Using ChatGPT isn't an AI strategy
Daphne Koller explains the hard truths of AI adoption in this week's Big Think Class.
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Why real AI transformation goes far beyond tool access | Daphne Koller
You’ve probably heard that artificial intelligence has untapped potential in today’s workplaces. And sure, many organizations have signed enterprise contracts and deployed different AI tools across all business units. But as insitro CEO and AI expert Daphne Koller stresses, making a tool available is not the same as intentionally leveraging it to transform your organization.
Learning objectives:
Envision ways AI can support innovative work.
Establish realistic expectations for physical AI.
Develop and evaluate AI use cases.
Choose AI tools based on pragmatics, not promises.
Cultivate risk-resilient AI practices.
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Lessons:
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The term “AI” is a catch-all that spans everything from large language models (LLMs) designed to generate text to machine learning aimed at modeling and optimizing real-world outcomes. Not only can the variety be staggering, but the media buzz can make it hard to parse which tools are relevant to which types of work. Fortunately, AI experts like Daphne Koller have been working with this technology for decades and have learned to tune into frequencies that can help you cut through the noise.
In this video lesson, Koller pinpoints when different types of AI can prove most fruitful, and what that looks like in practice.
Before the turn of the 21st century, science fiction speculated that we’d all be getting around with the aid of flying cars or jetpacks by now — but, as you’ve probably noticed, we aren’t quite there yet. The slow pace of some innovations (e.g., robot cars) can frustrate expectations shaped by rapid advances in the virtual world. But Daphne Koller stresses that the pace of AI’s impact in the physical world is a difference, not a hindrance.
In this video lesson, Koller discusses the reasons the rates at which AI can make meaningful change vary, and why it’s smart to adjust our expectations accordingly.
The tech panic of Y2K was a problem of shortsightedness. Computer programmers originally configured systems to represent years in 2 digits, not 4. This brevity was useful — until, of course, 1999 was about to become 2000. It was only then experts realized that computers wouldn’t be able to handle a rollback to the year “00.” To avert worldwide technological disaster, billions were spent reconfiguring systems. The lesson? Not simply to avoid cutting corners, but to design technology with its future use in mind.
In this video lesson, Daphne Koller shares her recommendations for deploying AI thoughtfully.
In the 19th century, Chinese laborers in the United States used snake oil as a joint pain reliever. The practice caught on, and European settlers began selling their own formulations. But consumers were scandalized when an investigation revealed that a popular brand contained no actual “snake oil.” The term became synonymous with grifting.
AI companies, too, can fail to deliver on their promises. In this video lesson, Daphne Koller offers a framework for making informed decisions about which AI products to invest in.
If everything always went according to plan, we wouldn’t need contracts, insurance, or contingency protocols. But the reality is that big investments demand foresight, not just optimism. The same principle applies when deploying AI, according to Daphne Koller. Clear guidelines and thoughtful questions can help businesses plan for a smoother rollout of AI tools.
In this video lesson, Koller emphasizes the need to set robust parameters for successful AI deployment.
When new technologies arrive, people often focus first on what (or who) they might replace. But as Daphne Koller reminds us, history suggests that’s only half the story. For everything that wanes, there’s often something of value gained in return.
In this video lesson, Koller brings hopeful insights to the nuanced conversation about skill atrophy in the face of AI.