🤖 AI is a general purpose technology with lots of different applications, similar to electricity.
🔍 Supervised learning is a powerful tool for labeling and recognizing things.
⚙️ Large-scale supervised learning using big data and powerful AI models can lead to improved performance.
📚 The last decade of AI has been driven by supervised learning and has seen significant progress.
🔥 This decade is introducing the exciting tool of generative AI, which can generate text based on prompts.
💡 Generative AI uses supervised learning to predict the next word and has led to the development of large language models.
🏗️ Building a commercial grade machine learning system traditionally takes around 6-12 months, but prompt-based AI can be deployed in a matter of hours or days.
🌊 The rise of prompt-based AI is expected to open up a flood of custom AI applications that can be built by many people.
💻 A sentiment classifier can be written with just a few lines of code, showcasing the simplicity and power of prompt-based AI.
🔑 Supervised learning is currently the most valuable AI technology, with companies like Google generating over $100 billion a year from it.
💡 Generative AI is an emerging technology with a lot of potential for growth in the next three years, attracting interest from developers, venture capitalists, and large corporations.
🌱 There are opportunities for new startups or existing companies to create and capture value in the AI market, particularly in the areas of new AI tools and applications.
🔑 The value of AI is currently concentrated in consumer software internet, but there are opportunities for AI in other industries.
💡 The traditional recipe of hiring a large group of engineers for a project is not feasible in industries outside of consumer software internet.
🛠️ The AI community is developing low-code and no-code tools that enable easy customization of AI systems.
🚀 There are diverse opportunities in AI, and starting multiple companies can help pursue these opportunities.
🔌 Integrating AI into existing businesses can be advantageous if done effectively.
💡 The AI stack consists of the hardware semiconductor layer, infrastructure layer, developer tooling layer, and application layer.
💑 Applications that combine AI expertise and specific domains, such as romance coaching, present exciting and less competitive opportunities.
🛳️ One example of an AI application is using AI to make ships more fuel-efficient.
📈 The process of building startups involves validating ideas, talking to customers, and recruiting a CEO.
🚀 Bearing AI successfully built a prototype and validated it with customers, resulting in external funding and fuel savings for ships.
🌊 Mitsui's expertise in maritime shipping, combined with AI expertise, led to the creation of Bearing AI and its impact on the industry.
💡 Concrete startup ideas are more efficient for validation and execution, as they provide a clear direction for the team and attract subject matter experts.
🤖 AI systems today are less biased and more fair than six months ago, but there are still problems to work on.
🤔 The biggest risk of AI is the disruption to jobs, particularly higher-wage jobs, and it is our responsibility to ensure well-being for those affected.
🌍 There is overblown hype about AI creating extinction risk, but AI can be a key part of the solution to real risks such as pandemics and climate change.