Each technology can not stand alone. It takes a saw to make a hammer, and it takes a hammer to make a saw. And it takes both tools to make a computer, and in today’s factory, it takes a computer to make saws and hammers. —Kevin Kelly
👋 On time for your weekend: a round-up of this week’s remarkable stories at the intersection of technology, business, design, and culture. Three reads and three listens; no fluff, just stuff⚡️
😔 Apologies for missing two editions in a row. There was no stopping the ‘AI’ rollercoaster, as it turned out. As always, one adapts, and Thoughtforms is back into its groove.
📚 Reading
GPT-4 Doesn't Have "Gender Bias." It's Just Bad At Language (Still):
Ultimately, though, current LLM tech faces a fundamental roadblock when it comes to resolving ambiguity: it has no durable internal mental model of you as a writer or interlocutor. An LLM can’t (yet) see you, or look at your profile picture, or creep on your socials or LinkedIn bio, so it can’t do what we humans do and think to itself, “What might this person with these qualities have meant or intended by this turn of phrase?”
Jon Stokes | 22 minutes
Just Calm Down About GPT-4 Already:
The large language models are a little surprising. I’ll give you that. And I think what they say, interestingly, is how much of our language is very much rote, R-O-T-E, rather than generated directly, because it can be collapsed down to this set of parameters [..] one of the deadly sins was how we humans mistake performance for competence [..] our models for generalizing from a performance to a competence don’t apply to AI systems.
Rodney Brooks—IEEE Spectrum | 18 minutes
Noise-Canceling Filters for the Internet are Coming:
[Is] it now possible to build tools that can effectively de-noise our information diets, the same way AirPods can silence the noise in our physical surroundings? How would they work? How would they make us feel? What first- and second-order consequences would widespread use of these tools have on culture, media, and politics?
Nathan Baschez—Divinations | 11 minutes
🎧 Listening
Lessons on building product sense, navigating AI, optimizing the first mile, and making it through the messy middle:
Our human potential has always been held back by the laws of physics, essentially. The mundane, repetitive labour you need to do to get anything done is what holds back our ingenuity. It's the friction; it's the 'work' in 'workflows.' Wouldn't it be great if we could just have flow and no work? That's what AI does: it gets us from workflow to flow; it gets us in this flow state where any idea in your mind's eye, you can start develop it.
Scott Belsky—Lenny’s Podcast | 62 minutes
AI, Web3, VR and the Future of Tech:
One of the elemental questions of building something on a blockchain is how do you get to a point that the user doesn't need to know what architecture you've used. If you can do that relatively easily, then what's the point? Because if the point of this is that it's portable and composable and addressable and they have ownership, if you abstract that away so that it becomes easy to use, then what was the point in doing it? There's an interesting product balance defined in that.
Benedict Evans—Upstream with Erik Torenberg | 80 minutes
The Human Experience:
[I]n organizations, they don't really want to hear the inconvenient truths because it gets in the way of their strategy [..] Even if you go and do the research, and you do go and understand what matters to customers at the thick end of the wedge [..], people in the room would rather question the methodology than accept the inconvenient truth. They'd rather say, "Well, is that the right sample size? And were they really the right customers? And maybe we ask them on the wrong day.”
John Sills—Infinite Loops | 79 minutes