July 2022 – AI in Media and Society

I guess it should be no surprise that people want to talk about sentient machines when the term artificial intelligence has become more common than bread and butter. I was hoping this July 2022 article in The Wall Street Journal would go further than it does to assert that there are no grounds at all for talking about sentience in today’s AI, but I was disappointed. The two authors at least did not try to “both sides” the spurious claims.

First, they state that there are a lot of exaggerated claims from companies selling so-called AI products and “solutions.” Second, they touch on the danger this holds for policy decisions — when our elected officials, lawyers, judges, etc., don’t have a clear idea of how AI systems work, they are bound to make poor laws and poor rulings. AI ethicists warn that the hype is “distorting policy makers’ views of the power and fallibility” of AI systems. The reporters quote Oren Etzioni, CEO of the nonprofit Allen Institute for Artificial Intelligence, as saying policy makers are “well-intentioned and ask good questions, but they’re not super well-informed.”

“The belief that AI is becoming — or could ever become — conscious remains on the fringes in the broader scientific community, researchers say.”
—Hao and Kruppa, in “Tech Giants Pour Billions into AI, but Hype Doesn’t Always Match Reality”

The WSJ article also covers the claims of a (now former) Google engineer who claimed the LaMDA chatbot is sentient. On July 22, The Verge was among several news organizations reporting that the engineer has been fired. That article links to a YouTube video that explains “how LaMDA works and how it could produce the responses that convinced [the engineer] without actually being sentient.”

I was dismayed that the media gave so much attention to the engineer’s claims — which he never should have made in the first place, being an engineer. If you take some time to learn about how chatbots are created (or voice assistants — my undergrad college students are decidedly unimpressed with Siri and Alexa), you’ll understand that they cannot possibly have sentience. These conversational systems are prediction machines — they predict “the likelihood of a token (character, word or string) given either its preceding context or … its surrounding context” (source: Bender et al., 2021). The results can be astoundingly good, or hilariously awful. Either way, the process that generates the responses is the result of computational predictions and not the product of a sentient being.

The same is true of the output from DALL-E (and the newer DALL-E 2), which creates an image based on a text description. The less you know about today’s algorithms and powerful AI hardware, the more likely you are to wonder whether there’s a humanlike intelligence behind the system that can produce these graphics. The output is extraordinary and literally was not even possible just a few years ago. What makes it possible today are the combination of massively parallel computational structures and the algorithms designed by humans to enable really, really good guesses (predictions) at what images would best match the descriptive text.

When I say we shouldn’t talk about sentience, I am being a bit coy. I do think we should be talking about what constitutes intelligence in humans and animals, how we know an entity is conscious, and what it means to think, to feel, to perceive the world. I don’t think we should be looking for sentience in our computers — not today and not for a long, long time to come. It distracts us from what today’s AI systems are actually doing, which is making guesses that then affect real sentient humans’ real lives.

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Today’s reading: How to get started with machine learning and AI.

Three factors that go into creating a new machine learning model:

Asking the right question: 25 percent
Data exploration and cleaning, feature engineering, feature selection: 50 percent
Training and evaluating the model: 25 percent

That’s according to Ellen Ambrose, director of AI at Protenus, a healthcare startup based in Baltimore, Maryland, and founded in 2014. She has a Ph.D. in neuroscience from Johns Hopkins University.

The article says: “Once a team has identified the right questions and has determined that the available data can answer those questions, the model needs to be configured.” I think this needs some reexamination: Once the team thinks they have the right question(s), and they think it’s likely that the available data can answer the question(s). Or even this: They think it’s likely that the available data can answer the question(s) adequately and with a high degree of accuracy.

Just as interface design has to be part of product development from the very beginning, a critical approach to the consequences of AI models must be in effect at every stage of the process. (The article doesn’t say this. This is me.) Your question might be harmful in ways you have not yet realized or acknowledged. Your data might contain imbalances or inadequacies that will not be apparent until you run wild data through your model.

The article lists three types of machine learning systems from which you might choose:

Neural network
Support vector machine (SVM)
Gradient boosted forest*

* Wikipedia says: “Gradient boosting is a machine learning technique used in regression and classification tasks, among others. It gives a prediction model in the form of an ensemble of weak prediction models, which are typically decision trees. When a decision tree is the weak learner, the resulting algorithm is called gradient-boosted trees; it usually outperforms random forest” (source). This is new to me; I’ve learned about random forests but not gradient boosted forests.

“Some algorithms are just better suited for certain tasks,” the article says. I wonder whether this might be glossed over in some quickie data science boot camps and courses. If you’re using a tool to build a machine learning model, and the tool lets you choose from various options, do you know enough to choose the one best suited to your task?

Like many descriptions of training an ML model, this article briefly glides over the process of adjusting hyperparameters. It always bothers me when a few principles of statistics and probability are dropped into an article about machine learning as if they were not part of an entire field that existed before ML. The text quickly moves on to: Now that your model is trained, it’s ready to go!

Photo of raw chocolate-chip cookie dough in a large white bowl — *What’s in the cookie dough? Photo by genniebee512 on Pixabay*

A bit later in the article, it’s noted that we can invoke a a trained ML model “with a single Python call” in a Jupyter Notebook. This is in fact how many students supposedly “learn” machine learning — but what are they learning, really, when they simply plug a given dataset into a pre-existing model? I’d say it’s like using the cookie dough sold in the refrigerated section of the supermarket. Sure, you get fresh hot cookies from your oven, but what do you know about making cookies? (What’s in that pre-made dough?)

The idea that someone else has built, tested, trained this ML model (many someones, in fact, with tons of resources you don’t have), and now you can skip all that and just use the model to do what you need to do — sure, that seems great! “The developer could then apply a specific business logic to generate value from an idea without needing to worry about the details of how the model was built and trained,” the article says. Wonderful!

But … you know, that cookie dough could contain an ingredient you’re allergic to. You’re going to want to read the label carefully. Does that ML model you’re using have an ingredients label? Chances are, the answer is no. What’s inside the black box? We can always go back to Crawford and Paglen for an example of how badly this can go. Or look at the many examples Hannah Fry examined.

Unusually for an article of this kind, here we find an acknowledgment that real-world data is constantly changing (and increasing). The ML system — once deployed — will need tending and oversight. MLOps was a new term to me — inspired by DevOps (the collaboration among developers and IT professionals in all stages of the software development lifecycle), the term MLOps refers to collaboration among data scientists, ML engineers, developers and IT professionals to manage the lifecycle of a machine learning system (algorithms and hardware). How well is it working now that it’s out in the world, and its output is relied on for decision-making that affects real people’s lives?

At the end, the article promises that Ars Technica “will be running an entire series on creating, evaluating, and running AI models.” I’m going to be on the lookout for that, but I’m a little skeptical after reading this one. There’s nothing wrong in it, per se, but in my opinion it misrepresents the use of ML models as something simple and safe, ignoring all the ways that your lack of knowledge about the details can lead to flawed results and unreliable outcomes.

Month: July 2022

We should not talk about sentience

A good intro to machine learning models?