AI Opportunities in 2023: Unlocking Potential with Supervised Learning and Generative AI

🤖 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.