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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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Emily

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14,648 次观看 • 5 个月前 •via X (Twitter)

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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.
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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

16,618 次观看 • 8 个月前

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.
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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.

LayerAI | AI2Earn

27,136 次观看 • 2 年前

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.
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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 次观看 • 5 个月前

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:
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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

108,801 次观看 • 3 个月前

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

23,901 次观看 • 8 个月前

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:
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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:

Vantor

17,050 次观看 • 6 个月前

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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:

NEARWEEK

23,509 次观看 • 1 年前

🌟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

18,919 次观看 • 1 年前

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."
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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

92,419 次观看 • 7 个月前

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 次观看 • 3 年前

Building AI infrastructure at yotta-scale requires more than raw performance; it demands an open, modular rack design that can evolve across product generations, combining leadership compute engines with high-speed networking to connect thousands of accelerators into a single, unified system. Take a look at the Helios. Our rack-scale AI platform powered by MI455X GPUs and EPYC “Venice” CPUs and designed for trillion-parameter training.
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Building AI infrastructure at yotta-scale requires more than raw performance; it demands an open, modular rack design that can evolve across product generations, combining leadership compute engines with high-speed networking to connect thousands of accelerators into a single, unified system. Take a look at the Helios. Our rack-scale AI platform powered by MI455X GPUs and EPYC “Venice” CPUs and designed for trillion-parameter training.

AMD

20,832 次观看 • 7 个月前

An exciting new course: Fine-tuning and Reinforcement Learning for LLMs: Intro to Post-training, taught by Sharon Zhou, VP of AI at AMD. Available now at Post-training is the key technique used by frontier labs to turn a base LLM--a model trained on massive unlabeled text to predict the next word/token--into a helpful, reliable assistant that can follow instructions. I've also seen many applications where post-training is what turns a demo application that works only 80% of the time into a reliable system that consistently performs. This course will teach you the most important post-training techniques! In this 5 module course, Sharon walks you through the complete post-training pipeline: supervised fine-tuning, reward modeling, RLHF, and techniques like PPO and GRPO. You'll also learn to use LoRA for efficient training, and to design evals that catch problems before and after deployment. Skills you'll gain: - Apply supervised fine-tuning and reinforcement learning (RLHF, PPO, GRPO) to align models to desired behaviors - Use LoRA for efficient fine-tuning without retraining entire models - Prepare datasets and generate synthetic data for post-training - Understand how to operate LLM production pipelines, with go/no-go decision points and feedback loops These advanced methods aren’t limited to frontier AI labs anymore, and you can now use them in your own applications. Learn here:
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An exciting new course: Fine-tuning and Reinforcement Learning for LLMs: Intro to Post-training, taught by Sharon Zhou, VP of AI at AMD. Available now at Post-training is the key technique used by frontier labs to turn a base LLM--a model trained on massive unlabeled text to predict the next word/token--into a helpful, reliable assistant that can follow instructions. I've also seen many applications where post-training is what turns a demo application that works only 80% of the time into a reliable system that consistently performs. This course will teach you the most important post-training techniques! In this 5 module course, Sharon walks you through the complete post-training pipeline: supervised fine-tuning, reward modeling, RLHF, and techniques like PPO and GRPO. You'll also learn to use LoRA for efficient training, and to design evals that catch problems before and after deployment. Skills you'll gain: - Apply supervised fine-tuning and reinforcement learning (RLHF, PPO, GRPO) to align models to desired behaviors - Use LoRA for efficient fine-tuning without retraining entire models - Prepare datasets and generate synthetic data for post-training - Understand how to operate LLM production pipelines, with go/no-go decision points and feedback loops These advanced methods aren’t limited to frontier AI labs anymore, and you can now use them in your own applications. Learn here:

Andrew Ng

132,304 次观看 • 10 个月前

From rewriting Google’s search stack in the early 2000s to reviving sparse trillion-parameter models and co-designing TPUs with frontier ML research, Jeff Dean has quietly shaped nearly every layer of the modern AI stack. As Chief AI Scientist at Google and a driving force behind Gemini, Jeff has lived through multiple scaling revolutions from CPUs and sharded indices to multimodal models that reason across text, video, and code. We sat down with Jeff to unpack what it really means to “own the Pareto frontier,” why distillation is the quiet force behind every generation of faster, cheaper models, how energy not FLOPs is becoming the true constraint on AI compute, what it takes to co-design hardware and models 2–6 years into the future, why unified multimodal systems will outperform specialized ones, what it was like leading the charge to unify all of Google’s AI teams, and his prediction that deeply personalized models with access to your full digital context will redefine what useful AI looks like. Jeff Dean Google DeepMind Google
0:44

From rewriting Google’s search stack in the early 2000s to reviving sparse trillion-parameter models and co-designing TPUs with frontier ML research, Jeff Dean has quietly shaped nearly every layer of the modern AI stack. As Chief AI Scientist at Google and a driving force behind Gemini, Jeff has lived through multiple scaling revolutions from CPUs and sharded indices to multimodal models that reason across text, video, and code. We sat down with Jeff to unpack what it really means to “own the Pareto frontier,” why distillation is the quiet force behind every generation of faster, cheaper models, how energy not FLOPs is becoming the true constraint on AI compute, what it takes to co-design hardware and models 2–6 years into the future, why unified multimodal systems will outperform specialized ones, what it was like leading the charge to unify all of Google’s AI teams, and his prediction that deeply personalized models with access to your full digital context will redefine what useful AI looks like. Jeff Dean Google DeepMind Google

Latent.Space

529,014 次观看 • 6 个月前

Spectre AI Soars and Secures Google Scale Tier Membership with $200,000 in Development Resources We're thrilled to announce a significant milestone for Spectre AI! After a lot of networking, and a rigorous selection process, we've been accepted into the prestigious Google Scale Tier program. We had Start Tier, now we have Scale Tier! This membership signifies Google's recognition of Spectre AI's potential to become a potential game-changer in the blockchain space, and it grants us access to a wealth of resources to fuel our growth – $200,000 in development funding from GoogleStartups to use their advanced tools. What is the Google Scale Tier? The Google Scale Tier is a highly selective program designed to nurture high-growth startups with exceptional potential. Going beyond simple funding, this program grants a comprehensive suite of benefits to empower us to scale our technology and achieve new heights. Unlocking Cutting-Edge Tech and Expertise Our Google Scale Tier membership unlocks a treasure trove of resources to accelerate our development journey: $200,000 in Google Development Resources: This crucial boost will allow us to leverage Google Cloud and cutting-edge tools, along with collaboration with top Google engineers. These experts will work closely with our team to integrate these powerful resources seamlessly into our entire suite of products, including AI Predictions, Sentiment Analysis, and Technical Analysis. Imagine the possibilities for enhanced accuracy, efficiency, and deeper market insights leveraged by Google's technology! Collaboration with Google Engineering Experts: As mentioned earlier, the $200,000 in development resources includes access to Google engineers – the masterminds behind cutting-edge technologies like Long Short-Term Memory (LSTM) models, Machine Learning (ML), and advanced graphing models. These experts will collaborate with our team to integrate these powerful tools into our products. Dedicated Google Representative: A dedicated Google representative from their Irish headquarters has become our go-to person, ensuring seamless collaboration and ongoing support throughout our journey. Thank you GoogleStartupUK The Future of Spectre AI: Enhanced All-in-One Products This partnership extends far beyond individual features. Here's what you can expect across our entire product suite: Next-Level Functionality: We'll leverage Google's advanced algorithms and massive datasets to refine all our tools, including AI Predictions, Sentiment Analysis, and Technical Analysis. This means more reliable and insightful information to guide your investment strategies. Expanded Capabilities: We're exploring groundbreaking new features for our entire product suite, like real-time analysis, multi-factor modeling, and even deeper market insights. Enhanced User Experience: Navigating through all our tools will be smoother than ever. We'll work with Google to refine the user interface across the board, making it easier to understand and leverage the power of AI in your crypto journey. The Data Visualization Revolution: Buckle up, X Bubblemaps users! Google's advanced graphing models are poised to transform how you visualize and explore data within Spectre AI. We can't wait to unveil a whole new level of visualization that will take your on-chain analysis to the next level. This is just the beginning! We're incredibly grateful for this opportunity to partner with Google and revolutionize the future of our all-in-one blockchain analysis suite. Stay tuned for exciting updates as we develop groundbreaking new features together. Thank you for being a part of the Spectre AI community! #google #googlecloud #spectre #ai #tech #innovation $spect
0:30

Spectre AI Soars and Secures Google Scale Tier Membership with $200,000 in Development Resources We're thrilled to announce a significant milestone for Spectre AI! After a lot of networking, and a rigorous selection process, we've been accepted into the prestigious Google Scale Tier program. We had Start Tier, now we have Scale Tier! This membership signifies Google's recognition of Spectre AI's potential to become a potential game-changer in the blockchain space, and it grants us access to a wealth of resources to fuel our growth – $200,000 in development funding from GoogleStartups to use their advanced tools. What is the Google Scale Tier? The Google Scale Tier is a highly selective program designed to nurture high-growth startups with exceptional potential. Going beyond simple funding, this program grants a comprehensive suite of benefits to empower us to scale our technology and achieve new heights. Unlocking Cutting-Edge Tech and Expertise Our Google Scale Tier membership unlocks a treasure trove of resources to accelerate our development journey: $200,000 in Google Development Resources: This crucial boost will allow us to leverage Google Cloud and cutting-edge tools, along with collaboration with top Google engineers. These experts will work closely with our team to integrate these powerful resources seamlessly into our entire suite of products, including AI Predictions, Sentiment Analysis, and Technical Analysis. Imagine the possibilities for enhanced accuracy, efficiency, and deeper market insights leveraged by Google's technology! Collaboration with Google Engineering Experts: As mentioned earlier, the $200,000 in development resources includes access to Google engineers – the masterminds behind cutting-edge technologies like Long Short-Term Memory (LSTM) models, Machine Learning (ML), and advanced graphing models. These experts will collaborate with our team to integrate these powerful tools into our products. Dedicated Google Representative: A dedicated Google representative from their Irish headquarters has become our go-to person, ensuring seamless collaboration and ongoing support throughout our journey. Thank you GoogleStartupUK The Future of Spectre AI: Enhanced All-in-One Products This partnership extends far beyond individual features. Here's what you can expect across our entire product suite: Next-Level Functionality: We'll leverage Google's advanced algorithms and massive datasets to refine all our tools, including AI Predictions, Sentiment Analysis, and Technical Analysis. This means more reliable and insightful information to guide your investment strategies. Expanded Capabilities: We're exploring groundbreaking new features for our entire product suite, like real-time analysis, multi-factor modeling, and even deeper market insights. Enhanced User Experience: Navigating through all our tools will be smoother than ever. We'll work with Google to refine the user interface across the board, making it easier to understand and leverage the power of AI in your crypto journey. The Data Visualization Revolution: Buckle up, X Bubblemaps users! Google's advanced graphing models are poised to transform how you visualize and explore data within Spectre AI. We can't wait to unveil a whole new level of visualization that will take your on-chain analysis to the next level. This is just the beginning! We're incredibly grateful for this opportunity to partner with Google and revolutionize the future of our all-in-one blockchain analysis suite. Stay tuned for exciting updates as we develop groundbreaking new features together. Thank you for being a part of the Spectre AI community! #google #googlecloud #spectre #ai #tech #innovation $spect

SPECTRE AI

46,699 次观看 • 2 年前

FRIEDBERG: VIDEO DATA WILL POWER THE NEXT GENERATION OF AI Friedberg broke down the scale shift coming to artificial intelligence, arguing that text based models like GPT are just the beginning, and that the real revolution will come from video-trained systems: “The internet and all these LLMs are language models trained on text from the internet, around 50 billion words total, maybe one to five terabytes of data in their training sets. But if you look at the video data out there, there are hundreds of billions of hours, much of it on YouTube. By some estimates, there’s a thousand exabytes of video data on the internet, about a billion times more than text data. I think we just saw that play out with the new video model that launched yesterday. Google has all this YouTube data, whether or not they’re using it to train, I don’t know. I’ve heard from insiders they’re not allowed to yet and would have to redo the terms of service.” Source: AIFinInsights david friedberg
2:12

FRIEDBERG: VIDEO DATA WILL POWER THE NEXT GENERATION OF AI Friedberg broke down the scale shift coming to artificial intelligence, arguing that text based models like GPT are just the beginning, and that the real revolution will come from video-trained systems: “The internet and all these LLMs are language models trained on text from the internet, around 50 billion words total, maybe one to five terabytes of data in their training sets. But if you look at the video data out there, there are hundreds of billions of hours, much of it on YouTube. By some estimates, there’s a thousand exabytes of video data on the internet, about a billion times more than text data. I think we just saw that play out with the new video model that launched yesterday. Google has all this YouTube data, whether or not they’re using it to train, I don’t know. I’ve heard from insiders they’re not allowed to yet and would have to redo the terms of service.” Source: AIFinInsights david friedberg

Mario Nawfal

15,855 次观看 • 9 个月前

NotebookLM is one of the most delightful, inspiring, and viral AI products out there right now, and I got a chance to chat with the PM behind the product, Raiza Martin (@raiza_abubakar). In our conversation, we cover: 🔸 The origin story of NotebookLM 🔸 The future road map for NotebookLM 🔸 How Google Labs operates differently from the rest of Google 🔸 The development of the “Audio Overviews” feature 🔸 Key metrics and growth of NotebookLM 🔸 Stories about collaborating with author Steven Johnson 🔸 Navigating potential misuse of AI technology 🔸 More Listen now 👇 - YouTube: - Spotify: - Apple: Raiza is a senior product manager for AI at Google Labs for AI at Google Labs, where she leads the team behind NotebookLM, an AI-powered research tool that includes a mind-blowing podcast-on-demand feature called “Audio Overviews.” NotebookLM started as a 20% project and has grown into a product that’s spreading across social media and has a Discord server with over 60,000 users. Raiza previously worked on AI Test Kitchen and has a background in startups, payments, and ads. Thank you to our wonderful sponsors for supporting the podcast: 🏆 Explo — Embed customer-facing analytics in your product: 🏆 Sprig — Build products for people, not data points: 🏆 Sidebar — Accelerate your career by surrounding yourself with extraordinary peers: Some key takeaways: 1. Embrace a startup mentality within large organizations: Google Labs operates with fewer processes and more agility than typical Google teams. This allows them to move faster and iterate quickly, much like a startup. 2. Often, powerful technology is already available; the magic lies in how you interact with it. For instance, by integrating powerful audio models with existing LLMs, NotebookLM created an innovative way for users to interact with content. Look for unique applications of the tools you already have. 3. Don’t wait for a perfect launch. Start with a working version of your product and use user feedback to iterate and improve. This approach can reveal unexpected insights and user preferences, helping you shape the final product.
1:07

NotebookLM is one of the most delightful, inspiring, and viral AI products out there right now, and I got a chance to chat with the PM behind the product, Raiza Martin (@raiza_abubakar). In our conversation, we cover: 🔸 The origin story of NotebookLM 🔸 The future road map for NotebookLM 🔸 How Google Labs operates differently from the rest of Google 🔸 The development of the “Audio Overviews” feature 🔸 Key metrics and growth of NotebookLM 🔸 Stories about collaborating with author Steven Johnson 🔸 Navigating potential misuse of AI technology 🔸 More Listen now 👇 - YouTube: - Spotify: - Apple: Raiza is a senior product manager for AI at Google Labs for AI at Google Labs, where she leads the team behind NotebookLM, an AI-powered research tool that includes a mind-blowing podcast-on-demand feature called “Audio Overviews.” NotebookLM started as a 20% project and has grown into a product that’s spreading across social media and has a Discord server with over 60,000 users. Raiza previously worked on AI Test Kitchen and has a background in startups, payments, and ads. Thank you to our wonderful sponsors for supporting the podcast: 🏆 Explo — Embed customer-facing analytics in your product: 🏆 Sprig — Build products for people, not data points: 🏆 Sidebar — Accelerate your career by surrounding yourself with extraordinary peers: Some key takeaways: 1. Embrace a startup mentality within large organizations: Google Labs operates with fewer processes and more agility than typical Google teams. This allows them to move faster and iterate quickly, much like a startup. 2. Often, powerful technology is already available; the magic lies in how you interact with it. For instance, by integrating powerful audio models with existing LLMs, NotebookLM created an innovative way for users to interact with content. Look for unique applications of the tools you already have. 3. Don’t wait for a perfect launch. Start with a working version of your product and use user feedback to iterate and improve. This approach can reveal unexpected insights and user preferences, helping you shape the final product.

Lenny Rachitsky

75,439 次观看 • 1 年前

99% of AI applications are cool-looking demos. Impressive, but don't get fooled by the hype. It takes a lot to build enterprise-grade products that deliver real value. I have at least three weekly conversations with companies that want to use a Large Language Model with their data. The demand is huge! Here is one idea about what you can do to help. The use cases that most of these companies want to solve are similar: They have an extensive knowledge base and want to build a simple application that uses that information to answer questions. In other words, they need help building Retrieval Augmented Generation (RAG) applications they can use in many different scenarios: 1. To train new employees 2. To help their support team 3. To search old meetings and documents 4. To help with their research However, building these systems is not straightforward. Yes, there's a lot of information online, but there aren't enough people who know how to create solutions that work. Here is the idea: Today, you can build an enterprise-grade RAG application without writing code. A couple of MIT PhDs with 10+ years of experience building AI applications created . It's a no-code platform for building applications using Large Language Models. They are partnering with me on this post. You can use Stack AI to create, test, and deploy an end-to-end production-ready AI system. It's SOC-2, HIPAA, and GDPR compliant and offers SSO, role management, access control, and on-premise deployments. Of course, you can use the platform with any LLM on the market now. It's the whole nine yards for building AI applications. Check them out here: 2023 was about models. 2024 is about the tools using these models to build production-ready applications. That's where I'd start.
0:31

99% of AI applications are cool-looking demos. Impressive, but don't get fooled by the hype. It takes a lot to build enterprise-grade products that deliver real value. I have at least three weekly conversations with companies that want to use a Large Language Model with their data. The demand is huge! Here is one idea about what you can do to help. The use cases that most of these companies want to solve are similar: They have an extensive knowledge base and want to build a simple application that uses that information to answer questions. In other words, they need help building Retrieval Augmented Generation (RAG) applications they can use in many different scenarios: 1. To train new employees 2. To help their support team 3. To search old meetings and documents 4. To help with their research However, building these systems is not straightforward. Yes, there's a lot of information online, but there aren't enough people who know how to create solutions that work. Here is the idea: Today, you can build an enterprise-grade RAG application without writing code. A couple of MIT PhDs with 10+ years of experience building AI applications created . It's a no-code platform for building applications using Large Language Models. They are partnering with me on this post. You can use Stack AI to create, test, and deploy an end-to-end production-ready AI system. It's SOC-2, HIPAA, and GDPR compliant and offers SSO, role management, access control, and on-premise deployments. Of course, you can use the platform with any LLM on the market now. It's the whole nine yards for building AI applications. Check them out here: 2023 was about models. 2024 is about the tools using these models to build production-ready applications. That's where I'd start.

Santiago

197,702 次观看 • 2 年前