Driving successful data driven

Expectations are not managed. If you start out by looking at some targeted use cases, some targeted pieces of implementation, and you have good metrics for success, and also investments in data management and organization; good governance, good policies; if you combine all that with a practical narrative about what the models can do, then you’ve controlled the hype and you’re less likely to fall into the trough. Q: Would you say the AI hype cycle is running faster than others you’ve looked at? A: The AI hype cycle does tend to

Have a skew

Towards innovations that do move quicker across the curve — and they tend to be more impactful in what they can do as well. At the moment, it’s front and center for funding initiatives, for VCs. It’s such a focal area, in business lead the research space as well. A lot of these things come out of academia, which is very active in this space. Q: Finally, AGI, or artifical general intelligence (AI that replicates human intelligence). You have that as

Coming in ten

Years or more. Are you hedging your bets because it might not be possible at all? A: Yes. We do have a marker which is “obsolete before plateau.” There is an argument to BT Lists say it’s never actually going to happen, but we’re saying it’s greater than 10 years because there are so many different interpretations of what AGI actually is. Lots of respected researchers are saying we’re on the path that will get us to AGI, but many more breakthroughs and innovations are needed to see what the path actually looks like. We think it’s something

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