Everything you need to know about artificial intelligence, or AI

Everything you need to know about artificial intelligence, or AI

Everything you need to know about artificial intelligence, or AI

Welcome to On Tech: AI, a contextual newsletter that will teach you about artificial intelligence, especially the new generation of chatbots like ChatGPT – all in just five days.

We’ll cover some of the big themes and questions around AI. By the end of the week, you’ll know enough to command the room at a dinner party or impress your colleagues.

Every day we will give you a quiz and a homework assignment. (A pro tip: Ask the chatbots themselves how they work or what concepts you don’t understand. Answering these questions is one of their most useful skills. But keep in mind that they sometimes get things wrong. .)

Let’s start at the beginning.

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

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

Progress was relatively slow until around 2012, when a single idea turned the whole field upside down.

It was called a neural network. It may look like a computerized brain, but, in reality, it’s a mathematical system that learns skills by finding statistical patterns in huge amounts of data. By analyzing thousands of photos of cats, for example, he can learn to recognize a cat. Neural networks allow Siri and Alexa to understand what you say, identify people and objects in Google Photos, and instantly translate dozens of languages.

The next big change: large language models.Around 2018, companies like Google, Microsoft, and OpenAI began building neural networks trained on large amounts of text from across the internet, including Wikipedia articles, e-books, and academic papers.

Somewhat to the surprise of experts, these systems have learned to write unique prose and computer code and conduct sophisticated conversations. This is sometimes called Generative AI (More on that later this week.)

The result: ChatGPT and other chatbots are now set to radically change our daily lives. Over the next four days, we’ll explain the technology behind these bots, help you understand their capabilities and limitations, and where they’re headed in the years to come.

Tuesday: How do chatbots work?

Wednesday: How can they be wrong?

Thursday: How can you use them right now?

Friday: where are they going?

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

The first step is to sign up for these chatbots. Bing and Bard chatbots are slowly rolling out, and you may need to join their waitlists to gain access. ChatGPT currently does not have a waitlist, but requires the creation of a free account.

Once you’re ready, just type your words (known as a prompt) into the text box, and the chatbot will respond. You might want to play around with different prompts and see if you get a different response.

Job of the Day: Ask ChatGPT or one of its competitors to write a cover letter for your dream job, like, for example, a NASA astronaut.

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

We have been covering developments in artificial intelligence for a long time and we have both written recent books on the subject. But this moment is markedly different from what came before. We recently chatted on Slack with our Editor-in-Chief, Adam Pasick, about how we’re each approaching this unique moment.

Cade: The technologies driving the new wave of chatbots have been creeping in for years. But the release of ChatGPT really opened people’s eyes. This sparked a new arms race in Silicon Valley. Tech giants like Google and Meta had been reluctant to release this technology, but they are now rushing to compete with OpenAI.

Kevin: Yeah, it’s crazy out there – I feel like I’m dizzy. There is a natural tendency to be skeptical of technology trends. Wasn’t crypto supposed to change everything? Aren’t we all talking about the metaverse? But it’s different with AI, in part because millions of users are already taking advantage of it. I interviewed teachers, filmmakers, and engineers who use tools like ChatGPT on a daily basis. And it came out only four months ago!

Adam: How do you balance the excitement there with the caution of where it might go?

Cade: The AI ​​is not as powerful as it seems. If you step back, you realize that these systems cannot entirely duplicate our common sense or reasoning. Remember the hype around self-driving cars: were those cars awesome? Yes, remarkably. Were they ready to replace human drivers? Not by far.

Kevin: I suspect tools like ChatGPT are actually more powerful than they look. We have not yet discovered everything they can do. And, at the risk of getting too existential, I’m not sure these models work so differently from our brains. Isn’t much of human reasoning just about recognizing patterns and predicting what’s next?

Cade: These systems mimic humans in some ways, but not others. They demonstrate what we can rightly call intelligence. But as OpenAI’s CEO told me, it’s “alien intelligence.” So, yes, they will do things that will surprise us. But they can also make us think they look more like us than they really do. They are both powerful and imperfect.

Kevin: Sounds like some humans I know!

Question 1 of 3

Start the quiz by choosing your answer.

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

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

  • Generative AI: Technology that creates 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 DALL-E and Midjourney for images.

Click here for more glossary terms.


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