• chevron_right

    Setting our heart-attack-predicting AI loose with “no-code” tools / ArsTechnica · 6 days ago - 13:00 · 1 minute

Ahhh, the easy button!

Enlarge / Ahhh, the easy button! (credit: Aurich Lawson | Getty Images)

This is the second episode in our exploration of "no-code" machine learning. In our first article , we laid out our problem set and discussed the data we would use to test whether a highly automated ML tool designed for business analysts could return cost-effective results near the quality of more code-intensive methods involving a bit more human-driven data science.

If you haven't read that article, you should go back and at least skim it . If you're all set, let's review what we'd do with our heart attack data under "normal" (that is, more code-intensive) machine learning conditions and then throw that all away and hit the "easy" button.

As we discussed previously, we're working with a set of cardiac health data derived from a study at the Cleveland Clinic Institute and the Hungarian Institute of Cardiology in Budapest (as well as other places whose data we've discarded for quality reasons). All that data is available in a repository we've created on GitHub, but its original form is part of a repository of data maintained for machine learning projects by the University of California-Irvine. We're using two versions of the data set: a smaller, more complete one consisting of 303 patient records from the Cleveland Clinic and a larger (597 patient) database that incorporates the Hungarian Institute data but is missing two of the types of data from the smaller set.

Read 38 remaining paragraphs | Comments

  • chevron_right

    No code, no problem—we try to beat an AI at its own game with new tools / ArsTechnica · Monday, 1 August - 13:00 · 1 minute

Is our machine learning yet?

Enlarge / Is our machine learning yet?

Over the past year, machine learning and artificial intelligence technology have made significant strides. Specialized algorithms, including OpenAI's DALL-E, have demonstrated the ability to generate images based on text prompts with increasing canniness. Natural language processing (NLP) systems have grown closer to approximating human writing and text. And some people even think that an AI has attained sentience . (Spoiler alert: It has not .)

And as Ars' Matt Ford recently pointed out here , artificial intelligence may be artificial, but it's not "intelligence"—and it certainly isn't magic. What we call "AI" is dependent upon the construction of models from data using statistical approaches developed by flesh-and-blood humans, and it can fail just as spectacularly as it succeeds. Build a model from bad data and you get bad predictions and bad output—just ask the developers of Microsoft's Tay Twitterbot about that.

For a much less spectacular failure, just look to our back pages. Readers who have been with us for a while, or at least since the summer of 2021, will remember that time we tried to use machine learning to do some analysis—and didn't exactly succeed. ("It turns out 'data-driven' is not just a joke or a buzzword," said Amazon Web Services Senior Product Manager Danny Smith when we checked in with him for some advice. "'Data-driven' is a reality for machine learning or data science projects!") But we learned a lot, and the biggest lesson was that machine learning succeeds only when you ask the right questions of the right data with the right tool.

Read 26 remaining paragraphs | Comments

  • chevron_right

    Ce chien robot apprend à marcher tout seul en une heure à peine / JournalDuGeek · Wednesday, 20 July - 15:30


Habituellement, ce sont plutôt les robots qui s'inspirent du vivant; mais ici, cette relation va dans les deux sens, ce qui est très intéressant pour les chercheurs.

Ce chien robot apprend à marcher tout seul en une heure à peine

  • chevron_right

    AR, meet ML: IKEA app lets you erase and replace your furniture / ArsTechnica · Thursday, 23 June - 21:54

IKEA Kreativ, the retailer's new AR app.

Enlarge / IKEA Kreativ, the retailer's new AR app. (credit: IKEA)

You might have seen augmented reality (AR) mobile apps that allow you to place 3D models of furniture in a camera view of your home, but a new app from IKEA will take that idea to a new level with the help of machine learning (ML).

Called IKEA Kreativ, the app allows you to take an accurate 3D scan of your room, then remove existing furniture items or clutter by replacing them with IKEA products you want to see in the space.

The delete-a-piece-of-furniture capability is reminiscent of the Google Pixel 6's Magic Eraser and iOS 16's upcoming "lift subject from background" features for smartphone photos. Those features are driven by similar AI/ML technology.

Read 10 remaining paragraphs | Comments

  • chevron_right

    How to get started with machine learning and AI / ArsTechnica · Wednesday, 22 June - 13:00 · 1 minute

"It's a cookbook?!"

Enlarge / "It's a cookbook?!" (credit: Aurich Lawson | Getty Images)

"Artificial Intelligence" as we know it today is, at best, a misnomer. AI is in no way intelligent, but it is artificial. It remains one of the hottest topics in industry and is enjoying a renewed interest in academia. This isn't new—the world has been through a series of AI peaks and valleys over the past 50 years. But what makes the current flurry of AI successes different is that modern computing hardware is finally powerful enough to fully implement some wild ideas that have been hanging around for a long time.

Back in the 1950s, in the earliest days of what we now call artificial intelligence, there was a debate over what to name the field. Herbert Simon, co-developer of both the logic theory machine and the General Problem Solver , argued that the field should have the much more anodyne name of “complex information processing.” This certainly doesn’t inspire the awe that “artificial intelligence” does, nor does it convey the idea that machines can think like humans.

However, "complex information processing" is a much better description of what artificial intelligence actually is: parsing complicated data sets and attempting to make inferences from the pile. Some modern examples of AI include speech recognition (in the form of virtual assistants like Siri or Alexa) and systems that determine what's in a photograph or recommend what to buy or watch next. None of these examples are comparable to human intelligence, but they show we can do remarkable things with enough information processing.

Read 23 remaining paragraphs | Comments

  • chevron_right

    Google places engineer on leave after he claims group’s chatbot is “sentient” / ArsTechnica · Monday, 13 June - 14:01

Google places engineer on leave after he claims group’s chatbot is “sentient”

Enlarge (credit: Yuchiro Chino | Getty Images)

Google has ignited a social media firestorm on the nature of consciousness after placing an engineer on paid leave who went public with his belief that the tech group’s chatbot has become “sentient.”

Blake Lemoine, a senior software engineer in Google’s Responsible AI unit, did not receive much attention last week when he wrote a Medium post saying he “may be fired soon for doing AI ethics work.”

But a Saturday profile in the Washington Post characterizing Lemoine as “the Google engineer who thinks the company’s AI has come to life” became the catalyst for widespread discussion on social media regarding the nature of artificial intelligence. Among the experts commenting, questioning or joking about the article were Nobel laureates, Tesla’s head of AI and multiple professors.

Read 16 remaining paragraphs | Comments

  • chevron_right

    How we learned to break down barriers to machine learning / ArsTechnica · Thursday, 19 May - 16:12

Dr. Sephus discusses breaking down barriers to machine learning at Ars Frontiers 2022. Click here for transcript . (video link)

Welcome to the week after Ars Frontiers! This article is the first in a short series of pieces that will recap each of the day's talks for the benefit of those who weren't able to travel to DC for our first conference. We'll be running one of these every few days for the next couple of weeks, and each one will include an embedded video of the talk (along with a transcript).

For today's recap, we're going over our talk with Amazon Web Services tech evangelist Dr. Nashlie Sephus. Our discussion was titled "Breaking Barriers to Machine Learning."

Read 27 remaining paragraphs | Comments

  • chevron_right

    Intelligence artificielle : la vision par ordinateur s’approche d’un cap crucial / JournalDuGeek · Saturday, 23 April - 08:00


La vision par ordinateur, une branche bien connue et très utile de l'IA, s'approche d'une petite révolution grâce aux progrès des systèmes d'étiquetage automatique.

Intelligence artificielle : la vision par ordinateur s’approche d’un cap crucial