On the internet, artificial intelligence is used for everything from speech recognition to spam filtering. The artificial intelligence or "AI" label is slapped on almost anything electronic these days, from "smart" toothbrushes to cancer-curing supercomputers. If you're like me you've become jaded by the AI rubric, realizing we're still a long way from true intelligence in machines. Jeff Hawkins is co-founder of machine intelligence company Numenta and author of a new book "A Thousand Brains: A New Theory of Intelligence" that offers a theory of what's missing in current AI. I don't normally do author interviews, but Jeff has a history of knowing where things are going in tech, including, in my opinion, being a primary developer of the modern smartphone at Handspring and Palm.

The digital revolution is here, but not everyone is benefiting equitably from it. And as Silicon Valley's ethos of "move fast and break things" spreads around the world, now is the time to pause and consider who is being left out and how we can better distribute the benefits of our new data economy. "Data is the main resource of a new digital economy," says Parminder Singh, executive director at nonprofit organization IT for Change. Global society will benefit because the economy will benefit, argues Singh, on decentralization of data and distributed digital models. Data commons--or open data sources--are vital to help build an equitable digital economy, but with that comes the challenge of data governance. "Not everybody is sharing data," says Singh. Big tech companies are holding onto the data, which stymies the growth of an open data economy, but also the growth of society, education, science, in other words, everything. According to Singh, "Data is a non-rival resource. It's not a material resource that if one uses it, other can't use it." Singh continues, "If all people can use the resource of data, obviously people can build value over it and the overall value available to the world, to a country, increases manifold because the same asset is available to everyone." One doesn't have to look very far to understand the value of non-personal data collected to help the public, consider GIS data from government satellites. Innovation plus the open access to geographic data helped not only create the Internet we know today, but those same tech companies.

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."