How we picked 35 of the world's top young scientists and engineers Our 2026 Innovators Under 35 list will be out soon. Here's what we looked for as we sifted through 550 nominations from around the world. Next month, on September 8, will reveal its 2026 list of Innovators Under 35, recognizing 35 young people from around the world who are doing groundbreaking scientific work and building clever technical fixes for sticky problems. By finding the top young innovators globally and learning what they're focused on in their work, we aim to give readers a sense of what advances to expect in the years to come. As a newsroom, we also use this exercise to help us spot rising talent and get to know some of the best early-career researchers in the fields that we cover. The editors of published the first Innovators Under 35 list in 1999, and it's become a beloved annual tradition alongside our lists of 10 Breakthrough Technologies, 10 Climate Tech Companies to Watch, and (new this year) 10 Things That Matter in AI Right Now .

There's a lot of hysteria around Generative AI (GAI) tools like ChatGPT, beyond the usual hype cycle of many technologies that have come to be in the world. There was even the case last year of the now former Google engineer who was convinced that an AI was, well, sentient. In human terms, this is absolutely impossible. This doesn't mean AI is terrible or that it can't do amazing things to help us. In fact, AI may be just the right technology humanity needs to survive our next phase of evolution. But there is no way, whatsoever, that AI can be in any way, shape or form, human.

We are victims of one common superstitionthe superstition that we understand the changes that are daily taking place in the world because we read about them and know what they are. The anthropological stories and the concept of memes were brought to my attention several years ago by Lynn Conway Much of the vision and some of the material was drawn from a paper that we worked on together but never published. The important distinction between process and product, was made crisp for me by John Seely Brown, who also has encouraged and made possible projects like Trillium, which I watched with interest, and like Colab, in which I participated. Joshua Lederberg kindled my interest in biological issues and a respect for knowledge processes and their partial automation that has not faded Dan Bobrow listened to my ramblings on several runs, agonized over my confusions, helped to get the kinks out of the arguments, and suggested the title for the article Sanjay Mittal and I have spent many hours speculating together on the issues in building community knowledge bases and knowledge servers and in understanding the principles of knowledge competitions Austin Henderson helped me to understand the Trillium story and to report it accurately. Austin and Sanjay hounded me to say, more precisely, what a knowledge medium is Agustin Araya and Mark Miller participated in a Colab session in which we tried to jointly lay out these ideas, and together asked me to make the prescriptions clearer Ed Feigenbaum persuaded me to be more precise in the discussion of the limits of today's expert systems technology Thanks to Agustin Araya, Dan Bobrow, John Seely Brown, Lynn Conway, Bob Engelmore, Ed Feigenbaum, Felix Frayman, Gregg Foster, Austin Henderson, Ken Kahn, Mark Miller, Sanjay Mittal, Julian Orr, Allen Sears, Lucy Suchman, and Paul Wallich for reading early drafts of this paper and for helping to clarify the ideas and improve the article's readability Stephen Cross triggered the writing of this article when he invited me to give the keynote address at the Aerospace Applications of Artificial Intelligence Conference in Dayton, Ohio, in September 1985.

More than 40,000 learners worldwide have used TLCTS courses. TLCTS utilizes artificial intelligence technologies during the authoring process and at run time to process learner speech, engage in dialogue, and evaluate and assess learner performance. This paper describes the architecture of TLCTS and the artificial intelligence technologies that it employs and presents results from multiple evaluation studies that demonstrate the benefits of learning foreign language and culture using this approach. It includes interactive lessons that focus on particular communicative skills and interactive games that apply those skills. Heavy emphasis is placed on spoken communication: learners must learn to speak the foreign language to complete the lessons and play the games.

Right now, Machine Learning and Data Science are two hot topics, the subject of many courses being offered at universities today. Above, you can watch a playlist of 18 lectures from a course called Learning From Data: A Machine Learning Course, taught by Caltech's Feynman Prize-winning professor Yaser Abu-Mostafa. This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. Learning From Data will be permanently added to our list of Free Online Computer Science Courses, part of our ever-growing collection, 1200 Free Online Courses from Top Universities.

These are some the most elite academic journals in the world. And last year, one tech company, Alphabet's Google, published papers in all of them. The unprecedented run of scientific results by the Mountain View search giant touched on everything from ophthalmology to computer games to neuroscience and climate models. For Google, 2016 was an annus mirabilis during which its researchers cracked the top journals and set records for sheer volume. Behind the surge is Google's growing investment in artificial intelligence, particularly "deep learning," a technique whose ability to make sense of images and other data is enhancing services like search and translation (see "10 Breakthrough Technologies 2013: Deep Learning").

These are some the most elite academic journals in the world. And last year, one tech company, Alphabet's Google, published papers in all of them. The unprecedented run of scientific results by the Mountain View search giant touched on everything from ophthalmology to computer games to neuroscience and climate models. For Google, 2016 was an annus mirabilis during which its researchers cracked the top journals and set records for sheer volume. Behind the surge is Google's growing investment in artificial intelligence, particularly "deep learning," a technique whose ability to make sense of images and other data is enhancing services like search and translation (see "10 Breakthrough Technologies 2013: Deep Learning").

And last year, one tech company, Alphabet's Google, published papers in all of them. According to the tally Google provided to MIT Technology Review, it published 218 journal or conference papers on machine learning in 2016, nearly twice as many as it did two years ago. "The top people care about advancing the world, and that means writing papers the world can use, and writing code the world can use." So when Apple hired computer scientist Russ Salakhutdinov from Carnegie Mellon last year as its new head of AI, he was immediately allowed to break Apple's code of secrecy by blogging and giving talks.

And last year, one tech company, Alphabet's Google, published papers in all of them. According to the tally Google provided to MIT Technology Review, it published 218 journal or conference papers on machine learning in 2016, nearly twice as many as it did two years ago. "The top people care about advancing the world, and that means writing papers the world can use, and write code the world can use." So when Apple hired computer scientist Russ Salakhutdinov from Carnegie Mellon last year as its new head of AI, he was immediately allowed to break Apple's code of secrecy by blogging and giving talks.

And last year, one tech company, Alphabet's Google, published papers in all of them. According to the tally Google provided to MIT Technology Review, it published 218 journal or conference papers on machine learning in 2016, nearly twice as many as it did two years ago. "The top people care about advancing the world, and that means writing papers the world can use, and write code the world can use." So when Apple hired computer scientist Russ Salakhutdinov from Carnegie Mellon last year as its new head of AI, he was immediately allowed to break Apple's code of secrecy by blogging and giving talks.