The progress of generative AI

1. The progress of generative AI

Let's discuss progress in generative AI, its players and driving factors.

2. The key players

Progress always relies on a variety of players, each bringing unique contributions. In the case of generative AI, universities lead fundamental research and produce researchers. They are often funded by governments and civic institutions. Startups and large companies hire researchers and integrate generative AI into products. Open-source communities build and iterate on datasets and models. Let's take a deeper look at each.

3. Universities

Universities play several key roles in generative AI's progress. First, they produce research and theory that is a foundation for progress in the field. For example, the GAN architecture was originally proposed by a group from the University of Montreal. Universities also train researchers in generative AI and related fields. Students often go on to found startup companies or lead research in other institutions. Finally, universities collaborate through partnerships with industry or government grants.

4. Government and civic institutions

Governments set the regulatory environment for development, but may also provide funding. For instance, the US government's DARPA (Defense Advanced Research Projects Agency) has funded research into making AI more explainable. Civic institutions contribute independent analyses, resources, and project funding. For instance, the Canadian Institute for Advanced Research (CIFAR) created CIFAR-10, a widely used image dataset.

5. Companies (large and small)

Companies large and small adopt generative AI to gain a market advantage. They bring generative AI to the broader world through their products. Smaller companies show off their latest technology to attract funding. Companies with large funding resources additionally publish research themselves. For instance, Google published the first research introducing transformer models. They also produce much of the hardware and cloud resources required by other contributors to do their work. Large companies also acquire startups and hire researchers to gain access to fresh talent and intellectual property.

6. Open-source community

Open-source projects provide open access to the tools used to develop generative AI, as well as some generative AI models themselves. This lowers barriers to deploying and experimenting with generative AI technology, speeding up innovation through collaboration. Many foundational models, like the image generator Stable Diffusion and large language model LLaMA, are open-source projects used broadly across the space. However, there are risks. Given the decreased ability to monetize, open-source projects are not always maintained for long. Also, giving open access to such powerful tools raises the risks of misuse by malicious actors.

7. Companies and open-source

A great synergy exists between tech companies and the open-source community. This is especially true in terms of research publication. Publishing openly will win support from developers, attract new talent, and provide more opportunities for feedback from a broader set of stakeholders. But companies are also faced with challenges when considering generative AI research publication. On the other hand, releasing too much risks losing competitive advantages and possibly allowing malicious actors to use the work for destructive ends.

8. Going forward

As we look to the future of generative AI development, several components will spur its progress while others will slow it down. Accelerators include continued decline of hardware costs, further research development, and pressure from market and geopolitical competition. Decelerators include hitting the physical limits of technology, a regulation that inhibits progress, and a lack of funding, data, or talent to fuel the field. The idea is to have a collaboration among players that balances openness, competition, and responsibility.

9. Let's practice!

Now let's see what we've learned!

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