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May 27, 2024

Welcome to On Tech: AI, a pop-up newsletter that will introduce you to artificial intelligence and especially new chatbots like ChatGPT in just five days.

We’ll be tackling some of the big topics and questions surrounding AI and by the end of the week, you’ll have enough knowledge to command the room at a dinner party, or impress your colleagues.

Every day, we’ll give you a quiz and a homework assignment. (Pro tip: Ask the chatbots themselves how they work, or about concepts you don’t understand. Answering these kinds of questions is one of their most useful skills. But remember, they sometimes get it wrong.)


Let’s start from the beginning.

The term “artificial intelligence” is often used to describe robots, self-driving cars, facial recognition technology, and pretty much anything else that seems vaguely futuristic.

A group of academics coined the term in the late 1950s as they set out to build a machine that could do anything a human brain can do—skills such as reasoning, problem solving, learning new tasks, and communicating using natural language.

Progress was relatively slow until around 2012, when an idea changed the entire field.

it’s known Neural Networks. It sounds like a computerized brain, but in reality, it’s a mathematical system that learns skills by looking for statistical patterns in vast amounts of data. For example, it can learn to recognize cats by analyzing thousands of photos of cats. Neural networks enable Siri and Alexa to understand what you say, recognize people and objects in Google Photos, and translate dozens of languages ​​instantly.

Next big change: large language model. Around 2018, companies like Google, Microsoft, and OpenAI began building neural networks trained on vast amounts of text from the Internet, including Wikipedia articles, digital books, and academic papers.

Somewhat to the experts’ surprise, the systems learned to write unique prose and computer code and engage in complex conversations.this is sometimes called generative artificial intelligence (More on that later this week.)

The result: ChatGPT and other chatbots are now poised to change our everyday lives in dramatic ways. Over the next four days, we’ll explain the technology behind these robots, helping you understand their capabilities and limitations, and where they might go in the years to come.


Tuesday: How do chatbots work?

Wednesday: How could they go wrong?

Thursday: How are you using them now?

Friday: Where are they going?


You have some homework to do! One of the best ways to learn about AI is to use it yourself.

The first step is to sign up for these chatbots. must and poet Chatbots are slowly rolling out, and you may need to be on their waitlist to gain access. Chat GPT There is currently no waitlist, but a free account needs to be set up.

When you’re ready, just enter your text (called a hint) into the text box and the chatbot will reply. You might want to try different prompts to see if you get a different response.

Today’s assignment: Ask ChatGPT or its competitors to write a cover letter for your dream job, like NASA Astronaut.

We want to see results! Share it as a comment and see what others have submitted.

We’ve been covering developments in artificial intelligence for a long time, and we’ve all written recent books on this topic. But this moment felt very different from any other. We recently chatted with our editor Adam Pasick on Slack about how each of us approaches this unique point in time.

Cade: The technology driving the new wave of chatbots has been percolating for years. But the release of ChatGPT really opened people’s eyes. It started a new arms race in Silicon Valley. Tech giants such as Google and Meta have been reluctant to release the technology, but now they are racing to compete with OpenAI.

Kevin: Yes, it’s crazy in there – I think I’m dizzy. It is a natural tendency to be skeptical of technological trends. Shouldn’t cryptocurrencies change everything? Aren’t we all talking about the metaverse? But AI feels different, in part because millions of users are already experiencing the benefits. I’ve interviewed teachers, filmmakers, and engineers who use tools like ChatGPT every day. And it came out only four months ago!

Adam: How do you balance excitement with caution?

Cade: Artificial intelligence is not as powerful as it seems. If you take a step back, you realize that these systems cannot fully replicate our common sense or reasoning. Remember the self-driving car hype: Are those cars impressive? Yes, very obvious. Are they ready to replace human drivers? Not by a long shot.

Kevin: I suspect that tools like ChatGPT are actually more Stronger than they look. We haven’t discovered everything they can do yet. And, at the risk of being too existential, I’m not sure how these models work any differently than our brains. Isn’t a lot of human reasoning about recognizing patterns and predicting what’s going to happen next?

Cade: These systems mimic humans in some ways, but differ in others. They demonstrate what we can rightly call intelligence. But it’s an “alien intelligence,” as OpenAI’s CEO told me. So, yes, they will do some things that will surprise us. But they can also fool us into thinking they are more like us than they really are. They are both powerful and flawed.

Kevin: Sounds like some people I know!


Question 1 of 3

Select your answer to start the quiz.


  • Neural Networks: A mathematical system modeled on the human brain that learns skills by finding statistical patterns in data. It consists of multiple layers of artificial neurons: the first layer receives input data, and the last layer outputs results. Even the experts who create neural networks don’t always understand what’s going on between the two.

  • Large language models: A neural network that learns skills by analyzing large amounts of text on the Internet, including generating prose, conducting conversations, and writing computer code. The basic function is to predict the next word in a sequence, but these models surprise experts by learning new abilities.

  • Generative AI: The technique of creating content (including text, images, video, and computer code) by identifying patterns in large amounts of training data and then creating new original material with similar characteristics.Examples include ChatGPT for text and Dahl-E and halfway for images.

Click here for more glossary terms.



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