Image Credit: Created with Midjourney
Join top executives in San Francisco on July 11-12 and learn how business leaders are getting ahead of the generative AI revolution. Learn More
New products like ChatGPT have captivated the public, but what will the actual money-making applications be? Will they offer sporadic business success stories lost in a sea of noise, or are we at the start of a true paradigm shift? What will it take to develop AI systems that are actually workable?
To chart AI’s future, we can draw valuable lessons from the preceding step-change advance in technology: the Big Data era.
2003–2020: The Big Data Era
The rapid adoption and commercialization of the internet in the late 1990s and early 2000s built and lost fortunes, laid the foundations of corporate empires and fueled exponential growth in web traffic. This traffic generated logs, which turned out to be an immensely useful record of online actions. We quickly learned that logs help us understand why software breaks and which combination of behaviors leads to desirable actions, like purchasing a product.
As log files grew exponentially with the rise of the internet, most of us sensed we were onto something enormously valuable, and the hype machine turned up to 11. But it remained to be seen whether we could actually analyze that data and turn it into sustainable value, especially when the data was spread across many different ecosystems.
Join us in San Francisco on July 11-12, where top executives will share how they have integrated and optimized AI investments for success and avoided common pitfalls.
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. 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. 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.
Countless 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.
What was just confusing babble at first ultimately delivered tremendous financial returns. It’s this very path that AI must follow.
2017–2034: The AI Era
Internet users have produced massive volumes of text written in natural language, like English or Chinese, available as websites, PDFs, blogs and more. 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. Fast-forward to ChatGPT arriving in late 2022 and parents calling their kids asking if the machines had finally come alive.
It is a watershed moment in the field of AI, in the history of technology, and maybe in the history of humanity.
Today’s AI hype levels are right where we were with big data. 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
To find viable, valuable long-term applications, AI platforms must embrace three essential elements.
- The generative AI models themselves
- 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
- 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
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.
Let’s look at each of these elements and where we are today:
Generative AI models
Generative AI is unique in its wildness, bringing challenges of unexpected behavior and requiring continual teaching to improve. We can’t fix bugs as we would with traditional, procedural software. 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 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.
The only way that these models can improve is through feedback and more opportunities to learn what good behavior looks like. 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.
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. 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.
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. 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.
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. 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. We’re already seeing massive improvements in coding efficiency using ChatGPT. What else will follow? Experimenting to find AI applications that provide a step-change in the user experience and business performance will be essential to creating Workable AI.
The companies that will build their fortune on this new class of technologies will break through these innovation barriers. They’ll solve the challenge of continuously and cost-effectively building trust in the AI while developing killer apps paired with sound monetization built on powerful underlying models.
Big data went through the same noise and nonsense cycle. Similarly, it will likely take a few generations and missteps, but by focusing on the tenets of Workable AI, this new discipline will quickly evolve to create a step-change platform that’s just as transformative as experts expect.
Florian Douetteau is CEO of Dataiku.
Welcome to the VentureBeat community!
DataDecisionMakers is where experts, including the technical people doing data work, can share data-related insights and innovation.
If you want to read about cutting-edge ideas and up-to-date information, best practices, and the future of data and data tech, join us at DataDecisionMakers.
You might even consider contributing an article of your own!