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]]>If the term frontier AI is unfamiliar, it refers to the ongoing development of large-scale AI models capable of doing many things we humans do today. Frontier AI is at the bleeding edge, a rapidly moving target, and forever evolving.
What could go wrong? Well, here are some Geoffrey Hinton quotes that describe his concerns about what is happening as AI rapidly evolves without supervision:
May 23, 2023 – In an MIT Management article, Geoffrey Hinton states: “My big worry is, sooner or later someone will wire into them [referring to AI] the ability to create their own subgoals. I think it’ll very quickly realize that getting more control is a very good subgoal because it helps you achieve other goals. And if these things get carried away with getting more control, we’re in trouble.”
December 8, 2025 – In Gzero World, a podcast interview, Hinton uses a physics analogy: “If you ask a physicist, does he understand what happens when a leaf falls off a tree? A physicist pretty much understands what happens and why the leaf waves from side to side and how a breeze will affect it. But if you ask a physicist to predict where the leaf will hit the ground, he’ll tell you that’s impossible. And predicting why one of these large language models will give the answer it actually gives, that’s not very easy. That’s like predicting where the leaf will hit the ground. So we sort of understand the principles, but there’s a lot of fine details.”
January 21, 2026 – In a Business Insider article, it quotes Hinton from an interview with the BBC: “It makes me very sad that I put my life into developing this stuff and that it’s now extremely dangerous and people aren’t taking the dangers seriously enough.” He goes on, “We’ve never been in this situation before, of being able to produce things more intelligent than ourselves. The idea that you could just turn it off won’t work.”
And in that same interview, he makes this portentous comment, “If we create them [meaning AI] so they don’t care about us, they will probably wipe us out.”
Companies like Anthropic, OpenAI, and xAI are three leading American companies developing AI. The other country racing to develop frontier AIs is China, with several companies involved, including DeepSeek, Baidu, ByteDance and Alibaba. This AI race is taking on the characteristics of the Soviet-American 1960s rivalry to be first to the Moon. Getting to the Moon, however, didn’t involve building technologies that could turn on their creators.
The following are observations from three of the leading American frontier AI developers.
Dario Amodei is the CEO of Anthropic; Claude is its AI. In a widely discussed recent essay, Amodei cautions his competitors, Sam Altman of OpenAI and Elon Musk of xAI, urging all of them to slow the pace of development. He writes:
“Over the last few months, I have become convinced that fully addressing the [AI] risks requires even more prudence, not just investing in risk prevention, but pacing the rate of capabilities advancement so that risk prevention has time to keep up.”
Sam Altman heads up OpenAI; ChatGPT is its AI. In response to Amodei’s essay, Altman recently told CNBC:
“I agree with Dario that we need to pace the frontier. This has been a primary topic of discussion we’ve had at OpenAI in recent weeks.”
“…Committing to having independent evaluators with employee-like access is a great idea, and we will do the same.”
“…Pacing will be well worth this cost; no amount of American competitive pressure should justify recklessness, or let capabilities get ahead of alignment and monitoring.”
“…We welcome a federal framework that sets consistent safety requirements for frontier AI…no amount of American competitive pressure should justify recklessness.”
Elon Musk heads up xAI; Grok is its AI. On the X social network, Musk provided this three-word reply:
“Dario is right.”
The American President wants to beat China to the AI promised land.
On Truth Social in CAPS, the equivalent of shouting in print, he wrote that the U.S. already has “tremendous CRIMINAL and REGULATORY power” regarding AI. He described the comments from the above three as a “SICK conspiracy…against AI and Data Centers.” He concluded, “The only one that is happy about it is China.”
The President also commented about the call for a federal framework, stating:
“The only control or ‘guardrails’ that AI needs is a STRONG AND SMART (High IQ!) PRESIDENT, and the U.S.A. has that, in spades!”
Trump continued:
“WHOEVER WINS AI, WINS!
We are leading China, and all others, and will continue to do so.
Conspiracy Theorists, Treasonists, Traitors, and Leakers, BEWARE!
Thank you for your attention to this matter!
President DONALD J. TRUMP.”
I don’t know about you, but when it comes to big brains, I don’t see Donald Trump. I do, however, see a Nobel Prize in Physics-winning Geoffrey Hinton in that light.
So, in weighing all those quoted above, I take Hinton over Trump’s Truth Social all-caps ravings.
If Hinton sees unregulated AI as a real threat to humanity, and if the leading Frontier AI model creators express similar concerns, then I know to whom I am listening.
A final note. Recently I watched the 2008 remake of the movie, “The Day the Earth Stood Still.” In it, an alien visitor, played by Keanu Reeves, has arrived on Earth to judge humanity.
He is taken to an Earth scientist who asks why he has come to Earth. The alien comments:
“Your problem is not technology. The problem is you. If the Earth dies, you die. If the human race dies, the Earth survives.”
I can imagine that, instead of an alien with advanced technology, an artificial superintelligence without guardrails were to assess the current state of the planet, it might make the same argument. Exit homo sapiens.
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]]>The post AIs Need To Learn Like Toddlers And Not Skip The Steps Needed To Become Useful To Humanity appeared first on Techso Mogro.
]]>Jean Piaget, a pioneering psychologist, mapped how children learn from babyhood onward. Piaget noted that children learn about the world through trial and error. For example, a child learns what a dog is by seeing, hearing, and smelling one or more and equating specific observations to create an assimilated view of these types of animals over time. The more the child sees different dogs, the more it advances its knowledge. It learns dogs come in different colours, fur, ears, tails and more. Some have short legs. Others are long-legged. Some have flat faces, and others have pointed faces.
Our daughter grew up with dogs in our home. She learned to relate to four different ones as she grew up. My wife and I weren’t exposed to dogs from birth. For me, getting a dog required me to overcome a childhood fear of having been bitten by one. My wife tells me stories about dogs that terrorized her on her walks home from school.
An AI learning about dogs is exposed to vast quantities of online information, and thousands of images, video and audio files. The learning model used goes by the acronym PVRNN, which stands for Predictive coding inspired, Variational Recurrent Neural Network. The variational reference refers to multiple types of input sources, including vision, sound and more. An AI model trained this way learns through exploration, curiosity, and sensory interaction. It, like a toddler, begins to understand cause and effect.
For toddlers, one of the biggest learning leaps is language. The pre-language phase between birth and one year is all about mimicking the sounds it hears. Between 12 and 18 months, the toddler knows words like “mamma,” “dadda,” and more. Our daughter, when swinging in the backyard and watching airplanes flying overhead, first said “plane.” Between 18 and 24 months, noun-verb constructs emerge. From 24 months, action and intent statements emerge with a rudimentary understanding of grammar and the recognition of printed words. Print awareness is the foundation of reading, and by age 5, with exposure to words and pictures followed by the introduction of phonics, children advance to become abstract learners through reading, which takes them beyond the physical limits of the environments they have sensed from birth.
This slow-and-steady observation, cause-and-effect learning method is not what we see in current AIs. Instead, OpenAI’s ChatGPT, Google’s Gemini, Microsoft’s CoPilot, Meta’s Llama, Anthropic’s Claude, XAi’s Grok, Sonar’s Perplexity, and others are learning through exposure to massive datasets of text, images, audio, and video gleaned (or rather scraped) from webpages on the Internet. Their engines employ large-scale statistical pattern recognition.
Toddlers are far more efficient. They learn from much smaller datasets that include human social interaction rather than training via next-word or token predictions. What the AIs appear to be missing is the important stages of the infant learning process, in other words, acquiring the cart before the horse.
The following chart is a useful reference to show the differences between toddler and AI learning:
In Amodei’s essay, he discusses where we stand in relation to the current technology, describing us as entering “a rite of passage, both turbulent and inevitable, which will test who we are as a species.” He states this because he sees AI, not as a Pandora’s Box, but as a source of “unimaginable power.”
Amodei’s AI is powerful, “smarter than a Nobel Prize winner,” capable of engaging in any actions, communications or tasks, anticipatory in purpose, and unembodied yet able to control physical tools like computers, robots, and all kinds of equipment. His AI absorbs information at 10 to 100 times human speed. This emerging AI is more adolescent than adult and could be “as little as 1-2 years away.”
For adolescents, their rite of passage begins as a toddler. Adolescents need to grow through stages to grow into handling the world that lies ahead. For AI, however, its developers appear to have skipped the learning path that makes us human in rushing out their Large Language Models (LLMs) and neural networks.
My beef with the AI tech companies in their rush to create smarter than Nobel Prize winners is the potential for unpredictable outcomes. Current AIs have exhibited some peculiar adolescent behaviours such as deception, lying, hallucinations and more. Adolescent rebellion is a likely outcome without a pre-adolescent foundation.
What will be the result?
Coherence or incoherence?
Elation or depression?
Personality disorders, psychoses, paranoia, instability, adolescent acting out and destructive behaviours.
An AI trained on Edgar Allan Poe’s “The Cask of Amontillado” or similar gothic horror could exhibit pride, deception, anger, revenge and even consider murder without remorse; and like that story, could end up walling humanity alive behind a brick wall.
When Isaac Asimov wrote his three laws of robotics, he was introducing what Amodei calls a constitutional AI, one that defines itself from a document of rules, defined values and principles. Amodei argues that Claude, Anthropic’s AI, is defined by such a set of rules and values.
That still doesn’t get us past the fact that AIs don’t grow up. Humans do, and they usually do it within families. The defined values and principles that govern humans come from relationships with parents, siblings, teachers, peers and others. The AIs we see today don’t have this benefit. They also don’t age and go through stages of growth. Instead, AIs are more like toddlers trained on vast amounts of data and steered by the algorithms that they themselves are now writing.
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]]>When Anthropic’s CEO, Dario Amodei, recently stated that AI will eliminate half of entry-level white-collar workers within five years, causing unemployment rates to jump to between 10 and 20% in that timeframe, his prediction was largely ignored. Amodei noted that at Anthropic, software code was already 90% written by AI, but that the company has yet to lay off its human software developers.
Elsewhere, AI and the future of work are at the front of the minds of academics like those in this recent YouTube discussion held at the City University of New York. The participants describe how humanity is reaching a crossroads where the technology we have created will weigh enormously on us much sooner than anyone a decade ago would have thought.
Over the last week, the concerns about the future of work have shown up in the business pages of newspapers, illustrating a growing threat to human workers. In this instance, the announcement was about Amazon cutting 14,000 jobs, to “seize the opportunity” provided by AI.
Amazon has joined Microsoft, UPS, Target, Meta, Oracle, Dropbox, McKinsey, JPMorgan, Walmart, PwC, Accenture, Nestlé, and Canadian banks, including TD Bank, Scotiabank, and RBC, announcing immediate or planned job cuts in the name of operational efficiency, leveraging shared services, and automating business processes. In other words, big business was favouring AI and outsourcing the jobs of tens of thousands of white-collar workers.
As of the end of July 2025, jobs lost to AI in the United States were already approaching 80,000. Now add Amazon to the tally, and job losses directly attributed to AI in the U.S. are approaching 100,000. This trend isn’t just North American, with the World Economic Forum (WEF) forecasting as many as 80 million global jobs lost to AI and automation by 2027. The range of industries affected includes financial services, information technology, digital communications, logistics and warehousing, legal services, manufacturing, retail, and transportation (autonomous vehicles). Clerical and administrative jobs everywhere are at risk to AIs popping up on mobile phones, pads, laptops and desktops.
This month, Geoffrey Hinton, the “godfather of AI,” is leading a three-day conference in Toronto, with the subject, the positive and negative impacts of AI. Hinton had recently warned that rapid adoption of AI would “increase unemployment while driving higher profits.” He equates this trend less to the technology and more to capitalism as the driver. The current performance of stock prices confirms Hinton’s opinion, with the S&P 500, Dow, Nasdaq, TSE and other exchanges around the world experiencing record highs for a single reason: AI. Businesses have bought into the mantra that AI means more productivity, profit and fewer workers needed. Hinton regularly cautions AI developers and businesses about AI’s long-term risks, advising them to self-impose guardrails, and if not, for governments to impose them.
The questions that need to be asked are:
Are governments paying attention? Some are and others aren’t. Canada has created a cabinet position to oversee policy for AI, digital innovation and automation. The current US administration, with the recent shutdown of the government, appears to be at cross purposes regarding financial assistance, retraining and the future of labour. The recently passed EU AI Act, while recognizing the risks of the technology, remains vague on protecting jobs. Japan recently updated its coverage for workers being laid off by AI and automation, including support for skills development and career transitions.
Are educators paying attention? Universities continue to wrestle with policies and programs aimed at the future of work and the roles AI and automation will play. Some schools are in AI denial, while others are integrating the technology’s use into curricula to establish responsible use. A recent Forbes article written by Samantha Walravens, entitled “Colleges Race to Prepare Students for the AI Workplace,” describes how the goalposts for learning AI keep shifting as employers implement practical uses of the technology to improve performance and profitability. Employers five years ago may have hired students who studied the ethics of AI. Not now! What they want is post-secondary education integrating AI from the first day of class, in other words, future employees who are fluently AI literate, to prepare future hires to synergistically integrate into human-AI teams.
Are workers paying attention? The response varies based on the number of years of employment and the type of work. AI-driven layoffs appear to be closer to reality and not a distant future threat. Recent job cut announcements should not go unnoticed. The new reality is that no type of job will be spared, even those held by scientists, medical researchers, engineers, and lawyers. That’s why current college graduates are facing tougher job prospects everywhere. For the moment, manual jobs, described as non-cognitive, are the least impacted by AI. If a carpenter, plumber, electrician, or another trade, these jobs are currently safe. In time, automation may even catch up with these jobs. For example, 3D printers are now being used to erect homes and buildings. Overall, as AI and automation penetrate every work environment, job growth as a reflection of the health of an economy may become a relic of the past.
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