Navigating the AI Frontier

In this episode, Nathan sits down with Adam Wenchel, CEO of Arthur.ai. Adam founded the AI security company back in 2019, before GPT-2 existed. In this episode, Adam shares his unique perspective on the AI security landscape, drawing from years building commercial AI systems. They discuss the attacks Adam set out to defend against, the changing priorities of executives in the rush to adopt LLMs, and the LLM-specific techniques Adam has developed. If you're looking for an ERP platform, check out our sponsor, NetSuite: http://netsuite.com/cognitive

We're hiring across the board at Turpentine and for Erik's personal team on other projects he's incubating. He's hiring a Chief of Staff, EA, Head of Special Projects, Investment Associate, and more. For a list of JDs, check out: eriktorenberg.com.

TIMESTAMPS:

(00:00:00) Episode Preview

(00:03:45) Adam's background in AI and starting Arthur AI in 2019

(00:05:52) The release of ChatGPT as a watershed moment for generative AI

(00:07:09) Differences between traditional cybersecurity and AI security

(00:09:51) Early examples of AI security issues like boundary detection attacks in fraud systems

(00:12:39) - Mitigating risks of AI systems through observability and robust training

(00:14:40) - Financial services governance of AI models and its challenges today

(00:15:12) Sponsors: Netsuite | Omneky

(00:21:18) - Motivations for governance like staying compliant with regulations

(00:21:40) - The mix of incentives shaping earlier AI governance, like explainability

(00:28:14) - Using LMs to evaluate the security of other LMs

(00:30:03) - Dynamics between training and evaluating future LMs

(00:38:10) - The state of reasoning capabilities in large LMs

(00:44:35) - Corporate urgency around adopting generative AI technologies

(00:46:51) - Common enterprise use cases for generative AI and security concerns

(00:50:45) - Techniques for reducing hallucinations in retrieval augmented LMs

(00:53:15) - Benchmarking LMs on specific organizational tasks versus generic benchmarks

(00:56:30) - Metrics beyond accuracy like concision and hedging

(01:01:20) - Automatically detecting anomalies and hallucinations

(01:09:20) - Relationships between Arthur AI and foundation model providers

(01:11:52) - Where Cohere shines: multilingualism and not hedging

(01:13:43) - Anticipating future watershed moments and steady progress

(01:19:03) - Whether we can ever fully solve AI alignment and safety

LINKS:

Arthur.ai: https://www.arthur.ai/

X/Social:

@apwenchel (Adam)

@itsArthurAI (Arthur.ai)

@labenz (Nathan)

@eriktorenberg

@CogRev_Podcast

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