Advances in large AI models and generative AI will continue to boost productivity, creativity, and satisfaction, enabling scientific breakthroughs and helping the world solve some of its biggest challenges. “Within this context, as Microsoft continues to responsibly scale AI advancements for customers, the cloud, infrastructure investments and a strong responsible AI approach are critical,” says KEVIN SCOTT, Microsoft’s Chief Technology Officer.
When we were heading into 2022, everyone in AI was anticipating really impressive things to take place. And even with those lofty expectations, it’s mind-blowing to look back at the magnitude of innovation that we saw left-to-right in AI. The things that researchers and other folks have done to advance the state-of-the-art are just light years beyond what we thought possible even a few years ago. And almost all of this is a result of the incredibly rapid advancement that has happened with large AI models.
We’ve also seen that AI models are becoming more powerful and delivering even more substantial gains for the problems that they’re being used to solve. I think the work on protein folding this year has been really good throughout the technology industry. That’s tremendously exciting. Anything that’s a force multiplier on science and medicine is just net beneficial to the world because those are where some of our biggest, nastiest problems live.
2023 is going to be the most exciting year that the AI community has ever had. And I say that after really, genuinely believing that 2022 was the most exciting year that we’d ever had. The pace of innovation just keeps rolling in at a fast clip.
Certain projects such as the newly launched GitHub Copilot – a large language model-based system that turns natural language prompts into code – are amazing but they are just the tip of the iceberg for what large AI models are going to be able to do going forward.
The entire knowledge economy is going to see a transformation in how AI helps out with repetitive aspects of one’s work and makes it generally more pleasant and fulfilling. This is going to apply to almost anything —from designing new molecules to create medicine, from manufacturing “recipes” from 3D models, or simply writing and editing.
Confidently I can say that 2023 is going to be the most exciting year that the AI community has ever had.
This is the big untold story of AI. To date, most of AI’s benefits are spread across 1,000 different things where you may not even fully appreciate how much of the product experience that you’re getting is coming from a machine learned system.
Let’s take Teams calls as an example. The system holds many parameters that were learned by a machine learning algorithm. There are jitter buffers for the audio system to smooth out the communication. The blur behind people on screens is a machine learning algorithm at work.
We’ve gone from machine learning in a few places to literally 1,000 machine learning things spread across different products, everything from how your Outlook email client works, your predictive text in Word, your Bing search experience, to what your feed looks like in Xbox Cloud Gaming and LinkedIn. There’s AI all over the place making these products better.
The most challenging problems we face as a society right now are in the sciences. How do we cure complicated diseases? How do we prepare ourselves for the next pandemic? How do we provide affordable, quality healthcare to an aging population? How do we educate more kids in the skills of the future? How do we develop technologies to reverse some of the negative effects of carbon emissions? We’re exploring how to take our exciting developments in AI to those problems.
The models in these basic science applications have the same scaling properties as large language models and there is immense opportunity there which means better medicines, it means a possibility to find a catalyst to fix our carbon emission problem, it means accelerating how scientists and those with big ideas can work to try to solve society’s biggest challenges.
The fundamental thing underlying almost all of the recent progress we’ve seen in AI is how critical the importance of scale has proven to be. It turns out that models trained on more data with more compute power just have a much richer and more generalized set of capabilities. If we want to keep driving this progress further, we need to optimize and scale up our compute power as much as we possibly can.
We live in a time of extraordinary complexity and historic macroeconomic change, and as we look out 5, 10 years into the future, even to just achieve a net neutral balance for the whole world, we’re going to need new forms of productivity for all of us to be able to continue enjoying progress.
We want to be building these AI tools as platforms that lots of people can use to build businesses and solve problems. We believe that these platforms democratize access to AI to far more people. With them, you’ll get a richer set of problems solved and you’ll have a more diverse group of people being able to participate in the creation of technology.