What jumps to mind when you think about the impending AI apocalypse? If you're partial to sci-fi movie cliches, you may envisage killer robots (with or without thick Austrian accents) rising up to terminate their hubristic creators. Or perhaps, a la The Matrix, you'll go for scary machines sucking energy out of our bodies as they distract us with a simulated reality. For Yuval Noah Harari, who has spent a lot of time worrying about AI over the past decade, the threat is less fantastical and more insidious. "In order to manipulate humans, there is no need to physically hook brains to computers," he writes in his engrossing new book Nexus.

This article was featured in the One Story to Read Today newsletter. "About 14 billion years ago, matter, energy, time and space came into being." So begins Sapiens: A Brief History of Humankind (2011), by the Israeli historian Yuval Noah Harari, and so began one of the 21st century's most astonishing academic careers. Sapiens has sold more than 25 million copies in various languages. Since then, Harari has published several other books, which have also sold millions. He now employs some 15 people to organize his affairs and promote his ideas. Check out more from this issue and find your next story to read. Harari might be, after the Dalai Lama, the figure of global renown who is least online.

Concern about doctored or manipulative media is always high around election cycles, but 2024 will be different for two reasons: deepfakes made by artificial intelligence (AI) and the sheer number of polls. The term deepfake refers to a hoax that uses AI to create a phoney image, most commonly fake videos of people, with the effect often compounded by a voice component. Combined with the fact that around half the world's population is holding important elections this year – including India, the US, the EU and, most probably, the UK – and there is potential for the technology to be highly disruptive. Here is a guide to some of the most effective deepfakes in recent years, including the first attempts to create hoax images. The banana where it all began.

The explosive growth in artificial intelligence in recent years -- crowned with the meteoric rise of generative AI chatbots like ChatGPT -- has seen the technology take on many tasks that, formerly, only human minds could handle. But despite their increasingly capable linguistic computations, these machine learning systems remain surprisingly inept at making the sorts of cognitive leaps and logical deductions that even the average teenager can consistently get right. In this week's Hitting the Books excerpt, A Brief History of Intelligence: Evolution, AI, and the Five Breakthroughs That Made Our Brains, AI entrepreneur Max Bennett explores the quizzical gap in computer competency by exploring the development of the organic machine AIs are modeled after: the human brain. Focusing on the five evolutionary "breakthroughs," amidst myriad genetic dead ends and unsuccessful offshoots, that led our species to our modern minds, Bennett also shows that the same advancements that took humanity eons to evolve can be adapted to help guide development of the AI technologies of tomorrow. In the excerpt below, we take a look at how generative AI systems like GPT-3 are built to mimic the predictive functions of the neocortex, but still can't quite get a grasp on the vagaries of human speech.

Multiple factors have driven the development of artificial intelligence (AI) over the years. The ability to swiftly and effectively collect and analyze enormous amounts of data has been made possible by computing technology advancements, which have been a significant contributing factor. Another factor is the demand for automated systems that can complete activities that are too risky, challenging or time-consuming for humans. Also, there are now more opportunities for AI to solve real-world issues, thanks to the development of the internet and the accessibility of enormous amounts of digital data. Moreover, societal and cultural issues have influenced AI.

Multiple factors have driven the development of artificial intelligence (AI) over the years. The ability to swiftly and effectively collect and analyze enormous amounts of data has been made possible by computing technology advancements, which have been a significant contributing factor. Another factor is the demand for automated systems that can complete activities that are too risky, challenging or time-consuming for humans. Also, there are now more opportunities for AI to solve real-world issues, thanks to the development of the internet and the accessibility of enormous amounts of digital data. Moreover, societal and cultural issues have influenced AI.

I maintain the popular Github repository satellite-image-deep-learning. This post provides the brief history of this repository, how I find material, and why you should create something similar. I started this repository in April 2018 whilst I was working at Surrey Satellites. The satellite constellation I had been hired to work on was on hold, and since I had demonstrated my ability to program in Python I was reassigned to assist with some development work. In particular the company had developed a basic catalogue for viewing satellite imagery & capture locations on a map. I was asked to add some new features after the original developer left, and most of the features were quite straightforward to implement. The catalogue quickly grew, and improving the search functionality became a high priority. One particular feature request was to add tags to imagery, so a user could search using terms such as golf course or harbour. What options are there for tagging an aerial image like this? Machine learning was suggested as an approach

How did we get to where we are today in the field of generative AI? Generative AI will be the most disruptive technological innovation since the advent of the personal computer and the inception of the Internet with the potential to create 10s of millions of new jobs, permanently alter the way we work, fuel the creator economy, and displace or augment 100s of millions of workers in roles from computer programmers to computer graphics artists, photographers, video editors, digital marketers and yes, even journalists. Even with all the hype around generative AI this year, it's true power has not yet been seen or felt, in 2023 there will be significant innovations that will begin a revolution that will leave no industry or job function un-impacted in one way or another. Although Generative AI has been a focused area of AI research since 2014, it really took off in the latter half of 2022 when the technology was put into the hands of consumers with the release of several text-to-image model services like MidJourney, Dall-E 2, Imagen, and the open-source release of Stability AI's Stable Diffusion. This was quickly followed up by OpenAI's ChatGPT which mesmerized consumers with a version of GPT-3 re-trained on conversational dialog that seemingly had an answer for everything and delivered responses in a very human-like manner. At the same time VCs looking for the hot new technology to invest in caught the generative AI bug and both Stability AI and Jasper both became instant unicorns with Series A funding exceeding $100 million.

To see what the future might look like it is often helpful to study our history. This is what I will do in this article. I retrace the brief history of computers and artificial intelligence to see what we can expect for the future. How rapidly the world has changed becomes clear by how even quite recent computer technology feels ancient to us today. Mobile phones in the '90s were big bricks with tiny green displays.

The language and image recognition capabilities of AI systems have developed very rapidly. The chart shows how we got here by zooming into the last two decades of AI development. The plotted data stems from a number of tests in which human and AI performance were evaluated in five different domains, from handwriting recognition to language understanding. Within each of the five domains the initial performance of the AI system is set to -100, and human performance in these tests is used as a baseline that is set to zero. This means that when the model's performance crosses the zero line is when the AI system scored more points in the relevant test than the humans who did the same test.2