Andrew Ng: Opportunities in AI - 2023

Andrew Ng: Opportunities in AI - 2023

Introduction (00:00:00)

  • Dr. Andrew Ng, the managing general partner of AI Fund, founder of DeepLearning.AI and Landing AI, chairman and co-founder of Coursera, and an adjunct professor of Computer Science at Stanford features in this video.
  • He previously led the Google Brain team and directed the Stanford AI lab.
  • He has educated roughly one in 1,000 people on the planet in AI.

What is AI (00:01:03)

  • AI is considered a general-purpose technology, indicating that it's useful for many different applications, similar to electricity.

Technology landscape (00:01:35)

  • AI is viewed as a collection of tools which includes supervised learning and generative AI.
  • Ng discusses two principal tools — supervised learning, suitable for recognizing or labeling things, and generative AI, a relatively new development.

Supervised learning (00:02:14)

  • Supervised learning is effective at computing inputs to outputs or A to B mappings.
  • It can be applied to various fields such as online advertising, self-driving cars, ship route optimization, automated visual inspection in factories, and sentiment analysis for restaurant reviews.

Supervised learning workflow (00:03:28)

  • The workflow of a supervised learning project includes collecting labeled data, training an AI model to learn from this data, and running the trained model on a cloud service.

Largescale supervised learning (00:04:21)

  • Over the last decade, large-scale supervised learning has emerged as a powerful approach.
  • Training larger AI models with more data consistently improves their performance.

Genes of AI (00:05:27)

  • The current decade is witnessing growth in generative AI, on top of everything that supervised learning has accomplished.
  • Generative AI uses supervised learning to repeatedly predict the next word, enabling large language models like ChatGPT.

Large language models (00:07:39)

  • Large language models are proving useful not just as consumer tools, but also as developer tools.
  • They aid in quickly building applications that conventionally would have taken several months to build.

Custom AI applications (00:09:51)

  • With prompt-based AI, customs AI applications that previously took 6-12 months to develop can now be built in a week.
  • This prompt-based approach is significantly reducing the time required to build and deploy AI systems.
  • Ng predicts a flood of custom AI applications in the future due to this trend.

AI Opportunities (00:11:14)

  • Andrew Ng identifies supervised learning as the primary driver of financial value in AI today, potentially worth over $100 billion US a year for a single company like Google.
  • The momentum behind supervised learning is substantial with millions of developers creating applications.
  • Generative AI, although much smaller today, is projected to more than double in value over the next three years due to developer interest, investments, and exploration of applications by large corporations.
  • Ng emphasizes the opportunities available for startups and large companies within the light shaded regions representing the growth potential of these AI technologies.
  • He identifies both supervised learning and generative AI as general-purpose technologies. The ongoing task is to identify and execute concrete use cases for each.
  • Ng cautions about short-term fads in the market, drawing a parallel with the Lensa app, which had short-lived success due to being easily replicated and its thin software layer not being defensible.

General Purpose Technology (00:15:42)

  • AI is seen as a general-purpose technology, with a main task being to identify diverse use cases and build them.
  • AI adoption is still heavily concentrated in consumer software internet, making wider adoption seem early.
  • The most valuable AI projects (multi-billion dollar projects) are centered around advertising, web search and e-commerce product recommendations while other potential AI projects in different industries may not be as vast but still significant.
  • Examples provided include a pizza making company that used AI for quality control (a $5 million project) and an agriculture company that used AI to improve crop yields (another $5 million project).
  • A trend highlighted by Ng is the developing toolset in the AI community, enabling aggregation of use cases and user customization, with a focus on low code and no code tools.
  • These tools allow individual industries to train AI systems on their specific data, which isn't readily available online, enabling better customization and application of AI systems for their specific needs.
  • The focus is shifting from writing a lot of code to providing useful data, a concept referred to as "data-centric AI".
  • This trend is fundamental to distributing the value of AI from the concentrated tech world to all other industries, effectively expanding the application of AI throughout the economy.

How to go after AI opportunities (00:20:24)

  • Many valuable AI projects are available now in diverse fields such as maritime shipping, education, financial services, etc.
  • The speaker suggests starting multiple companies to pursue these diverse AI opportunities and has initiated AI Fund, a venture studio that builds startups.
  • Existing companies also have plenty of opportunities to integrate AI into their businesses, using their distribution as an advantage.
  • The AI Stack is structured into four layers, hardware-semiconductor, infrastructure, developer tool, and application. Each layer represents various opportunities but also carries their own challenges such as capital intensiveness and competition.
  • The Application Layer has a very large market with less intense competition compared to other layers, providing numerous exciting opportunities in areas where there is deep expertise.

Building startups (00:24:56)

  • The process of building startups begins with the generation of ideas and their validation.
  • A CEO is recruited early in the process to lead the project.
  • With the CEO, the team spends three months to build a prototype and validate the concept with the customers.
  • If the prototype survives this stage (66% survival rate), the first investment is made, enabling the hiring of an executive team and the creation of a minimum viable product (MVP).
  • After building the MVP, the startup then seeks additional external funding to continue growth and scaling.

Concrete Ideas (00:29:05)

  • The speaker prefers engaging when there's a concrete idea to work on, despite common advice suggesting exploration of multiple alternatives before settling on a solution.
  • Concrete ideas can be validated or falsified efficiently and offer clear direction for the team.
  • Many subject matter experts possess deeply thought-out ideas but lack a build partner, making idea identification and partnership a more efficient means of establishing startups.

Risks (00:31:17)

  • Andrew Ng's teams only work on projects that benefit humanity. They have terminated projects that, although financially viable, were deemed unethical.
  • Despite current issues with bias, fairness, and accuracy in AI, the technology is improving rapidly. AI systems today are less biased and more fair than they were six months prior.
  • Effort continues to mitigate these issues, and many teams are working towards making AI much better.
  • One of the major risks of AI is job disruption.
  • AI automation may have significant impacts on jobs, particularly high-wage jobs, as many of their tasks are exposed to automation.
  • While AI brings immense value, there is a societal responsibility to ensure that people, particularly those whose livelihoods may be disrupted by AI, are well cared for and treated well.

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