Rosenblatt led the design of a computer to implement this idea and tried to train it to recognize the differences between males and females in photos. "the embryo of an electronic computer that [the Navy] expects will be able to walk, talk, see, write, reproduce itself and be conscious of its existence."

The security view of our overall failings to support AI innovation and tech investment push us into action, but it hasn't worked with health insecurity. The broader issue that might motivate and pull us forward is recognition that modern AI, driven in part by advances in machine learning, the harnessing of Big Data and powerful computing, has the potential to amplify human ingenuity and help solve the challenges, including more productive work and climate change mitigation.

Michael Wooldridge is the winner of the 2021 Educational Advances in Artificial Intelligence (EAAI) Outstanding Educator Award. He gave a plenary talk at AAAI/EAAI in February this year, focussing on lessons he has learnt in communicating AI to the public. Michael's public science journey began in 2014 when the press and social media became awash with stories of AI. He wondered who was going to respond to these, often exaggerated, narratives and to add some nuance to the discussion. It turned out that nobody did, and there was a noticeable absence of expert opinion reported.

In this technical talk, Chad Jenkins from the University of Michigan posed the following question: "who will pay the cost for the likely mistakes and potential misuse of AI systems?" As he states, "we are increasingly seeing how AI is having a pervasing impact on our lives, both for good and for bad. So, how do we ensure equal opportunity in science and technology?" It would be great to talk about the many compelling ideas, innovations, and new questions emerging in robotics research. I am fascinated by the ongoing NeRF Explosion, prospects for declarative robot programming by demonstration, and potential for a reemergence of probabilistic generative inference.

Several years ago, Bella Abrahams, the public affairs director at Intel Israel, spoke to a group of female students from the Ben-Gurion University of the Negev. She discussed her career journey and shared her challenges and decisions along the way. She also provided some insights on how to prepare for job applications and sending resumes. After some time, Abrahams got a call. A young student on the line told her how helpful her speech was and how, by using Abrahams' tools, the young woman got the job of her dreams.

If reading that sentence generates a small knot in the pit of my stomach, I have to wonder, what does it feel like to say it? That's exactly what I read in a recent article (AI maths whiz creates tough new problems for humans to solve, Nature, 3 February 2021). It is a quote from Doron Zeilberger, a professor of mathematics at Rutgers University. Whatever: what prompted it is something known as the Ramanujan Machine (ramanujanmachine.com). Named, as you can imagine, for the great mathematician Srinivasa Ramanujan, the Machine will, its authors tell us, "harness your computer power to make new [mathematical] discoveries."

A few months ago, Generative Pre-Trained Transformer-3, or GPT-3, the biggest artificial intelligence (AI) model in history and the most powerful language model ever, was launched with much fanfare by OpenAI, a San Francisco-based AI lab. Over the last few years, one of the biggest trends in natural language processing (NLP) has been the increasing size of language models (LMs), as measured by the size of training data and the number of parameters. The 2018-released BERT, which was then considered the best-in-class NLP model, was trained on a dataset of 3 billion words. The XLNet model that outperformed BERT was based on a training set of 32 billion words. Shortly thereafter, GPT-2 was trained on a dataset of 40 billion words. Dwarfing all these, GPT-3 was trained on a weighted dataset of roughly 500 billion words.

It's "time to wake up and do a better job," says publisher Tim O'Reilly--from getting serious about climate change to building a better data economy. And the way a better data economy is built is through data commons--or data as a common resource--not as the giant tech companies are acting now, which is not just keeping data to themselves but profiting from our data and causing us harm in the process. "When companies are using the data they collect for our benefit, it's a great deal," says O'Reilly, founder and CEO of O'Reilly Media. "When companies are using it to manipulate us, or to direct us in a way that hurts us, or that enhances their market power at the expense of competitors who might provide us better value, then they're harming us with our data." And that's the next big thing he's researching: a specific type of harm that happens when tech companies use data against us to shape what we see, hear, and believe. It's what O'Reilly calls "algorithmic rents," which uses data, algorithms, and user interface design as a way of controlling who gets what information and why. Unfortunately, one only has to look at the news to see the rapid spread of misinformation on the internet tied to unrest in countries across the world. We can ask who profits, but perhaps the better question is "who suffers?" According to O'Reilly, "If you build an economy where you're taking more out of the system than you're putting back or that you're creating, then guess what, you're not long for this world." That really matters because users of this technology need to stop thinking about the worth of individual data and what it means when very few companies control that data, even when it's more valuable in the open. After all, there are "consequences of not creating enough value for others." We're now approaching a different idea: what if it's actually time to start rethinking capitalism as a whole? "It's a really great time for us to be talking about how do we want to change capitalism, because we change it every 30, 40 years," O'Reilly says. He clarifies that this is not about abolishing capitalism, but what we have isn't good enough anymore. "We actually have to do better, and we can do better. And to me better is defined by increasing prosperity for everyone."

My friend Mike Carey, who has died aged 71, was a pioneer in the development of speech recognition and digital audio, including digital audio broadcasting (DAB). After a formative period at the Post Office research labs, Keele University and Mitel Telecom, he left in 1985 with Adrian Anderson and me to establish Ensigma, a company that quickly established itself as a leader in the research and development of digital speech and audio applications, with uses in mobile telephony, broadcasting and automated speech recognition systems. Mike was born in Preston, to Ethel (nee Glover), a weaver and housewife, and Stephen, a miner-cum-decorator. At Preston Catholic college, a local grammar, he met Elizabeth Mercer at a school dance, for once beating the cross-country champion across the floor. They married in 1969, by which time Mike was on a Post Office scholarship to study electrical engineering at Imperial College, London, supported throughout by extra work taken on by his parents.

When Chen Qiufan took a trip to the southwest Chinese province of Yunnan 15 years ago, he noticed that time seemed to slow down as he reached the city of Lijiang. Chen was a recent college graduate with a soul-sucking real estate job in the pressure-cooker metropolis of Shenzhen, and Lijiang was a backpacker's refuge. Wandering through the small city, he was enchanted by the serrated rows of snow-capped mountains on the horizon and the schools of fish swimming through meandering canals. But he was also unnerved by the throngs of city dwellers like himself--burned out, spiritually lost, adrift. He wove his observations together into a short story called " The Fish of Lijiang," about a depressed office worker who travels to a vacation town, only to discover that everything is artificially engineered--from the blue sky to the fish in the streams to the experience of time itself.