Data + Code

KI, ML, etc.: Es dauert noch und das ist ganz normal so

AI hier, AI da. Aber wer setzt es wirklich ein? MIT Technology Review macht den Realitätscheck und bestätigt vieles derjenigen, die sich praktisch damit befassen.

It’s one thing to see breakthroughs in artificial intelligence that can outplay grandmasters of Go, or even to have devices that turn on music at your command. It’s another thing to use AI to make more than incremental changes in businesses that aren’t inherently digital.

Denn Google, Amazon, Facebook, Netflix und die anderen großen Firmen haben extrem viele Mitarbeiter, die sich nur damit befassen und das Geschäftsmodell ist inheränt auf Daten ausgelegt. In anderen Branchen ist das nicht so.

Data scientists at IBM and Fluor didn’t need long to mock up algorithms the system would use, says Leslie Lindgren, Fluor’s vice president of information management. What took much more time was refining the technology with the close participation of Fluor employees who would use the system. In order for them to trust its judgments, they needed to have input into how it would work, and they had to carefully validate its results, Lindgren says.

To develop a system like this, “you have to bring your domain experts from the business—I mean your best people,” she says. “That means you have to pull them off other things.” Using top people was essential, she adds, because building the AI engine was “too important, too long, and too expensive” for them to do otherwise.

Es wird also, so das Fazit, noch etwas dauern, bis Künstliche Intelliganz und Maschinelles Lernen auch in nicht-Tech-Branchen in der Breite ankommt. Ungewönlich ist das nicht:

What (…) economists confirmed, is that the spread of technologies is shaped less by the intrinsic qualities of the innovations than by the economic situations of the users. The users’ key question is not, as it is for technologists, “What can the technology do?” but “How much will we benefit from investing in it?”


Politik + Wirtschaft

Eine gute Beschreibung von Artificial Intelligence beim Economist

„One way of understanding this [Artificial Intelligence] is that for humans to do things they find difficult, such as solving differential equations, they have to write a set of formal rules. Turning those rules into a program is then pretty simple. For stuff human beings find easy, though, there is no similar need for explicit rules—and trying to create them can be hard. To take one famous example, adults can distinguish pornography from non-pornography. But describing how they do so is almost impossible, as Potter Stewart, an American Supreme Court judge, discovered in 1964. Frustrated by the difficulty of coming up with a legally watertight definition, he threw up his hands and wrote that, although he could not define porn in the abstract, “I know it when I see it.”

Machine learning is a way of getting computers to know things when they see them by producing for themselves the rules their programmers cannot specify. The machines do this with heavy-duty statistical analysis of lots and lots of data.“

Das Ende des Textes:

„But for now, the best advice is to ignore the threat of computers taking over the world—and check that they are not going to take over your job first.“

aus: Rise of the machines