We’re already seeing massive improvements in coding efficiency using ChatGPT. The simple prompt of ChatGPT could potentially surpass the search engine of the Big Data Era - but many more applications could be just as powerful and profitable in different verticals and applications. Hundreds of companies have sprouted up in a matter of months providing applications of generative AI, from creating marketing collateral to crafting new music to creating new medicines. Much as with nutrition labels or movie ratings, consumers deserve to know what they’re getting into - and I believe many will be pleasantly surprised by the quality of AI-generated products. In addition, I would require that any media (text, audio, image, video) generated by AI be clearly labeled as “Made with AI” when used in a commercial or political context. There is much to applaud in initial moves by industry leaders to create thoughtful guardrails with real teeth, and I urge rapid adoption of smart regulation. Governance, transparency and explainability, enforced through real regulation, are essential to give companies confidence that they can understand what AI is doing when missteps inevitably occur so that they can limit the damage and work to improve the AI. Constant vigilance around data quality and algorithm performance is essential to avoid devastating hallucinations that can alienate potential customers from using models in high-stakes environments where real dollars are spent. The only way that these models can improve is through feedback and more opportunities to learn what good behavior looks like. We just don’t know which weights between which neurons need to be set to which values to prevent a chatbot from telling a journalist to divorce his wife. These models are software that has been built by other software, composed of hundreds of billions of equations that interact in ways we cannot understand. We can’t fix bugs as we would with traditional, procedural software. Generative AI is unique in its wildness, bringing challenges of unexpected behavior and requiring continual teaching to improve. Let’s look at each of these elements and where we are today: Generative AI models Just as Google united these elements to create workable big data, the AI success stories must do the same to create what I call Workable AI. A system to ensure trust in the models, including the ability to continually and cost-effectively monitor a model’s performance and to teach the model so that it may improve its responses.The interfaces and business applications that will allow users to interact with the models, which could be a standalone product or a generative AI-augmented back office process.To find viable, valuable long-term applications, AI platforms must embrace three essential elements. The key question the industry must answer is: How can AI deliver the sustainable business outcomes essential to bring this step-change forward for good? Workable AI: Let’s put AI to work Today’s AI hype levels are right where we were with big data. It is a watershed moment in the field of AI, in the history of technology, and maybe in the history of humanity. Fast-forward to ChatGPT arriving in late 2022 and parents calling their kids asking if the machines had finally come alive. Thanks to big data, storing and analyzing this text is easy - enabling researchers to develop software that can read all that text and teach itself to write. Internet users have produced massive volumes of text written in natural language, like English or Chinese, available as websites, PDFs, blogs and more. What was just confusing babble at first ultimately delivered tremendous financial returns. Now, we all expect to find exactly what we need in seconds, as well as perfect turn-by-turn directions, collaborative documents and cloud-based storage.Ĭountless fortunes have been built on Google’s ability to turn data into compelling products, and many other titans, from a rebooted IBM to the new goliath of Snowflake, have built successful empires by helping organizations capture, manage and optimize data. Google’s search results were consistently excellent and built trust, but the company couldn’t have kept providing search at scale - or all the additional products we rely on Google for today - until Adwords enabled monetization. Google’s big data success story is worth revisiting as a symbol of how data turned it into a trillion-dollar company that transformed the market forever.
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