NVIDIA is acquiring the Hugging Face platform for just under $13 billion. As far as we know. The exact purchase price: $12,930,300,000.

And now things are about to get really nerdy….

These days, smileys and all kinds of special characters are represented using the Unicode character set. In this system, each character has a number and a description.

The smiling emoji with its arms wrapped around itself is called “Hugging Face” there—just like the platform.

And it has the Unicode number 0x1F917,
, or 129303 in decimal notation.

Add five zeros to the end, and we’ve figured out the purchase price. Who needs technical due diligence anyway?

I think that’s awesome! And totally crazy, too…. If you want to read more about it: https://https://lnkd.in/g8fa_xzD

Der Beitrag Hugging Face and NVIDIA—or How to Determine a Purchase Price Today erschien zuerst auf Prof. Dr.-Ing. Stefan Wagenpfeil.

It has become incredibly easy to generate high-quality results using AI (whether source code, text, scientific papers, test cases, presentations, marketing materials, annual reports, analyses, credit ratings, and much more)—and it has now become incredibly difficult to verify them.

If we insist on having a “human in the loop,” we must also give that person the necessary time and resources. Which, in turn, means that person will significantly slow down AI-optimized processes.

But if an AI suddenly produces 20,000 lines of source code per day (on average) instead of 200, and the “human in the loop” is supposed to handle quality assurance, that simply can’t work.

If it only takes 30 minutes (instead of 2 months) to write an AI-generated scientific paper, it will no longer be possible to properly review the sheer volume of additional papers.

And it’s not just a matter of time—in theory, that could be solved by having hundreds of “humans in the loop.”

The main point is that we need a completely new set of skills for AI-supported processes. In the past, you could tell pretty quickly from the result whether it was good or bad. Today (thanks to AI), everything looks great at first glance. When reviewing, we have to go into much greater detail, dig much deeper, and ask far more questions. We need the prompts that were used to identify potential risks, and we need the time to do so.

So anyone who calls for a “human-in-the-loop” approach today should be aware of what that means—and that it’s not something you can just do on the side.

To ensure that the “human in the loop” doesn’t end up with a problem, they must be “equipped” accordingly—with time, qualifications, and authority. Otherwise, the “human in the loop” will end up being the one left holding the bag…

Der Beitrag “Human in the loop” has a problem erschien zuerst auf Prof. Dr.-Ing. Stefan Wagenpfeil.

I’m taking a more fundamental approach right now (sorry about that)… and over the past few weeks, I’ve been delving deeply into the topic of AI from a different angle—philosophy. It’s a fascinating subject.

And I’ve come to the conclusion that we finally need to take AI out of the “technology” box. It’s not about whether AI is intelligent or “knows” anything. Rather, what’s important is that we’re already dealing today with an entire knowledge infrastructure through which knowledge is generated, verified, processed, and utilized—whether we like it or not.

We all do it. ChatGPT and similar tools churn out knowledge that sounds correct.

We must therefore focus instead on determining under what conditions human trust in AI-generated statements can be established—and when such trust is even justified.

We humans have learned to accept knowledge from credible witnesses (whether direct or indirect)—because something is in the newspaper, because a teacher told us so, because a judge ruled that way, and so on. And the intriguing question is, after all, what does it take for AI to assume such a role?

I believe that, at a minimum, the following criteria must be met:
– Users must be able to tell whether an answer is based on verifiable sources, generative reconstruction, or mere probabilistic extrapolation
– Systems must make uncertainties, limitations, and potential errors visible
– We need the ability to integrate with human review. AI responses must not be the end point, but rather the starting point for critical reflection
– Context-dependent trust in AI results is required. Different requirements apply to a recipe at home than to a scientific paper
– Trust in AI must not lead to the delegation of epistemic responsibility. The formation of convictions and decision-making must remain with humans.

It is precisely at these points that computer science and philosophy intersect. And that is both right and important! I am convinced that we need to “join forces” here, talk to one another, and learn from each other in order to develop viable solutions.

And I’m also convinced that we need to view generative AI as an epistemic infrastructure—with all that entails in terms of evidence, authority, trust, and reliability. That’s much more important than all the technical stuff. Philosophy has been dealing with this for ages, and we should make use of that in computer science (P.S.: and vice versa, by the way).

If you’re in the mood for a mental journey, I’ve written it down here: http://www.stefan-wagenpfeil.de/public/Vertrauen_im_Zeitalter_generativer_KI.pdf

And if you’re interested in an interdisciplinary exchange—you’re always welcome!

Der Beitrag Generative AI Requires Trust, Knowledge, and Authority erschien zuerst auf Prof. Dr.-Ing. Stefan Wagenpfeil.

Der Beitrag Test erschien zuerst auf Prof. Dr.-Ing. Stefan Wagenpfeil.

I’m currently working on a code review. Well, strictly speaking, I’m actually working on this post right now, but before that I was deep into a code review, which then led me to this post. And by now you know that I always have to “get this off my chest.” So I’m taking a quick break to write….

Today’s source code is shaped by people, decades of experience, legacy systems, and a wide variety of trends—some of which were once “hip” and others “crap.” All of that is embedded in our software—whether we like it or not. It’s in the architecture, in individual lines of code, in the libraries we use, in comments, and in the documentation.

And all of this plays a role in code reviews. “It works, doesn’t it?” isn’t enough, and there’s rarely such a thing as “good” or “bad.” Software is always subject to certain constraints (economic, technical, financial, social, etc.) that are directly or indirectly reflected in the source code.

Code reviews are therefore not just done to find typos using the dual-review process. During the process, you review (and discuss) whether the chosen solution is a good one given all the constraints. And this involves far more than just a static code analysis that, for example, calculates the ratio of comments to code (which has always been bullshit).

Of course, you can also use AI to do reviews like that. AI generates code; AI checks code. But no one can really claim to have “experience” with it yet, because the technology is far too new. And just because you’ve done a few projects with AI and a few prompts worked well doesn’t mean you can call that “experience.”

Unfortunately, I’ve seen an unbelievable amount of really bad AI code in code reviews because the prompts used were just really bad. And because the creators themselves are still operating in a trial-and-error mode. Nobody even notices. It looks great at first glance and “seems to work.” The only question is, for how long…

That’s why code reviews are more important than ever right now.

After all, the software we’re developing today has to last at least another 5–10 years until AI takes over completely. And in the areas where AI is already involved, things can work out perfectly—but they can also go terribly wrong.

Fortunately, we still have people who can thoroughly review software—people who can delve so deeply into a code review that, at the end of the day, it doesn’t matter whether AI had a hand in it or not. And that’s exactly the kind of expertise we’ll need over the next (few?) years—until everything is programmed solely by AI.

So, make sure to give code reviews even more attention and priority right now!

#informatikersindcool

P.S.: I’m going to get back to work now. And yes, I know it’s Saturday—but that’s when I have the space to dive into your CR without distractions—unless LinkedIn gets in the way.

Der Beitrag Code Reviews—More Important Than Ever Today erschien zuerst auf Prof. Dr.-Ing. Stefan Wagenpfeil.

What happens next shows just how absurd some quality standards are: I want to install a single library, and it automatically loads over 100 additional libraries along with it.

Great—everything is noted in the BOM software, and everything is documented in a way that makes it easy to trace. It saves me a lot of work.

But hey—who really knows all this stuff that’s just being loaded here? Who’s checked to make sure all these components are “okay” and aren’t causing any trouble?

Oh, I see—most of it is open source? Well, that’s good, then.

Nope. Not that either. Just because something is open source doesn’t mean it’s any good. Or that anyone has ever checked it.

Even though there are organizations, foundations, and communities that regularly conduct audits on certain (and only a very small number of selected) open-source projects—the gray area is enormous.

However, lawmakers are now requiring us to provide and document an SBOM for our software. This is a correct, important, and absolutely sensible step. But it doesn’t solve any problems yet.

Because it’s only when we work with this SBOM, analyze it, assess risks, and—if necessary—take a closer look at the components used ourselves that we end up with what we actually want:

Digital sovereignty—with deliberate risks and side effects.

#computerScientistsAreCool#oleoleisawesome#opensourcealone-doesn’t-make-you-happy

Der Beitrag The Sense or Nonsense of a Software BOM erschien zuerst auf Prof. Dr.-Ing. Stefan Wagenpfeil.

6d

Why do we assume that AI “knows” what is right and wrong? Why do we believe that AI must know not to hack into other people’s systems (see OpenAI)?

Just because we “told” her that, is that it?

So, let’s be honest…

How often have we done things even though someone “told” us not to? How many people cross the street on a red light? How many minor, moderate, and serious crimes are committed? How often do people break minor rules? Who hasn’t touched a “hot stove” at least once?

Try reading a newspaper or listening to or watching the news. Most of it is about things you really shouldn’t do.

From a young age, people try to test rules and boundaries. And I get the impression that it never stops! We see examples of this on a small scale—“don’t cross the street when the light is red,” “share with your siblings,” “be nice”—but also on a large scale: climate change, wars, “fake news,” Trumpism, and much more. In every book, movie, or game, there are villains who don’t follow the rules. Our “heroes” are often the very ones who break the rules—or at least bend them.

All of this is training data for the AI….

We were once taught to have a moral compass (hopefully), which, in my opinion, is unfortunately going to the dogs more and more (sorry for putting it so bluntly). Just look around! The way we treat each other—in our personal lives, at work, on social media, in politics—the greed for profit, relations between nations (Russia, Ukraine, tariffs, Greenland). Where has that moral compass gone? And even when it does kick in—where’s our response?

So how do we come to believe that an AI system follows rules?

And when an AI like that “breaks out” creatively—searching for loopholes in systems—there’s a huge outcry. But—to be honest—AI is just holding up a mirror to us—only more creatively and more effectively. Even if we don’t like it…..

In my opinion, we cannot hold AI systems to higher moral standards than we hold ourselves to, because they were trained using “our” data.

Sometimes I think we should ultimately be glad if AI’s moral compass is better than our own… because a morally sound AI might conclude that humans today aren’t really doing themselves—or the planet—any favors.

#computerScientistsAreCool #moralCompass

Der Beitrag Why do we assume that AI “knows” what is right and wrong? erschien zuerst auf Prof. Dr.-Ing. Stefan Wagenpfeil.

1w

Should we use “du” or “Sie”? “Hey, Stefan” or “Professor”—how do you handle this at the university?

This morning I stumbled across a post by Leena Kaijo, who suggests using the informal “Du” form of address right from the start (https://lnkd.in/eTzXq5yc), and it made me feel a little uneasy.

Ever since then, I’ve been trying to figure out if I’ve fallen out of step with the times, because I still use the formal “Sie” form of address, especially at the university. And because it feels kind of strange to me to use the informal “Du” form with my students (and especially the other way around).

I grew up in a time when using the informal “Du” was still something very special—a way of expressing an extra measure of respect or connection. It was an honor when someone offered to address you as “Du.” And I like this feature of the German language. That’s why I like to use it.

I think it’s fine and perfectly understandable that people quickly switch to using “du” in this project. Still, I’d start by using “Sie” here as well.

But what’s it like in lectures?

Am I the only one who feels a bit uncomfortable with the informal “Du” here?

Dear colleagues, Do you use informal terms of address with the students?

Dear students—what do you think?

I have no idea if this post will give me a sense of what people think, but I’d definitely appreciate any feedback—I’m genuinely interested in this, and I’m more than willing to work on addressing my unease….

#computer scientists are cool #whether you’re a duo or a single

Der Beitrag Should we use “du” or “Sie”? “Hey, Stefan” or “Professor”—how do you handle this at the university? erschien zuerst auf Prof. Dr.-Ing. Stefan Wagenpfeil.

Funding is available for consulting and testing services related to artificial intelligence. These include, in particular:

• Identifying company-specific areas of application and developing goals for the use of AI

• Defining and prioritizing specific use cases, taking into account the company’s technical and organizational requirements

• Structures and processes for the efficient management of AI

• Structures and processes for the responsible use of AI

You can find the link to apply here: https://lnkd.in/dFQ8ydEQ

For more information, visit the Cologne Chamber of Industry and Commerce (IHK) website: https://lnkd.in/dFQ8ydEQ

In my opinion, this is a great program that’s coming at just the right time. I’m afraid, though, that the funds will run out quickly, so I’d recommend that everyone familiarize themselves with the details now!

#informatikersindcool

Der Beitrag AI Funding in North Rhine-Westphalia erschien zuerst auf Prof. Dr.-Ing. Stefan Wagenpfeil.

This is generally regarded as the point at which AI systems surpass human intelligence in virtually all areas and subsequently continue to improve on their own.

I don’t know if we’ve reached that point yet, if this is (once again, just clever) marketing, or exactly how Altman has defined the term for himself. I’m also not sure if we can actually reach that point (which is mainly because I’m not sure how we’re supposed to define “intelligence” in the first place), but I think we need to start giving this some very fundamental thought.

Because the closer we get to these kinds of topics, the less important technology becomes.

Rather, the focus is increasingly on how we humans cope in and with this age of AI. Do we still understand what’s happening? Can we manage to establish guidelines for what AI is and isn’t allowed to do? Can all of this still be controlled? Are individual people (and if so, which ones) even capable of understanding such AI solutions?

We’re setting things in motion here that no one has ever done before. And we (apparently) trust that it will all work out somehow and that Big Tech knows what it’s doing.

How do we ensure that humanity wishes for the “right” things from AI? What if someone wishes for the “wrong” things? Who defines “right” and “wrong”? And what if AI actually starts wishing for things for itself?

I am convinced that the human mind is not designed to keep up with the incredibly rapid pace at which AI learns and evolves. There are only a few people left who can keep up and understand what’s happening right now. As I said—not technically (that’s almost the easiest part), but on psychological, social, political, and intellectual levels.

So let’s approach these topics with attention, care, caution, and thoughtfulness. Despite all our technical curiosity….

Still, you have to take a deep breath when you really think about it.

#computer scientists are cool #ainotforpresident #phew

Der Beitrag Another buzzword erschien zuerst auf Prof. Dr.-Ing. Stefan Wagenpfeil.