Applications – Page 2 – AI in Media and Society

New AI strategy from U.S. Department of Health and Human Services

January 30, 2021January 30, 2021 Mindy McAdams

The Biden Administration is working hard in a wide range of areas, so maybe it’s no surprise that HHS released this report, titled Artificial Intelligence (AI) Strategy (PDF), this month.

“HHS recognizes that Artificial Intelligence (AI) will be a critical enabler
of its mission in the future,” it says on the first page of the 7-page document. “HHS will leverage AI to solve previously unsolvable problems,” in part by “scaling trustworthy AI adoption across the Department.”

So HHS is going to be buying some AI products. I wonder what they are (will be), and who makes (or will make) them.

“HHS will leverage AI capabilities to solve complex mission challenges and generate AI-enabled insights to inform efficient programmatic and business decisions” — while to some extent this is typical current business jargon, I’d like to know:

Which complex mission challenges? What AI capabilities will be applied, and how?
Which programmatic and business decisions? How will AI-enabled insights be applied?

These are the kinds of questions journalists will need to ask when these AI claims are bandied about. Name the system(s), name the supplier(s), give us the science. Link to the relevant research papers.

I think a major concern would be use of any technologies coming from Amazon, Facebook, or Google — but I am no less concerned about government using so-called solutions peddled by business-serving firms such as Deloitte.

The following executive orders (both from the previous administration) are cited in the HHS document:

Maintaining American Leadership in Artificial Intelligence (13859), February 2019
Promoting the Use of Trustworthy Artificial Intelligence in the Federal Government (13690), December 2020

The department will set up a new HHS AI Council to identify priorities and “identify and foster relationships with public and private entities aligned to priority AI initiatives.” The council will also establish a Community of Practice consisting of AI practitioners (page 5).

Four key focus areas:

An AI-ready workforce and AI culture (includes “broad, department-wide awareness of the potential of AI”)
AI research and development in health and human services (includes grants)
“Democratize foundational AI tools and resources” — I like that, although implementation is where the rubber meets the road. This sentence indicates good aspirations: “Readily accessible tools, data assets, resources, and best practices will be critical to minimizing duplicative AI efforts, increasing reproducibility, and ensuring successful enterprise-wide AI adoption.”
“Promote ethical, trustworthy AI use and development.” Again, a fine statement, but let’s see how they manage to put this into practice.

The four focus areas are summarized in a compact chart (image file).

AI in Media and Society by Mindy McAdams is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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Would you let AI create a recipe for you?

September 18, 2020 Mindy McAdams

On Fridays I try to find something to write about that’s a little less heavy than explanations of neural networks and examinations of embedded biases in AI systems. I call it Friday AI Fun.

The BBC recently wrote about a mobile app that uses AI to help you concoct a meal from the ingredients you already have at home. Plant Jammer is available for both iOS and Android, and it doesn’t merely take your ingredients and find an existing recipe for you — it actually creates a new recipe.

According to BBC journalist Nell Mackenzie, the results are not always delicious. She made some veggie burgers that came out tasting like oatmeal.

I was interested in how the app uses AI, and this is what I found: The team behind Plant Jammer consists of 15 chefs and data scientists, based in Copenhagen, Denmark. They admit that “AI is only a fraction” of what powers the app, framing that as a positive because the app incorporates “gastronomical learnings from chefs.”

The app includes multiple databases, including one of complete recipes. An aspect of the AI is a recommender system, which they compare to Netflix’s. As Plant Jammer learns more about you, it will improve at creating recipes you like, based on “people like you.”

“We asked the chefs which ingredients are umami, and how umami they are. This part reflects the ‘human intelligence’ we used to build our system, a great ‘engine’ that has led to very interesting findings.”
—Michael Haase, CEO, Plant Jammer

My searches led me to an interview with Michael Haase, Plant Jammer’s CEO, in which he described the “gastro-wheel” feature in the app. The wheel encourages you to find balance in your ingredients among a base, something fresh, umami, crunch, sweet-spicy-bitter, and something that ties the ingredients together in harmony.

I’ve downloaded the app but, unlike Mackenzie, I haven’t been brave enough yet to let it create a recipe for me. Exploring some of the recommended recipes in the app, I did find the ability to select any ingredient and instantly see substitutions for it — that could come in handy!

Mackenzie’s article for the BBC also describes other AI–powered food and beverage successes, such as media agency Tiny Giant using AI to help clients “find new combinations of flavors for cupcakes and cocktails.”

What’s the use of machine learning?

September 16, 2020September 18, 2020 Mindy McAdams

I’m interested in applications of machine learning in journalism. This is natural, as my field is journalism. In the field of computer science, however, accolades and honors tend to favor research on new algorithms or procedures, or new network architectures. Applications are practical uses of algorithms, networks, etc., to solve real-world problems — and developing them often doesn’t garner the acclaim that researchers need to advance their careers.

Hannah Kerner, a professor and machine learning researcher at the University of Maryland, wrote about this in the MIT Technology Review. Her essay is aptly titled “Too many AI researchers think real-world problems are not relevant.”

“The first image of a black hole was produced using machine learning. The most accurate predictions of protein structures, an important step for drug discovery, are made using machine learning.”
—Hannah Kerner

Noting that applications of machine learning are making real contributions to science in fields outside computer science, Kerner (who works on machine learning solutions for NASA’s food security and agriculture program) asks how much is lost because of the priorities set by the journals and conferences in the machine learning field.

She also ties this focus on ML research for the sake of advancing ML to the seepage of bias out from widely used datasets into the mainstream — the most famous cases being in face recognition, with systems (machine learning models) built on flawed datasets that disproportionately skew toward white and male faces.

“When studies on real-world applications of machine learning are excluded from the mainstream, it’s difficult for researchers to see the impact of their biased models, making it far less likely that they will work to solve these problems.”
—Hannah Kerner

Machine learning is rarely plug-and-play. In creating an application that will be used to perform useful work — to make new discoveries, perhaps, or to make medical diagnoses more accurate — the machine learning researchers will do substantial new work, even when they use existing models. Just think, for a moment, about the data needed to produce an image of a black hole. Then think about the data needed to make predictions of protein structures. You’re not going to handle those in exactly the same way.

I imagine the work is quite demanding when a number of non–ML experts (say, the biologists who work on protein structures) get together with a bunch of ML experts. But either group working separately from the other is unlikely to come up with a robust new ML application. Kerner linked to this 2018 news report about a flawed cancer-detection system — leaked documents said that “instead of feeding real patient data into the software,” the system was trained on data about hypothetical patients. (OMG, I thought — you can’t train a system on fake data and then use it on real people!)

Judging from what Kerner has written, machine learning researchers might be caught in a loop, where they work on pristine and long-used datasets (instead of dirty, chaotic real-world data) to perfect speed and efficiency of algorithms that perhaps become less adaptable in the process.

It’s not that applications aren’t getting made — they are. The difficulty lies in the priorities for research, which might dissuade early-career ML researchers in particular from work on solving interesting and even vital real-world problems — and wrestling with the problems posed by messy real-world data.

I was reminded of something I’ve often heard from data journalists: If you’re taught by a statistics professor, you’ll be given pre-cleaned datasets to work with. (The reason being: She just wants you to learn statistics.) If you’re taught by a journalist, you’ll be given real dirty data, and the first step will be learning how to clean it properly — because that’s what you have to do with real data and a real problem.

So the next time you read about some breakthrough in machine learning, consider whether it is part of a practical application, or instead, more of a laboratory experiment performed in isolation, using a tried-and-true dataset instead of wild data.