Maybe I’ve been avoiding AI – so please forgive this rush of posts.
That may well be because it seems to be everywhere and constant at the moment. The hype is overwhelming. I use the word hype deliberately, certainly AI – specific massive neural-net systems – do make possible incredible changes, and will effect the way we work for decades to come.
I do not buy the argument that this means that everything that came before is irrelevant, or that anyone (like me) who does not lace every single statement with AI is in someway a cynic and needs to be left behind. Rather, I see these as arguments that are used to sideline naysayers.
I’ve been keeping my AI thoughts to myself because I feel it would be detrimental to share. I know I’m not alone here; discussing AI with a friend before Christmas he felt the need to add “Please don’t share these comments.”
This came home to me when I read this: “OpenAI in particular should beware hubris. One vc says discussion of cash burn is taboo at the firm, even though leaked figures suggest it will incinerate more than $115bn by 2030.” (OpenAI’s cash burn …, The Economist, December 30.)
So here are some thoughts on where we are with AI
#1 Hype makes it difficult
Between the bubble and the hype it is very difficult to have a have an informed conversation about AI. Even without the hype it would be difficult because this is an emerging technology.
#2 Fear over hope
Rationally I know that technology advances benefit humans, create new jobs and improve living standards. However, one can’t help fearing what is to come because of the constant repetition of “AI will cut jobs” (and who is saying it, #6 below.)
#3 Applications
While an LLM writing a document is impressive few of us spend our days writing documents. This is the equivalent of early micros shipping with BASIC. This was cool if you could programme (or learn); it was useful, to some degree but only if you knew what you were doing. Ultimately it was the emergence of games and then basic word processing and calculation applications which made micros worth the investment.
That is why Apple II was a hit and MSX was not, VisiCalc beat Microsoft BASIC. It is the ARM powered Archimedes failed (no killer apps) but ARM powered phones are omnipresent.
To realise the potential of AI/LLM/neural-nets those applications need building. Some are emerging, for example in healthcare, in law enforcement and environmental.
#4 What problem are you solving?
Applying AI to a problem means we need to have an idea what the problem is (requirements), then we need to construct a product (development), somewhere along the line we need to understand the details (specifications), as I described last time, we need to test the result (testing), get it into hands of users (deployment) and refine the result (feedback and iteration).
Recognise that? Just because it is a shiny new technology doesn’t mean those things go away.
This is one of the reason’s AI initiatives are failing. “Just use AI” may impress investors but simply asking an LLM for a document is little more than a party trick. While we need experimentation people are trying to force AI into every conversation and neglecting the basics.
#5 Unappreciated costs
AI is creating jobs, at the moment many of those jobs are low paid, tedious and hidden away behind sub-contractors in Africa, e.g. tagging and moderation.
Then there is the great unmentionable: Power consumption.
In an age of climate change, where we know the damage our power systems are doing to the environment it is disgusting that these systems are given away free.
Please don’t say “they are powered by renewables.” The world hasn’t finished removing fossil fuels so every data centre powered by renewables is reduces the fossil fuel removed form the mix. Nor is it just power consumption: there are grid connections.
Where I live in London companies are building data centres. But London has a shortage of homes. The data centres v. homes debate is only just getting going. Sometimes it can feel like machines already have mastery: people are loosing jobs and homes to machines.
#6 The rise of the right (sorry)
The AI cheer leaders – Thiel, Musk, Andreessen, Altman, etc. – are aligned with the right of American politics. It sometimes seems the AI revolution and the destruction of post-1945 world order are the same thing. For AI to succeed, must we jettison post-1945 morals?
The arrival of the internet was associated with the creation of opportunities. People like Vince Cerf and Tim Berners-Lees were positive role models who kept their politics quiet. The American oligarchs leading the AI boom envisage a Brave New World rather than The Culture.
(Anyone else see Huxley’s “T” icon in the Tesla badge?)
Looping back, ironically, the “absolute free speech” espoused those oligarchs is not extended to anyone expressing scepticism about the brave new world.
Ultimately, it would be easier to be positive about AI if, instead of emphasising about job cuts we talked about new opportunities. But that itself is a political decision that few talk about.
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