Artificial Intelligence
International Desk — September 13, 2025
If you zoom out far enough, artificial intelligence looks less like a single invention and more like a long conversation—between our curiosity and our tools. The conversation begins long before computers: in philosophers’ arguments about reasoning, in mathematicians’ rules for logic, in engineers’ dreams of automata. But AI as a field, with a name and a plan, starts in mid‑20th‑century America and then advances in waves—surges of confidence, followed by hard lessons, followed by better ideas.
The modern story opens with two sparks. In 1950, Alan Turing asked a disarmingly simple question—“Can machines think?”—and proposed an operational test for intelligent behavior. Six years later, a summer workshop at Dartmouth College gathered a small group of researchers and put a label on the ambition: artificial intelligence. That meeting didn’t produce a working machine, but it set an agenda and a community. From there, universities and labs began writing the first programs that could play checkers, prove theorems, and reason about blocks on a table. The goal was audacious, the mood optimistic. Dartmouth+1
Early AI was mostly symbolic: hand‑written rules and search procedures that manipulated logic like chess pieces. It worked beautifully on tidy puzzles and brittlely on the messy, ambiguous world. By the 1960s and 70s, researchers had impressive demos and grant proposals that promised more. Then reality pushed back. Language is not just rules; vision is not just edges and corners; commonsense is not a checklist you can finish. Funding cooled. The field learned a phrase it would hear again: AI winter—periods when expectations outran capabilities and money followed. Encyclopedia Britannica
And yet, even in the winters, ideas were accumulating. One camp kept faith with symbols and expert knowledge. Another camp—connectionism—pursued artificial neurons that learn from data. The two lines of thought often argued, but the friction helped refine both: better knowledge representations here, better learning algorithms there. By the 1980s, expert systems showed how codified know‑how could solve real business problems. They also showed the limits of rule books: expensive to maintain, hard to scale. Meanwhile, neural networks were relearning to learn. The field didn’t settle the debate; it got more precise about what each approach was good at. Encyclopedia Britannica
A public turning point arrived in 1997 when IBM’s Deep Blue beat world chess champion Garry Kasparov. It was not general intelligence; it was focus and horsepower—clever evaluation functions, deep search, specialized hardware. Still, the symbolism was impossible to miss. A machine had outplayed a grandmaster in his home arena. For many people, that was the first time AI felt tangible rather than theoretical, an achievement you could watch unfold on a board. IBM
The 2000s stitched together ingredients that would power the next leap: bigger datasets, faster GPUs, and better algorithms. In 2012, a deep neural network shocked computer vision benchmarks; within a few years, deep learning escaped the lab and spread across translation, speech, and recommendation engines. Then, in 2017, a new architecture—the Transformer—replaced a lot of hand‑crafted complexity with one scalable idea: attention. Transformers trained on vast text corpora would become the backbone of today’s language and multimodal models, not because they “understood” like we do, but because they could represent patterns at scale with uncanny flexibility. arXiv
Another watershed came from a very old board game. In 2016, DeepMind’s AlphaGo beat Lee Sedol using deep neural networks plus search. It didn’t just play well; in critical moments it played strangely—moves that looked wrong to human experts until, several turns later, they looked inevitable. That match flipped a switch in the public imagination. If a machine could find winning paths that decades of human study had missed, perhaps AI could also help in domains where our intuitions run thin. Google Cloud Storage
The 2020s pushed AI from impressive to everywhere. Large language models learned to chat, summarize, draft, translate, and code. When OpenAI released ChatGPT in late 2022, general audiences discovered that an AI system could talk to them in plain language and feel useful within minutes. It wasn’t magic—it was pattern prediction trained on massive text—but the experience lowered a psychological barrier. The conversation about AI moved from research labs and boardrooms into classrooms, living rooms, and every inbox. OpenAI
What does this history teach? First, AI advances when ideas, data, and compute line up. Take away any one and progress slows. Second, the field cycles: bold claims drive funding; constraints force humility; new tools reopen possibilities. Winters are not failures; they’re the compost that fertilizes the next bloom. Third, definitions matter less than outcomes. People rarely ask whether their translation app uses rules or neural nets; they care that it helps them write a clearer email in a language that isn’t theirs.
It also teaches caution. Systems that look fluent are not necessarily reliable; they can be biased, brittle, or overconfident. We’ve learned to build guardrails, to test beyond benchmarks, to think about safety as a design constraint rather than a patch. That’s not a brake on progress; it’s the steering wheel. Each generation of AI has forced us to ask not just what we can do, but what we should deploy, where, and with whom in control.
The conversation continues. Researchers are exploring models that remember, reason with tools, and mix modalities—text, images, audio, code—without dropping context. Policymakers are writing rules in real time. And the rest of us are deciding, day by day, which tasks to hand off and which to keep human. If the past seventy‑five years are a guide, the next chapter won’t be a straight line. It will be a braid: new math with old questions; bigger machines with human stakes. AI will keep changing how we work and learn. Our job is to make sure it also changes how we care for each other.
Selected references for key milestones: Dartmouth naming of “AI,” Britannica’s historical overview, IBM’s Deep Blue archive, Nature’s AlphaGo paper, the original Transformer paper, and OpenAI’s ChatGPT announcement. OpenAI+5Dartmouth+5Encyclopedia Britannica+5
