Cohere's SVP Technology - Saurabh Baji

Cohere's SVP Technology - Saurabh Baji

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.

Denne episoden er hentet fra en åpen RSS-feed og er ikke publisert av Podme. Den kan derfor inneholde annonser.

Episoder(271)

What Most People Get Wrong About Evolution | Akarsh Kumar

What Most People Get Wrong About Evolution | Akarsh Kumar

A lot of people in AI treat evolution as a dumb fallback, basically random search for when you can't take a gradient. Akarsh Kumar thinks that is wrong. Selection hangs on to partial solutions, so mut...

10 Okt 47min

How a Voice Agent Learns the Rhythm of Conversation — Shawn Wen

How a Voice Agent Learns the Rhythm of Conversation — Shawn Wen

Tsung-Hsien (Shawn) Wen, CTO of PolyAI, tells Tim Scarfe why voice agents are harder than text agents. Voice adds time, and a good conversation depends on adapting to the person on the line, not just ...

1 Okt 1h 10min

Who Checks a Proof No Human Can Read? — Leo de Moura

Who Checks a Proof No Human Can Read? — Leo de Moura

Leonardo de Moura created Lean and co-created Z3. ---This episode is sponsored by Parallel.Parallel, where agents find answers: web search, extraction and deep research APIs built for AI agents.Start ...

30 Sep 1h 14min

When AI Research Starts Moving Faster Than Human Research - Zhengyao Jiang

When AI Research Starts Moving Faster Than Human Research - Zhengyao Jiang

Weco let an AI coding agent rewrite the harness around another agent for eight days: its code, prompts and tools, while the underlying language model stayed fixed. Tim Scarfe asks Weco co-founder Zhen...

26 Sep 43min

How Deep Learning Finally Cracked Messy Tables - Frank Hutter

How Deep Learning Finally Cracked Messy Tables - Frank Hutter

Frank Hutter, co-founder of Prior Labs, talks about TabPFN, a tabular foundation model that makes predictions in a single forward pass, and the research behind it.TabPFN is pre-trained on synthetic da...

23 Sep 1h 53min

Why Scaling Prediction Cannot Create Intelligence - Alexander Mattick

Why Scaling Prediction Cannot Create Intelligence - Alexander Mattick

Alexander Mattick is a researcher at Fraunhofer IIS and a PhD researcher at the University of Technology Nuremberg (UTN), and a regular on Yannic Kilcher's Discord. He first came on MLST in 2022, afte...

21 Sep 2h 14min

How Physical AI Learns Across Language, Video and Action — Ming-Yu Liu

How Physical AI Learns Across Language, Video and Action — Ming-Yu Liu

The car making a left turn at the start of this episode was never filmed. Cosmos 3 generated it. Ming-Yu Liu, who leads the Cosmos research at NVIDIA, explains how one model can describe a video, gene...

15 Sep 25min

Speech Recognition Is Not a Solved Problem — Pavan Kumar Reddy

Speech Recognition Is Not a Solved Problem — Pavan Kumar Reddy

Pavan Kumar Reddy leads audio research at Mistral AI. He joins Tim Scarfe for a deep technical tour of Voxtral — and explains why the frontier of deployed voice is still a cascade of specialised model...

14 Sep 1h 42min

Populært innen Teknologi

energi-og-klima
teknisk-sett
lydartikler-fra-aftenposten
elektropodden
nasjonal-sikkerhetsmyndighet-nsm
tomprat-med-gunnar-tjomlid
rss-ai-forklart
rss-alt-som-gar-pa-strom
rss-kunstig-intelligens-med-elisabeth-maren-og-morten
shifter
smart-forklart
fornybaren
rss-alt-vi-kan
rss-bak-skyen
pedagogisk-intelligens
rss-romnasjonen
rss-teknologioptimistene-en-podkast-om-teknologi-og-mennesker
rss-heis
rss-larervarelset
rss-hvorfor-ble-det-sann