No Priors: AI, Machine Learning, Tech, & Startups
No Priors Ep. 6 | With Daphne Koller from Insitro
updated
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Show Notes:
0:00 Introduction
0:47 Google releases NotebookLM
5:20 Integrating AI into consumer apps and gaming
9:11 Future of AI companionship and procreation
14:45 OpenAI o1 model improves on iterative reasoning
18:06 Sarah and Elad reflect on Nobel Prizes going to AI researchers
21:23 Jobs and businesses at risk of disruption
27:18 AI-durable companies
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Show Notes:
0:00 Introduction
0:38 Ankur’s path to Braintrust
3:05 Braintrust’s solution
5:46 AI tooling trends
7:58 Instruction tuning vs. fine-tuning
8:57 Open-source AI adoption
10:42 Future of data infrastructure and synthetic data
14:45 Designing technical interviews
18:04 Rethinking agent-based approaches
19:34 Building out an AI team
23:35 Typescript as the language of AI
25:12 The shift away from using frameworks
26:02 Vendor consolidation among enterprises
27:16 Coding as a CEO
30:16 Collaborating with customers
33:00 Future of Braintrust and evals
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Show Notes:
0:00 Introduction
0:56 Lina Khan’s background and path to the FTC
2:35 Amazon’s Antitrust Paradox
4:20 Frameworks for regulating M&A in young markets
8:50 Khan’s perspective on AI acquisitions
12:18 What founders can expect from Khan’s M&A environment
14:55 Promoting competition at the large model layer
17:01 Creating fair AI regulation
18:40 FTC’s work to ban non-competes
20:31 Why so few young people hold power in government today
22:18 The realities of running a government agency
24:20 Measuring the impact of FTC
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Show Notes:
0:00 Introduction
0:32 Rippling’s mission and product offerings
2:13 Compound startups
3:53 Evaluating human performance with Talent Signal
13:19 Incorporating AI evaluations into decision-making at Rippling
14:56 Leveraging work outputs as inputs for models
18:23 How Rippling chose which AI product to build first
20:53 Building out bundled products
23:26 Merging and scaling diverse data sources
25:16 Early adopters and integrating AI into decision-making processes
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Show Notes:
0:00 Intro
0:42 Defining agentic systems and types of agents
3:55 Customer-facing company agents
5:43 Sierra AI
8:11 Transforming customer service and reducing costs
9:57 Challenges in implementing LLMs for company agents
14:45 Drawing parallels between AI and the cloud market’s evolution
17:50 Future of the AI landscape
19:15 Building durable AI products
24:39 Outcome-based business models and tangible ROI in AI solutions
29:22 Next wave of AI sectors for enterprise adoption
31:15 Customizing goals and guardrails with customers
35:55 Creating distinct personalities for Sierra's agents
41:05 Bret’s insights on upcoming technology and hardware shifts
46:50 How AI software could enhance human agency
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Show Notes:
0:00 Introduction
0:24 LLM market consolidation
2:18 Competition and decreasing API costs
3:58 Innovation in LLM productization
8:20 Comparing the LLM and social network market
11:40 Increasing competition in image generation
13:21 Trend in smaller models with higher performance
14:43 Areas of innovation
17:33 Legacy of AirBnB and Uber pushing boundaries
24:19 AMD Acquires ZT
25:49 Elad’s looking for a Robot
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Show Notes:
0:00 Introduction
0:33 Evolution of self-driving cars
2:23 The Tesla vs. Waymo approach to self-driving
6:32 Training Optimus with automotive models
10:26 Reasoning behind the humanoid form factor
13:22 Existing challenges in robotics
16:12 Bottlenecks of AI progress
20:27 Parallels between human cognition and AI models
22:12 Merging human cognition with AI capabilities
27:10 Building high performance small models
30:33 Andrej’s current work in AI-enabled education
36:17 How AI-driven education reshapes knowledge networks and status
41:26 Eureka Labs
42:25 What young people study to prepare for the future
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Show Notes:
0:00 Introduction
0:45 Eric’s journey to founding Magic.dev
4:01 Long context windows for more accurate outcomes
10:53 Building a path toward AGI
15:18 Defining what is enough compute for AGI
17:34 Achieving Magic’s final UX
20:03 What makes a good AI assistant
22:09 Hiring at Magic
27:10 Impact of AGI
32:44 Eric’s north star for Magic
36:09 How Magic will interact in other tools
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Show Notes:
00:00 Introduction
00:23 Matt’s early days at Amazon
02:53 Early conception of AWS
06:36 Understanding the full opportunity of cloud compute
12:21 Blockers to cloud migration
14:19 AWS reaction to Gen AI
18:04 First-party models at hyperscalers
20:18 AWS point of view on open source
22:46 Grounding and knowledge bases
26:07 Semiconductors and data center capacity for AI workloads
31:15 Infrastructure investment for AI startups
33:18 Value creation in the AI ecosystem
36:22 Enterprise adoption
38:48 Near-future predictions for AWS usage
41:25 AWS’s role for startups
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Show Notes:
00:00 Introduction
02:42 Foundry background
03:57 GPU utilization for large models
07:29 Systems to run a large model
09:54 Historical value proposition of the cloud
14:45 Sharing cloud compute to increase efficiency
19:17 Foundry’s new releases
23:54 The current state of GPU capacity
29:50 GPU market dynamics
36:28 Compound systems design
40:27 Improving open-ended tasks
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Show Notes:
0:00 Introduction to Ramp
3:17 Working with startups
8:13 Ramp’s implementation of AI
14:10 Resourcing and staffing
17:20 Deciding when to build vs buy
21:20 Selling productivity
25:01 Risk mitigation when using AI
28:48 What the AI stack is missing
30:50 Marketing with AI
37:26 Designing a modern marketing team
40:00 Giving creative freedom to marketing teams
42:12 Augmenting bookkeeping
47:00 AI-generated podcasts
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Show Notes:
0:00 Introduction
0:32 Brex’s business and transitioning to solo CEO
3:04 Building AI into Brex
7:09 Solving for risk and reliability in AI-enabled financial products
11:41 Allocating resources toward AI investment
14:00 Innovating data use in marketing
20:00 Building durable businesses in the face of AI
25:36 AI’s impact on finance
29:15 Brex’s decision to focus on startups and enterprises
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Show Notes:
00:00 Introduction to Oriol Vinyals
00:55 The Gemini Project and Its Impact
02:04 AI in Google Search and Chat Models
08:29 Infinite Context Length and Its Applications
14:42 Scaling AI and Reward Functions
31:55 The Future of General Models and Specialization
38:14 Reflections on AGI and Personal Insights
43:09 Will the Next Generation Study Computer Science?
45:37 Closing thoughts
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Show Notes:
(00:00) Introduction
(00:29) The Origin and Evolution of Airtable
(02:31) Challenges and Successes in Building Airtable
(06:09) Airtable's Transition to Enterprise Solutions
(09:44) Insights on Product Management
(16:23) Integrating AI into Airtable
(21:55) The Future of No Code and AI
(30:30) Workshops and Training for AI Adoption
(36:28) The Role of Code Generation in No Code Platforms
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Show Notes:
(0:00) Introduction
(0:16) Old school operators AI misunderstandings
(5:10) Tech history is repeating itself with slow AI adoption
(6:09) New AI Markets
(8:48) AI-backed buyouts
(13:03) AI incubation
(17:18) Exciting incubating applications
(18:26) AI and the public markets
(22:20) Staffing AI companies
(25:14) Competition and shrinking head count
Watch or listen to the full episodes here:
Emily Glassberg Sands from Stripe: youtu.be/wiD1BfNEi-U
Dylan Field from Figma: youtu.be/k7F0yRs1IWY
Brett Adcock from Figure: youtu.be/O3fp1Xf7Ztw
Aditya Ramesh, Tim Brooks and Bill Peebles from OpenAI’s Sora team: youtu.be/reMnn6bV_fI
Cognition’s Scott Wu: youtu.be/OvBiqmcnjHY
Alexandr Wang from Scale: youtu.be/2SWRU7YOd6c
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Show Notes:
0:00 Introduction
0:46 Emily Glassberg Sands on the Future of AI and Fintech
4:23 Dylan Field on AI and Human Creative Potential
9:03 Brett Adcock on Running Figure AI’s Hardware and Software Processes
12:43 OpenAI’s Sora Team on Artists’ Creative Experiences with their Model
17:43 Scott Wu Gives Advice for Human Engineers Co-Working with AI
21:06 Alexandr Wang on How Quality Data Builds Confidence in AI Systems
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Show Notes:
0:00 Introduction
0:28 Use Cases for Cartesia and Sonic
1:32 Karan Goel & Albert Gu’s professional backgrounds
5:06 State Space Models (SSMs) versus Transformer Based Architectures
11:51 Domain Applications for Hybrid Approaches
13:10 Text to Speech and Voice
17:29 Data, Size of Models and Efficiency
20:34 Recent Launch of Text to Speech Product
25:01 Multi-modality & Building Blocks
25:54 What’s Next at Cartesia?
28:28 Latency in Text to Speech
29:30 Choosing Research Problems Based on Aesthetic
31:23 Product Demo
32:48 Cartesia Team & Hiring
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Show Notes:
0:00 Introduction
3:08 Applications of AI content creation
5:49 Best use cases for Hey Gen
7:34 Building for quality in AI video generation
11:17 The models powering HeyGen
14:49 Research approach
16:39 Safeguarding against deep fakes
18:31 How AI video generation will change video creation
24:02 Challenges in building the model
26:29 HeyGen team and company
In this episode, they also get into why Mike thinks LLMs will not get us to AGI, how Zapier is incorporating AI into their products and the power of agents, and why it’s dangerous to regulate AGI before discovering its full potential.
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Show Notes:
0:00 Introduction
1:10 Redefining AGI
2:16 Introducing ARC Prize
3:08 Definition of AGI
5:14 LLMs and AGI
8:20 Promising techniques to developing AGI
11:0 Sentience and intelligence
13:51 Prize model vs investing
16:28 Zapier AI innovations
19:08 Economic value of agents
21:48 Open source to achieve AGI
24:20 Regulating AI and AGI
They also discuss methods for growing context windows and managing latency budgets, how Tengyu’s research has informed his work at Voyage, and the role academia should play as AI grows as an industry.
Show Notes:
0:00 Introduction
1:59 Key points of Tengyu’s research
4:28 Academia compared to industry
6:46 Voyage AI overview
9:44 Enterprise RAG use cases
15:23 LLM long-term memory and token limitations
18:03 Agent chaining and data management
22:01 Improving enterprise RAG
25:44 Latency budgets
27:48 Advice for building RAG systems
31:06 Learnings as an AI founder
32:55 The role of academia in AI
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Show Notes:
0:00 Introduction
0:53 Transitioning from founder to investing
5:10 Early social media startups
7:50 Trend predicting at YC
10:03 Selecting YC founders
12:06 AI trends emerging in YC batch
18:34 Motivating culture at YC
20:39 Choosing the startups with longevity
24:01 Shifting YC found demographics
29:24 Building in San Francisco
31:01 Making YC a beacon for creators
33:17 Garry Tan is bringing San Francisco back
In this episode, they get into the importance of data quality in building trust in AI systems and a possible future where we can build better self-improvement loops, AI in the enterprise, and where human and AI intelligence will work together to produce better outcomes.
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0:00 Introduction
3:01 Data infrastructure for autonomous vehicles
5:51 Data abundance and organization
12:06 Data quality and collection
15:34 The role of human expertise
20:18 Building trust in AI systems
23:28 Evaluating AI models
29:59 AI and government contracts
32:21 Multi-modality and scaling challenges
In this episode, Elad, Sarah, And Mikey talk about how the Suno team took their experience making at transcription tool and applied it to music generation, how the Suno team evaluates aesthetics and taste because there is no standardized test you can give an AI model for music, and why Mikey doesn’t think AI-generated music will affect people’s consumption of human made music.
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Show Notes:
0:00 Mikey’s background
3:48 Bark and music generation
5:33 Architecture for music generation AI
6:57 Assessing music quality
8:20 Mikey’s music background as an asset
10:02 Challenges in generative music AI
11:30 Business model
14:38 Surprising use cases of Suno
18:43 Creating a song on Suno live
21:44 Ratio of creators to consumers
25:00 The digitization of music
27:20 Mikey’s favorite song on Suno
29:35 Suno is hiring
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Show Notes:
0:00 Intro
1:25 Music AI generation
4:02 Apple’s LLM
11:39 The role of AI-specific hardware
15:25 AI platform updates
18:01 Forward thinking in investing in AI
20:33 Unlimited context
23:03 Energy constraints
In this episode, they talk about why the team built Devin with a UI that mimics looking over another engineer’s shoulder as they work and how this transparency makes for a better result. Scott discusses why he thinks Devin will make it possible for there to be more human engineers in the world and what will be important for software engineers to focus on as these roles evolve. They also get into how Scott thinks about building the Cognition team and that they’re just getting started.
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Show Notes:
0:00 Introduction
1:12 IOI training and community
6:39 Cognition’s founding team
8:20 Meet Devin
9:17 The discourse around Devin
12:14 Building Devin’s UI
14:28 Devin’s strengths and weakness
18:44 The evolution of coding agents
22:43 Tips for human engineers
26:48 Hiring at Cognition
Sora team leads, Aditya Ramesh, Tim Brooks, and Bill Peebles join Elad and Sarah to talk about developing Sora. The generative video model isn’t yet available for public use but the examples of its work are very impressive. However, they believe we’re still in the GPT-1 era of AI video models and are focused on a slow rollout to ensure the model is in the best place possible to offer value to the user and more importantly they’ve applied all the safety measures possible to avoid deep fakes and misinformation. They also discuss what they’re learning from implementing diffusion transformers, why they believe video generation is taking us one step closer to AGI, and why entertainment may not be the main use case for this tool in the future.
Show Notes:
0:00 Sora team Introduction
1:05 Simulating the world with Sora
2:25 Building the most valuable consumer product
5:50 Alternative use cases and simulation capabilities
8:41 Diffusion transformers explanation
10:15 Scaling laws for video
13:08 Applying end-to-end deep learning to video
15:30 Tuning the visual aesthetic of Sora
17:08 The road to “desktop Pixar” for everyone
20:12 Safety for visual models
22:34 Limitations of Sora
25:04 Learning from how Sora is learning
29:32 The biggest misconceptions about video models
In this episode, Suhail talks with Sarah and Elad about how the integration of language and vision models enhances the multimodal capabilities, how the Playground team thought about creating a user-friendly interface to make AI-generated content more accessible, and the future of AI-powered image generation and editing.
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Show Notes:
0:00 Introduction
0:52 Focusing on image generation
3:01 Differentiating from other AI creative tools
5:58 Training a Stable Diffusion model
8:31 Long term vision for Playground AI
15:00 Evolution of AI architecture
17:21 Capabilities of multimodal models
22:30 Parallels between audio AI tools and image-generation
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Show Notes:
0:00 Intro
0:32 How to think about scaling in 2024
3:21 Microsoft/Inflection deal
5:28 Voice cloning
7:02 Investing climate
12:50 Whitespace in AI
16:36 AI video landscape
19:54 Agentic user experiences
22:21 Prosumer as the first wave of application AI
In this episode, Brett and Sarah discuss why right now is the correct time to build a fleet of AI robots and how implementation in industrial settings will be a stepping stone into AI robots coming into the home. They also get into how Brett built a team of world class engineers, commercial partnerships with BMW and OpenAI that are accelerating their growth, and the plan to achieve social acceptance for AI robots.
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Show Notes:
(0:00) Brett’s background
(3:09) Figure AI Thesis
(5:51) The argument for humanoid robots
(7:36) Figure AI public demos
(12:38) Mitigating risk factors
(15:20) Designing the org chart and finding the team
(16:38) Deployment timeline
(20:41) Build vs buy and vertical integration
(23:04) Product management at Figure
(28:37) Corporate partnerships
(31:58) Humans at home
(33:38) Social acceptance
(35:41) AGI vs the robots
Show Notes:
0:00 Introduction to LangChain
1:45 Managing an open source environment
4:30 Developing useful AI agents
10:03 Sophistication and limitations of AI app development
14:17 Switching between model APIs
17:10 Context windows, fine tuning and functionality
21:37 Evolution of AI open source environment
23:53 The next big breakthroughs
Show Notes:
(0:00) Introduction
(1:19) Capabilities of efficient code enabled development
(4:11) Difference in training inference workloads
(6:12) AI product acceleration
(8:48) Leading on inference benchmarks at BaseTen
(12:08) Optimizations for different types of models
(16:11) Internal vs open source models
(19:01) timeline for enterprise scale
(21:53) Rethinking investment in compute spend
(27:50) Defensibility in AI industries
(31:30) Hardware and the chip shortage
(35:47) Speed is the way to win in this industry
(38:26) Wrap
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Show Notes:
0:00 Introduction
2:01 No more Adobe acquisition
4:20 What’s next for Figma
7:16 FigJam, digital collaboration, and expanding beyond design
10:50 Figma DevMode
13:06 Incorporating AI at Figma
15:03 How AI will change design
19:19 Creativity augmentation and the iterative loop
22:44 Automating repetitive design tasks
25:35 The future of AI UI
29:44 Investing philosophy
31:28 Leadership evolution
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Show Notes:
0:00 Introduction
0:27 Model news and product launches
5:01 Google enters the competitive space with Gemini 1.5
8:23 Biology and robotics using LLMs
10:22 Agent-centric companies
14:22 NVIDIA earnings
17:29 ROI in AI
20:43 Impact from AI
25:45 Building effective AI tools in house
29:09 What would it take to compete with NVIDIA
33:23 The architectural approach to compute
35:42 the roadblocks to chip production in the US
38:30 The virtuous tech cycles in AI
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Show Notes:
0:00 Introduction and Mark’s background
2:35 AMD background and current markets
4:40 AMD shifting to AI space
8:54 AI applications coming out of AMD
10:57 Software investment
15:15 The benefits of open-source stacks
16:58 Evolving GPU market
20:21 Constraints on GPU production
24:11 Innovations in chip technology
27:57 Chip supply chain
30:18 Future of innovative hardware products
35:42 What’s next for AMD
In this episode, they talk about how Pinecone’s Canopy product is making search more accurate by using larger data sets in a way that is more efficient and cost effective—which was almost impossible before there were serverless options. They also get into how RAG architecture uniformly increases accuracy across the board, how these models can increase “operational sanity” in the dataset for their customers, and hybrid search models that are using keywords and embeds.
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Show Notes:
0:00 Introduction to Edo and Pinecone
2:01 Use cases for Pinecone and RAG models
6:02 Corporate internal uses for syntax search
10:13 Removing the limits of RAG with Canopy
14:02 Hybrid search
16:51 Why keep Pinecone closed source
22:29 Infinite context
23:11 Embeddings and data leakage
25:35 Fine tuning the data set
27:33 What’s next for Pinecone
28:58 Separating reasoning and knowledge in AI
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Show Notes:
0:00 Introduction
2:09 AI and Computing literacy
5:39 Building the Notion AI team
8:43 Notion as an application company
12:09 Prioritizing AI investment
14:53 The rapid evolution cycle of AI development
17:46 Notion Q&A
20:00 Workflow and AI for calendars
22:43 Moving past the need for organization
24:36 History of SaaS doesn’t repeat, it rhymes
30:14 Design at Notion
34:26 Notion office design
36:52 How RAG will change the future
38:30 Building our the software in the Notionscape
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Show Notes:
0:00 Background
0:38 Emily’s role at Stripe
2:31 Adopting early gen AI models
4:44 Promoting internal usage of AI
8:17 Applied ML accelerator teams
10:36 Radar fraud assistant
13:30 Sigma assistant
14:32 How will AI affect Stripe in 3 years
17:00 Knowing when it’s time to invest more fully in AI
18:28 Deciding how to proliferate models
22:04 Whitespace for fintechs employing AI
25:41 Leveraging payments data for customers
27:51 Labor economics and data
30:10 Macro economic trends for strategic decisions
32:54 How will AI impact education
35:36 Unique needs of AI startups
Today on No Priors, Glen Coates, the VP of core product at Shopify (and former founder of b2b wholesale platform Handshake), joins Sarah and Elad. They talk about the releases from Shopify Editions, why they are deploying “copilot” rather than “autopilot,” AI innovation-at-scale, how to change the basement of a house while people are living in it, and building a leadership team of entrepreneurs.
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0:00 Background
2:22 Calling a “Code Red” at Shopify
4:04 Integrating acquisitions, entrepreneurial leaders
12:15 AI adoption
15:51 Deciding when to ship AI products, evaluations
17:33 Shopify’s risk orientation
18:50 Changing the core Shopify data model, enabling AI features
26:05 What’s missing from LLMs for merchants
28:47 Most interesting AI developments in the industry
33:22 What users want from LLMs and search
38:20 No Priors social
Today on No Priors, Peter joins Sarah to talk about how the Covariant team is developing multimodal models that have precise grounding and understanding so they can adapt to solve problems in the physical world. They also discuss how they plan their roadmap at Covariant, what could be next for the company, and what use case will bring us to the Chat-GPT moment for AI robots.
00:00 Peter Chen Background
00:58 How robotics AI will drive AI forward
03:00 Moving from research to a commercial company
05:46 The argument for building incrementally
08:13 Manufacturing robotics today
12:21 Put wall use case
15:45 What’s next for Covariant Brain
18:42 Covariant’s customers
19:50 Grounding concepts in Ai
25:47 How scaling laws apply to Covariant
29:21 Covariant’s driving thesis
32:54 the Chat-GPT moment for robotics
35:12 Manufacturing center of the future
37:02 Safety in AI robotics
Sarah and Beyang talk about how Sourcegraph is thinking about augmenting the coding process in a way that ensures accuracy and efficiency starting with robust and high-quality context. They also think about what the future of software development could look like in a world where AI can generate high-quality code on its own and where that leaves humans in the coding process.
0:00 Beyang Liu’s experience
0:52 Sourcegraph premise
2:20 AI and finding flow
4:18 Developing LLMs in code
6:46 Cody explanation
7:56 Unlocking AI code generation
11:00 search architecture in LLMs
16:02 Quality-assurance in data set
18:03 Future of Cody
22:48 Constraints in AI code generation
30:28 Lessons from Beyang’s research days
33:17 Benefits of small models
35:49 Future of software development
42:14 What skills will be valued down the line
What is Digital Life? with OpenAI Co-Founder & Chief Scientist Ilya Sutskever
youtu.be/Ft0gTO2K85A
How AI can help small businesses with Former Square CEO Alyssa Henry
youtu.be/llMFYc4_vik
Will Everyone Have a Personal AI? With Mustafa Suleyman, Founder of DeepMind and Inflection
youtu.be/g4VszCFonPk
How will AI bring us the future of medicine? With Daphne Koller from Insitro
youtu.be/k5FvyrJdEcI
The case for AI optimism with Reid Hoffman from Inflection AI
youtu.be/_Hprred2E7M
Your AI Friends Have Awoken, With Noam Shazeer
youtu.be/emCoG-hA7AE
Mistral 7B and the Open Source Revolution With Arthur Mensch, CEO Mistral AI
youtu.be/EMOFRDOMIiU
0:00 Introduction
0:27 Ilya Sutskever on the cap profit model
3:11 Alyssa Henry on how AI can small business owners
5:25 Mustafa Suleyman on defining intelligence
8:53 Reid Hoffman’s advice for co-working with AI
11:47 Daphne Koller on probabilistic graphical models
13:15 Noam Shazeer on the possibilities of LLMs
14:27 Arthur Mensch on keeping AI open
17:19 Jensen Huang on how Nvidia decides what to work on
Aside from his storied experience in technology, Reid is an author, podcaster, and political activist. Most recently, he co-authors a book with GPT 4 called Impromptu: Amplifying Our Humanity Through AI.
00:00 Reid Hoffman’s birdseye view on the state of AI
03:37 AI and human collaboration in workflows
5:23 What’s causing AI doomerism
12:28 Advice for whitecollar workers
16:45 Why Reid isn’t retiring
18:25 How Inflection started
22:06 Surprising ways people are using Inflection
25:34 Western bias and AI ethics
30:58 Structural challenges in governing AI
33:15 Most exciting whitespace in AI
35:00 GPT 5 and Innovations coming in the next two years
44:00 What future should we be building?
Alyssa recently retired from being longtime CEO of Square, within Block. Before that she was a vice president of AWS running, amongst other things, the storage products, or the digital storage bucket for the world. And before AWS, she ran order management software at Amazon Retail and started her tech career at Microsoft. She remains on the boards of Intel, Confluent and was previously on the board of Unity.
0:00 Alyssa’s experience and career trajectory
2:30 Transition from engineer to manager
4:09 AI implementation at Square
7:46 Small business AI applications
12:14 Latent demand for content generation
15:04 The origin story of Square’s GPT-2 products
16:54 Consolidating ecommerce workflows
18:46 How will AI change cloud services
23:07 hyperscaler foundation models and the AI land grab
25:16 Enterprise demand for open source models
28:08 Startups in the AI semiconductor space
31:02 Scale up architectures vs scaling out
34:32 What’s next for Alyssa
36:08 What Elad and Sarah are excited about in 2024
Clara Shih is the Chief Executive Officer of Salesforce AI where she leads the AI efforts across Salesforce including AI co-pilot and agent platform, model development, go-to-market growth, adoption, partnerships, ecosystems, and secure responsible AI. Before that was the CEO of Salesforce Service Cloud She is also the co-founder and previous CEO of Hearsay Systems. She is also on the Board of Directors at Starbucks.
0:00 Clara’s Background
0:50 From cloud services to AI
3:25 Internal Model Development vs Open Source
5:20 The Co-Pilot Approach
8:50 Enterprise AI Adoption
10:54 The future of Enterprise AI
13:23 Cross-team collaboration
14:40 AI is the new UI
19:11 Structuring the Dataset
21:25 What’s next for generative AI in Enterprise
23:18 Pricing challenges in AI
26:30 Startups and AI
28:22 Collaboration in AI Industry
0:00 Recapping the OpenAI saga
9:56 AI video products
16:14 Moving from Diffusion Models to LLMs
19:47 The beneficial margins of AI investing
04:55 - The Spectrum of Agent Tasks
10:23 - Specialization and Generalization With Agents
14:08 - Code and Language in AI Agents
21:00 - Evaluating AI Development Tools Efficiently
26:39 - Prioritizing GPU Usage
Arthur Mensch is Chief Executive Officer and co-founder of Mistral AI. A graduate of École Polytechnique, Télécom Paris and holder of the Master Mathématiques Vision Apprentissage at Paris Saclay, he completed his thesis in machine learning for functional brain imaging at Inria (Parietal team). He spent two years as a post-doctoral fellow in the Applied Mathematics department at ENS Ulm, where he carried out work in mathematics for optimization and machine learning. In 2020, he joined DeepMind as a researcher, working on large language models, before leaving in 2023 to co-found Mistral AI with Guillaume Lample and Timothee Lacroix.
00:00 - Why he co-founded Mistral
04:22 - Chinchilla and Proportionality
06:16 - Mistral 7b
09:17 - Data and Annotations
10:33 - Open Source Ecosystem
17:36 - Proposed Compute and Scale Limits
19:58 - Threat of Bioweapons
23:08 - Guardrails and Safety
29:46 - Mistral Platform
31:31 - French and European AI Startups
Ilya Sutskever is Co-founder and Chief Scientist of OpenAI. He leads research at OpenAI and is one of the architects behind the GPT models. He co-leads OpenAI's new "Superalignment" project, which tries to solve the alignment of superintelligences in 4 years. Prior to OpenAI, Ilya was co-inventor of AlexNet and Sequence to Sequence Learning. He earned his Ph.D in Computer Science from the University of Toronto.
00:00 - Early Days of AI Research
06:49 - Origins of Open Ai & CapProfit Structure
13:54 - Emergent Behaviors of GPT Models
18:05 - Model Scale Over Time & Reliability
23:51 - Roles & Boundaries of Open Source in the AI Ecosystem
28:38 - Comparing AI Systems to Biological & Human Intelligence
32:56 - Definition of Digital Life
35:11 - Super Alignment & Creating Pro Human AI
41:20 - Accelerating & Decelerating Forces
Ryan co-founded Material Security in 2017 after seeing high profile email hacks in the 2016 Presidential election. Previously, he led various engineering teams at Dropbox after it acquired his first company, Parastructure. Prior to Parastructure, he led engineering at a data analysis company spun out of Stanford by DARPA. He holds both an MS in Computer Networks and Security and a BS in Computer Science from Stanford.
00:00 - How 2016 Election Hacking Inspired Ryan to Start Material Security
05:02 - Generative AI Use Cases in Cyber Security & Fine Tuning
11:50 - Predictions on Effective Threat Levels from AI Hacks
15:39 - Democracy, the Department of Defence, DARPA and Cyber Security
20:17 - Is there room for startups in the Cyber Security industry?
27:13 - New Challenges On Horizon After 7 Years as Cofounder
30:32 - Advice to Founders
Kawal Gandhi works in the Office of the CTO of Google Cloud, where his main focus is AI/ML. He has worked at Google for nearly a decade in search and ad roles before focusing on the development and marketing of AI tools.
00:00 - Generative AI in Google Cloud
09:05 - AI Adoption for Enterprise
13:31 - Multi-Modal AI Models
16:19 - AI Adoption, Investment Cost, Anti-patterns
24:43 - Google's TPU and NVIDIA GPU shortage
31:00 - Data Marketplace and Model Training