle participating in Perle Labs community campaign

Big_Wealth❤️‍🔥

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🔍How do we make sense of messy, real-world documents? We (the EPIC Lab at UC Berkeley) are building DocETL, an open-source system for LLM-native data processing. We've started to create a showcase of demos. Here's our first: an analysis of public feedback on US AI strategy. 🇺🇸

Shreya Shankar

35,294 次观看 • 1 年前

Yesterday I interviewed Sean Cai about AI data. This is essentially a guide for founders on how to sell data and RL envs to AI labs. "I've never seen a data contract get turned down by a top lab, if it's good quality data, for budget reasons." 00:00 What areas of data are underserved? 02:10 For bio data, is it real-world or purely digital? 04:21 For cyber data, which subsets are most underserved? 05:50 What is the sales process like? 07:04 Why would a lab not renew or increase their purchase volume? 10:13 When a researcher is exploring a new direction, what's the first step? 11:35 In robotics data, what do you view as underserved? 13:12 What does the initial data delivery look like, what format? 13:53 Do labs have more sophisticated internal setups for running environments? 14:32 Are the non-frontier labs buying off-the-shelf data from Anthropic / OpenAI vendors? 16:11 Do Anthropic data vendors put expiry timeframes on the exclusivity? 16:42 Are purchase decisions researcher-led? 17:41 Decagon, Sierra, Ramp: what kinds of data are they buying? 19:06 Long-term, when do labs still need to buy external data vs train on user traces? 21:15 Will end-vendor benchmarks shift to performance per dollar? 22:04 How many labs are spending at the 1B+/yr data level? 23:53 Delta between Anthropic's stated $1B and your 10-20B/lab number? 26:05 What makes inference providers / neoclouds a good fit to acquire RL env cos?

Yesterday I interviewed Sean Cai about AI data. This is essentially a guide for founders on how to sell data and RL envs to AI labs. "I've never seen a data contract get turned down by a top lab, if it's good quality data, for budget reasons." 00:00 What areas of data are underserved? 02:10 For bio data, is it real-world or purely digital? 04:21 For cyber data, which subsets are most underserved? 05:50 What is the sales process like? 07:04 Why would a lab not renew or increase their purchase volume? 10:13 When a researcher is exploring a new direction, what's the first step? 11:35 In robotics data, what do you view as underserved? 13:12 What does the initial data delivery look like, what format? 13:53 Do labs have more sophisticated internal setups for running environments? 14:32 Are the non-frontier labs buying off-the-shelf data from Anthropic / OpenAI vendors? 16:11 Do Anthropic data vendors put expiry timeframes on the exclusivity? 16:42 Are purchase decisions researcher-led? 17:41 Decagon, Sierra, Ramp: what kinds of data are they buying? 19:06 Long-term, when do labs still need to buy external data vs train on user traces? 21:15 Will end-vendor benchmarks shift to performance per dollar? 22:04 How many labs are spending at the 1B+/yr data level? 23:53 Delta between Anthropic's stated $1B and your 10-20B/lab number? 26:05 What makes inference providers / neoclouds a good fit to acquire RL env cos?

Chris Barber (in SF)

130,324 次观看 • 2 个月前

Our Llama-3.1-Nemotron-70B-Instruct model is a leading model on the 🏆 Arena Hard benchmark (85) from Arena. Arena Hard uses a data pipeline to build high-quality benchmarks from live data in Chatbot Arena, and is known for its predictive ability of Chatbot Arena Elo score as well as separability between helpful and less helpful models. Use our customized model Llama-3.1-Nemotron-70B to improve the helpfulness of LLM generated responses in your applications. 📥 Try on our API catalog: 📥 On GitHub: 📥 Or on Hugging Face:

Our Llama-3.1-Nemotron-70B-Instruct model is a leading model on the 🏆 Arena Hard benchmark (85) from Arena. Arena Hard uses a data pipeline to build high-quality benchmarks from live data in Chatbot Arena, and is known for its predictive ability of Chatbot Arena Elo score as well as separability between helpful and less helpful models. Use our customized model Llama-3.1-Nemotron-70B to improve the helpfulness of LLM generated responses in your applications. 📥 Try on our API catalog: 📥 On GitHub: 📥 Or on Hugging Face:

NVIDIA AI Developer

140,756 次观看 • 1 年前

We don’t want ICE in the UK. Leicester’s policing data is not Palantir’s to harvest. These are our communities. Our families. Our lives. Not commodities. The public has a right to know who is watching. And why.

We don’t want ICE in the UK. Leicester’s policing data is not Palantir’s to harvest. These are our communities. Our families. Our lives. Not commodities. The public has a right to know who is watching. And why.

Shockat Adam MP

34,621 次观看 • 5 个月前

The internet has been exhausted as a data source for AI. Our co-founder Liam Fedus joined Elad Gil on No Priors to discuss what comes next, and how we are building AI scientists and the autonomous laboratories for them to operate.

The internet has been exhausted as a data source for AI. Our co-founder Liam Fedus joined Elad Gil on No Priors to discuss what comes next, and how we are building AI scientists and the autonomous laboratories for them to operate.

Periodic Labs

10,953 次观看 • 3 个月前