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Ed Elson speaks with Nick Frosst, a co-founder of Cohere. They discuss why the company chose an enterprise-only strategy, how he sees the future of AI unfolding, and whether an IPO is on the horizon. Learn more about your ad choices. Visit podcastchoices.com/adchoices
This episode is sponsored by tastytrade. Trade stocks, options, futures, and crypto in one platform with low commissions and zero commission on stocks and crypto. Built for traders who think in probabilities, tastytrade offers advanced analytics, risk tools, and an AI-powered Search feature. Learn more at https://tastytrade.com/ In this episode of Eye on AI, Nick Frosst, Co-Founder of Cohere and former Google Brain researcher, explains why Cohere is betting on enterprise AI instead of chasing AGI. While much of the AI industry is focused on artificial general intelligence, Cohere is building practical, capital-efficient large language models designed for real-world enterprise deployment. Nick breaks down why scaling transformers does not equal AGI, why inference cost and ROI matter, and how enterprise AI differs from consumer AI hype. We discuss enterprise LLM deployment, private data, regulated industries like banking and healthcare, agentic systems, evaluation benchmarks, and why AI will likely become embedded infrastructure rather than a headline breakthrough. If you care about enterprise AI, AGI debates, large language models, and the future of AI in business, this conversation delivers a grounded perspective from inside one of the leading AI companies. Stay Updated: Craig Smith on X: https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI (00:00) From Google Brain to Cohere (03:54) Discovering Transformers (06:39) The Transformer Dominance (09:44) What AGI Actually Means (12:26) Planes vs Birds: The AI Analogy (14:08) Why Cohere Isn't Chasing AGI (18:38) Distillation & Model Efficiency (21:42) What Enterprise AI Really Does (25:20) Private Data & Secure Deployment (26:59) Enterprise Use Cases (RBC Example) (32:22) Why AI Benchmarks Mislead (34:55) Why Most AI Stays in Demo (38:23) What "Agents" Actually Are (43:32) The Problem With AGI Fear (49:15) Scaling Enterprise AI (53:24) Why AI Will Get "Boring"
Joelle Pineau is the chief AI officer at Cohere. Pineau joins Big Technology Podcast to discuss where the cutting edge of AI research is headed — and what it will take to move from impressive demos to reliable agents. Tune in to hear why memory, world models, and more efficient reasoning are emerging as the next big frontiers, plus what current approaches are missing. We also cover the “capability overhang” in enterprise AI, why consumer assistants still aren’t lighting the world on fire, what AI sovereignty actually means, and whether the major labs can ever pull away from each other. Hit play for a cool-headed, deeply practical look at what’s next for AI and how it gets deployed in the real world. --- Enjoying Big Technology Podcast? Please rate us five stars ⭐⭐⭐⭐⭐ in your podcast app of choice. Want a discount for Big Technology on Substack + Discord? Here’s 25% off for the first year: https://www.bigtechnology.com/subscribe?coupon=0843016b Learn more about your ad choices. Visit megaphone.fm/adchoices
In this recap episode, we highlight the best moments from our 2025 interviews and reflect on the ideas that defined the year. Featuring: David Rubenstein (co-founder of Carlyle) Yamini Rangan (CEO of HubSpot) Ben Chestnut (co-founder of Mailchimp) Winston Weinberg (co-founder and CEO of Harvey) Garrett Lord (co-founder of Handshake) Aidan Gomez (co-founder and CEO of Cohere) Michelle Zatlyn (co-founder of Cloudflare) Evan Spiegel (co-founder and CEO of Snap) Connect with Joubin X: https://x.com/Joubinmir LinkedIn: https://www.linkedin.com/in/joubin-mirzadegan-66186854/ Email: grit@kleinerperkins.com Follow on LinkedIn: https://www.linkedin.com/company/kpgrit Follow on X: https://x.com/KPGrit Learn more about Kleiner Perkins: https://www.kleinerperkins.com/
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How do companies like Salesforce and Dell scale intelligence across every cloud? Aidan Gomez, co-founder and CEO of Cohere, explains how they’re building AI that works across all enterprise systems and deploys anywhere, giving companies true flexibility and security. He joins Joubin Mirzadegan for a wide-ranging conversation on why synthetic data went from dismissed to indispensable, and how the race among AI labs is really unfolding. Guest: Aidan Gomez, co-founder and CEO of Cohere Connect with Aidan: X LinkedIn Connect with Joubin: X LinkedIn Email: grit@kleinerperkins.com Learn more about Kleiner Perkins
Joelle Pineau is the Chief AI Officer at Cohere, where she leads research on advancing large language models and practical AI systems. Before joining Cohere, she was VP of AI Research at Meta, where she founded and led Meta AI's Montreal lab. A professor at McGill University, Joelle is renowned for her pioneering work in reinforcement learning, robotics, and responsible AI development. AGENDA: 00:00 Introduction to AI Scaling Laws 03:00 How Meta Shaped How I Think About AI Research 04:36 Challenges in Reinforcement Learning 10:00 Is It Possible to be Capital Efficient in AI 15:52 AI in Enterprise: Efficiency and Adoption 22:15 Security Concerns with AI Agents 28:34 Can Zuck Win By Buying the Galacticos of AI 32:15 The Rising Cost of Data 35:28 Synthetic Data and Model Degradation 37:22 Why AI Coding is Akin to Image Generation in 2015 48:46 If Joelle Was a VC Where Would She Invest? 52:17 Quickfire: Lessons from Zuck, Biggest Mindset Shift
Nick Frosst is a Canadian AI researcher and entrepreneur, best known as co-founder of Cohere, the enterprise-focused LLM. Cohere has raised over $900 million, most recently a $500 million round, bringing its valuation to $6.8 billion. Under his leadership, Cohere hit $100M in ARR. Prior to founding Cohere, Nick was a researcher at Google Brain and a protégé of Geoffrey Hinton. AGENDA: 00:00 – Biggest lessons from Geoff Hinton at Google Brain? 02:10 – Did Google completely sleep at the wheel and miss ChatGPT? 05:45 – Is data or compute the real bottleneck in AI's future? 07:20 – Does GPT5 Prove That Scaling Laws are BS? 13:30 – Are AI benchmarks just total BS? 17:00 – Would Cohere spend $5M on a single AI researcher? 19:40 – What is nonsense in AI that everyone is talking about? 25:30 – What is no one talking about in AI that everyone should be talking about? 33:00 – How do Cohere compete with OpenAI and Anthropic's billions? 44:30 – Why does being American actually hurt tech companies today? 45:10 – Should countries fund their own models? Is model sovereignty the future? 52:00 – Why has Sam Altman actually done a disservice to AI?
Dr. Max Bartolo from Cohere discusses machine learning model development, evaluation, and robustness. Key topics include model reasoning, the DynaBench platform for dynamic benchmarking, data-centric AI development, model training challenges, and the limitations of human feedback mechanisms. The conversation also covers technical aspects like influence functions, model quantization, and the PRISM project. Max Bartolo (Cohere): https://www.maxbartolo.com/ https://cohere.com/command TRANSCRIPT: https://www.dropbox.com/scl/fi/vujxscaffw37pqgb6hpie/MAXB.pdf?rlkey=0oqjxs5u49eqa2m7uaol64lbw&dl=0 TOC: 1. Model Reasoning and Verification [00:00:00] 1.1 Model Consistency and Reasoning Verification [00:03:25] 1.2 Influence Functions and Distributed Knowledge Analysis [00:10:28] 1.3 AI Application Development and Model Deployment [00:14:24] 1.4 AI Alignment and Human Feedback Limitations 2. Evaluation and Bias Assessment [00:20:15] 2.1 Human Evaluation Challenges and Factuality Assessment [00:27:15] 2.2 Cultural and Demographic Influences on Model Behavior [00:32:43] 2.3 Adversarial Examples and Model Robustness 3. Benchmarking Systems and Methods [00:41:54] 3.1 DynaBench and Dynamic Benchmarking Approaches [00:50:02] 3.2 Benchmarking Challenges and Alternative Metrics [00:50:33] 3.3 Evolution of Model Benchmarking Methods [00:51:15] 3.4 Hierarchical Capability Testing Framework [00:52:35] 3.5 Benchmark Platforms and Tools 4. Model Architecture and Performance [00:55:15] 4.1 Cohere's Model Development Process [01:00:26] 4.2 Model Quantization and Performance Evaluation [01:05:18] 4.3 Reasoning Capabilities and Benchmark Standards [01:08:27] 4.4 Training Progression and Technical Challenges 5. Future Directions and Challenges [01:13:48] 5.1 Context Window Evolution and Trade-offs [01:22:47] 5.2 Enterprise Applications and Future Challenges REFS: [00:03:10] Research at Cohere with Laura Ruis et al., Max Bartolo, Laura Ruis et al. https://cohere.com/research/papers/procedural-knowledge-in-pretraining-drives-reasoning-in-large-language-models-2024-11-20 [00:04:15] Influence functions in machine learning, Koh & Liang https://arxiv.org/abs/1703.04730 [00:08:05] Studying Large Language Model Generalization with Influence Functions, Roger Grosse et al. https://storage.prod.researchhub.com/uploads/papers/2023/08/08/2308.03296.pdf [00:11:10] The LLM ARChitect: Solving ARC-AGI Is A Matter of Perspective, Daniel Franzen, Jan Disselhoff, and David Hartmann https://github.com/da-fr/arc-prize-2024/blob/main/the_architects.pdf [00:12:10] Hugging Face model repo for C4AI Command A, Cohere and Cohere For AI https://huggingface.co/CohereForAI/c4ai-command-a-03-2025 [00:13:30] OpenInterpreter https://github.com/KillianLucas/open-interpreter [00:16:15] Human Feedback is not Gold Standard, Tom Hosking, Max Bartolo, Phil Blunsom https://arxiv.org/abs/2309.16349 [00:27:15] The PRISM Alignment Dataset, Hannah Kirk et al. https://arxiv.org/abs/2404.16019 [00:32:50] How adversarial examples arise, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, Aleksander Madry https://arxiv.org/abs/1905.02175 [00:43:00] DynaBench platform paper, Douwe Kiela et al. https://aclanthology.org/2021.naacl-main.324.pdf [00:50:15] Sara Hooker's work on compute limitations, Sara Hooker https://arxiv.org/html/2407.05694v1 [00:53:25] DataPerf: Community-led benchmark suite, Mazumder et al. https://arxiv.org/abs/2207.10062 [01:04:35] DROP, Dheeru Dua et al. https://arxiv.org/abs/1903.00161 [01:07:05] GSM8k, Cobbe et al. https://paperswithcode.com/sota/arithmetic-reasoning-on-gsm8k [01:09:30] ARC, François Chollet https://github.com/fchollet/ARC-AGI [01:15:50] Command A, Cohere https://cohere.com/blog/command-a [01:22:55] Enterprise search using LLMs, Cohere https://cohere.com/blog/commonly-asked-questions-about-search-from-coheres-enterprise-customers
Laura Ruis, a PhD student at University College London and researcher at Cohere, explains her groundbreaking research into how large language models (LLMs) perform reasoning tasks, the fundamental mechanisms underlying LLM reasoning capabilities, and whether these models primarily rely on retrieval or develop procedural knowledge. SPONSOR MESSAGES: *** CentML offers competitive pricing for GenAI model deployment, with flexible options to suit a wide range of models, from small to large-scale deployments. https://centml.ai/pricing/ Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on o-series style reasoning and AGI. Are you interested in working on reasoning, or getting involved in their events? Goto https://tufalabs.ai/ *** TOC 1. LLM Foundations and Learning 1.1 Scale and Learning in Language Models [00:00:00] 1.2 Procedural Knowledge vs Fact Retrieval [00:03:40] 1.3 Influence Functions and Model Analysis [00:07:40] 1.4 Role of Code in LLM Reasoning [00:11:10] 1.5 Semantic Understanding and Physical Grounding [00:19:30] 2. Reasoning Architectures and Measurement 2.1 Measuring Understanding and Reasoning in Language Models [00:23:10] 2.2 Formal vs Approximate Reasoning and Model Creativity [00:26:40] 2.3 Symbolic vs Subsymbolic Computation Debate [00:34:10] 2.4 Neural Network Architectures and Tensor Product Representations [00:40:50] 3. AI Agency and Risk Assessment 3.1 Agency and Goal-Directed Behavior in Language Models [00:45:10] 3.2 Defining and Measuring Agency in AI Systems [00:49:50] 3.3 Core Knowledge Systems and Agency Detection [00:54:40] 3.4 Language Models as Agent Models and Simulator Theory [01:03:20] 3.5 AI Safety and Societal Control Mechanisms [01:07:10] 3.6 Evolution of AI Capabilities and Emergent Risks [01:14:20] REFS: [00:01:10] Procedural Knowledge in Pretraining & LLM Reasoning Ruis et al., 2024 https://arxiv.org/abs/2411.12580 [00:03:50] EK-FAC Influence Functions in Large LMs Grosse et al., 2023 https://arxiv.org/abs/2308.03296 [00:13:05] Surfaces and Essences: Analogy as the Core of Cognition Hofstadter & Sander https://www.amazon.com/Surfaces-Essences-Analogy-Fuel-Thinking/dp/0465018475 [00:13:45] Wittgenstein on Language Games https://plato.stanford.edu/entries/wittgenstein/ [00:14:30] Montague Semantics for Natural Language https://plato.stanford.edu/entries/montague-semantics/ [00:19:35] The Chinese Room Argument David Cole https://plato.stanford.edu/entries/chinese-room/ [00:19:55] ARC: Abstraction and Reasoning Corpus François Chollet https://arxiv.org/abs/1911.01547 [00:24:20] Systematic Generalization in Neural Nets Lake & Baroni, 2023 https://www.nature.com/articles/s41586-023-06668-3 [00:27:40] Open-Endedness & Creativity in AI Tim Rocktäschel https://arxiv.org/html/2406.04268v1 [00:30:50] Fodor & Pylyshyn on Connectionism https://www.sciencedirect.com/science/article/abs/pii/0010027788900315 [00:31:30] Tensor Product Representations Smolensky, 1990 https://www.sciencedirect.com/science/article/abs/pii/000437029090007M [00:35:50] DreamCoder: Wake-Sleep Program Synthesis Kevin Ellis et al. https://courses.cs.washington.edu/courses/cse599j1/22sp/papers/dreamcoder.pdf [00:36:30] Compositional Generalization Benchmarks Ruis, Lake et al., 2022 https://arxiv.org/pdf/2202.10745 [00:40:30] RNNs & Tensor Products McCoy et al., 2018 https://arxiv.org/abs/1812.08718 [00:46:10] Formal Causal Definition of Agency Kenton et al. https://arxiv.org/pdf/2208.08345v2 [00:48:40] Agency in Language Models Sumers et al. https://arxiv.org/abs/2309.02427 [00:55:20] Heider & Simmel’s Moving Shapes Experiment https://www.nature.com/articles/s41598-024-65532-0 [01:00:40] Language Models as Agent Models Jacob Andreas, 2022 https://arxiv.org/abs/2212.01681 [01:13:35] Pragmatic Understanding in LLMs Ruis et al. https://arxiv.org/abs/2210.14986
In this episode of No Priors, Sarah is joined by Aidan Gomez, cofounder and CEO of Cohere. Aidan reflects on his journey to co-authoring the groundbreaking 2017 paper, “Attention is All You Need,” during his internship, and shares his motivations for building Cohere, which delivers AI-powered language models and solutions for businesses. The discussion explores the current state of enterprise AI adoption and Aidan’s advice for companies navigating the build vs. buy decision for AI tools. They also examine the drivers behind the flattening of model improvements and discuss where large language models (LLMs) fall short for predictive tasks. The conversation explores what the market has yet to account for in the rapidly evolving AI ecosystem, as well as Aidan’s personal perspectives on AGI—what it might look like and when it could arrive. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @AidanGomez Show Notes: 0:00 Introduction 0:36 Co-authoring “Attention is all you need” 2:27 Leaving Google and founding Cohere 4:04 Cohere’s mission and models 6:15 Pitfalls of current AI 8:14 How enterprises are deploying AI today 10:58 Build vs. buy strategy for AI tools 14:37 Barriers to enterprise adoption 20:04 Which types of companies should pretrain models? 24:25 Addressing flaws in open-source models 25:12 Current and expected progress in scaling laws 29:54 Advances in multi-step problem solving and reasoning 32:29 Key drivers behind the flattening curve of model improvements 36:25 Exploring AGI 39:59 Limitations of LLMs 42:10 What the market has mispriced
Aidan Gomez is the co-author of the "Attention Is All You Need" paper that launched the AI revolution and CEO of Cohere, an enterprise AI company. Gomez joins Big Technology to discuss the myths, facts, and realities of today's AI landscape. Tune in to hear why the real value of AI isn't in flashy consumer apps but in automating crucial back-office processes that could save businesses billions. We also cover the truth about AI capabilities, the likelihood of AGI, synthetic data training, and whether an intelligence explosion is possible. Hit play for a refreshingly grounded discussion about where AI is actually making an impact, from one of the field's pioneering voices. --- Enjoying Big Technology Podcast? Please rate us five stars ⭐⭐⭐⭐⭐ in your podcast app of choice. For weekly updates on the show, sign up for the pod newsletter on LinkedIn: https://www.linkedin.com/newsletters/6901970121829801984/ Want a discount for Big Technology on Substack? Here’s 40% off for the first year: https://tinyurl.com/bigtechnology Questions? Feedback? Write to: bigtechnologypodcast@gmail.com Learn more about your ad choices. Visit megaphone.fm/adchoices
Ivan Zhang, co-founder of Cohere, discusses the company's enterprise-focused AI solutions. He explains Cohere's early emphasis on embedding technology and training models for secure environments. Zhang highlights their implementation of Retrieval-Augmented Generation in healthcare, significantly reducing doctor preparation time. He explores the shift from monolithic AI models to heterogeneous systems and the importance of improving various AI system components. Zhang shares insights on using synthetic data to teach models reasoning, the democratization of software development through AI, and how his gaming skills transfer to running an AI company. He advises young developers to fully embrace AI technologies and offers perspectives on AI reliability, potential risks, and future model architectures. https://cohere.com/ https://ivanzhang.ca/ https://x.com/1vnzh TOC: 00:00:00 Intro 00:03:20 AI & Language Model Evolution 00:06:09 Future AI Apps & Development 00:09:29 Impact on Software Dev Practices 00:13:03 Philosophical & Societal Implications 00:16:30 Compute Efficiency & RAG 00:20:39 Adoption Challenges & Solutions 00:22:30 GPU Optimization & Kubernetes Limits 00:24:16 Cohere's Implementation Approach 00:28:13 Gaming's Professional Influence 00:34:45 Transformer Optimizations 00:36:45 Future Models & System-Level Focus 00:39:20 Inference-Time Computation & Reasoning 00:42:05 Capturing Human Thought in AI 00:43:15 Research, Hiring & Developer Advice REFS: 00:02:31 Cohere, https://cohere.com/ 00:02:40 The Transformer architecture, https://arxiv.org/abs/1706.03762 00:03:22 The Innovator's Dilemma, https://www.amazon.com/Innovators-Dilemma-Technologies-Management-Innovation/dp/1633691780 00:09:15 The actor model, https://en.wikipedia.org/wiki/Actor_model 00:14:35 John Searle's Chinese Room Argument, https://plato.stanford.edu/entries/chinese-room/ 00:18:00 Retrieval-Augmented Generation, https://arxiv.org/abs/2005.11401 00:18:40 Retrieval-Augmented Generation, https://docs.cohere.com/v2/docs/retrieval-augmented-generation-rag 00:35:39 Let’s Verify Step by Step, https://arxiv.org/pdf/2305.20050 00:39:20 Adaptive Inference-Time Compute, https://arxiv.org/abs/2410.02725 00:43:20 Ryan Greenblatt ARC entry, https://redwoodresearch.substack.com/p/getting-50-sota-on-arc-agi-with-gpt Disclaimer: This show is part of our Cohere partnership series
Dr. Patrick Lewis, who coined the term RAG (Retrieval Augmented Generation) and now works at Cohere, discusses the evolution of language models, RAG systems, and challenges in AI evaluation. MLST is sponsored by Brave: The Brave Search API covers over 20 billion webpages, built from scratch without Big Tech biases or the recent extortionate price hikes on search API access. Perfect for AI model training and retrieval augmented generation. Try it now - get 2,000 free queries monthly at http://brave.com/api. Key topics covered: - Origins and evolution of Retrieval Augmented Generation (RAG) - Challenges in evaluating RAG systems and language models - Human-AI collaboration in research and knowledge work - Word embeddings and the progression to modern language models - Dense vs sparse retrieval methods in information retrieval The discussion also explored broader implications and applications: - Balancing faithfulness and fluency in RAG systems - User interface design for AI-augmented research tools - The journey from chemistry to AI research - Challenges in enterprise search compared to web search - The importance of data quality in training AI models Patrick Lewis: https://www.patricklewis.io/ Cohere Command Models, check them out - they are amazing for RAG! https://cohere.com/command TOC 00:00:00 1. Intro to RAG 00:05:30 2. RAG Evaluation: Poll framework & model performance 00:12:55 3. Data Quality: Cleanliness vs scale in AI training 00:15:13 4. Human-AI Collaboration: Research agents & UI design 00:22:57 5. RAG Origins: Open-domain QA to generative models 00:30:18 6. RAG Challenges: Info retrieval, tool use, faithfulness 00:42:01 7. Dense vs Sparse Retrieval: Techniques & trade-offs 00:47:02 8. RAG Applications: Grounding, attribution, hallucination prevention 00:54:04 9. UI for RAG: Human-computer interaction & model optimization 00:59:01 10. Word Embeddings: Word2Vec, GloVe, and semantic spaces 01:06:43 11. Language Model Evolution: BERT, GPT, and beyond 01:11:38 12. AI & Human Cognition: Sequential processing & chain-of-thought Refs: 1. Retrieval Augmented Generation (RAG) paper / Patrick Lewis et al. [00:27:45] https://arxiv.org/abs/2005.11401 2. LAMA (LAnguage Model Analysis) probe / Petroni et al. [00:26:35] https://arxiv.org/abs/1909.01066 3. KILT (Knowledge Intensive Language Tasks) benchmark / Petroni et al. [00:27:05] https://arxiv.org/abs/2009.02252 4. Word2Vec algorithm / Tomas Mikolov et al. [01:00:25] https://arxiv.org/abs/1301.3781 5. GloVe (Global Vectors for Word Representation) / Pennington et al. [01:04:35] https://nlp.stanford.edu/projects/glove/ 6. BERT (Bidirectional Encoder Representations from Transformers) / Devlin et al. [01:08:00] https://arxiv.org/abs/1810.04805 7. 'The Language Game' book / Nick Chater and Morten H. Christiansen [01:11:40] https://amzn.to/4grEUpG Disclaimer: This is the sixth video from our Cohere partnership. We were not told what to say in the interview. Filmed in Seattle in June 2024.
Saurabh Baji discusses Cohere's approach to developing and deploying large language models (LLMs) for enterprise use. * Cohere focuses on pragmatic, efficient models tailored for business applications rather than pursuing the largest possible models. * They offer flexible deployment options, from cloud services to on-premises installations, to meet diverse enterprise needs. * Retrieval-augmented generation (RAG) is highlighted as a critical capability, allowing models to leverage enterprise data securely. * Cohere emphasizes model customization, fine-tuning, and tools like reranking to optimize performance for specific use cases. * The company has seen significant growth, transitioning from developer-focused to enterprise-oriented services. * Major customers like Oracle, Fujitsu, and TD Bank are using Cohere's models across various applications, from HR to finance. * Baji predicts a surge in enterprise AI adoption over the next 12-18 months as more companies move from experimentation to production. * He emphasizes the importance of trust, security, and verifiability in enterprise AI applications. The interview provides insights into Cohere's strategy, technology, and vision for the future of enterprise AI adoption. https://www.linkedin.com/in/saurabhbaji/ https://x.com/sbaji https://cohere.com/ https://cohere.com/business MLST is sponsored by Brave: The Brave Search API covers over 20 billion webpages, built from scratch without Big Tech biases or the recent extortionate price hikes on search API access. Perfect for AI model training and retrieval augmentated generation. Try it now - get 2,000 free queries monthly at http://brave.com/api. TOC (*) are best bits 00:00:00 1. Introduction and Background 00:04:24 2. Cloud Infrastructure and LLM Optimization 00:06:43 2.1 Model deployment and fine-tuning strategies * 00:09:37 3. Enterprise AI Deployment Strategies 00:11:10 3.1 Retrieval-augmented generation in enterprise environments * 00:13:40 3.2 Standardization vs. customization in cloud services * 00:18:20 4. AI Model Evaluation and Deployment 00:18:20 4.1 Comprehensive evaluation frameworks * 00:21:20 4.2 Key components of AI model stacks * 00:25:50 5. Retrieval Augmented Generation (RAG) in Enterprise 00:32:10 5.1 Pragmatic approach to RAG implementation * 00:33:45 6. AI Agents and Tool Integration 00:33:45 6.1 Leveraging tools for AI insights * 00:35:30 6.2 Agent-based AI systems and diagnostics * 00:42:55 7. AI Transparency and Reasoning Capabilities 00:49:10 8. AI Model Training and Customization 00:57:10 9. Enterprise AI Model Management 01:02:10 9.1 Managing AI model versions for enterprise customers * 01:04:30 9.2 Future of language model programming * 01:06:10 10. AI-Driven Software Development 01:06:10 10.1 AI bridging human expression and task achievement * 01:08:00 10.2 AI-driven virtual app fabrics in enterprise * 01:13:33 11. Future of AI and Enterprise Applications 01:21:55 12. Cohere's Customers and Use Cases 01:21:55 12.1 Cohere's growth and enterprise partnerships * 01:27:14 12.2 Diverse customers using generative AI * 01:27:50 12.3 Industry adaptation to generative AI * 01:29:00 13. Technical Advantages of Cohere Models 01:29:00 13.1 Handling large context windows * 01:29:40 13.2 Low latency impact on developer productivity * Disclaimer: This is the fifth video from our Cohere partnership. We were not told what to say in the interview, and didn't edit anything out from the interview. Filmed in Seattle in Aug 2024.
Aidan Gomez is the Co-founder & CEO at Cohere, the leading AI platform for enterprise, having raised over $1BN from some of the best with their last round pricing the company at a whopping $5.5BN. Prior to Cohere, Aidan co-authored the paper "Attention is All You Need," which introduced the groundbreaking Transformer architecture. He also collaborated with a number of AI luminaries, including Geoffrey Hinton and Jeff Dean, during his time at Google Brain, where the team focused their efforts on large-scale machine learning. In Today's Episode with Aidan Gomez We Discuss: 1. Compute vs Data: What is the Bottleneck: Does Aidan believe that more compute will result in an equal increase in performance? How much longer do we have before it becomes a case of diminishing returns? What does Aidan mean when he says "he has changed his mind massively on the role of data"? What did he believe? How has it changed? 2. The Value of the Model: Given the demand for chips, the consumer need for applications, how does Aidan think about the inherent value of models today? Will any value accrue at the model layer? How does Aidan analyze the price dumping that OpenAI are doing? Is it a race to the bottom on price? Why does Aidan believe that "there is no value in last year's model"? Given all of this, is it possible to be an independent model provider without being owned by an incumbent who has a cloud business that acts as a cash cow for the model business? 3. Enterprise AI: It is Changing So Fast: What are the biggest concerns for the world's largest enterprises on adopting AI? Are we still in the experimental budget phase for enterprises? What is causing them to move from experimental budget to core budget today? Are we going to see a mass transition back from Cloud to On Prem with the largest enterprises not willing to let independent companies train with their data in the cloud? What does AI not do today that will be a gamechanger for the enterprise in 3-5 years? 4. The Wider World: Remote Work, Downfall of Europe and Relationships: Given humans spending more and more time talking to models, how does Aidan reflect on the idea of his children spending more time with models than people? Does he want that world? Why does Aidan believe that Europe is challenged immensely? How does the UK differ to Europe? Why does Aidan believe that remote work is just not nearly as productive as in person?
Enterprise AI is booming so it’s no wonder that, as companies figure out how to implement it, the industry of AI infrastructure is emerging. This week, Equity is bringing you an episode of our sister show, Found. Becca and Dom talk to Nick Frosst from Cohere, the AI company building natural language models for enterprise customers. They discuss why Frosst thinks the AI boom isn’t built on a bubble, whether or not AI companies are building toward a “digital god”, and how AI regulation could be a good thing. Equity is TechCrunch’s flagship podcast, produced by Theresa Loconsolo, and posts every Wednesday and Friday. Subscribe to us on Apple Podcasts, Overcast, Spotify and all the casts. You also can follow Equity on X and Threads, at @EquityPod. For the full episode transcript, for those who prefer reading over listening, check out our full archive of episodes over at Simplecast. Credits: Equity is produced by Theresa Loconsolo with editing by Kell. Bryce Durbin is our Illustrator. We'd also like to thank the audience development team and Henry Pickavet, who manages TechCrunch audio products. Learn more about your ad choices. Visit megaphone.fm/adchoices
Jay Alammar, renowned AI educator and researcher at Cohere, discusses the latest developments in large language models (LLMs) and their applications in industry. Jay shares his expertise on retrieval augmented generation (RAG), semantic search, and the future of AI architectures. MLST is sponsored by Brave: The Brave Search API covers over 20 billion webpages, built from scratch without Big Tech biases or the recent extortionate price hikes on search API access. Perfect for AI model training and retrieval augmentated generation. Try it now - get 2,000 free queries monthly at http://brave.com/api. Cohere Command R model series: https://cohere.com/command Jay Alamaar: https://x.com/jayalammar Buy Jay's new book here! Hands-On Large Language Models: Language Understanding and Generation https://amzn.to/4fzOUgh TOC: 00:00:00 Introduction to Jay Alammar and AI Education 00:01:47 Cohere's Approach to RAG and AI Re-ranking 00:07:15 Implementing AI in Enterprise: Challenges and Solutions 00:09:26 Jay's Role at Cohere and the Importance of Learning in Public 00:15:16 The Evolution of AI in Industry: From Deep Learning to LLMs 00:26:12 Expert Advice for Newcomers in Machine Learning 00:32:39 The Power of Semantic Search and Embeddings in AI Systems 00:37:59 Jay Alammar's Journey as an AI Educator and Visualizer 00:43:36 Visual Learning in AI: Making Complex Concepts Accessible 00:47:38 Strategies for Keeping Up with Rapid AI Advancements 00:49:12 The Future of Transformer Models and AI Architectures 00:51:40 Evolution of the Transformer: From 2017 to Present 00:54:19 Preview of Jay's Upcoming Book on Large Language Models Disclaimer: This is the fourth video from our Cohere partnership. We were not told what to say in the interview, and didn't edit anything out from the interview. Note also that this combines several previously unpublished interviews from Jay into one, the earlier one at Tim's house was shot in Aug 2023, and the more recent one in Toronto in May 2024. Refs: The Illustrated Transformer https://jalammar.github.io/illustrated-transformer/ Attention Is All You Need https://arxiv.org/abs/1706.03762 The Unreasonable Effectiveness of Recurrent Neural Networks http://karpathy.github.io/2015/05/21/rnn-effectiveness/ Neural Networks in 11 Lines of Code https://iamtrask.github.io/2015/07/12/basic-python-network/ Understanding LSTM Networks (Chris Olah's blog post) http://colah.github.io/posts/2015-08-Understanding-LSTMs/ Luis Serrano's YouTube Channel https://www.youtube.com/channel/UCgBncpylJ1kiVaPyP-PZauQ Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks https://arxiv.org/abs/1908.10084 GPT (Generative Pre-trained Transformer) models https://jalammar.github.io/illustrated-gpt2/ https://openai.com/research/gpt-4 BERT (Bidirectional Encoder Representations from Transformers) https://jalammar.github.io/illustrated-bert/ https://arxiv.org/abs/1810.04805 RoPE (Rotary Positional Encoding) https://arxiv.org/abs/2104.09864 (Linked paper discussing rotary embeddings) Grouped Query Attention https://arxiv.org/pdf/2305.13245 RLHF (Reinforcement Learning from Human Feedback) https://openai.com/research/learning-from-human-preferences https://arxiv.org/abs/1706.03741 DPO (Direct Preference Optimization) https://arxiv.org/abs/2305.18290
Sara Hooker is VP of Research at Cohere and leader of Cohere for AI. We discuss her recent paper critiquing the use of compute thresholds, measured in FLOPs (floating point operations), as an AI governance strategy. We explore why this approach, recently adopted in both US and EU AI policies, may be problematic and oversimplified. Sara explains the limitations of using raw computational power as a measure of AI capability or risk, and discusses the complex relationship between compute, data, and model architecture. Equally important, we go into Sara's work on "The AI Language Gap." This research highlights the challenges and inequalities in developing AI systems that work across multiple languages. Sara discusses how current AI models, predominantly trained on English and a handful of high-resource languages, fail to serve the linguistic diversity of our global population. We explore the technical, ethical, and societal implications of this gap, and discuss potential solutions for creating more inclusive and representative AI systems. We broadly discuss the relationship between language, culture, and AI capabilities, as well as the ethical considerations in AI development and deployment. YT Version: https://youtu.be/dBZp47999Ko TOC: [00:00:00] Intro [00:02:12] FLOPS paper [00:26:42] Hardware lottery [00:30:22] The Language gap [00:33:25] Safety [00:38:31] Emergent [00:41:23] Creativity [00:43:40] Long tail [00:44:26] LLMs and society [00:45:36] Model bias [00:48:51] Language and capabilities [00:52:27] Ethical frameworks and RLHF Sara Hooker https://www.sarahooker.me/ https://www.linkedin.com/in/sararosehooker/ https://scholar.google.com/citations?user=2xy6h3sAAAAJ&hl=en https://x.com/sarahookr Interviewer: Tim Scarfe Refs The AI Language gap https://cohere.com/research/papers/the-AI-language-gap.pdf On the Limitations of Compute Thresholds as a Governance Strategy. https://arxiv.org/pdf/2407.05694v1 The Multilingual Alignment Prism: Aligning Global and Local Preferences to Reduce Harm https://arxiv.org/pdf/2406.18682 Cohere Aya https://cohere.com/research/aya RLHF Can Speak Many Languages: Unlocking Multilingual Preference Optimization for LLMs https://arxiv.org/pdf/2407.02552 Back to Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMs https://arxiv.org/pdf/2402.14740 Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence https://www.whitehouse.gov/briefing-room/presidential-actions/2023/10/30/executive-order-on-the-safe-secure-and-trustworthy-development-and-use-of-artificial-intelligence/ EU AI Act https://www.europarl.europa.eu/doceo/document/TA-9-2024-0138_EN.pdf The bitter lesson http://www.incompleteideas.net/IncIdeas/BitterLesson.html Neel Nanda interview https://www.youtube.com/watch?v=_Ygf0GnlwmY Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet https://transformer-circuits.pub/2024/scaling-monosemanticity/ Chollet's ARC challenge https://github.com/fchollet/ARC-AGI Ryan Greenblatt on ARC https://www.youtube.com/watch?v=z9j3wB1RRGA Disclaimer: This is the third video from our Cohere partnership. We were not told what to say in the interview, and didn't edit anything out from the interview.
Aidan Gomez, CEO of Cohere, reveals how they're tackling AI hallucinations and improving reasoning abilities. He also explains why Cohere doesn't use any output from GPT-4 for training their models. Aidan shares his personal insights into the world of AI and LLMs and Cohere's unique approach to solving real-world business problems, and how their models are set apart from the competition. Aidan reveals how they are making major strides in AI technology, discussing everything from last mile customer engineering to the robustness of prompts and future architectures. He also touches on the broader implications of AI for society, including potential risks and the role of regulation. He discusses Cohere's guiding principles and the health the of startup scene. With a particular focus on enterprise applications. Aidan provides a rare look into the internal workings of Cohere and their vision for driving productivity and innovation. https://cohere.com/ https://x.com/aidangomez Check out Cohere's amazing new Command R* models here https://cohere.com/command Disclaimer: This is the second video from our Cohere partnership. We were not told what to say in the interview, and didn't edit anything out from the interview.
Joelle Pineau’s curiosity led her to pursue a doctorate in engineering with a focus on robotics, which she describes as her “gateway into AI.” As vice president of AI research at Meta, Joelle leads a team committed to openness in the service of high-quality research, responsible AI development, and community contribution. In this episode, Joelle, who is also a professor at McGill University, weighs the advantages industry and academia each have for conducting artificial intelligence research. She also describes specific AI research projects Meta is working on, including scientific discovery initiatives focused on addressing societal problems like carbon capture. Read the episode transcript here. Guest bio: Joelle Pineau is vice president of AI research at Meta and a professor at McGill University. Her research focuses primarily on developing new models and algorithms for planning and learning in complex, partially observable domains. She also applies these algorithms to robotics, health care, games, and conversational agents. Pineau serves on the board of the <cite>Journal of Artificial Intelligence Research</cite> and the <cite>Journal of Machine Learning Research</cite>. She has a bachelor’s degree in engineering from the University of Waterloo and master’s degree and doctorate in robotics from Carnegie Mellon University. Me, Myself, and AI is a collaborative podcast from MIT Sloan Management Review and Boston Consulting Group and is hosted by Sam Ransbotham and Shervin Khodabandeh. Our engineer is David Lishansky, and the coordinating producers are Allison Ryder and Andy Goffin. Stay in touch with us by joining our LinkedIn group, AI for Leaders at mitsmr.com/AIforLeaders or by following Me, Myself, and AI on LinkedIn. We encourage you to rate and review our show. Your comments may be used in Me, Myself, and AI materials.
Nick Frosst, co-founder of Cohere, on the future of LLMs, and AGI. Learn how Cohere is solving real problems for business with their new AI models. This is the first podcast from our new Cohere partnership! Nick talks about his journey at Google Brain, working with AI legends like Geoff Hinton, and the amazing things his company, Cohere, is doing. From creating the must useful language models for businesses to making tools for developers, Nick shares a lot of interesting insights. He even talks about his band, Good Kid! Nick said that RAG is one of the best features of Cohere's new Command R* models. We are about to release a deep-dive on RAG with Patrick Lewis from Cohere, keep an eye out for that - he explains why their models are specifically optimised for RAG use cases. Learn more about Cohere Command R* models here: https://cohere.com/commandhttps://github.com/cohere-ai/cohere-toolkit Nick's band Good Kid: https://goodkidofficial.com/ Nick on Twitter: https://x.com/nickfrosst Disclaimer: We are in a partnership with Cohere to release content for them. We were not told what to say in the interview, and didn't edit anything out from the interview. We are currently planning to release 2 shows per month under the partnership about their AI platform, research and strategy.
Cohere is one of the buzziest AI startups around right now. It's not making consumer products; it's focused on the enterprise market and making AI products for big companies. And there's a huge tension there: up until recently, computers have been deterministic. If you give computers a certain input, you usually know exactly what output you’re going to get. There’s a logic to it. But if we all start talking to computers with human language and getting human language back, well, human language is messy. And that makes the entire process of knowing what to put in and what exactly we’re going to get out of our computers different than it ever has been before. Links: Attention is all you need On the Dangers of Stochastic Parrots Introducing the AI Mirror Test, which very smart people keep failing | The Verge AI isn’t close to becoming sentient | The Conversation These are Microsoft’s Bing AI secret rules and why it says it’s named Sydney | The Verge ‘Godfather of AI’ quits Google with regrets and fears about his life’s work | The Verge Microsoft CTO Kevin Scott on Bing’s quest to beat Google | The Verge Top AI researchers and CEOs warn against ‘risk of extinction’ | The Verge Google Zero is here — now what? | The Verge Cara grew from 40k to 650k in a week because artists are fed up with Meta’s AI policies | TechCrunch How AI copyright lawsuits could make the whole industry go extinct | The Verge Transcript: https://www.theverge.com/e/23937899 Credits: Decoder is a production of The Verge and part of the Vox Media Podcast Network. Our producers are Kate Cox and Nick Statt. Our editor is Callie Wright. Our supervising producer is Liam James. The Decoder music is by Breakmaster Cylinder. Learn more about your ad choices. Visit podcastchoices.com/adchoices
Joelle Pineau is the head of Meta's AI Research division. She joins Big Technology Podcast to discuss the company's recent proclamation that it intends to build Artificial General Intelligence, digging into how we get there and why it feels it must. In a wide-ranging discussion, we cover the latest research trends, the company's open-source practices, what actual products developers have been built with AI, video generation, and the reasons why NVIDIA chips are so in demand. Tune in for a deep dive into the world's most crucial technology from someone directing one of its most important research labs. --- Enjoying Big Technology Podcast? Please rate us five stars ⭐⭐⭐⭐⭐ in your podcast app of choice. For weekly updates on the show, sign up for the pod newsletter on LinkedIn: https://www.linkedin.com/newsletters/6901970121829801984/ Questions? Feedback? Write to: bigtechnologypodcast@gmail.com Learn more about your ad choices. Visit megaphone.fm/adchoices
Today we’re joined by Sara Hooker, director at Cohere and head of Cohere For AI, Cohere’s research lab. In our conversation with Sara, we explore some of the challenges with multilingual models like poor data quality and tokenization, and how they rely on data augmentation and preference training to address these bottlenecks. We also discuss the disadvantages and the motivating factors behind the Mixture of Experts technique, and the importance of common language between ML researchers and hardware architects to address the pain points in frameworks and create a better cohesion between the distinct communities. Sara also highlights the impact and the emotional connection that language models have created in society, the benefits and the current safety concerns of universal models, and the significance of having grounded conversations to characterize and mitigate the risk and development of AI models. Along the way, we also dive deep into Cohere and Cohere for AI, along with their Aya project, an open science project that aims to build a state-of-the-art multilingual generative language model as well as some of their recent research papers. The complete show notes for this episode can be found at twimlai.com/go/651.
Welcome to Eye on AI, the podcast that keeps you informed about the latest trends, obstacles, and possibilities in the realm of artificial intelligence. In this episode, we have the privilege of engaging in a thought-provoking discussion with Aidan Gomez, an exceptional AI developer and co-founder of Cohere. Aidan's passion lies in enhancing the efficiency of massive neural networks and effectively deploying them in the real world. Drawing from his vast experience, which includes leading a team of researchers at For.ai and conducting groundbreaking research at Google Brain, Aidan provides us with unique insights and anecdotes that shed light on the AI landscape. During our conversation, Aidan explains his collaboration with the legendary Geoffrey Hinton and their remarkable project at Google Brain. We delve into the intricate architecture of AI systems, demystifying the construction of the transformative transformer algorithm. Aidan generously shares his knowledge on the creation of attention within these models and the complexities of scaling such systems. As we explore the fascinating domain of language models, Aidan discusses their learning process, bridging the gap between code and data. We uncover the immense potential of these models to suggest other large-scale counterparts. We gain invaluable insights into Aidan's journey as a co-founder of Cohere, an innovative platform revolutionizing the utilization of language technology. Tune in to Eye on AI now to immerse yourself in a captivating conversation that will expand your understanding of this ever-develop field. (00:00) Preview (00:33) Introduction & sponsorship (02:00) Aidan's background with machine learning & AI (05:10) Geoffrey Hinton & Aidan Gomez working together (07:55) Aidan Gomez & Google Brain's project (12:53) Aidan's role in building AI architecture (15:25) How the transformer algorithm is built (18:25) How do you create attention? (20:40) How do you scale the model? (25:10) How language models learn from code and data (29:55) Did you know the potential of the project? (34:15) Can LLMs suggest other large models? (36:45) How Aidan Gomez started Cohere (41:10) How do people use Cohere? (46:50) Examples of language technology models (48:40) How Cohere handles hallucinations (52:53) The dangers of AI Craig Smith Twitter: https://twitter.com/craigss Eye on A.I. Twitter: https://twitter.com/EyeOn_AI
On this episode, we’re joined by Aidan Gomez, Co-Founder and CEO at Cohere. Cohere develops and releases a range of innovative AI-powered tools and solutions for a variety of NLP use cases. We discuss: - What “attention” means in the context of ML. - Aidan’s role in the “Attention Is All You Need” paper. - What state-space models (SSMs) are, and how they could be an alternative to transformers. - What it means for an ML architecture to saturate compute. - Details around data constraints for when LLMs scale. - Challenges of measuring LLM performance. - How Cohere is positioned within the LLM development space. - Insights around scaling down an LLM into a more domain-specific one. - Concerns around synthetic content and AI changing public discourse. - The importance of raising money at healthy milestones for AI development. Aidan Gomez - https://www.linkedin.com/in/aidangomez/ Cohere - https://www.linkedin.com/company/cohere-ai/ Thanks for listening to the Gradient Dissent podcast, brought to you by Weights & Biases. If you enjoyed this episode, please leave a review to help get the word out about the show. And be sure to subscribe so you never miss another insightful conversation. Resources: - https://cohere.ai/ - “Attention Is All You Need” #OCR #DeepLearning #AI #Modeling #ML
Aidan Gomez, co-founder and CEO of Cohere joins Host Pieter Abbeel to discuss transformers, large language models, command and instruct models, prompt engineering, and LLMs competitive landscape. Subscribe to the Robot Brains Podcast today | Visit therobotbrains.ai and follow us on YouTube at TheRobotBrainsPodcast and Twitter @therobotbrains. Hosted on Acast. See acast.com/privacy for more information.
We’re super excited to welcome Jay Alammar to the show. Jay is a well-known AI educator, applied NLP practitioner at co:here, and author of the popular blog, “The Illustrated Transformer.” In this episode, he shares his ideas on creating applied NLP solutions, working with large language models, and creating educational resources for state-of-the-art AI. Sponsors: Fastly – Our bandwidth partner. Fastly powers fast, secure, and scalable digital experiences. Move beyond your content delivery network to their powerful edge cloud platform. Learn more at fastly.com Fly.io – The home of Changelog.com — Deploy your apps and databases close to your users. In minutes you can run your Ruby, Go, Node, Deno, Python, or Elixir app (and databases!) all over the world. No ops required. Learn more at fly.io/changelog and check out the speedrun in their docs. Featuring: Jay Alammar – Website, GitHub, X Chris Benson – Website, GitHub, LinkedIn, X Daniel Whitenack – Website, GitHub, X Show Notes: Jay’s popular blog (with posts including “The Illustrated Transformer”) co:here Topically sandbox - topic modeling co:here’s prompt engineering guide Upcoming Events: Register for upcoming webinars here!
Support us! https://www.patreon.com/mlst MLST Discord: https://discord.gg/aNPkGUQtc5 YT: https://youtu.be/i9VPPmQn9HQ Edward Grefenstette is a Franco-American computer scientist who currently serves as Head of Machine Learning at Cohere and Honorary Professor at UCL. He has previously been a research scientist at Facebook AI Research and staff research scientist at DeepMind, and was also the CTO of Dark Blue Labs. Prior to his move to industry, Edward was a Fulford Junior Research Fellow at Somerville College, University of Oxford, and was lecturing at Hertford College. He obtained his BSc in Physics and Philosophy from the University of Sheffield and did graduate work in the philosophy departments at the University of St Andrews. His research draws on topics and methods from Machine Learning, Computational Linguistics and Quantum Information Theory, and has done work implementing and evaluating compositional vector-based models of natural language semantics and empirical semantic knowledge discovery. https://www.egrefen.com/ https://cohere.ai/ TOC: [00:00:00] Introduction [00:02:52] Differential Semantics [00:06:56] Concepts [00:10:20] Ontology [00:14:02] Pragmatics [00:16:55] Code helps with language [00:19:02] Montague [00:22:13] RLHF [00:31:54] Swiss cheese problem / retrieval augmented [00:37:06] Intelligence / Agency [00:43:33] Creativity [00:46:41] Common sense [00:53:46] Thinking vs knowing References: Large language models are not zero-shot communicators (Laura Ruis) https://arxiv.org/abs/2210.14986 Some remarks on Large Language Models (Yoav Goldberg) https://gist.github.com/yoavg/59d174608e92e845c8994ac2e234c8a9 Quantum Natural Language Processing (Bob Coecke) https://www.cs.ox.ac.uk/people/bob.coecke/QNLP-ACT.pdf Constitutional AI: Harmlessness from AI Feedback https://www.anthropic.com/constitutional.pdf Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (Patrick Lewis) https://www.patricklewis.io/publication/rag/ Natural General Intelligence (Prof. Christopher Summerfield) https://global.oup.com/academic/product/natural-general-intelligence-9780192843883 ChatGPT with Rob Miles - Computerphile https://www.youtube.com/watch?v=viJt_DXTfwA
Dr. Patrick Lewis is a London-based AI and Natural Language Processing Research Scientist, working at co:here. Prior to this, Patrick worked as a research scientist at the Fundamental AI Research Lab (FAIR) at Meta AI. During his PhD, Patrick split his time between FAIR and University College London, working with Sebastian Riedel and Pontus Stenetorp. Patrick’s research focuses on the intersection of information retrieval techniques (IR) and large language models (LLMs). He has done extensive work on Retrieval-Augmented Language Models. His current focus is on building more powerful, efficient, robust, and update-able models that can perform well on a wide range of NLP tasks, but also excel on knowledge-intensive NLP tasks such as Question Answering and Fact Checking. YT version: https://youtu.be/Dm5sfALoL1Y MLST Discord: https://discord.gg/aNPkGUQtc5 Support us! https://www.patreon.com/mlst References: Patrick Lewis (Natural Language Processing Research Scientist @ co:here) https://www.patricklewis.io/ Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (Patrick Lewis et al) https://arxiv.org/abs/2005.11401 Atlas: Few-shot Learning with Retrieval Augmented Language Models (Gautier Izacard, Patrick Lewis, et al) https://arxiv.org/abs/2208.03299 Improving language models by retrieving from trillions of tokens (RETRO) (Sebastian Borgeaud et al) https://arxiv.org/abs/2112.04426
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