Month: October 2024

Google’s ex-CEO Eric Schmidt believes that AI growth demands for electricity will outpace any preventative measures to reduce harm to the environment (extraction, carbon emissions, etc.), our mitigation efforts aren’t going to work anyway, and we’ll risk “constraining” AI development.

So, we should go “all in” on destroying the planet now, and instead bet that AI will figure out how to save it sometime later on, according to his remarks at some “expert” meeting in DC earlier this month.

I’m all for optimism and I’m hopeful that minds greater than mine will find ways to save me from some of Fate’s cruelty or my own ineptitude, whether those minds are organic or artificial.

Mr. Schmidt has every right to make his personal problems worse because of some vague belief that doing so will enable their solution, but passing off that fantasy as a viable public policy option is irresponsible, at best, and willful deceit, more likely.

Why deceit? Well, he has much to gain from unrestrained AI development, both through an arms developer he founded last year to develop AI-powered drones and his likely investments in technology companies building less overt agents of death.

He also subscribes to a Silicon Valley theology called “Effective Altruism,” which posits that rich, smart tech nerds such as himself have the capacity (and not just the power) to make decisions in the best interests of the rest of us. He has funded lots of organizations, scholarships, and other activities to promote it.

This is how I get to the “willful deceit” analysis.

He knows that people can walk and chew gum at the same time, and that there’s no public policy that doesn’t have to address multiple needs and often competing interests. The idea that building AI and fighting climate change is a binary choice is simply not true; we can and should do both at the same time, with one pursuit informing and at times mitigating the other.

He also knows that relying on AI to do any specific thing at a specific time is a fool’s errand. This is especially true when it comes to solving particularly huge and complicated problems, and climate change is perhaps the hugest and most complex problem we can imagine.

Solving it won’t just require a description of the solution but a series of solutions, most or all of which will themselves be huge and complicated and rely on people, communities, and institutions doing two things at the same time (or more).

And there’s no guarantee that there’ll even be a viable fix by the time that the commensurately smart and electrically well-fed AI comes online to offer it. If there’s some Big Data model that specified with a dependable level of certainty the delivery of a climate change fix that also confirms our ability and willingness to implement it, well, that would have been a nice addition to Mr. Schmidt’s comments.

But it doesn’t exist. He’s “all in” on a wish wrapped in a hope inside a fantasy.

I am regularly dumbfounded by the level of blather and nonsense that passes for “expert” opinion on AI, especially when it comes to how it will impact our lives and world. The wrong people are dictating the wrong terms for our public discourse about AI. We should not be surprised when we reach the wrong conclusions, or when we’re told that they were inevitable.

Why there aren’t more of us standing up when we’re told this dreck and yelling “What the fuck are you talking about?” is beyond me.

Instead, we have to rely on “experts” like Mr. Schmidt telling us that sometime in the distant future, as we waft clouds of carbon from our eyes and gasp for our next breath, an AI will magically appear and tell us what we should do to save the planet.

What if it tells us that we never should have spent all that money and time making AI and destroying the planet in the first place?

Mr. Schmidt will have long since died a very rich man.

And that’s the only certainty he’s banking on.

AI researcher Geoffrey Hinton won a Nobel Prize earlier this month for his work pioneering the neural networks that help make AI possible. 

Also, he believes that his creation will hasten the spread of misinformation, eliminate jobs, and might one day decide to annihilate humankind.

So, now he’s madly working on ways to keep us safe?

Not quite. He says that he’s “too old to do technical work” and that he consoles himself with what he calls “the normal excuse” that if he hadn’t done it, someone else would have.”

He’s just going to keep reminding us he regrets that we might be doomed.

I guess there’s some intellectual and moral honesty in his position. Since he didn’t help invent a time machine, he can’t go back and undo his past work, and he never intentionally created a weapon of mass destruction. His mental capacity today at 75 is no match for the brainpower he possessed as a young man.

And he gave up whatever salary he was getting at Google so he could sound the alarm (though he’ll likely make more on the speaker’s circuit).

History gives us examples of other innovators who were troubled by and/or tried to make amends for the consequences of their inventions.

In 1789, an opponent of capital punishment named Joseph-Ignace Guillotin proposed a swiftly efficient bladed machine to behead people, er, attached to recommendations for its fair use and protections for its victims’ families. He also hoped that less theatrical executions would draw fewer spectators and reduce public support for the practice.

After 15,000+ people were guillotined during the French Revolution, he spent the remainder of his life speaking on the evils of the death penalty.

In 1867, Alfred Nobel patented an explosive using nitroglycerin called “Nobel’s Safety Powder” – otherwise known as dynamite – that could make mining safer and more efficient. He also opened 90+ armaments factories while claiming that he hoped that equipping two opposing armies with his highly efficient weapons would make them “recoil with horror and disband their troops.”

He created his Peace Prize in his will almost 40 years later to honor “the most or the best work for fraternity among nations.”  While the medal has been awarded annually ever since, there’ve been no reports of opposing armies disbanding because their guns are too good.

In 1945, Robert Oppenheimer and his Manhattan Project team detonated the first successful nuclear weapon, after which he reported quipped “I guess it worked.” Bombs would be dropped on Hiroshima and Nagasaki about a month later, and Oppenheimer’s mood would shift, telling President Truman that “I feel I have blood on my hands,” and he went on to host or participate in numerous learned conclaves on arms control.

No, I’m not overly bothered that Geoffrey Hinton follows in a long tradition of scientists having late-in-life revelations. What frightens and angers me is that the tradition continues.

How many junior Guillotins blindly believe that they can fix a problem with AI without causing other ones? How many Nobels are turning a deaf ear to the reports of their chatbot creations lying or being used to do harm? 

How many Oppenheimers are chasing today’s Little Boy AI – a generally aware AI, or “AGI” – without contemplating the broad implications for their intentions…or planning to take any responsibility for them, whether known or as-yet to be revealed?

You’d think that history would have taught us that scientists need to be more attuned to the implications of their actions. If it had, maybe we’d require STEM students to take courses in morals and personal behavior, or make researchers working on particularly scary stuff submit to periodic therapeutic conversations with psych experts who could help them keep their heads on straight?

Naw, instead we’re getting legislation intended to make sure AI abuses all of us equally, and otherwise absolves its inventors of any culpability if those impacts are deemed onerous.

Oh, and allows its inventors like Mr. Hinton to tell us we’re screwed, collect a prize, and go off to make peace with his conscience.

Stay tuned for a new generation of AI researchers to get older and follow in his footsteps.

And prepare to live with the consequences of their actions, however much or little they regret them.

Turns out that when large language models (“LLMS”) get larger, they get better at certain tasks and worse on others.

Researchers in a group called BigScience found that feeding LLMs more data made them better at solving difficult questions – likely those that required access to that greater data and commensurate prior learning – but at the cost of delivering reliably accurate answers to simpler ones.

The chatbots also got more reckless in their willingness to tee-up those potentially wrong answers.

I can’t help but think of an otherwise smart human friend who gets more philosophically broad and sloppily stupid after a few cocktails.

The scientists can’t explain the cause of this degraded chatbot performance, as the machinations of evermore complex LLMs make such cause/effect assessments more inscrutable. They suspect that it has something to do with user variables like query structure (wording, length, order) or maybe how the results themselves are evaluated, as if a looser definition of accuracy or truth would improve our satisfaction with the outcomes.

The happyspeak technical term for such gyrations is “reliability fluctuations.”

So, don’t worry about the drunken friend’s reasoning…just smile at the entertaining outbursts and shrug at the blather. Take it all in with a grain of salt.

This sure seems to challenge the merits of gigantic, all-seeing and knowing AIs that will make difficult decisions for us.

It also begs questions about why the leading tech toffs are forever searching for more data to vacuum into their ever-bigger LLMs. There’s a mad dash to achieve artificial general intelligence (“AGI”) because it’s assumed there’s some point of hugeness and complexity that will yield a computer that thinks and responds like a human being.

Now we know that the faux person might be a loud drunk.

There’s a contrarian school of thought in AI research and development that suggests smaller is better because a simplified and shortened list of tasks can be accomplished with less data, use less energy, and spit out far more reliable results.

Your smart thermostat doesn’t need to contemplate Nietzsche, it just needs to sense and respond to the temperature. It’s also less likely to decide one day that it wants to annihilate life on the planet.

We already have this sort of AI distributed in devices and processes across our work and personal lives. Imagine if development was focused on making these smaller models smarter, faster, and more efficient, or finding new ways to clarify and synthesize tasks that suggested new ways to build and connect AIs to find big answers by asking ever-smaller questions?

Humanity doesn’t need AGI or evermore garrulous chatbots to solve even our most seemingly intractable problems

We know the answers to things like slowing or reversing climate change, for instance, but we just don’t like them. Our problems are social, political, economic, psychological…not really technological.

And the research coming from BigScience suggests that we’d need to take any counsel from an AI on the subject with that grain of salt anyway.

We should just order another cocktail.

It sounds like the title of a Roald Dahl story, but researchers have devised a robot that moves in response to the wishes of a mushroom.

OK, so a shroom might not desire to jump or walk across a room, but they possess neuron-like branch-things called hyphae that transmit electrical impulses in response to changes in light, temperature, and other stimuli.

These impulses can vary in amplitude, frequency, and duration, and mushrooms can share them with one another in a quasi-language that one researcher believes yields at least 50 words that can be organized into sentences.

Still, to call that thinking is probably too generous, though a goodly portion of our own daily cognitive activity is no more, er, thoughtful than similar responses to prompts with the appropriate grunt or simple declaration.

But doesn’t it represent some form of intelligence, informed by some type of awareness?

The video of the dancing mushroom robot suggests that the AI sensed the mushroom’s intentionality to move. It’s not necessarily true, since the researchers had to make some arbitrary decisions about which stimuli would trigger what actions, but the connection between the organism and machine is still quite real, and it suggests stunning potential for the further development of an AI that mediates that interchange.

Much is written about the race to make AI sentient so that we can interact with it as if we were talking to one another, and then it could go on to resolve questions as we would but only better, faster, and more reliably.

Yet, like our own behavior, a majority of what happens around the world doesn’t require such higher-level conversation or contemplation.

There are already many billions of sensors in use that capture changes in light, temperature, and other stimuli, and then prompt programmed responses.

Thermostats trigger HVAC units to start or stop. Radars in airplanes tell pilots to avoid storms and trigger a ping when your car drifts over a lane divider. My computer turned on this morning because the button I pushed sensed my intention and translated it into action.

Big data reads minds, of a sort, by analyzing enough external data so that a predictive model can suggest what we might internally plan to do next. It’s what powers those eerily prescient ads or social media content that somehow has a bulls-eye focus on the topics you love to get angry about.

The mushroom robot research suggests ways to make these connections – between observation and action, between internal states of being and the external world – more nuanced and direct.

Imagine farms where each head of lettuce manages its own feeding and water supply.  House pets that articulate how they feel beyond a thwapping tail or sullen quiet. Urban lawns that can flash a light or shoot a laser to keep dogs from peeing on them.

AI as a cross-species Universal Translator.

It gets wilder after that. Imagine the complex systems of our bodies being able to better manage their interaction, starting with prescribing a bespoke vitamin to start every day and leading to more real-time regulation of water intake, etc. (or microscopic AIs that literally get inside of us and encourage our organs and glands to up their game).

Think of how the AI could be used by people who have infirmities that impede their movement or even block their interaction with the outside world. Faster, more responsive exoskeletons. Better hearing and sight augmentation. Active sensing and responses to counter the frustrating commands of MS or other neurological diseases.

Then, how about looking beyond living things and applying AI models to sense the “intentionally” of, say, a building or bridge to stay upright or resist catching on fire, and then empowering them to “stay healthy” by adjusting stresses of weight and its allocation.

It’s all a huge leap beyond a dancing mushroom robot, but it’s not impossible.

Of course, there’s a downside to such imagined benefits: The same AI that can sense when a mushroom wants to dance will know, by default, how to trigger that intention. Tech that better reads us will be equally adept at reading to us.

The Universal Translator will work both ways.

There are ethical questions here that are profound and worthy of spirited debate, but I doubt we’ll ever have them. AI naysayers will rightly point out that a dancing mushroom robot is a far cry from an AI that reads the minds of inanimate objects, let alone people.

But AI believers will continue their development work.

The dance is going to continue.