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Andrew Ng: Opportunities in AI - 2023
@ Stanford Online
08/30/23
Takeaways
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Andrew Ng
08/30/23
@ Stanford Online
AI is a general-purpose technology, similar to electricity, which means it can be applied to a wide range of applications rather than just one specific use.
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Andrew Ng
08/30/23
@ Stanford Online
Supervised learning is effective for labeling and recognizing data, such as identifying whether an email is spam or not.
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Andrew Ng
08/30/23
@ Stanford Online
The most lucrative application of supervised learning I've worked on is online advertising, where we can predict which ads users are likely to click on.
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Andrew Ng
08/30/23
@ Stanford Online
In self-driving cars, supervised learning can help label the positions of other vehicles based on sensor readings.
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Andrew Ng
08/30/23
@ Stanford Online
One project involved optimizing ship routes by predicting fuel consumption based on the route taken.
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Andrew Ng
08/30/23
@ Stanford Online
Supervised learning is also useful in automated visual inspection, such as identifying defects in manufactured products.
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Andrew Ng
08/30/23
@ Stanford Online
The last decade focused on large-scale supervised learning, where increasing data and model size led to improved performance.
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Andrew Ng
08/30/23
@ Stanford Online
This decade is characterized by the addition of generative AI tools, which build on the foundations of supervised learning.
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Andrew Ng
08/30/23
@ Stanford Online
Generative AI can produce varied outputs based on a given prompt, demonstrating its versatility in applications.
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Andrew Ng
08/30/23
@ Stanford Online
The core of generative AI relies on supervised learning to predict the next word in a sequence, enabling the creation of coherent text.
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Andrew Ng
08/30/23
@ Stanford Online
Large language models, like ChatGPT, are trained on vast amounts of text data, allowing them to generate human-like responses.
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Andrew Ng
08/30/23
@ Stanford Online
The timeline for building a commercial-grade AI system typically ranges from six to twelve months, depending on the complexity of the project.
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Andrew Ng
08/30/23
@ Stanford Online
In contrast, using prompt-based AI can significantly reduce development time, allowing for quicker deployment of AI solutions, with applications that used to take six months to a year to build now being developed in about a week, opening up a flood of new AI applications.
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Andrew Ng
08/30/23
@ Stanford Online
Many people underestimate the magnitude of custom AI applications that are going to emerge in the near future.
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Andrew Ng
08/30/23
@ Stanford Online
Developers around the world can now build a sentiment classifier in just about 10 minutes using minimal code, which is a significant advancement in AI development.
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Andrew Ng
08/30/23
@ Stanford Online
The majority of financial value from AI today comes from supervised learning, which can generate over 100 billion US dollars a year for companies like Google.
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Andrew Ng
08/30/23
@ Stanford Online
Generative AI is an exciting new area that is currently smaller in scale but is expected to grow significantly in the next few years due to increased developer interest and venture capital investments.
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Andrew Ng
08/30/23
@ Stanford Online
General-purpose technologies like supervised learning and generative AI are valuable for a wide range of applications, and there is still much work to be done in identifying and executing concrete use cases.
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Andrew Ng
08/30/23
@ Stanford Online
The rise of generative AI presents opportunities to create deep, complex applications that can provide long-term value, unlike simpler applications that may not be defensible in the long run.
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Andrew Ng
08/30/23
@ Stanford Online
While AI has been discussed for many years, its adoption outside consumer software remains limited, with most value still concentrated in areas like advertising and web search.
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Andrew Ng
08/30/23
@ Stanford Online
Many potential AI projects exist, but outside of consumer software, few have the scale to apply a single piece of software to a billion users, making it challenging to generate similar financial value.
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Andrew Ng
08/30/23
@ Stanford Online
There are numerous smaller-scale AI projects, such as those in agriculture and food production, that require a different approach than the traditional model of hiring large teams of engineers for development.
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Andrew Ng
08/30/23
@ Stanford Online
The AI community is developing better tools that allow end users to easily customize AI systems without needing extensive technical knowledge.
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Andrew Ng
08/30/23
@ Stanford Online
Exciting low-code and no-code tools are emerging, enabling users to customize AI systems based on their specific needs.
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Andrew Ng
08/30/23
@ Stanford Online
Companies can now train AI systems using their own unique data, such as images of their products, which may not be available online.
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Andrew Ng
08/30/23
@ Stanford Online
The trend of Data Centric AI allows businesses to provide data instead of writing extensive code, making AI implementation more feasible.
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Andrew Ng
08/30/23
@ Stanford Online
AI's value is currently concentrated in the tech sector, but there is a significant opportunity to expand its benefits across all industries.
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Andrew Ng
08/30/23
@ Stanford Online
Two key trends in AI are the development of general-purpose technology with concrete use cases and the rise of user-friendly tools that enable AI deployment across various industries.
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Andrew Ng
08/30/23
@ Stanford Online
To capitalize on diverse AI opportunities, it's essential to start multiple companies that can pursue different markets and applications.
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Andrew Ng
08/30/23
@ Stanford Online
Integrating AI into existing businesses can be advantageous, especially for companies with strong distribution channels, allowing for efficient product enhancements.
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Andrew Ng
08/30/23
@ Stanford Online
The AI stack consists of several layers, including hardware, infrastructure, and application layers, each with unique opportunities and challenges.
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Andrew Ng
08/30/23
@ Stanford Online
While the infrastructure and developer tooling layers are receiving media attention, the success of these layers depends on the success of the application layer.
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Andrew Ng
08/30/23
@ Stanford Online
An example of a successful AI application is a startup that uses AI for romantic relationship coaching, combining expertise in AI and relationships.
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Andrew Ng
08/30/23
@ Stanford Online
The startup Bearing AI, which aims to make ships more fuel-efficient using AI, was validated through customer feedback and technical feasibility checks before development.
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Andrew Ng
08/30/23
@ Stanford Online
The process of building startups has evolved to include bringing on a CEO early in the project, which improves efficiency and knowledge transfer.
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Andrew Ng
08/30/23
@ Stanford Online
Today, hundreds of ships are using Bearing AI's technology, resulting in significant fuel savings and operational efficiency, which emerged from the collaboration between Mitsui's maritime expertise and my team's AI knowledge, showcasing how diverse expertise can lead to innovative solutions.
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Andrew Ng
08/30/23
@ Stanford Online
I've realized that my focus should be solely on AI, as trying to master multiple fields like maritime shipping or healthcare is overwhelming and inefficient.
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Andrew Ng
08/30/23
@ Stanford Online
Partnering with subject matter experts often leads to exciting new opportunities, as they bring deep insights that can be combined with AI technology for effective solutions.
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Andrew Ng
08/30/23
@ Stanford Online
I prefer to engage in startup ideas only when there's a concrete concept, as this allows for efficient validation and execution, contrary to the common advice of exploring many alternatives.
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Andrew Ng
08/30/23
@ Stanford Online
Concrete ideas can be validated or falsified quickly, providing clear direction for teams and enabling faster progress in the ideation stage.
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Andrew Ng
08/30/23
@ Stanford Online
In today's AI landscape, many subject matter experts have already explored problems deeply, and collaborating with them can accelerate the validation and building process of new ideas.
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Andrew Ng
08/30/23
@ Stanford Online
My team only works on projects that advance humanity, and we often reject financially sound ideas that are ethically questionable, highlighting the importance of ethical considerations in AI.
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Andrew Ng
08/30/23
@ Stanford Online
AI technology is rapidly improving, with current systems being less biased and more fair than they were six months ago, although challenges with bias and fairness still exist.
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Andrew Ng
08/30/23
@ Stanford Online
The biggest risk of AI is job disruption, as the current wave of automation affects a broader range of jobs, including higher-wage positions, compared to previous automation waves.
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Andrew Ng
08/30/23
@ Stanford Online
As AI creates value, we have a responsibility to ensure that those whose jobs are disrupted are supported and treated well, emphasizing the need for societal care in the face of technological change.
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Andrew Ng
08/30/23
@ Stanford Online
While there is hype around AI potentially causing extinction risks, I believe that AI will not create meaningful extinction risks for humanity, and we can manage its development responsibly.
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Andrew Ng
08/30/23
@ Stanford Online
AI technology will develop gradually, allowing us time to provide oversight and ensure safety, rather than experiencing a sudden, uncontrollable leap in intelligence.
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Andrew Ng
08/30/23
@ Stanford Online
To ensure humanity thrives in the future, we should embrace AI's rapid development rather than slow it down, as AI can be a key part of solving significant global challenges.
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Andrew Ng
08/30/23
@ Stanford Online
AI as a general-purpose technology presents numerous opportunities, and our focus should be on building concrete use cases to harness its potential effectively.