by Malcolm Murray

When it comes to AI, or even worse, “AGI”, we are facing a crisis of language. Different people use the terms to mean drastically different things. This is deeply unhelpful for productive debate. This point was hammered home to me several times this week. On LinkedIn, I debated appropriate risk management techniques for AI with a professor, and it turned out we were talking about very different kinds of AI. In New York, the proposed RAISE act made the A16Z lobbying army, fresh from its bloody victories in the California legislature, reload its weapons, despite the two sides talking about very different kinds of AI.
AI, as the current buzzword, is an extremely big tent and in effect a screen on to which people project their distinct hopes and fears. To make some progress in the AI debate, we should separate AI into its different archetypes. I believe there are at least five: Tool AI, Robot AI, Oracle AI, Golem AI and Agent AI, and they are all distinct, with different lineages and different purposes. Let’s examine each in turn.
First, there is Tool AI. Its lineage can be traced to big data, the buzzword in the business world in the early 2010s. This is the AI we have had for more than a decade, the AI that gets advertised in B2B SaaS solutions. It is AI as a prediction engine, deployed in the Amazon storefront to recommend your next purchase. This is AI in the TikTok feed, optimizing content for your engagement. It is statistics, but statistics on steroids. This is the type of AI for which VC firms like Andreessen Horwitz (A16Z) are techno-optimists and that can lead to large productivity increases for companies. It is a complement to humans, not a substitute.
Second, there is Robot AI. Its lineage includes the first use of the word robot, in the Czech play R.U.R. (Rossum’s Universal Robots) in 1920, and Robotic Process Automation (RPA), a buzzword in the business world in the late 2010s. This signifies a machine that can automate and therefore replace a hitherto human-conducted process, whether analog or digital. Since the Industrial Revolution, repetitive factory processes have become automated. Instead of the human, blue-collar worker picking up the product to be manufactured and painted, say, the machine does it. More recently, we are seeing digital processes, on computer systems, also becoming possible to automate. Instead of the human, white-collar worker picking up the piece of data from one database and pasting it in another, the machine does it. This is also an AI that the A16Zs of the world would approve of, that can lead to large-scale productivity enhancements. At the same time, this is a type of AI that politicians worry about, since it will inevitably lead to job losses, as it is a substitute to humans rather than a complement. Read more »

Sughra Raza. Light Tricks, Seattle, March, 2022.
I have been thinking about artificial intelligence and its implications for most of my adult life. In the mid-1970s I conducted research in computational semantics which I used in
At about 6:30 am, we pulled up to the Labor Ready office in the Central District. My friend – who for the sake of this column will be called Rick – and I were responding to a trespassing call: a woman who was asked to leave the day-labor agency office was refusing.

Donald Trump is a con man. He was that for a very long time before he entered politics. Because he is a con man, it is tempting for critics to describe his presidential victories as successful cons. However, I think that interpretation does not hold up. Because while Trump at his essence may be little more than a sociopathic con man lacking a sophisticated and flexible inferiority, voters and citizens are not simply “marks.” The electorate, especially one as large as the United States’ (over 73 million registered voters), is maddeningly complex. It reflects a stunning amount of views, ideals, fears, and nuance. And the catch is that while the elected government can never hope to fully reflect this complexity, it can unduly influence it.

In February, after a month-long consideration, I set my New Year’s resolutions into a five-by-five grid. I made a BINGO card—twenty-four resolutions plus the FREE space. It was my attempt to gamify the whole tired resolution process that I’ve failed at so well. Surprisingly the trick seems to have worked, at least partially.
In the context of growing concern about educational equity, the persistent racial disparities associated with the Specialized High School Admissions Test in New York City continue to spark debate. As cities and school systems nationwide reconsider the role of standardized testing, the story of the origins of this test shed light on how deeply embedded policies can appear neutral while, in reality, reinforcing inequality.


Nirmal Raja. Entangled / The Weight of Our Past, 2022.

