War in Europe, a momentous volcanic eruption and a surprise finding that could rewrite our understanding of reality – 2022 really has been a busy year for science, technology, health and environment news, and all that happened in just the first few months. From stunning space imagery to pig heart transplants, here are the New Scientist news editors' picks of the biggest scientific developments, discoveries and events of the year. Russia's invasion of Ukraine in February has sparked devastation across the country and affected many areas of life around the world, as both nations play a key role in the global supply chains for energy, food and more. It has also raised the spectre of nuclear weapons, with Russian president Vladimir Putin making not-so veiled threats about deploying his atomic arsenal. Thankfully, Armageddon has been avoided, but Russia's offensive has sparked discussion of a new kind of nuclear war, as Ukraine's nuclear power plants became a battleground this year.

I think there's an important set of lessons for us about what the next decade's going to be like for AI. The first is where it came from, which is a team of three people poking at an idea in, like, a random corner of the OpenAI building. This one single idea about diffusion models, just a little breakthrough in algorithms, took us from making something that's not very good to something that can have a huge impact on the world. Another thing that's interesting is that this was the first AI that everyone used, and there's a few reasons why that is. But one is that it creates, like, full finished products. If you're using Copilot, our code generation AI, it has to have a lot of help from you.

As they tinkered with the model, everyone involved realized this was something special. "It was very clear that this was it--this was the product," says Altman. We never even had a meeting about it." But nobody--not Altman, not the DALL-E team--could have predicted just how big a splash this product was going to make. "This is the first AI technology that has caught fire with regular people," says Altman. DALL-E 2 dropped in April 2022. In May, Google announced (but did not release) two text-to-image models of its own, Imagen and Parti. Then came Midjourney, a text-to-image model made for artists. And August brought Stable Diffusion, an open-source model that the UK-based startup Stability AI has released to the public for free. The doors were off their hinges. OpenAI signed up a million users in just 2.5 months. More than a million people started using Stable Diffusion via its paid-for service Dream Studio in less than half that time; many more used Stable Diffusion through third-party apps or installed the free version on their own computers. And then in October we had Round Two: a spate of text-to-video models from Google, Meta, and others. Instead of just generating still images, these can create short video clips, animations, and 3D pictures. The pace of development has been breathtaking. In just a few months, the technology has inspired hundreds of newspaper headlines and magazine covers, filled social media with memes, kicked a hype machine into overdrive--and set off an intense backlash. This story is part of our upcoming 10 Breakthrough Technologies 2023 series. Sign up for The Download to get the full list in January. "The shock and awe of this technology is amazing--and it's fun, it's what new technology should be," says Mike Cook, an AI researcher at King's College London who studies computational creativity. "But it's moved so fast that your initial impressions are being updated before you even get used to the idea.

We predicted that tensions would flare between the U.S. and China over AI in 2022. This proved all ... [ ] too true. As we do every year, last December we published a list of 10 predictions about the world of artificial intelligence in 2022. To keep ourselves honest, with 2022 now coming to a close, let's revisit these predictions to see how things actually played out. There is much to learn from these retrospectives about the state and trajectory of AI today.

The AI research team OpenAl has released a beta version of ChatGPT, a chatbot built on the GPT-3.5 language model. ChatGPT is a chatbot that uses deep learning to generate text that mimics that of a human. It was developed by the firm so that users could get responses that were both technical and plain-spoken. Similar to a personal tutor who is versed in all disciplines, it may react to a variety of inquiries in a natural way. As a result, it is promoted as a Google substitute.

On Wednesday, a designer and engineer named Arvind Sanjeev revealed his process for creating Ghostwriter, a one-of-a-kind repurposed Brother typewriter that uses AI to chat with a person typing on the keyboard. The "ghost" inside the machine comes from OpenAI's GPT-3, a large language model that powers ChatGPT. The effect resembles a phantom conversing through the machine. To create Ghostwriter, Sanjeev took apart an electric Brother AX-325 typewriter from the 1990s and reverse-engineered its keyboard signals, then fed them through an Arduino, a low-cost microcontroller that is popular with hobbyists. The Arduino then sends signals to a Raspberry Pi that acts as a network interface to OpenAI's GPT-3 API.

Check out all the on-demand sessions from the Intelligent Security Summit here. One of the biggest topics underlying the hype bonanza since OpenAI's release of ChatGPT two weeks ago has been: What does this mean for Google search? But it was only on Tuesday evening that Google appeared to finally weigh in on the topic: CNBC reported that employees raised concerns at a recent all-hands meeting that the company was losing its competitive edge in artificial intelligence (AI) given ChatGPT's quick rise. "Is this a missed opportunity for Google, considering we've had Lamda for a while?" read one top-rated question. Alphabet CEO Sundar Pichai and Jeff Dean, the long-time head of Google's AI division, responded to the question by saying that the company has similar capabilities in its LaMDA model, but that Google has more "reputational risk" in providing wrong information and therefore is moving "more conservatively than a small startup."

ChatGPT's beta launch exceeded 1 million users in less than a week, attracting the attention of almost everyone in the entire tech ecosystem. I read articles about it in the New York Times, the Financial Times and The Atlantic, three top media sources in my books. The AI garners work-place buzz under the possibility that its generation is so effective, it might pose a threat to human jobs such as copywriting, answering customer service inquiries, writing news reports, and creating legal documents. Large Language Models (LLMs), and generative AI like ChatGPT to the workplace--especially where the reliability of information is paramount. I met with the executive team at Hebbia AI, a startup leading research efforts on LLMs, to dig in.

This is the future of search. The online interface can provide instant answers to any query using natural language processing and machine learning technologies like OpenAI Codex to translate natural language into SQL. This allows anyone to navigate large data sets like Twitter.

While recent work on text-conditional 3D object generation has shown promising results, the state-of-the-art methods typically require multiple GPU-hours to produce a single sample. This is in stark contrast to state-of-the-art generative image models, which produce samples in a number of seconds or minutes. In this paper, we explore an alternative method for 3D object generation which produces 3D models in only 1-2 minutes on a single GPU. Our method first generates a single synthetic view using a text-to-image diffusion model, and then produces a 3D point cloud using a second diffusion model which conditions on the generated image. While our method still falls short of the state-of-the-art in terms of sample quality, it is one to two orders of magnitude faster to sample from, offering a practical trade-off for some use cases. We release our pre-trained point cloud diffusion models, as well as evaluation code and models, at https://github.com/openai/point-e.