Dear friends,
Last week, I returned home from Asia, where I spoke at Seoul National University in Korea, the National University of Singapore, and the University of Tokyo in Japan and visited many businesses. As I discussed the state of AI with students, technologists, executives, and government officials, something struck me: Around the world, everyone is wrestling with similar AI-related issues.
In every country:
- Business leaders are asking how AI will affect their companies.
- Governments are wondering how it will affect the labor market, what risks it poses, and how to regulate it.
- Companies are trying to figure out how to use it without “giving away” their data to one of the platform vendors.
- Developers are experimenting with creative uses of generative AI. The two most common applications remain building customer service chatbots and answering questions based on documents. But I also heard about numerous creative projects in medical records, financial records, privacy protection, and much more.
When the deep learning revolution started about a decade ago, I advised teams to (i) learn about the technology, (ii) start small and build projects quickly to hone intuition about what’s possible, and (iii) use learnings from smaller projects to scale to bigger ones. With the generative AI revolution, my advice remains the same. This time, though, the barrier to entry is lower and thus the time-to-value seems to be shorter. It takes substantial effort to collect data and train and deploy a neural network, but less effort to prompt a large language model and start getting results.
For developers, this means richer opportunities than ever! Leaders are looking for helpful perspectives. If you’re able to experiment, learn, identify successful use cases (and even some failures — which is fine, too), and share your insights with colleagues, perhaps you can influence the trajectory of your business.
Last Friday, I discussed how businesses can plan for generative AI with Erik Brynjolfsson, Andrew McAfee, James Milin, and Daniel Rock, who co-founded Workhelix (a portfolio company of AI Fund, which I lead). Workhelix helps its customers break down jobs into tasks to see which tasks can be augmented by AI. You can listen to the conversation here.
For instance, a radiologist’s tasks include (i) capturing images, (ii) reading them, (iii) communicating with patients, and so on. Which of these tasks can take advantage of AI to make a radiologist’s job more productive and enjoyable? Can it help optimize image acquisition (perhaps by tuning the X-ray machine controls), speed up interpretation of images, or generate takeaways text for patients?
Although Workhelix is applying this recipe at scale, it’s also useful for teams that are exploring opportunities in AI. Consider not jobs but their component tasks. Are any of them amenable to automation or assistance by AI? This can be a helpful framework for brainstorming interesting project ideas.
The way generative AI is taking off in many places around the world means that our markets are increasingly global. Wherever in the world you live, this is a wonderful time to build your AI knowledge and increase your AI skills. Exciting opportunities lie ahead!
Special thanks to Ian Park of the Korean Investment Corporation, Chong Yap Seng of the National University of Singapore, and Yuji Mano of Mitsui, who made my visits much more productive and enjoyable. I also hope to visit other countries soon. Stay tuned!
Keep learning,
Andrew
P.S. DeepLearning.AI just launched “Evaluating and Debugging Generative AI,” created in collaboration with Weights & Biases and taught by Carey Phelps. Machine learning development is an iterative process, and we often have to try many things to build a system that works. I used to keep track of all the different models I was training in a text file or spreadsheet. Thankfully better tools are available now. This course will teach you how to use them, focusing on generative AI applications. I hope you enjoy the course!