This video is a rare, crystal clear glimpse into the future
@NandoPr1m3
20 күн бұрын
Fantastic Interview! It's very tough to talk about AI in general because it's still moving quite fast. I equate it to trying to fetch water from a river using a strainer. I'm grateful for the caliber of people like Clay working on turning it into real world solutions.
@chanrox69
8 күн бұрын
Awesome interview. Working on AI ethics and fairness. There are really good open source frameworks
@sirishkumar-m5z
17 күн бұрын
The Strawberry project by OpenAI is fascinating! It's exciting and possibly transformative for education that a single AI model can cover such a wide range of topics.
@darkmatter9583
20 күн бұрын
Given the rapid evolution of AI in the past 18 months, how do you envision its impact on hardware optimization for large scale deployment in the next decade?
@foregroundtreble05
12 күн бұрын
0:15 -- useful insight for the most common user
@DylanKane-u2j
14 күн бұрын
Kinda took a dark turn when he basically admitted to misleading or hiding information from customers, whereas this tech could be used to educate, cure and advance society.
@vicaya
20 күн бұрын
Pretty much admitting that the current AI solutions fundamentally don't scale, just like all previous enterprise software that needs heavy post sale customization for many customers, albeit with even less certainty.
@420_gunna
20 күн бұрын
0:15 "They're better at detecting errors in their own output than not making those errors in the first place." Haven't listened yet but there's a GDM paper "LLMs cannot self-correct reasoning yet" that refutes this, assuming he's talking about intrinsic self-correction without shitty pre-hoc prompting.
@railtorail
10 күн бұрын
Looks like he's a good fund raiser given his story telling abilities.
@twoplustwo5
15 күн бұрын
🎯 Key points for quick navigation: 00:00:00 *🚀 Introduction to Clay Bavor and Background* - Clay's background and professional history, - His tenure and roles at Google, - The foundation and vision of Sierra AI. 00:05:37 *🌌 Development and Impact of AI Technologies* - Early experiences and breakthroughs with AI at Google, - Significance of transformative AI technologies, - Noteworthy AI capabilities and their demonstrations. 00:09:55 *🛠️ Revolutionizing Customer Interaction with AI* - How AI redefines customer interactions, - The evolution of digital engagement tools, - Use cases highlighting seamless customer experiences. 00:15:29 *🏆 Transition to Customer Satisfaction with AI* - Comparison between traditional chatbots and new AI models, - Improvements in customer satisfaction with AI agents, - Techniques enhancing AI-agent interactions and trustworthiness. 00:20:17 *🔧 Building Robust AI Systems for Customer Service* - Challenges in deploying AI safely and reliably, - The concept and application of agent OS, - Frameworks and solutions to ensure effective AI-driven customer service. 22:17 *🛠️ Integrating AI Agents with Systems and SDKs* - Discussion on integrating AI agents with systems of records, mobile apps, and websites using agent SDK. - Explanation of agent OS abstracting multiple LLM calls to streamline responses. - Importance of building fast, safe, and reliable AI agents. 23:36 *🧠 Supervisory AI and Error Detection* - Importance of using supervisory AI agents to review the work of primary agents. - Explanation of how LLMs detecting their errors can be more effective when given supervisory personas. - Use of multiple prompts and reflections to improve accuracy and reliability of responses. 26:04 *🧩 Enhancing AI Performance through Prompt Engineering* - Discussion on improving LLM performance through better prompt engineering and emotional manipulation techniques. - Techniques like Chain of Thought and task decomposition to enhance reasoning and accuracy. - Importance of applying advanced prompt engineering methods to improve AI agent outputs. 28:40 *🧪 Benchmarking AI Agents with TOA Bench* - Introduction of TOA Bench for evaluating AI agents' real-world performance with user simulation. - Tests involving interactions with various personas and tools to measure accuracy and reliability. - Findings showing the need for sophisticated architectures to improve AI agents' success rates in complex tasks. 33:28 *⛏️ Engineering vs. Research in AI Agents* - Discussion on engineering challenges versus research tasks in developing reliable AI agents. - Strategies involving composing different models and tools for better performance. - Examples of how agent OS aids in safely deploying AI agents with a mix of advanced engineering and research methods. 36:55 *🌐 Deploying AI Agents for Customer Interactions* - Current capabilities of AI agents in handling tasks from answering questions to complex troubleshooting. - Use cases of AI agents assisting in various customer service tasks including dealing with churn and troubleshooting technical issues. - Future potential for more sophisticated and broader AI agent applications in customer service and beyond. 40:51 *🚀 Future Potential and Broader AI Agent Applications* - Discussion on the potential of AI agents to handle increasingly complex tasks. - Vision of agents assisting throughout the customer journey from pre-purchase to troubleshooting. - Implications of integrating AI agents to enhance customer experience and business metrics. 44:02 *❓ Redefining Customer Service Expectations* - Consideration of how AI agents could change standard metrics for conversion and retention if they consistently deliver excellent experiences. - The potential impact of AI on making customer interactions easier and more accessible. - Reflection on traditional customer service challenges like long wait times and poor agent interactions and how AI could mitigate these issues. 00:45:26 *🐱 Humorous anecdotes and customer support enhancement* - Clay Bavor recounts a humorous incident of an AI agent meowing during a support call, - Discussion shifts to the potential of greatly improving customer support through flexible, fluent AI interactions. 00:46:22 *🛠️ Practical challenges in deploying AI for customer service* - Describes the complexities and considerations in real-world AI deployment, - Mentions the need to adapt AI to the specific business logic and customer interaction styles. 00:47:16 *🧩 Development and architecture of AI agents* - Explains the unique aspects of AI agents being non-deterministic, unlike traditional software, - Introduction of the "agent development life cycle" and the use of conversation simulators for testing. 00:50:08 *🔧 Building AI as a comprehensive solution* - Emphasis on creating a complete solution that includes technology, training, and auditing, - Collaboration between product managers, engineers, and customer experience teams to develop AI agents. 00:56:00 *📊 Tools for managing AI interactions* - Introduction of the "Experience Manager" for real-time analytics and issue spotting, - Feedback mechanisms to improve AI responses based on customer interactions. 01:01:30 *🔄 Holistic approach to building a company* - Discussion on the interplay between engineering, research, and customer experience teams within Sierra, - Emphasis on continuous self-improvement and learning from mistakes through internal processes. 01:05:16 *📊 Outcome-based pricing model* - Explanation of Sierra's resolution-based pricing strategy, - Alignment of incentives between Sierra and its clients for efficient AI problem-solving. 01:06:10 *🌌 Future excitement in AI* - Exploration of the rapid advancements in AI over the past few years, - Speculations on the future, including AI-generated feature-length films and evolving AI capabilities. 01:08:23 *🌐 The Future of Computer Graphics and AI* - Discussion on the future advancements in computer graphics powered by AI, - Potential to create complex virtual worlds with simple descriptions, - Evolution of rendering techniques, making traditional tools obsolete. 01:09:05 *🚀 Technology as a Force Multiplier* - Technology's ability to enhance the capabilities of people and organizations, - Speculative scenarios where companies operate at their best in all aspects, - Using AI to replicate top-performing sales forecasts and customer interactions, - Example of having AI emulate the most knowledgeable and experienced employees. Made with HARPA AI
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