Virtual Teacher 💯 @vt_cathy

Here's how I would explain why a negative divided by a negative equals a positive to kids

Authors, share the worlds your readers dream of this Tuesday #WritersLift 🌙📚✨ Reply with your books, poetry, blogs, podcasts, or other creative work below 👇 𝑰’𝒍𝒍 𝑹𝒆𝒑𝒐𝒔𝒕 𝒕𝒐 𝒃𝒐𝒐𝒔𝒕 𝒚𝒐𝒖𝒓 𝒓𝒆𝒂𝒄𝒉 🚀💫 💛 #IndieAuthors #AmWriting Follow @karonsbooks ✨

@karonsbooks Amazing image what program did you use to generate it?

The Peter Principle is the satirical theory that employees are generally promoted to their level of incompetence. In 1974, the author of the Peter a principle, Dr. Laurence J. Peter, explained how he first got the idea and what can be done about it

Yann LeCun sat across from Lex Fridman and quietly proved that intelligence has nothing to do with thinking. He did it with two sentences about a trophy. “The trophy doesn’t fit in the suitcase because it’s too big.” “The trophy doesn’t fit in the suitcase because it’s too small.” Same words. One swap at the end. In the first, “it” is the trophy. In the second, “it” is the suitcase. You solved both before you finished reading. Nobody taught you that. There is no rule for it. No logic chain. No formula. You knew because you’ve held things. Packed things. Felt the resistance of something too large for the space it was meant to fill. LeCun calls this grounding. “A big object doesn’t fit in a small object.” The machine has read that line a billion times. It has never once picked anything up. It knows the word “big.” It has never been small enough to be lifted, or large enough to be the problem. So when the sentence turns, it has nothing to turn on. You didn’t solve that riddle by thinking. You solved it by having lived. Every object your hands ever closed around. Every door you misjudged. Every suitcase you overpacked and forced shut. Decades of physics written into your nervous system so deep you can’t even find it. That is what answered the question. Not your mind. Your life. LeCun: “You have this knowledge of how the world works, of geometry, and things like that.” Now point that at yourself. Most of what you understand, you could never explain. You cannot describe how you catch a ball. How you judge the weight of a bag before you lift it. How you know a staircase is wrong before your foot confirms it. Your deepest intelligence has no language in it at all. We spent centuries convinced that thinking was the highest act of the mind. LeCun is pointing at something underneath it. Something older. Something the body learned long before the mouth could speak. Intelligence was never computation. It was accumulation. The slow, silent record of a life spent touching the world. The machine holds every word ever written about it. It has never once been in it. We keep asking whether it thinks. It cannot even tell us which “it” we mean.

cathylynnebrown.substack.com/p/ai-the-force…

AI does not reduce creativity. It amplifies it. One person can now write, illustrate, animate, compose music, edit video and build ideas that once required entire teams. The future belongs increasingly to people who can think creatively, adapt quickly and turn imagination into reality. Education needs to stop asking: “Should students use AI?” And start asking: “Are we helping students become the kind of humans who can use these tools meaningfully?” My latest newsletter in comment to follow...

And the next question "How could you use this in your classroom tomorrow?" And then some quotes from Seymour Pappert from the 2004 Apple Conference virtualteacher.com.au/papert/ “....in a professional environment Professionals develop.” “How can we make the magic persist.” “Magic is hard. You need books, to learn programs, to go to conferences, to ask questions. It’s absolutely worth it.” “If we love children and we know what’s possible how can we do anything else.” “How does a school that has seen the magic turn it’s back on it.” Amazing man amazing conference.

“Teachers aren’t scared of change. They are scared of wasting time on things that make no significant impact in their classrooms.” In an upcoming blog post, I reflected on this quote and shared a few thoughts. When developing professional learning, does it feel relevant to staff and connected to what they actually want and need to learn? It’s easy to blame people for not implementing new ideas, but more often than not, that’s a leadership issue. If professional learning happens during staff days and nothing changes afterward, the problem is likely the process and implementation, not the staff. A simple way to measure the value of professional learning is to ask two questions at the end of the day: What did you learn today? And because of that learning, how will students benefit? If we can’t clearly explain how learning will improve students' outcomes, then our time is probably better spent elsewhere.

#BREAKING Pauline Hanson’s budget reply: “We will abolish divisive cultural departments and race based programs, that divide Australians by skin colour or ancestry” “Every Australian will treated equal under one flag and one culture” “No more taxpayer funded ‘Welcome to Country’ rituals” She’s got my vote.

Charlie Munger called bureaucracy a cancer. Not a problem. Not friction. Cancer. A thing that grows for no reason other than to keep growing. Munger: “I think a bureaucracy is sort of like a cancer. And it functions sort of like a cancer.” He wasn’t exaggerating. He was diagnosing. Bureaucracy doesn’t solve problems. It metabolizes them. Every problem becomes a department. Every department becomes a budget. Every budget becomes proof the department is needed. The organism feeds itself. Munger: “I think the decisions get made better if you eliminate the bureaucracy.” Not faster. Better. That word matters. Everyone assumes more oversight means better outcomes. More layers. More approvals. More hands on every decision before it can move. What actually happens is the opposite. Decisions get diluted until nobody can trace who made them. The thing that should take four hours takes four months. And by the time it arrives, the original purpose is already dead. Now apply that to government. The same cancer Munger diagnosed in boardrooms runs entire nations. Departments that exist to oversee departments. Agencies duplicating the work of other agencies. Committees formed to evaluate whether other committees should continue to exist. Taxpayers fund all of it. And now there’s an alternative. AI can already do most of it. Not in theory. Right now. Permit processing. Budget allocation. Regulatory review. Policy analysis. Compliance. All pattern recognition and rule application. The two things machines already do better than any human in the building. An algorithm doesn’t protect its own budget. Doesn’t lobby for headcount. Doesn’t slow a process down to justify hiring more people to manage the slowness. That’s the trick bureaucracy has always run. Make everything worse. Then point at the mess as proof more resources are needed. Munger: “And so we’re very anti-bureaucracy, and I think it’s doing us a lot of good.” He ran Berkshire Hathaway with roughly 30 people at headquarters. Hundreds of billions in value. Managed by a team you could seat at a dinner table. Meanwhile the average government agency employs thousands to do what a single system could process before lunch. The reason bureaucracy survives has nothing to do with performance. It survives because it votes. It lobbies. It self-perpetuates. It is the only organism on earth that grows by making the host sicker. Munger didn’t call it waste. He didn’t call it bloat. He called it cancer. The scan came back years ago. The patient keeps hiring more doctors.

@realBigBrainAI

Elon Musk just put the entire university system on trial. Not the curriculum. Not the professors. The premise. Musk: “You don’t need college to learn stuff. Everything is available basically for free. You can learn anything you want for free.” For a thousand years, universities held one monopoly. Access. You paid the toll or you stayed ignorant. The internet erased that in a decade. Every lecture. Every framework. Every textbook. Free. From any screen on Earth. The six-figure tuition is no longer buying knowledge. It is buying a signal. Musk: “There is a value that colleges have, which is seeing whether somebody can work hard at something, including a bunch of annoying homework assignments, and still do their homework assignments.” That is the product. Not intelligence. Not creativity. Not vision. Compliance. You are paying $200,000 to prove you can tolerate bureaucracy on a schedule. Musk: “Colleges are basically for fun and to prove you can do your chores. But they’re not for learning.” The entire system is a sorting machine for corporate HR. It does not measure what you can build. It measures whether you can sit still, follow directions, and deliver on command. Four years of obedience dressed as education. Musk: “If you’re trying to do something exceptional, you must have evidence of exceptional ability. I don’t consider going to college evidence of exceptional ability.” The system optimizes for average. It rewards the compliant. It certifies the patient. It quietly filters out everyone who refuses to wait for permission. The ones who reshaped the modern world never finished the test. Musk: “Gates is a pretty smart guy, he dropped out. Jobs is pretty smart, he dropped out. Larry Ellison, smart guy, he dropped out.” They did not drop out because it was too hard. They dropped out because the speed limit was too low. The most dangerous thing a university does is convince a generational talent that finishing the syllabus is the achievement. It is not. It is the floor. A degree is a receipt for compliance. The future has never belonged to people who finish their homework. It belongs to the on

@nobodysview And ask yourself the most important question of all. What do YOU like? virtualteacher.com.au/what-do-you-li…

Yann LeCun ignored every rule in the textbook. And the textbook was wrong. He built neural networks with more parameters than data points. Every statistician alive said that shouldn’t work. LeCun: “Every pre-deep learning textbook told you, you need to have fewer parameters than you have data samples.” He trained them on non-convex objectives with no guarantee of convergence. Every optimization theorist alive said he was out of his mind. LeCun: “All those things you read in textbooks and they tell you to stay away from this. And they’re all wrong.” Not most of them. All of them. An entire field spent decades warning people away from the one path that actually led somewhere. LeCun didn’t argue. He just looked at biology. LeCun: “We know that the brain works. We don’t know how, but we know it works.” Billions of parameters. Learns from almost nothing. Runs on messy non-convex biology that violates every optimization theorem ever written. And it works. In the late 1800s, theorists mathematically proved heavier-than-air flight was impossible. The birds never read the paper. The AI field spent decades arguing over symbolic systems, hand-crafted rules, and carefully constrained architectures. LeCun kept pointing at the thing sitting inside every skull in the room. LeCun: “Intelligence is inseparable from learning.” You cannot program a mind into existence. You cannot engineer understanding. Every intelligent thing that has ever existed on this planet arrived there one way. It learned. That’s not a computer science insight. That’s a claim about the nature of mind itself. And the scariest part isn’t that the textbooks were wrong. It’s that the people who wrote them were the smartest ones in the room. The biggest threat to discovery has never been ignorance. It’s the expert who knows exactly why something is impossible while it’s already happening inside their own skull.