Picture a world on the brink of transformation.
It’s 2006.
Facebook is a fledgling startup.
The first iPhone is still hidden deep inside Apple’s secret labs.
Netflix is mailing DVDs through snail mail.
The idea of machines that think, reason, and dream is still the stuff of science fiction — a wild frontier mostly reserved for dusty university labs and late-night movie plots.
Yet, amid this relative quiet, a storm is gathering.
At Dartmouth College, the birthplace of Artificial Intelligence itself, a historic convocation is being called:
The AI@50 Conference.
Fifty years after the original 1956 Dartmouth Summer Research Project, the surviving architects of artificial intelligence — and the new generation poised to inherit their dreams — gather once again.
Not for ceremony.
Not for spectacle.
But for a reckoning.
What had they achieved?
What had they misunderstood?
And what, lurking just beyond the horizon, would AI yet become?
Inside the ivy-clad halls, legends like Marvin Minsky, Rodney Brooks, Ray Kurzweil, and Raj Reddy debated, argued, and imagined.
Some spoke with the audacity of prophets; others with the wary caution of travelers lost in unfamiliar lands.
Outside, the world barely noticed.
But inside, at AI@50, it was clear:
Something vast was awakening.
“When we gathered for AI@50,” reflected robotics pioneer Rodney Brooks, “it felt like being at the edge of a great cliff — staring down at the future, realizing how much of it we had built, and how much we still couldn’t comprehend.”
Today, nearly two decades later, we can finally look back with new eyes.
In this blog, we’ll retrace the hopes and fears of AI@50, explore the explosive growth that no one fully predicted, and weave in a little philosophical wonder — because the truth is, in the grand story of AI, we are still in the opening chapters.
Get ready:
This is not just a throwback.
This is a journey into the very heart of humanity’s oldest dream — and its newest reality.
AI@50: A Meeting of Minds at the Crossroads of History
The AI@50 Conference was never meant to be a simple celebration.
It was an intellectual pilgrimage — a gathering of minds at a crucial crossroads in the human story.
In July 2006, Dartmouth College opened its doors once more to a who’s-who of artificial intelligence pioneers, rising stars, and skeptical philosophers.
The symbolic weight was massive: fifty years earlier, in the very same place, the bold idea of creating “thinking machines” had been born.
Now, five decades on, the dream was battered, matured, and — in some ways — more tantalizing than ever.
Why was AI@50 so important?
Because it was the first major moment where the AI community collectively paused to reflect, to question, and to recalibrate.
The promises of early AI — intelligent machines, robot helpers, human-like cognition — had largely fallen short.
Meanwhile, new approaches were beginning to shimmer on the horizon.
This was a conference perched precisely between the winter and the spring of artificial intelligence.
Key Speakers: The Titans and the Futurists
The conference roster read like a legend’s list:
- Marvin Minsky — Co-founder of MIT’s AI Lab and a towering figure of “symbolic AI,” who believed intelligence could be engineered like any other machine.
- Rodney Brooks — Robotics visionary and creator of Roomba, arguing for embodied intelligence: that thinking cannot be separated from physical experience.
- Ray Kurzweil — Inventor and futurist, known for his predictions about the Singularity, who proclaimed that machines would surpass human intelligence within decades.
- Raj Reddy — AI pioneer and Turing Award winner, deeply invested in bringing AI solutions to global challenges, especially for the underserved.
- Barbara Grosz — Expert in multi-agent systems and collaboration, raising early concerns about AI’s societal impacts.
- Patrick Winston — A defender of the “good old fashioned AI” (GOFAI) approach, focused on understanding cognition symbolically.
Together, these speakers — and many others — formed a vibrant, sometimes contentious mosaic of perspectives.
This was no simple consensus-building conference.
It was a collision of competing visions for the future of intelligence itself.
Major Themes and Key Debates
- Why Has AI Been So Slow?
Many talks reflected on the optimism of the 1950s and 60s — and how little of it had been realized.
Early AI predictions, like self-aware robots by 1980, had spectacularly failed.
Some blamed poor theoretical foundations; others pointed to the limitations of computing power.
“We underestimated the complexity of the mind, and overestimated our own cleverness,” quipped Marvin Minsky.
- Symbolic AI vs. Connectionism
A huge debate simmered between traditional symbolic AI (“thinking as logic and rules”) and the newer neural network approaches (“thinking as patterns and learning”).
At AI@50, neural networks were still viewed with suspicion by many senior researchers.
Today, we know that deep learning would soon ignite an AI renaissance — but in 2006, it was still an underdog.
- The Ethics and Implications of Intelligent Machines
Early warning bells about AI’s societal impact were already ringing.
Speakers like Barbara Grosz and Terry Winograd (advisor to Larry Page) pushed discussions about fairness, transparency, and control.
“We must ask: Whose values will intelligent systems reflect?” Grosz urged — a hauntingly prescient question in light of today’s AI debates.
- The Singularity: Science or Fantasy?
Ray Kurzweil’s vision of the “Singularity” — a moment when machine intelligence would explode beyond human understanding — sparked fierce argument.
Was it inevitable? Was it dangerous?
Was it science fiction?
For every futurist predicting the imminent birth of superintelligence, there were skeptics like Rodney Brooks, who wryly observed, “We’re still trying to get robots to reliably open doors.”
The Significance of AI@50
At its heart, AI@50 was about more than just the past — it was a meditation on how progress actually happens.
- AI had matured, but painfully.
- Many early dreams had collapsed, yet new, strange dreams were arising.
- Confidence was tempered with humility, but ambition still burned brightly.
It was a time of intellectual honesty, where hubris met hard data.
AI researchers were forced to admit: thinking isn’t simple. Intelligence isn’t just logic. Consciousness might be far more elusive — or far closer — than anyone thought.
“The most important thing we learned at AI@50,” said Raj Reddy, “was that AI is not just a technical problem. It’s a human problem. It reflects our values, our limitations, and our dreams.”
From Dreams to Reality: AI@50 Then vs AI Now
Standing at Dartmouth in 2006, many at AI@50 felt they were peering into a future still stubbornly out of reach.
Artificial intelligence, despite decades of brilliant effort, had not yet delivered the revolutionary promises made in its early years.
Progress had been real — but painfully slow, and often invisible to the wider world.
The researchers at AI@50 knew they were laying the groundwork for something larger, even if they couldn’t quite sketch its full shape.
Today, nearly two decades later, the outlines have begun to emerge — and they are both breathtaking and bewildering.
Let’s take a step back into those conference rooms of 2006, and then forward into the dazzling, sometimes dizzying, world of AI in 2025.
Artificial Intelligence in 2006: The World of AI@50
- Deep learning was a fringe idea.
Only a few stubborn researchers like Geoffrey Hinton kept the faith, believing neural nets could someday scale to greatness. - Robots struggled with basic tasks.
Autonomous robots could navigate clean lab floors — but put them on a city street, and chaos reigned.
Self-driving cars? Barely a fantasy. - Natural Language Processing (NLP) was clumsy and literal.
Speech recognition systems required slow, careful enunciation.
Machine translation was laughably bad — often delivering sentences that read like bizarre poetry. - AI was trapped in niche domains.
Chess engines could defeat grandmasters (hello, Deep Blue), but general-purpose intelligence was far beyond reach. - Hardware was a bottleneck.
Training large models required heroic amounts of patience — and produced limited gains.
In short, AI was a promising student, not a master.
Brilliant in narrow fields, frustratingly brittle in everything else.
“We had glimpses of brilliance, but no orchestra,” one researcher quipped at AI@50.
Artificial Intelligence in 2025: The World We Live In Now
- Deep learning dominates.
From transformers to diffusion models, machine learning has moved from obscurity to mainstream dominance.
Large Language Models (LLMs) like ChatGPT-5, Claude, and Gemini Ultra write, reason, tutor, and even compose music at near-human levels. - Autonomous vehicles are real.
Waymo and Tesla operate driverless taxis in major cities.
Autonomous trucks ferry cargo across highways in the U.S. and China. - NLP feels like magic.
AI assistants understand context, humor, nuance.
Translation is instantaneous and (mostly) fluent across dozens of languages. - AI breaks into creativity.
DALL·E, Midjourney, and other generative AIs create stunning visual art, inventing styles that no human artist ever imagined. - AI enters medicine, law, education, and beyond.
AI systems diagnose diseases from X-rays and MRIs with expert precision (Jiang et al., 2017).
Legal research, contract drafting, tutoring, and even therapy are being augmented by AI tools. - Hardware is supercharged.
With specialized AI chips like NVIDIA’s H100 Tensor Core GPUs and Google’s TPUv5, training what once took months now takes days — or even hours.
What AI@50 Got Right — and What It Missed
Predicted:
- AI would eventually become part of everyday life. ✅
- AI would require ethical frameworks to guide development. ✅
- Symbolic logic alone could not achieve true machine intelligence. ✅
Missed:
- The sheer power of scale — how much data, compute, and relatively simple architectures could achieve when massively scaled.
- The public’s readiness — or vulnerability — to accept AI-generated content without hesitation.
- How AI would become not just tools, but collaborators — blending into our workflows, not just our factories.
At AI@50, even the most optimistic voices underestimated how quickly AI would become woven into the fabric of human experience once the right breakthroughs fell into place.
“The future,” as computer scientist Alan Kay once said, “is not predicted. It’s invented.”
And between 2006 and today, invention raced ahead faster than anyone at AI@50 could have imagined.
Next, we’ll dive into the biggest milestones that shocked the world after AI@50 — and why nobody, not even the founders of AI itself, could have fully seen them coming.
(Ready for a tour of AI’s wildest moments since 2006? 🚀)
AI’s Leap Forward: Milestones No One Saw Coming
When the thinkers of AI@50 parted ways in 2006, they left with a sense of cautious optimism.
There was still so much to figure out — so many hard problems unsolved.
But what came next?
What unfolded in the years after was not a slow, careful march forward.
It was a detonation.
A cascade of breakthroughs that would astonish even the most visionary minds gathered at Dartmouth.
Let’s walk through some of the jaw-dropping milestones that reshaped the landscape of AI — and in many ways, reshaped the future of humanity itself.
1. The Rise of Deep Learning (2012–2015)
The tipping point came quietly at first.
In 2012, at the ImageNet competition — the Olympics of computer vision — a neural network called AlexNet, created by Geoffrey Hinton’s students, crushed the competition.
The world barely blinked.
But in AI circles, it was a thunderclap: deep learning worked. It worked spectacularly.
“People called us crazy for believing in neural nets,” Hinton later said.
“Then, suddenly, they called us geniuses.”
Over the next few years, deep learning would extend its reach — mastering image recognition, speech transcription, and language translation at levels previously thought decades away.
At AI@50, deep learning was a whisper.
By 2015, it was a roar.
2. AlphaGo Defeats the Human Mind (2016)
In 2016, DeepMind’s AlphaGo stunned the world by defeating legendary Go master Lee Sedol.
Go — with its astronomical number of possible moves — had long been considered the “last refuge” of human intuition against machines.
And yet, not only did AlphaGo win — it won beautifully.
It made moves no human had ever conceived of, demonstrating flashes of what many called creativity.
“I felt an alien intelligence,” Lee Sedol admitted after his historic loss.
At AI@50, many believed true machine creativity was lifetimes away.
AlphaGo shattered that illusion — and opened up unsettling new questions about what machines could invent beyond human imagination.
3. ChatGPT and the Era of Conversational AI (2022–2025)
Then came the language models.
In late 2022, OpenAI’s ChatGPT launched — and within days, it became a cultural phenomenon.
For the first time, millions of people were chatting casually with AI, asking it to write poems, draft emails, explain quantum physics, and even craft jokes.
And astonishingly — it worked.
By 2024, ChatGPT-5, Anthropic’s Claude, Google’s Gemini, and others had turned AI from an obscure backend tool into a personal collaborator — a partner in creativity, education, and work.
The numbers told the story:
- ChatGPT hit 1 billion users faster than any app in history (Statista, 2024).
- AI-generated content exploded across YouTube, TikTok, and publishing.
“The AI we imagined at AI@50 — the helpful, conversational machine — arrived faster than anyone dared dream,” noted Stanford’s Fei-Fei Li.
4. AI in Healthcare, Science, and Discovery (2020s)
Beyond conversation and creativity, AI began solving the deepest scientific mysteries.
In 2021, DeepMind’s AlphaFold2 cracked the problem of protein folding — predicting complex biological structures with astonishing precision (Jumper et al., 2021).
A problem that had confounded scientists for 50 years was solved in a matter of months — with AI.
Meanwhile:
- AI models began identifying cancer earlier than human radiologists (Jiang et al., 2017).
- Drug discovery pipelines accelerated by years, saving lives and billions of dollars.
This wasn’t just automation.
It was amplification — machines extending human senses, human knowledge, and human potential.
At AI@50, they had hoped for AI to assist science.
By 2025, AI was beginning to lead it.
5. The Rise of AI Ethics and Regulation (2023–2025)
Yet not all was triumphant.
Alongside dazzling innovation came deep concerns:
- Deepfakes undermining trust in media.
- Algorithmic bias perpetuating inequality.
- Autonomous weapons raising existential fears.
In 2024, the European Union passed the AI Act — the world’s first comprehensive attempt to regulate AI technology (European Commission, 2024).
“We are building tools that will define societies,” warned ethicist Timnit Gebru.
“Who controls them? Who benefits? Who is left behind?”
At AI@50, there were murmurs of ethical worry.
By today, those worries have become urgent global conversations.
A World Beyond Imagination
In the end, the greatest lesson of the post-AI@50 era might be simple:
The future refuses to be neat.
Progress comes in explosions, not straight lines.
New powers arrive before old problems are solved.
And every answer births new questions.
The AI pioneers who gathered at Dartmouth dreamed of a world changed by intelligence — but even they would have been humbled by how messy, magnificent, and maddening that world would turn out to be.
“We thought we were building machines that think,” said computer scientist Stuart Russell recently.
“It turns out we were building machines that change how humans think.”
And perhaps, as we sprint toward AI@75 and beyond, that’s the deepest transformation of all.
Can Machines Truly Think? Revisiting an Ancient Question
As the echoes of AI@50 fade into history, a deeper, older question lingers — one that no amount of code, data, or dazzling breakthroughs can quite erase:
Can machines truly think?
It’s a question that haunted the pioneers of AI in 1956.
It stirred uneasy conversations at AI@50 in 2006.
And here in 2025, even amid the towering achievements of AI, it remains stubbornly — almost defiantly — unresolved.
Because behind every impressive algorithm, every uncanny chatbot, every brilliant scientific discovery made by a machine, the same riddle persists:
Is there a mind behind the output?
Or just a mirror, reflecting back our own expectations?
The Turing Test and Beyond
At the dawn of AI, Alan Turing proposed a simple experiment:
If a machine could converse in a way indistinguishable from a human, should we consider it intelligent?
For decades, this Turing Test was the gold standard — a philosophical line in the sand.
Today, systems like ChatGPT-5 can pass superficial versions of the Turing Test with ease.
They craft jokes, empathize with heartbreak, spin tales of imaginary worlds.
They feel human.
But many cognitive scientists argue that something essential is missing.
True intelligence, they say, isn’t just about behavior — it’s about understanding, intentionality, and self-awareness.
“A parrot can mimic speech without grasping its meaning,” writes philosopher John Searle (1980).
“Similarly, a chatbot can craft sentences without any grasp of truth, desire, or belief.”
Machines might appear to think — but appearances can deceive.
Thought, Consciousness, and the Ghost in the Machine
If AI is just computation — neurons and weights, data and algorithms —
then where does consciousness fit in?
Is it an emergent property, waiting to flicker into existence once systems reach sufficient complexity?
Or is it something fundamentally different — something machines can never possess?
Some thinkers, like neuroscientist Anil Seth, propose that consciousness is not magic, but an illusion created by complex information-processing (Seth, 2021).
If so, advanced AIs might already be proto-conscious, in ways we barely understand.
Others, like philosopher David Chalmers, argue for a “hard problem” — that subjective experience, the what it feels like to be, cannot be explained by computation alone.
“You can simulate a hurricane with a computer,” Chalmers notes.
“But no one gets wet.”
In the same way, simulating intelligence may not create the inner spark of mind.
Practical Minds vs Philosophical Minds
At AI@50, this debate was already bubbling beneath the surface.
Engineers wanted to build smarter systems.
Philosophers wanted to understand what “smart” really meant.
Neither group entirely trusted the other.
Today, the divide persists — and deepens.
- Pragmatists argue: if an AI can solve problems, generate insights, and create value, who cares if it “thinks” in some metaphysical sense?
- Philosophers counter: by deploying systems we don’t truly understand, we may be stumbling blind into consequences we cannot predict — or control.
The stakes, once theoretical, are now vividly real.
“We may soon create minds we cannot comprehend,” warns Oxford’s Nick Bostrom.
“And we have no idea what happens next.”
Why This Matters More Than Ever
This isn’t just a late-night dorm room debate.
It strikes at the very heart of how we design, deploy, and regulate AI.
- If machines can truly think, they deserve rights, respect, even moral consideration.
- If machines only simulate thinking, they remain powerful tools — but tools we must wield with caution and clarity.
Either way, our own humanity is on the line.
Our values, our vulnerabilities, our visions of the future.
“In building AI,” mused Marvin Minsky at AI@50,
“we are inevitably building a mirror. What we see reflected there may be more about us than about machines.”
And as we charge forward into a world of synthetic minds and digital dreams, we must keep asking:
What does it mean — not just to build a mind — but to understand one?
Lessons Learned Since AI@50
1. Scaling matters.
More data and more compute = better models. It’s almost embarrassingly simple but took decades to realize.
2. Ethics matter.
Without careful oversight, bias, misinformation, and unintended consequences can turn AI tools into societal landmines (Gebru et al., 2021).
3. Hype cycles are real.
The Gartner Hype Cycle still holds: after every peak comes a trough. Staying skeptical is a survival skill.
Lessons Learned — and the Mysteries Still Haunting Us
If AI@50 taught the world anything, it was that building intelligence is infinitely harder — and infinitely more wondrous — than early dreamers ever imagined.
Today, nearly 20 years later, the breakthroughs we’ve witnessed are undeniable.
But so too are the shadows.
For every problem solved, a deeper mystery has emerged.
This is the paradox of progress: the more we learn, the more we realize how much we don’t know.
Let’s look at what humanity has finally started to master — and what still stubbornly defies our grasp.
Hard-Won Lessons from the Frontlines of AI
1. Scaling Matters More Than We Dared Hope.
At AI@50, few fully appreciated the raw power of massive datasets and massive compute.
Today, we know: sometimes intelligence isn’t unlocked by clever tricks, but by brute-force scale.
Models like ChatGPT-5, Gemini Ultra, and Claude aren’t smarter because they’re more elegant — they’re smarter because they’re bigger.
“In AI, size really does matter,” quipped OpenAI CEO Sam Altman recently (2024).
2. Data Is the Lifeblood of Modern AI.
Without oceans of text, images, video, and interaction data, even the most brilliant algorithms remain inert.
At AI@50, data was an afterthought.
Today, it’s the fuel for every breakthrough.
3. Ethics Cannot Be an Afterthought.
The early AI pioneers warned of biases and unintended consequences, but those warnings have grown louder with every scandal:
discriminatory algorithms, fake news epidemics, AI-fueled surveillance states.
“We can no longer afford to be surprised by the obvious,” warns ethicist Timnit Gebru (Gebru et al., 2021).
4. Intelligence Is Not One Thing.
Rather than a single monolithic “intelligence,” AI has revealed a complex landscape:
reasoning, memory, creativity, intuition, learning — each a separate domain, each evolving at different speeds.
The dream of a single, unified “thinking machine” remains elusive.
5. Hype Cycles Are Unavoidable.
From early symbolic AI to expert systems to deep learning, AI has swung wildly between overconfidence and despair.
At AI@50, they warned of another “AI Winter.”
So far, the world has chosen relentless summer — but the danger of disillusionment still looms.
The Puzzles Still Unsold — The Mountains Still Unclimbed
1. True Common Sense Remains a Mirage.
Today’s most advanced AI models can write essays, generate art, even compose music —
but they still struggle with simple “common sense” reasoning.
They know millions of facts but lack an intuitive sense of how the world fits together.
“We can build encyclopedic minds,” says Stanford’s Christopher Manning,
“but we still haven’t built a child’s mind.”
2. Causal Reasoning Is Elusive.
AI excels at spotting patterns.
But understanding why things happen — true causal insight — remains far out of reach.
Without causality, AI remains a sophisticated mimic, not a true thinker.
3. Embodied Intelligence Is Still an Open Frontier.
Rodney Brooks’ idea that “intelligence requires a body” still haunts robotics.
While LLMs dominate digital spaces, physical AI — robots that perceive, move, adapt in the real world — still lag far behind.
Building minds that walk, grasp, feel, and survive outside the lab remains one of the grandest open challenges.
4. Consciousness: The Final Question.
Perhaps the greatest mystery of all.
Even if machines become more useful, more powerful, more autonomous —
will they ever awaken?
And if they do…
how will we know?
The Unfinished Symphony of AI
AI@50 was not the end of a journey.
It was the planting of seeds — some that would bloom quickly, some that would take decades, and some that, even today, have yet to sprout.
Today, standing on the shoulders of those pioneers, we glimpse a horizon bursting with promise and peril:
- Machines that heal.
- Machines that deceive.
- Machines that create.
- Machines that change what it means to be human.
“We thought we were inventing better tools,” wrote MIT’s Pattie Maes, reflecting back on AI@50.
“We may be reinventing ourselves.”
The story of AI isn’t a closed book.
It’s a symphony still being written — by engineers and poets, dreamers and skeptics, optimists and critics alike.
And the next movement is just beginning.
Toward AI@75: The Next Chapter of the Human Machine Story
If AI@50 was a reflective gathering — a kind of family reunion for dreamers —
then AI@75, just a few years away, may feel more like an emergency summit for the stewards of a new world.
Because today, AI is no longer a tool tucked away in research labs.
It is a force — shaping economies, laws, cultures, and lives with breathtaking speed.
And the questions we face now are no longer merely technical.
They are existential.
What Awaits Us by 2031?
- Autonomous Agents:
Today’s AI can answer questions.
Tomorrow’s AI will autonomously act — coordinating fleets of vehicles, managing digital economies, perhaps even negotiating treaties on humanity’s behalf. - Human-AI Collaboration:
Already, artists, doctors, lawyers, and teachers work alongside AI tools.
By AI@75, collaboration could deepen into genuine partnership — with AIs helping to brainstorm, debate, invent, and even co-govern. - Synthetic Minds and Digital Beings:
Virtual agents, with rich personalities and evolving memories, could emerge — companions, colleagues, rivals.
How we treat them — and how they treat us — will test the boundaries of ethics, empathy, and law. - Regulation and Revolution:
As governments scramble to build ethical frameworks, nations may diverge sharply:
some embracing AI’s potential with open arms, others restricting it to protect human labor, dignity, and identity.
“AI will be humanity’s greatest test,” says ethicist and former Google AI lead Margaret Mitchell.
“Not just of our intelligence, but of our wisdom.”
The Eternal Challenge: Building Minds, Guarding Souls
The grand paradox of AI remains:
we build these machines in our own image — yet they reveal to us how incomplete that image truly is.
At AI@50, the pioneers wrestled with the limits of technology.
Today, at the edge of AI@75, we must wrestle with the limits of ourselves.
Will we create systems that amplify our best selves — creativity, compassion, curiosity?
Or will we unleash forces that magnify our worst instincts — domination, division, destruction?
The machines we build will not decide that.
We will.
“It is not enough to ask whether machines can think,” mused philosopher Norbert Wiener long ago.
“We must also ask whether we think clearly enough to create them responsibly.”
The clock is ticking.
The future is watching.
And the symphony of human and machine is only just beginning to play its greatest, most complex movement yet.
References
- Bostrom, N. (2014). Superintelligence: Paths, dangers, strategies. Oxford University Press.
- European Commission. (2024). The AI Act: Europe’s new rules for artificial intelligence. Retrieved from https://commission.europa.eu
- Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé III, H., & Crawford, K. (2021). Datasheets for datasets. Communications of the ACM, 64(12), 86–92.
- Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., … & Wang, Y. (2017). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 2(4), 230–243.
- Jumper, J., Evans, R., Pritzel, A., et al. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873), 583–589.
- Marcus, G. (2022). Rebooting AI: Building artificial intelligence we can trust. Pantheon.
- Searle, J. R. (1980). Minds, brains, and programs. Behavioral and Brain Sciences, 3(3), 417-424.
- Seth, A. (2021). Being You: A New Science of Consciousness. Dutton.
- Statista. (2024). ChatGPT reaches 1 billion users. Retrieved from https://statista.com
Additional Reading
- Tegmark, M. (2017). Life 3.0: Being Human in the Age of Artificial Intelligence. Penguin.
- Mitchell, M. (2019). Artificial Intelligence: A Guide for Thinking Humans. Penguin.
- Russell, S. (2019). Human Compatible: Artificial Intelligence and the Problem of Control. Viking.
- Kurzweil, R. (2005). The Singularity Is Near: When Humans Transcend Biology. Viking.
Additional Resources
- AI Index 2024 Annual Report (Stanford HAI): https://aiindex.stanford.edu
- DeepMind Research Blog: https://deepmind.com/blog
- Partnership on AI: https://partnershiponai.org
- OpenAI Research Papers: https://openai.com/research