Das ist interessant (siehe die Screenshots) und lässt mich nicht los, seit ich es vor ein paar Tagen gesehen habe:

„On the left is GPT-3.5. On the right is GPT-4.

If you think the answer on the left indicates that GPT-3.5 does not have a world-model….

Then you have to agree that the answer on the right indicates GPT-4 does. [..]

It’s pretty evident to me that we’re no longer in „stochastic parrot“ territory. GPT-4 is capable of genuinely reasoning about the world. It’s pretty dumb sometimes, and makes odd mistakes. But it does seem to have some emergent simple world-model.“

GPT-4 liegt bei solchen Aufgaben noch oft genug falsch, dass man sicher davon ausgehen kann, dass es kein Welt-Modell hat. ABER man sieht die Anfänge.


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Solche Anekdoten sehe ich mittlerweile täglich:

„Everyone’s laughing at all the ChatGPT threads, but I (a man who can’t code), just built and shipped a functioning and IMO useful Chrome Extension in ~45 minutes using just that and @Replit – it was the weirdest feeling ever.“

LLMs bringen das Abstraktionslevel von No-Code-Tools auf die Überholspur.

Das ist auch Empowerment. Und zwar massiv. Und natürlich ist dieser Weg nicht für alles geeignet, yadda yadda. Muss es auch gar nicht sein, um empowering zu sein und Software itself nachhaltig zu verändern.

Aber Entwickler:innen, die laut öffentlich dagegen hetzen, haben wahrscheinlich nur Angst um ihren eigenen Turf? 😉


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Don’t mind me, just browsing the Internet here

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Ich habe gerade scite.ai entdeckt und das sieht mal fantastisch aus. ML-Analyse von 33 Millionen akademischer Papers, eine extrem nützliche Hilfe. Unter anderem:

„See how your references have been cited all in one place and evaluate your references to see if they have editorial notices, retractions, or seem to be heavily contrasted at a fraction of the time it would normally take you.“

scite.ai/

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Artificial Digging: How Google’s AI Now Reveals What Producers Sampled:

„But it wasn’t until mid-2022 that Google’s song recognition turned from another mid-Shazam alternative to a groundbreaking discovery for them. A Sample Hunting member by the name of DJPasta found a new way to utilize the technology to the fullest: „I figured out a method to run audio directly from my PC into Google Assistant with software called Bluestacks. I was mostly trying out a few Todd Edwards samples that I’d been looking for at the time. To my surprise, Google Assistant’s song recognition found most of them. Eventually, I had the idea to try out shorter samples, like Carrie Lucas‘ ‚Sometimes a Love Goes Wrong.'“

A stint of discoveries followed, also by other members of the community who started using Google Assistant. From there, they had pretty much taken Google Assistant on board as the new default for sample hunting, on top of their continued discoveries by ear, knowledge of music, and labor of endlessly digging through music.

Lobelia recalls there was a sample drought for Daft Punk’s „Face To Face“ up till July 2022. Lobelia: „I slept through what we now call the Night of Many Samples when I’d say a dozen samples were found. I can’t describe how crazy waking up to all of that was!“ Since then, they tongue-in-cheek call Google’s song recognition technology The Blessed AI. A divine status with the community members as its disciples.“

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Solar set to overtake other energy sources by 2027 | Financial Times:

Next year, solar photovoltaic capacity will leapfrog that of hydropower, according to the International Energy Agency. In three years, it will overtake gas-fired generation. And, in four years, it will push past coal — to boast the largest share of generation capacity of any power source. […]

“There is a boom, there is exponential growth, there is acceleration,” says Heymi Bahar, senior analyst for renewable energy markets and policy at the IEA. “Solar accounts for almost 60 per cent of every power installation that will be built in the coming five years.”

It begins.

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