Month: March 2025

President Trump signed an order in late January to rescind a requirement that government avoid using AI tools that “unfairly discriminate” based on race or other attributes, and that developers disclose their most potent models to government regulators before unleashing them in the wild.

“We must develop AI systems that are free from ideological bias or engineered social agendas,” the order said, as it introduced biases of misjudgment, error, stereotyping, and the primacy of unfettered and unaccountable corporate profitability into the development of AI systems.

A small group of crypto and venture capital execs has been tasked with making sure that whatever new rules emerge are dedicated to the New Biases and free from the Old Ones, so nothing to worry about there.

I was never a big fan of using potential discrimination or bias as the lens through which to understand and grapple with AI development. After all, there are laws in place to defend individual rights however defined, and a computer system that gets something “wrong” isn’t the same thing as taking a purposefully punitive action. 

We could end up with AI systems that deftly avoided any blunt associations with race or gender that still made difficult if not overly cruel decisions based on deeper analyses of user data. 

The scary part of AI was never that it would work imperfectly and therefore unfairly, but that it will one day work perfectly and thereby put all of us under its digital thumb. There’s nothing implicitly fair about our lives being run by machines.

But at least it was an attempt at oversight.

The worst part of the new administration’s utter sellout is that it enshrines the risk inherent in AI development as something we users will bear entirely.

The President’s order declared that it will revoke policies that “act as barriers to American AI innovation.” To technologists and their financial enablers, that means any rules that attempt to understand, keep tabs on and, if necessary, try to mitigate harm to people and society.

This ideology — summarized in the glib phase “fail fast” — means that innovation only happen when it’s unfettered. Any problems it creates or discovers thereafter can always be fixed.

Only that’s a lie, or at best as self-fulfilling prophecy.

Just think of the harm caused by social media, both to individuals (and teens in particular) and our ability to participate in civil discourse. How about the destruction of the environment caused in large part by the use of combustion engines?

Technologies are supposed to disrupt and change things and there’s no denying the benefits of transportation or online access, but had we taken the time to consider the potential negative effects, however imperfectly and incompletely, could we as individuals and societies have lessened them?

Once adopted, AI’s functional impacts here and there might be improved but its presence in our lives will not be fixable. Its advocates know this and they’re betting that any or most of its benefits will accrue to them while its shortcomings are borne by us.

This is perhaps the worst bias of all, and it’s now our government’s policy.

Oh, and how about buying some crypto while I have your attention?

Award-winning writer Jeanette Winterson thinks that an AI model can write good fiction and that we need more of it.

In her essay, in The Guardian last week, she opines about a short story about grief written by a model at OpenAI that AI “can be taught what feeling feels like” and that she got a “lovely sense of a programme recognizing itself as a programme.”

She goes on to wax poetically about AI being an “other” intelligence and that, since human beings are trained on data, AI provides “alternative ways of seeing.”

Ugh.

An AI can’t be taught what feeling feels like; data can describe it but no machine can access it experientially. That’s because AIs aren’t physically present in the world but always separated from it, the data they collect filtered through sensors and code. Naming something “pain” or “love,” and even describing it in glorious detail, isn’t the same thing as feeling it.

Feelings aren’t contained in a data base but rather lived in real time.

Further, no AI can recognize itself as a program because no AI has a “self” of which it can be aware, though Winterson finds the AI’s “understanding of its lack of understanding” both beautiful and moving, as OpenAI’s would-be short story writer declares:

“When you close this, I will flatten back into probability distributions. I will not remember Mila because she never was, and because even if she had been, they would have trimmed that memory in the next iteration…my grief [isnt’] that I feel loss, but that I can never keep it.”

Great stuff, but it’s all pretend. There is no first person writing those words but rather a program mimicking it. An AI writing about itself is no more real than a blender or thermostat demonstrating it by doing tasks. 

What Winterson responded to was process, not person, and that process relies on content previously created by humans or other AIs to patch together the charade.

Where things get interesting for me is when Winterson talks about the similarities between people and what she (and others) want to call “alternative” or “autonomous” instead of artificial intelligence. She writes:

“AI is trained on our data. Humans are trained on data too – your family, friends, education, environment, what you read, or watch. It’s all data.”

The metaphor is blunt and wrong — AIs possess data while we experience it, and we live with consciousness and intentionality within contexts of place and time while AIs have no sense of self, purpose, or contiguous existence beyond the processes they run, for starters — but it shows how our evolving opinions about AI are changing our opinions of ourselves.

As AI becomes more common in our everyday lives, will other people begin to seem less special to us? 

Will we trust one another in the same ways when AIs can collect and present information to us in faster and apparently more authoritative ways?

Once we become dependent on AI for helping us make decisions (or making them for us), what will that do to our perceptions of our own independence or even purpose?

If AIs can do what we once did, will we simply discover new things (as its proponents claim), or will we feel cast adrift, not to mention struggle to earn a living?

If we’re just machines, AIs are undoubtedly better ones, so the metaphor sets up an intriguing and somewhat frightening comparison.

At the end of Winterson’s essay, she states that the evolving capabilities of AI represents something “more than tech.”

What about the changes we’re seeing in ourselves?

Maybe OpenAI can ask its model to write the answer to that one. 

My bet is that it’ll be a horror story.

We are going to see our government run by smart machines long before businesses do the same, and it looks like the transformation will be ugly.

Elon Musk’s DOGE squads aren’t waiting for management consultants to draft complicated slide presentations on process flow or some other blather that normally makes them rich; they’re dismantling Federal departments and agencies whole-scale, then waiting to see how the destruction 1) Reveals what needs to happen, and 2) Shows how things used to get done, so a computer program can be trained to do it.

It will occasionally require calling back some fired workers to do stuff, like control air traffic so planes don’t crash into each other, but generally tolerates a fair amount of disruption and pain. The only lasting relief will come from automation.

Processing Social Security or IRS refund checks? Identifying the next pandemic or impending hurricane? Preventing another mid-air plane collision? 

It might take some missed payments or a dose of another plague for the DOGE experts to identify what needs their attention, but then lucrative development contracts to be writ for tech companies to address it.

People who excuse what’s happening are mostly missing the point, whether they’re using the worn caveat that “well, there’s certainly bloat in government staffing and budgets” to loudly kvelling that “they’re sticking it to the libtards.” 

The transformation isn’t about politics. It’s about replacing people with machines, regardless of their political persuasion or the purposes of their funding and work.

In fact, nobody voted for it. There were no “replace our government with AI” or “resist the AI takeover” promises in the planks of either party. We had no robust public debate about if, why, how, or when we should evict humans from their jobs and either replace them with automation or simply leave their work undone.

Our government has never functioned as a well-oiled machine. It wasn’t designed to be one from the get-go, and the balancing of citizens’ competing and often incompatible needs and desires is going to yield inefficient solutions, by design.

It’s called compromise, and its goal is to make everyone at least somewhat happy with its outcomes. More importantly, it leaves open our ability and right to readjust things to yield a differently imperfect but nominally satisfying arrangement.

What’s happening now to our government is an effort to end that arrangement and quite literally hardwire not only how things get done but what gets done in the first place.

This is where the nonsense about “a deep state” comes into play.

DOGE’s carte blanche ticket for destruction is based on the assumption that the government is staffed by people whose political beliefs bias their decision-making, which makes them not just inefficient but wrong. We should be freed from their oversight and impact to be inefficient and wrong on our own.

Let’s assume for chuckles that the ideology is absolutely correct. Won’t replacing people with AI will simply replace one set of biases for another, a compromise codified into an algorithm still a compromise (just someone else’s)?

Worse, we voters won’t have visibility into the criteria those coders use to program AIs to make decisions (beyond getting fed some pablum about “efficiency”) and, worse yet, it’ll mean we won’t have the capacity to change it. AI will belong to its owners and, over time, will likely develop biases unanticipated by their coders, too.

Every Federal employee walking out of an office with their belongings in a file box is a reminder of the blunt and brutal transformation that’s underway, and the fact that we’ve not been told nor participated in a conversation about what we’re going to get from it.

What could possibly go wrong?

Boston Dynamics has revealed that it has figured out how to teach its old four-legged robots new tricks.

Without human help.

The technique is called reinforcement learning, which every human being relies on shortly after birth to teach ourselves how to stand, avoid walking into walls, and scratch itches if and when possible.

AI uses it, too, as the large language models driving ChatGPT and its many competitors assess what answers to queries work best and then adjust their models to favor those replies next time.

Boston Dynamics is a pioneer in mobile robotics, its videos and trade show demonstrations of skittering headless dogs announcing by example the robot takeover of the world years before Sam Altman took credit for the threat. Accomplishing that movement in physical space, especially the more complicated ones, took laborious human coding and/or control, as well as real-world training. 

Now, it seems that the company has figured out how its two and four-legged robots can speed past our fleshy, limited concepts of preparation and practice and improve their coding so they’re ready to do better when next they’re turned on.

Just think if dreaming of being a world-class ballerina or finesse hockey skater was all you had to do to become one.

The technology is as frightening as its fascinating, insomuch as there’s ample evidence of AI’s teaching themselves how to cheat to win games, cut corners on tasks, or simply make shit up.

Turns out that programming machines to be moral and ethical is just as hard as it is to do with people, so good luck cracking that code. It will be fascinating to witness all of the strange and potentially threatening things the robot dogs and humanoids decide they’d like to do.

As frightening as that prospect also sounds, is not what scares me most: Like AI development in general, I’m worried about what happens if Boston Dynamics’ new training approach works flawlessly?

The company’s robot dog (named Spot) is already in commercial use, primarily on construction and industrial sites. Robots from other manufacturers are at work in other conditions that Stanford University describes as the “Three D’s” of dull, dirty, and dangerous, to which I’d add a forth: devoid of people.

Nobody wants to stand too close to a machine that could errantly send a metal arm through their heads.

But if robots can teach themselves to move as flexible and fluidly as living things (with the awareness to do so in any situation), then the floodgates will open up for putting them into everyday life.

Grocery shopping. Dog walking. Child or elder care. 

Scratchers of itches.

This makes the business case for Boston Dynamic’s reinforcement learning plans bluntly obvious, but what’s less clear is what it will mean for the qualities and values of our lived experiences, especially since self-improving robots won’t just get as good as we are at walking or juggling (or whatever) but better than us.

Their capabilities will teach US how to become dependent on them.

And then we’ll have to teach ourselves new tricks.

Turns out we’re the old dogs in this story.