Y’know, I started reading this great piece by Claire L. Evans thinking about its connections to systems thinking, but I ended up thinking more about prototyping. And microbes.
In this piece published a year ago, Ted Chiang pours cold water on the idea of a bootstrapping singularity.
How much can you optimize for generality? To what extent can you simultaneously optimize a system for every possible situation, including situations never encountered before? Presumably, some improvement is possible, but the idea of an intelligence explosion implies that there is essentially no limit to the extent of optimization that can be achieved. This is a very strong claim. If someone is asserting that infinite optimization for generality is possible, I’d like to see some arguments besides citing examples of optimization for specialized tasks.
Whatever the merit of the scientific aspirations originally encompassed by the term “artificial intelligence,” it’s a phrase that now functions in the vernacular primarily to obfuscate, alienate, and glamorize.
Do “cloud” next!
What would Wiener think of the current human use of human beings? He would be amazed by the power of computers and the internet. He would be happy that the early neural nets in which he played a role have spawned powerful deep-learning systems that exhibit the perceptual ability he demanded of them—although he might not be impressed that one of the most prominent examples of such computerized Gestalt is the ability to recognize photos of kittens on the World Wide Web.
Thorough (and grim) research from Chris.
A terrific six-part series of short articles looking at the people behind the history of Artificial Intelligence, from Babbage to Turing to JCR Licklider.
- When Charles Babbage Played Chess With the Original Mechanical Turk
- Invisible Women Programmed America’s First Electronic Computer
- Why Alan Turing Wanted AI Agents to Make Mistakes
- The DARPA Dreamer Who Aimed for Cyborg Intelligence
- Algorithmic Bias Was Born in the 1980s
- How Amazon’s Mechanical Turkers Got Squeezed Inside the Machine
The history of AI is often told as the story of machines getting smarter over time. What’s lost is the human element in the narrative, how intelligent machines are designed, trained, and powered by human minds and bodies.
We hoped for a bicycle for the mind; we got a Lazy Boy recliner for the mind.
Nicky Case on how Douglas Engelbart’s vision for human-computer augmentation has taken a turn from creation to consumption.
When you create a Human+AI team, the hard part isn’t the “AI”. It isn’t even the “Human”.
It’s the “+”.
Spot-on take by Ted Chiang:
I used to find it odd that these hypothetical AIs were supposed to be smart enough to solve problems that no human could, yet they were incapable of doing something most every adult has done: taking a step back and asking whether their current course of action is really a good idea. Then I realized that we are already surrounded by machines that demonstrate a complete lack of insight, we just call them corporations.
Related: if you want to see the paperclip maximiser in action, just look at the humans destroying the planet by mining bitcoin.
Questions prompted by the Clearleft gathering in Norway to discuss AI.
I like Richard’s five reminders:
- Just because the technology feels magic, it doesn’t mean making it understandable requires magic.
- Designers are going to need to get familiar with new materials to make things make sense to people.
- We need to make sure people have an option to object when something isn’t right.
- We should not fall into the trap of assuming the way to make machine learning understandable should be purely individualistic.
- We also need to think about how we design regulators too.
A good, if somewhat dispiriting, overview of Artificial Intelligence. (There's some nice typesetting on this page)