What is AI? Everything, everywhere, all at once
As Research Software Engineers (RSEs) in a university, we get lots of AI questions and projects. But what is “AI” exactly? It can mean almost anything, and it makes a bit of a problem when we and our customers may not be starting from a shared understanding of what “AI” is. Let’s discuss.
For us research engineers, a “real AI” project might mean “writing Python code and optimizing deep learning training using a large computer cluster” or “does a Mixture of Experts-model produce better accuracy compared to a traditional feed-forward network in my use case?”. In practice, “AI” questions to us have included:
Problems with web APIs
Problems with PyTorch
Problems with HPC libraries
Problems with laws and regulations
Problems with web servers
Problems with Python installations
I think the effect of “AI” is to reduce the effort needed to use computing tools, so the amount of computing people want to do increases. These increases are proportional to all the usual computing projects we get, even if the projects aren’t exactly deep learning training. Thus the increased need for our team and a general increase in computing and data literacy.
In the end, “AI” is such an overloaded term that it can mean anything. To better ask for help with “AI”, it’s good to be able to decipher it down to the actual topic. This post isn’t about scientific computing general, so let’s look deeper below at what the core “AI” use cases are:
Types of AI
This blog post evolved out of a colleague’s talk at NoBSC 2026, where they pointed the issue that “AI” can mean anything, thus it isn’t a good to speak of “AI” projects without further clarification. In that talk, we thought of two broad categories for the actual uses of “AI”:
“AI” can mean pattern matching and decision making. In this, you have some input data and predict some output based on that.
“AI” can mean content generation, as in generating text, images, or more. This is, in fact, also a prediction of an output based on an input prompt.
You notice these two categories are pretty normal things? That’s because they aren’t, it’s just that modern machine learning (deep learning) has gotten so much better at it than even a decade ago. It’s not intelligence, it’s predictions that seem human-like or better.
These categories aren’t scientific - it’s just what we use as a base for discussion with our customers.
Pattern matching and decision making
This is a traditional use of machine learning. Here, you take input data and predict some output. This is usually done by having a lot of sample input data and corresponding “true” outputs for that data, and training a model. When you give the model new input data, it can predict an output. This is known as “supervised learning”.
This needs specific training data and the output model is usually specific to that domain or model. Deep learning can certainly find patterns that humans or traditional machine learning can’t - assuming such patterns exist in the first place.
For an advanced example, an industrial plant has sensors monitoring the whole process and records of each time it broke down. By using this data, “AI” might be able to predict breakdowns more accurately that a human or non-deep machine learning tools could. These types of uses are relatively un-objectionable to society.
Other examples include things such as insurance companies using all of their data to analyze claims and preemptively deny coverage to those who think are more likely to get sick. Or using pattern matching to approve/deny claims without a human taking responsibility. These have much more societal impact and thus lead to lots of suspicion about “AI”, since it’s being used to diffuse responsibility.
Another type of pattern matching is classifying things without having any true labels. This is called “unsupervised learning”. One example would be the “Netflix Challenge” where scientists tried to use watching data to predict what would be relevant recommendations. It doesn’t matter what the detected categories are, just that things go together.
Content generation
Just like the section title says, this is generating content. Examples could be chatbots (generating text) or image generators based on some input. This has become so widespread since 2022 that it’s easy to think that this is “AI”.
Under the hood, this is actually pattern matching, since it takes a prompt, uses all the previous input data, and generates a predicted output. There isn’t actually “intelligence” under the hood, and it’s all limited by the power of the algorithms and the source data. It can be wrong, not useful, etc. Content generation is definitely not at human-level, and does not have the wide background and task knowledge of a human. It is much faster, though.
Some people can use content-generating methods to make predictions, which isn’t as refined as actual pattern-matching/decision-making method, but because of the general-purpose nature of content generation, it can work without much effort. One example I’ve heard of is using large language models (LLMs) with an input such as “is this social media post positive or negative sentiment? Answer with one word ‘positive’ or ‘negative’”. You get a sentiment analyzer with very little work, that has some large implicit background knowledge. This is the power of these so-called “foundation models” that can do many tasks. The downside is it uses much more computing resources and more chance of going off the rails (hallucination, implicit biases of input data, etc.).
Content generation is powerful, but has potential for misinformation or misuse on a massive scale. One probably wants to be careful when using content generation for predictive tasks. The prevalence of generated content and its potential for misuse is behind a lot of the backlash to “AI”.
Commercial platforms
While not a category, there are also commercial platforms that do the above things. They are set up to be easy to use by a broad audience. We can help with these things, but most of the actual “AI” work is already done. ChatGPT’s early dominance in “AI” was probably caused almost as much by figuring out a useful, usable interface for the general public as their underlying “AI” technology.
So, while commercial platforms and purchasable tools may be easy, they usually have very limited information about how they work (hidden behind “AI” to make you think it’s intelligent), and similar things can be done locally given enough time and effort. The delegation of accountability (and thinking) to third-party platforms is also behind part of the backlash to “AI”.
Examples include the chatbots that everyone uses, coding assistants, text summarizers, etc.
What you should do
If you, or someone, has an “AI” project, the first step is to think deeper and figure what is really the goal. rkdarst has an old saying, “explain it to me again without any terms invented or made popular in the last ten years”. This helps to peel back these layers and get a description of what is actually needed, and is probably quite useful when trying to figure out what an “AI” project actually is. If you can explain your project without saying “AI”, you are well on the way to solving it with “AI”.
Also, we shouldn’t separate “AI” from computing skills in general. “AI” lets people do more computing with less work, but it means everyone needs a higher base level of knowledge (even about no-exactly-“AI” topics). Think back to when cars became cheaper and more reliable. Once cars became more common, people didn’t have to know as much about their intervals, but many more people had to learn how to drive and interact with them. (We’re not saying we want our cities infested with cars, nor AI). This is true even when the field of study isn’t “computing”. Don’t let “AI literacy” become “ChatGPT literacy”, it is literacy in computing, data, and problem solving - and a healthy suspicion for details hidden behind jargon.
You certainly can benefit from “AI” without knowing all the details. It’s very hard to benefit from it without knowing your actual goal.
Note: this blog post was written with zero “AI” content generation. My anonymous colleague has contributed some of the key ideas and title.
Definitions
Machine learning: A field of study using statistical algorithms to learn from data and generalize to unseen data.
Deep learning: Machine learning using multilayered neural networks.
Supervised learning: Machine learning methods taking input and labeled “true” outputs as the training data.
Unsupervised learning: Compared to the above, algorithms learning from input data which is unlabeled.
Training: The process of iterating through the input data to find patterns, resulting in the trained model.
Model: The learned parameters from input data and training, which can be combined with the right code to make predictions.
Application programming interface (API): An interface that makes it easy for a computer code to interact with something (as opposed to a human-optimized interface).
“Artificial intelligence”: Did you think I’m going to give a single simple definition here?