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I used NotebookLM to study Google new breakthrough with TurboQuant and used Video overview to study the subject, best learning tool in the world at the moment. TurboQuant: Redefining AI Efficiency with Extreme Compression Google Research has introduced TurboQuant, a suite of advanced algorithms designed to dramatically compress the...

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14,648 görüntüleme • 3 ay önce •via X (Twitter)

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This week at Google I/O Box highlighted how Gemini 2.5 Pro can be used with Box AI to power complex data extraction. There's a tremendous amount of value trapped in unstructured data -like PDFs, docs, images, and more- that we can now tap into with AI Agents.
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This week at Google I/O Box highlighted how Gemini 2.5 Pro can be used with Box AI to power complex data extraction. There's a tremendous amount of value trapped in unstructured data -like PDFs, docs, images, and more- that we can now tap into with AI Agents.

Aaron Levie

49,142 görüntüleme • 1 yıl önce

Dominate Google Search by reverse-engineering your competitors with Gemini and NotebookLM. No more manual keyword guesswork. No more content that fails to rank. Here’s the new play 👇 → Map the top 10 search results to find hidden content gaps. → Use Gemini to identify exactly what Google rewards today. → Build a centralized AI research database in NotebookLM. → Create high-authority outlines based on real ranking data. → Scale expert-level content with data-backed citations. Save this post. The SEO landscape is shifting faster than ever. Want the full guide? DM me.
9:05

Dominate Google Search by reverse-engineering your competitors with Gemini and NotebookLM. No more manual keyword guesswork. No more content that fails to rank. Here’s the new play 👇 → Map the top 10 search results to find hidden content gaps. → Use Gemini to identify exactly what Google rewards today. → Build a centralized AI research database in NotebookLM. → Create high-authority outlines based on real ranking data. → Scale expert-level content with data-backed citations. Save this post. The SEO landscape is shifting faster than ever. Want the full guide? DM me.

Julian Goldie SEO

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Introducing: Advanced Data Assets™ By LayerAI 🧬 Data is used to power Big Data and AI. The increasing demand for high quality data to improve AI models will lead to new companies creating value in the global data market. The problem: current data collection methods make data fall short of its true potential. Data is limited. Building a decentralized solution where users choose to productize and earn from their data opens untapped sources of income. 🌐 Creating Advanced Data Assets™ There exists a market opportunity to create a digital blockchain-based global data marketplace. Data collected using our nodes, our native network apps, settled on LayerAI. The Advanced Data. Meet the Ecosystem that empowers users to participate in the Data Economy.
0:32

Introducing: Advanced Data Assets™ By LayerAI 🧬 Data is used to power Big Data and AI. The increasing demand for high quality data to improve AI models will lead to new companies creating value in the global data market. The problem: current data collection methods make data fall short of its true potential. Data is limited. Building a decentralized solution where users choose to productize and earn from their data opens untapped sources of income. 🌐 Creating Advanced Data Assets™ There exists a market opportunity to create a digital blockchain-based global data marketplace. Data collected using our nodes, our native network apps, settled on LayerAI. The Advanced Data. Meet the Ecosystem that empowers users to participate in the Data Economy.

LayerAI | AI2Earn

27,136 görüntüleme • 2 yıl önce

Google just had its DeepSeek moment — and almost nobody's talking about it. Here's the story you need to know. 🧵 When DeepSeek dropped in early 2025, it didn't just impress people. It scared them. A model competing with the biggest AI players — at a fraction of the cost — through math, not hardware. Chip stocks tanked. The industry panicked. Then on March 24th, 2026, Google published a research paper called TurboQuant. Cloudflare's CEO immediately called it Google's DeepSeek moment. Memory chip stocks for Micron and Western Digital fell on the news. Why? Because TurboQuant compresses AI memory by 6x — through software alone. → No new chips needed → No retraining required → One server can now host more models than before The era of throwing hardware at AI problems is ending. The era of mathematical efficiency is here. ✅Save this post, you'll thank yourself when this reshapes every AI tool you use. 📌 Want the SOP? DM me.
8:21

Google just had its DeepSeek moment — and almost nobody's talking about it. Here's the story you need to know. 🧵 When DeepSeek dropped in early 2025, it didn't just impress people. It scared them. A model competing with the biggest AI players — at a fraction of the cost — through math, not hardware. Chip stocks tanked. The industry panicked. Then on March 24th, 2026, Google published a research paper called TurboQuant. Cloudflare's CEO immediately called it Google's DeepSeek moment. Memory chip stocks for Micron and Western Digital fell on the news. Why? Because TurboQuant compresses AI memory by 6x — through software alone. → No new chips needed → No retraining required → One server can now host more models than before The era of throwing hardware at AI problems is ending. The era of mathematical efficiency is here. ✅Save this post, you'll thank yourself when this reshapes every AI tool you use. 📌 Want the SOP? DM me.

Julian Goldie SEO

12,731 görüntüleme • 3 ay önce

We’re expanding the Gemini API File Search tool 🔍 with 3 new updates that enable developers to more easily build multimodal RAG systems with enhanced precision: + Multimodal Support: By leveraging our Gemini Embedding 2 model, File Search can now reason across image and text simultaneously. + Custom Metadata Filtering: Bring structure to unstructured data by tagging files with custom key-value labels. This pre-filters your data and boosts search speed. + Exact citations: File Search can now capture and return the exact source (down to the page number) for every piece of information indexed. See multimodal File Search in action with our example app in Google AI Studio. Chat with your entire image and doc library, ask questions, and trace answers back to the source:
0:20

We’re expanding the Gemini API File Search tool 🔍 with 3 new updates that enable developers to more easily build multimodal RAG systems with enhanced precision: + Multimodal Support: By leveraging our Gemini Embedding 2 model, File Search can now reason across image and text simultaneously. + Custom Metadata Filtering: Bring structure to unstructured data by tagging files with custom key-value labels. This pre-filters your data and boosts search speed. + Exact citations: File Search can now capture and return the exact source (down to the page number) for every piece of information indexed. See multimodal File Search in action with our example app in Google AI Studio. Chat with your entire image and doc library, ask questions, and trace answers back to the source:

Google AI Developers

108,603 görüntüleme • 1 ay önce

$MU $00060 $00530 $TSM Evolution of the AI Memory Hierarchy: HBM, DRAM, & SRAM Overview and Market Analysis The video outlines the strategic shift of modern computing toward a memory-centric architecture driven by the demands of generative AI. It explains that the industry is moving away from a traditional commodity model to a tightly coupled hierarchy of specialized technologies, where High Bandwidth Memory (HBM) and advanced packaging act as primary bottlenecks for performance. Leading manufacturers like SK hynix, Samsung, and Micron are navigating complex transitions to HBM4 and CXL-based expansion to overcome data movement constraints. Beyond the memory producers, the ecosystem relies on integration enablers like TSMC for packaging and specialized equipment providers for high-precision manufacturing. Ultimately, the analysis suggests that memory intensity and power efficiency have become the decisive factors in the success of gigawatt-scale data centers.
14:38

$MU $00060 $00530 $TSM Evolution of the AI Memory Hierarchy: HBM, DRAM, & SRAM Overview and Market Analysis The video outlines the strategic shift of modern computing toward a memory-centric architecture driven by the demands of generative AI. It explains that the industry is moving away from a traditional commodity model to a tightly coupled hierarchy of specialized technologies, where High Bandwidth Memory (HBM) and advanced packaging act as primary bottlenecks for performance. Leading manufacturers like SK hynix, Samsung, and Micron are navigating complex transitions to HBM4 and CXL-based expansion to overcome data movement constraints. Beyond the memory producers, the ecosystem relies on integration enablers like TSMC for packaging and specialized equipment providers for high-precision manufacturing. Ultimately, the analysis suggests that memory intensity and power efficiency have become the decisive factors in the success of gigawatt-scale data centers.

TheValueist

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How to Make AI Models More Useful? Discover the potential of AI and blockchain, and how features like RAG and TEEs can help NEAR unlock its full potential to build the world's best truly open AI. TL;DR - RAG (Retrieval-Augmented Generation) enhances AI agents by personalizing responses with relevant context. - Blockchain ensures transactions are processed transparently, efficiently, and securely. - NEAR Protocol is designed for AI, featuring a sharding architecture that supports high transaction volumes at low fees without compromising performance. It also offers privacy, user-owned data, and monetization opportunities. Study the NEAR stack, explore how to make AI models useful:
3:05

How to Make AI Models More Useful? Discover the potential of AI and blockchain, and how features like RAG and TEEs can help NEAR unlock its full potential to build the world's best truly open AI. TL;DR - RAG (Retrieval-Augmented Generation) enhances AI agents by personalizing responses with relevant context. - Blockchain ensures transactions are processed transparently, efficiently, and securely. - NEAR Protocol is designed for AI, featuring a sharding architecture that supports high transaction volumes at low fees without compromising performance. It also offers privacy, user-owned data, and monetization opportunities. Study the NEAR stack, explore how to make AI models useful:

NEARWEEK

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Foundation models reach their full potential when paired with data at planetary scale. By combining the capabilities of Google Earth AI imagery models with the scale, depth, and quality of Vantor’s satellite imagery, Vantor customers can rapidly unlock complex, high-value use cases. Running Earth AI embedding models on Vantor’s 30 cm-class satellite imagery allows analysts to instantly surface visually similar features, patterns, and objects across massive datasets. A single reference image becomes a search query. From there, the system can: • Identify similar vessels across thousands of square kilometers • Detect infrastructure patterns at scale • Accelerate damage assessment workflows • Narrow broad-area searches before human review Our agreement with Google marks the first commercial deployment of Earth AI imagery models against a dataset offering global 2D coverage of virtually all landmass, global-scale 3D terrain, a 20+ year high-resolution archive, and constellation revisit rates of up to 15 times per day. That combination matters. When advanced imagery models are applied to a globally consistent, temporally deep, and geometrically precise spatial foundation, insight shifts from experimental to operational. And when deployed within sovereign, air-gapped environments, these capabilities move beyond cloud-based analytics to deliver mission-ready intelligence. Learn more:
1:21

Foundation models reach their full potential when paired with data at planetary scale. By combining the capabilities of Google Earth AI imagery models with the scale, depth, and quality of Vantor’s satellite imagery, Vantor customers can rapidly unlock complex, high-value use cases. Running Earth AI embedding models on Vantor’s 30 cm-class satellite imagery allows analysts to instantly surface visually similar features, patterns, and objects across massive datasets. A single reference image becomes a search query. From there, the system can: • Identify similar vessels across thousands of square kilometers • Detect infrastructure patterns at scale • Accelerate damage assessment workflows • Narrow broad-area searches before human review Our agreement with Google marks the first commercial deployment of Earth AI imagery models against a dataset offering global 2D coverage of virtually all landmass, global-scale 3D terrain, a 20+ year high-resolution archive, and constellation revisit rates of up to 15 times per day. That combination matters. When advanced imagery models are applied to a globally consistent, temporally deep, and geometrically precise spatial foundation, insight shifts from experimental to operational. And when deployed within sovereign, air-gapped environments, these capabilities move beyond cloud-based analytics to deliver mission-ready intelligence. Learn more:

Vantor

17,050 görüntüleme • 4 ay önce

🚀 Introducing the AI Alliance: Shaping the Future of AI and Data Security We’re proud to unveil the AI Alliance, a collaboration of expert teams from ARC, DMAIL.AI and XAlpha AI Agent to name a few, who are working together to drive innovation in AI and data security. By leveraging the power of LLMs (Large Language Models), we’re building the foundation for future AI applications. 🌐✨ 💡 This is just the beginning! Stay tuned for more insights and updates from this recently held AI Alliance AMA. We’ll dive deeper into how these teams are helping to shape the future of AI.
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🚀 Introducing the AI Alliance: Shaping the Future of AI and Data Security We’re proud to unveil the AI Alliance, a collaboration of expert teams from ARC, DMAIL.AI and XAlpha AI Agent to name a few, who are working together to drive innovation in AI and data security. By leveraging the power of LLMs (Large Language Models), we’re building the foundation for future AI applications. 🌐✨ 💡 This is just the beginning! Stay tuned for more insights and updates from this recently held AI Alliance AMA. We’ll dive deeper into how these teams are helping to shape the future of AI.

ARC

20,658 görüntüleme • 1 yıl önce

Excited to announce AlphaEvolve A powerful AI coding agent developed by our team in Google DeepMind that is able to discover impactful new algorithms for important problems in Maths and Computing by combining the creativity of large language models with automated evaluators.
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Excited to announce AlphaEvolve A powerful AI coding agent developed by our team in Google DeepMind that is able to discover impactful new algorithms for important problems in Maths and Computing by combining the creativity of large language models with automated evaluators.

Pushmeet Kohli

203,443 görüntüleme • 1 yıl önce

🌟Quilibrium’s AI Breakthrough: Encrypted Training on CPUs In her latest live stream ( - minute 14) Cassie unveiled a groundbreaking AI training method that allows models to be trained on encrypted data using CPUs while achieving performance comparable to Nvidia’s A100 GPU (blue line in the graph below). Traditionally, AI training requires expensive GPUs because matrix multiplications—the core of deep learning—are highly computational. Running these calculations on CPUs is painfully slow, often taking hours or days for even small models. The problem worsens when trying to train AI on encrypted data, as standard encryption methods add a massive computational burden. Quilibrium’s breakthrough removes this bottleneck. Instead of relying on traditional matrix multiplication, their method uses a completely different mathematical approach, allowing AI models to be trained securely and efficiently without exposing the raw data. Cassie didn’t reveal the exact technique, only hinting that it’s inspired by existing AI research and will be detailed in a future open-source AGPL-licensed paper. The key advantage? AI can now be trained at GPU speeds on standard CPUs, making privacy-preserving machine learning far more accessible. This innovation has major implications. It slashes AI infrastructure costs, allowing organizations to train powerful models without investing in expensive hardware. It also enables private AI training on personal or corporate data without revealing sensitive information, a game-changer for industries like healthcare and finance. If Quilibrium’s method delivers on its promise, it could reshape AI development, making privacy-first computing the new standard. $QUIL $wQUIL
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🌟Quilibrium’s AI Breakthrough: Encrypted Training on CPUs In her latest live stream ( - minute 14) Cassie unveiled a groundbreaking AI training method that allows models to be trained on encrypted data using CPUs while achieving performance comparable to Nvidia’s A100 GPU (blue line in the graph below). Traditionally, AI training requires expensive GPUs because matrix multiplications—the core of deep learning—are highly computational. Running these calculations on CPUs is painfully slow, often taking hours or days for even small models. The problem worsens when trying to train AI on encrypted data, as standard encryption methods add a massive computational burden. Quilibrium’s breakthrough removes this bottleneck. Instead of relying on traditional matrix multiplication, their method uses a completely different mathematical approach, allowing AI models to be trained securely and efficiently without exposing the raw data. Cassie didn’t reveal the exact technique, only hinting that it’s inspired by existing AI research and will be detailed in a future open-source AGPL-licensed paper. The key advantage? AI can now be trained at GPU speeds on standard CPUs, making privacy-preserving machine learning far more accessible. This innovation has major implications. It slashes AI infrastructure costs, allowing organizations to train powerful models without investing in expensive hardware. It also enables private AI training on personal or corporate data without revealing sensitive information, a game-changer for industries like healthcare and finance. If Quilibrium’s method delivers on its promise, it could reshape AI development, making privacy-first computing the new standard. $QUIL $wQUIL

Quilibrium Community

18,887 görüntüleme • 1 yıl önce

A study from the German Agency for International Cooperation found that just 2% of data used to train global AI models comes from Africa. With over 2000 languages and a growing but uneven digital footprint, how does Africa build AI that truly reflects the continent’s diversity?
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A study from the German Agency for International Cooperation found that just 2% of data used to train global AI models comes from Africa. With over 2000 languages and a growing but uneven digital footprint, how does Africa build AI that truly reflects the continent’s diversity?

BBC News Africa

69,850 görüntüleme • 6 ay önce

Flapping Airplanes co-founder Asher Spector explains why data efficiency is the greatest bottleneck to AI adoption: "To the extent that AI has been hard to integrate into the economy, I really think it's because models are much less data-efficient than humans. If you want it to learn a new task, or put it in a new vertical, it takes thousands of times more effort than it does to just tell a human what to do." "If you can make a model a million times more data-efficient, it's a million times easier to put into the economy. There's a ton of cool stuff that you can do in really data-constrained regimes. For example, whether it's robotics, or scientific discovery, or even something like trading, these problems have very limited data, and existing AI systems aren't quite as good at them as they are at other things. I think that learning to learn with less data is just tremendously valuable in all of these domains."
1:44

Flapping Airplanes co-founder Asher Spector explains why data efficiency is the greatest bottleneck to AI adoption: "To the extent that AI has been hard to integrate into the economy, I really think it's because models are much less data-efficient than humans. If you want it to learn a new task, or put it in a new vertical, it takes thousands of times more effort than it does to just tell a human what to do." "If you can make a model a million times more data-efficient, it's a million times easier to put into the economy. There's a ton of cool stuff that you can do in really data-constrained regimes. For example, whether it's robotics, or scientific discovery, or even something like trading, these problems have very limited data, and existing AI systems aren't quite as good at them as they are at other things. I think that learning to learn with less data is just tremendously valuable in all of these domains."

TBPN

90,711 görüntüleme • 5 ay önce

NVIDIA open models provide the foundation to innovate across agentic AI, robotics, autonomous vehicles, and research. 🤖 This release contributes 10 trillion language tokens, 500,000 robotics trajectories, and 100 terabytes of vehicle sensor data to the community. These frameworks are designed to significantly accelerate specialized AI development and discovery. Learn more:
1:39

NVIDIA open models provide the foundation to innovate across agentic AI, robotics, autonomous vehicles, and research. 🤖 This release contributes 10 trillion language tokens, 500,000 robotics trajectories, and 100 terabytes of vehicle sensor data to the community. These frameworks are designed to significantly accelerate specialized AI development and discovery. Learn more:

NVIDIA

36,399 görüntüleme • 3 ay önce

Google presents AudioPaLM: A Large Language Model That Can Speak and Listen paper page: introduce AudioPaLM, a large language model for speech understanding and generation. AudioPaLM fuses text-based and speech-based language models, PaLM-2 [Anil et al., 2023] and AudioLM [Borsos et al., 2022], into a unified multimodal architecture that can process and generate text and speech with applications including speech recognition and speech-to-speech translation. AudioPaLM inherits the capability to preserve paralinguistic information such as speaker identity and intonation from AudioLM and the linguistic knowledge present only in text large language models such as PaLM-2. We demonstrate that initializing AudioPaLM with the weights of a text-only large language model improves speech processing, successfully leveraging the larger quantity of text training data used in pretraining to assist with the speech tasks. The resulting model significantly outperforms existing systems for speech translation tasks and has the ability to perform zero-shot speech-to-text translation for many languages for which input/target language combinations were not seen in training. AudioPaLM also demonstrates features of audio language models, such as transferring a voice across languages based on a short spoken prompt.
4:10

Google presents AudioPaLM: A Large Language Model That Can Speak and Listen paper page: introduce AudioPaLM, a large language model for speech understanding and generation. AudioPaLM fuses text-based and speech-based language models, PaLM-2 [Anil et al., 2023] and AudioLM [Borsos et al., 2022], into a unified multimodal architecture that can process and generate text and speech with applications including speech recognition and speech-to-speech translation. AudioPaLM inherits the capability to preserve paralinguistic information such as speaker identity and intonation from AudioLM and the linguistic knowledge present only in text large language models such as PaLM-2. We demonstrate that initializing AudioPaLM with the weights of a text-only large language model improves speech processing, successfully leveraging the larger quantity of text training data used in pretraining to assist with the speech tasks. The resulting model significantly outperforms existing systems for speech translation tasks and has the ability to perform zero-shot speech-to-text translation for many languages for which input/target language combinations were not seen in training. AudioPaLM also demonstrates features of audio language models, such as transferring a voice across languages based on a short spoken prompt.

AK

290,517 görüntüleme • 3 yıl önce

Learn to optimize RAG for cost and performance in our new short course, Prompt Compression and Query Optimization, created with MongoDB and taught by Richmond Alake. This course teaches you to combine traditional database capabilities with vector search using MongoDB for RAG. You'll learn these techniques: - Vector search: For semantic matching of user queries - Filtering using metadata: Pre- and post-filtering to narrow search results - Projections: Selecting only necessary fields to minimize data returned - Boosting: Reranking results to improve relevance - Prompt compression: Using a small LLM to compress context, significantly reducing token count and processing costs These methods address scaling, performance, and security challenges in large-scale RAG applications. You can sign up here:
3:12

Learn to optimize RAG for cost and performance in our new short course, Prompt Compression and Query Optimization, created with MongoDB and taught by Richmond Alake. This course teaches you to combine traditional database capabilities with vector search using MongoDB for RAG. You'll learn these techniques: - Vector search: For semantic matching of user queries - Filtering using metadata: Pre- and post-filtering to narrow search results - Projections: Selecting only necessary fields to minimize data returned - Boosting: Reranking results to improve relevance - Prompt compression: Using a small LLM to compress context, significantly reducing token count and processing costs These methods address scaling, performance, and security challenges in large-scale RAG applications. You can sign up here:

Andrew Ng

71,672 görüntüleme • 2 yıl önce