Andrew Ng: Opportunities in AI - 2023

This paragraph introduces Dr. Andrew Ng, covering his impressive background and achievements. It highlights his various roles, such as being the founder of DeepLearning.AI and Landing AI, co-founder of Coursera, and an adjunct professor at Stanford. It also mentions his leadership roles at Google Brain and the Stanford AI lab, and the significant impact he has had on AI education, with millions of people taking his classes. The paragraph sets the stage for Dr. Ng's insights and perspectives on AI.

Dr. Ng explains that AI can be viewed as a collection of tools, with supervised learning and generative AI being the two most important ones currently. He discusses supervised learning, which is excellent at recognizing and labeling things or computing input-output mappings. He provides examples of its applications, such as online advertising, self-driving cars, ship route optimization, and automated visual inspection. He then delves into generative AI, explaining how it works by using supervised learning to repeatedly predict the next word, enabling the generation of text like in ChatGPT. Dr. Ng also mentions additional techniques like RLHF for further tuning the AI output.

In this paragraph, Dr. Ng demonstrates the power of prompt-based AI by writing a few lines of code to build a sentiment classifier. He imports tools from OpenAI, writes a prompt that asks the AI to classify text as having positive or negative sentiment, and runs the code live, showing how quickly and easily a developer can build such an application using prompting. This example highlights the exciting development of using prompting as a developer tool, enabling rapid AI application development.

Dr. Ng discusses the opportunities in AI, starting with the current and projected value of different AI technologies. He highlights that while supervised learning is already massively valuable, generative AI is expected to more than double in value in the next three years due to developer interest and investments. He then addresses why AI adoption has been slower outside the tech and consumer software industries, identifying a long tail of smaller projects (e.g., $5 million) that were previously difficult to execute due to high customization costs. Dr. Ng sees the development of low-code and no-code tools, like prompting and data-centric AI, as a key enabler for deploying AI in more industries and unlocking these opportunities.

In this paragraph, Dr. Ng explains how he started AI Fund, a venture studio that builds startups to pursue diverse AI opportunities across various industries. He provides an example of Bearing AI, a startup that uses AI to make ships more fuel-efficient, highlighting the importance of subject matter expertise from partners like Mitsui in identifying valuable ideas. He also shares the process his team follows for building startups, from idea validation and prototyping to securing funding and scaling. Dr. Ng emphasizes the efficiency of starting with concrete ideas and partnering with subject matter experts rather than trying to learn industries from scratch.

In this paragraph, Dr. Ng transparently shares the recipe his team has developed for building AI startups. He walks through the process step-by-step, using the example of Bearing AI, a company that uses AI to make ships more fuel-efficient. The process includes validating the idea, recruiting a CEO, building a prototype and conducting customer validation, securing funding, hiring an executive team, building an MVP, and obtaining real customers. Dr. Ng highlights the importance of partnering with subject matter experts and utilizing concrete ideas rather than exploring broad domains from scratch.

Dr. Ng discusses the risks and social impact of AI. He emphasizes that his teams only work on projects that move humanity forward and have killed projects on ethical grounds. He acknowledges the issues of bias, fairness, and accuracy in current AI systems but notes that the technology is rapidly improving. Dr. Ng identifies job disruption as one of the biggest risks of AI, particularly for higher-wage jobs, and stresses the need for society to support those whose livelihoods are disrupted. He also addresses the hype around artificial general intelligence (AGI) and extinction risks, arguing that AGI is still decades away and that AI is more likely to be part of the solution for real existential risks like pandemics and climate change.

In his closing remarks, Dr. Ng reiterates that AI is a general-purpose technology that creates numerous opportunities for everyone. He emphasizes that the important work ahead lies in building concrete use cases and applications across various domains. Dr. Ng expresses his willingness to engage with the audience on these opportunities in the future, highlighting the potential for collaboration and further advancements in AI.