a16z Podcast: On Data and Data Scientists in the Age of AI
Dec 5, 2017
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Ion Stoica, co-founder of Databricks and RISElab director, and Scott Clark, co-founder of SigOpt, dive into the complexities of utilizing AI with abundant data. They emphasize that quality matters—a simple truth often overlooked. With AI initiatives, understanding business goals is paramount. The discussion also addresses overcoming initial hurdles for companies and stresses the importance of context in shaping successful data strategies. Practical advice flows on navigating the evolving AI landscape and ensuring alignment with organizational objectives.
Effective implementation of AI hinges on accurate data representation and continuous monitoring to avoid the pitfalls of 'garbage in, garbage out'.
Organizations facing the cold start problem can mitigate challenges by committing to multiple AI projects rather than relying on a single initiative.
Deep dives
Importance of Data in AI Implementation
Data is a critical foundation for organizations beginning their AI journeys, often requiring more time and resources than the actual model development. It is essential to ensure that the data collected accurately represents real-world scenarios and is continuously monitored for accuracy. Organizations must develop key performance indicators (KPIs) based on this data to operationalize efforts towards becoming data-driven. Failure to manage data effectively can lead to poor outcomes, as the principle of 'garbage in, garbage out' applies directly to AI model performance.
Navigating the Cold Start Problem
The cold start problem presents a significant challenge for enterprises attempting to adopt AI, as they often feel overwhelmed by the rapid pace of technological change. Successful companies tend to approach AI by committing to multiple projects rather than relying on a single initiative, allowing them to hedge their bets and find effective solutions. It's observed that smaller companies tend to adopt an AI mindset from the start, while large enterprises may struggle to pivot from existing systems to new technologies. The advancements in AI tools have made it quicker for organizations to initiate projects, reducing the barriers traditionally associated with the cold start problem.
The Evolving Role of Data Scientists
As AI tools continue to improve and become more accessible, the role of data scientists is expected to evolve significantly. While these tools can facilitate faster processes, their effectiveness hinges on the ability of data scientists to set appropriate business objectives and understand the unique context of their organizations. Simply utilizing advanced tools without a strategic focus can result in efforts that do not drive meaningful progress or competitive advantages. Therefore, a deep understanding of business goals remains crucial for data scientists, ensuring that AI efforts align with overall organizational success.
Data, data, everywhere, nor any drop to drink. Or so would say Coleridge, if he were a big company CEO trying to use A.I. today -- because even when you have a ton of data, there's not always enough signal to get anything meaningful from AI.
Why? Because, "like they say, it's 'garbage in, garbage out' -- what matters is what you have in between," reminds Databricks co-founder (and director of the RISElab at U.C. Berkeley) Ion Stoica. And even then it's still not just about data operations, emphasizes SigOpt co-founder Scott Clark; your data scientists need to really understand "What's actually right for my business and what am I actually aiming for?" And then get there as efficiently as possible.
But beyond defining their goals, how do companies get over the "cold start" problem when it comes to doing more with AI in practice, asks a16z operating partner Frank Chen (who also released a microsite on getting started with AI earlier this year)? The guests on this short "a16z Bytes" episode of the a16z Podcast -- based on a conversation that took place at our recent annual Summit event -- share practical advice about this and more.
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