Research AI vs Applied AI vs Generative AI
- (Stanford University - Hank Ping Han Hsieh)
- Research AI
One important distinction to draw is between the research side of AI and the applied side. Research AI is rather academic in nature and requires a heavy dose of math across a variety of disciplines before you even get to those parts that are specific to AI.
This aspect of AI focuses on the algorithms and tools that drive the state of AI forward. For example, what neural network structures might improve vision recognition results? How might we make unsupervised learning a more generally useful approach? Can we find ways to understand better how deep learning pipelines come up with the answers they do?
- Applied AI
Applied AI refers to the use of AI technology and techniques to solve real-world problems or achieve specific goals. This may involve using AI to analyze data and make predictions or decisions, automate processes or tasks, improve the efficiency or effectiveness of the organization, or provide personalized experiences or recommendations to users.
Applied AI systems use machine learning algorithms to analyze data and make predictions or decisions based on that data.
Applied AI can be used in a wide range of industries and applications, such as healthcare, finance, manufacturing, transportation, and customer service. The goal of applying AI is to use the capabilities of AI to create value and solve practical problems.
- Generative AI
Generative AI refers to systems that create new, original content (such as images, videos, and text) that did not exist before. These systems use algorithms to generate content based on patterns and data, and they can learn from their output and improve over time.
Generative AI is commonly used in creative fields such as art, music and design, as well as in research and development.
With generative AI, the potential for research and testing is limitless, as it has the ability to generate raw material that has not yet been created.
Generative AI can enhance current algorithms by generating training data for new neural networks or developing advanced deep learning architectures. In essence, generative AI functions like a machine that creates improved machines.
- From Research To Production
To get from research into production takes far more than just a model. It takes a village of both research and engineering efforts in tandem to make these things work. It takes hardware, it takes scalable hosting, it takes DevOps, it takes great data science, and much more.
Thankfully, more and more startups are building solutions for each building block, and big players (Uber and Netflix come to mind) are joining in as they open source more and more of their tooling.
Applied AI is anything to do with taking AI research from the lab to a use-case and everything in-between. From the infrastructure and tooling, to the hardware, to the deployment surfaces in industry, to the models themselves, it takes a village to get a bleeding edge advance in AI research to a use-case.
One great test for maturation in our field is the time it takes for a new advance to get from paper to production. Even just a few years ago you could skim some of the major advances in the field and struggle to find real use-cases; this is quickly starting to change.
Applied AI is more about using existing tools to obtain useful results. Open source has played a big role here in providing free and often easy-to-use software in a variety of languages.
Public cloud providers have also devoted a lot of attention to providing machine learning services, models, and datasets that make the onramp to getting started with AI much simpler than it would be otherwise.
