In 2025, we will see AI and machine learning begin to amplify the impact of Crispr genome editing in medicine, agriculture, climate change, and the basic research that underpins these fields. It's worth saying upfront that the field of AI is awash with big promises like this. With any major new technological advance there is always a hype cycle, and we are in one now. In many cases, the benefits of AI lie some years in the future, but in genomics and life science research we are seeing real impacts right now. This story is from the WIRED World in 2025, our annual trends briefing.

A top AMD executive says that the company is thinking about deeper integration of AI across the Ryzen product line, but one ingredient is missing: client applications and operating systems that actually take advantage of them. AMD launched the Ryzen 7000 series in January, which includes the Ryzen 7040HS. In early May, AMD announced the Ryzen 7040U, a lower-power version of the 7040HS. Both are the first chips to include Ryzen AI "XDNA" hardware, among the first occurrences of AI logic for PCs. So far, however, AI remains a service that (aside from Stable Diffusion and some others) exclusively runs in the cloud.

I firmly believe that artificial intelligence (AI) has the potential to be among the most disruptive technologies we will ever develop. So why – more than 50 years since the first machine learning research – is its impact still, in many ways, limited? This is the question at the heart of a new book called Power and Prediction - The Disruptive Economics of Artificial Intelligence, co-authored by Joshua Gans, along with Ajay Agrawal and Avi Goldfarb. I got the chance to once again catch up with Gans, holder of the Jeffrey S Skoll Chair of Technical Innovation and Entrepreneurship at Toronto's Rotman School of Management. The last time I spoke to Joshua, he had just released his first book, Prediction Machines – The Simple Economics of Artificial Intelligence.

I firmly believe that artificial intelligence (AI) has the potential to be among the most disruptive technologies we will ever develop. So why – more than 50 years since the first machine learning research – is its impact still, in many ways, limited? This is the question at the heart of a new book called Power and Prediction - The Disruptive Economics of Artificial Intelligence, co-authored by Joshua Gans, along with Ajay Agrawal and Avi Goldfarb. I got the chance to once again catch up with Gans, holder of the Jeffrey S Skoll Chair of Technical Innovation and Entrepreneurship at Toronto's Rotman School of Management. The last time I spoke to Joshua, he had just released his first book, Prediction Machines – The Simple Economics of Artificial Intelligence.

Almost every enterprise believes data to be one of their most important assets, but most would admit they are not leveraging their data to its full potential. That's because making data easily accessible to employees is surprisingly hard work. It requires a concerted, ongoing effort to gather, structure, and tag data in order to turn it into knowledge, capable of being found in the moments when it can be most useful, using a voice AI knowledge assistant. Of course, this has always been true regardless of the state of technology, from ancient libraries indexing vast physical volumes to today's cloud-based search engines crawling millions of gigabytes of data. We can take for granted the magic of the now-ubiquitous digital keyword search, which has delivered us the power to have any data point just a few mouse and keyboard clicks away.

Xbox Game Studios head Matt Booty has said it's his "dream" to turn to "transformational" AI and machine learning as an alternative to human QA testing. Booty dove into his desire for AI testers during his "Storytime With Matt Booty" panel during PAX West. He said QA testing has "not really kept up with how quickly we can make content," as reported by VGC (opens in new tab), and went on to compare changing something minor in a game as opposed to a movie. "If we're working on a movie and you come in and say'hey, this ending let's tighten this up, let's edit this, let's cut that scene,' it usually doesn't break anything at the beginning of the movie," he said. "But in a game you can be ready to ship and a designer's like'I've got this one little feature, I'm just going to change the colour on this one thing' and then it somehow blows up something and now the first 10 minutes of the game doesn't play. So that testing aspect, every single time anything new goes into a big game the whole game has to be tested, front-to-back, side-to-side."

Recently, I had the honor of addressing the "Commercialising Quantum" conference on how organisations should prepare for quantum today, held under the stewardship of The Economist's editors. My message was that some aspects of quantum technologies are on the horizon, and we should be prepared to invest ensure that quantum capabilities for economic prosperity and national security are developed accordingly.

AlphaFold, DeepMind's artificial intelligence that predicts the structure of proteins, is a gift to biologists. To understand life, we need to understand proteins. Living things are molecular machines, and most of the key components are made of proteins. You are reading this article, for instance, with proteins in your retina that can detect light, various proteins that make your muscles move and so on. In one way, proteins are simple.

Artificial Intelligence is transmuting the system and methods of the healthcare industries. Artificial Intelligence and healthcare were found together over half a century. The healthcare industries use Natural Language Processes to categorize certain data patterns. Artificial Intelligence can be used in clinical trials, to hasten the searches and validation of medical coding. This can help reduce the time to start, improve and accomplish clinical training.

I have been doing NLP/ML research for the last 6 years. I have come across a lot of machine learning resources and papers. Today, I kept thinking about the machine learning / NLP / deep learning related blog posts (not papers) that have been transformational for me. In this blog post, I provide a short collection of a few high-impact blog posts that come to mind. This post was originally a Twitter thread.