On a basic level, there are two main types of AI – narrow AI and strong AI. Strong AI, on the other hand, would be capable of handling a wide range of tasks instead of one particular task. It could potentially have human-level cognition and would be able to complete any intellectual task that a person could. Narrow AI exists today, while strong AI has yet to emerge – as a matter of fact, many experts question whether it is even possible. For this reason, it's important to take a closer look at how they may interact in the future.

Mayor of London Sadiq Khan has announced £500,000 in funding to support the capital's data economy, coincident with the first day of London Tech Week. The money will go into Data for London, a platform which will, according to a statement from the Mayor's office, be a "central library for the vast amount of data held across the capital, enabling Londoners to access both public and private data more easily". The new "library" is a development of the Greater London Authority's London Datastore, set up in 2010, which contains 6,000 datasets. It houses a Coronavirus Hub, which was accessed more than five million times in nine months during the pandemic. It also contains a Planning Datahub, which holds data on more than 450,000 planning proposals, as well as an Infrastructure Mapping Application, which is used by utility companies to try to reduce the congestion and disruption caused by roadworks.

The new world order taking shape is likely to be more complex than a simple bi-polar structure, especially since data is being produced at a pace that boggles the mind. For one, we recognize that the digital trace that is generated by computers around the world spans a very wide range of activities, from sending an SMS text message to making a financial transaction. That said, we acknowledge that in the near-term there could be some countries – China being the pre-eminent example – where data-sharing between public and private sector agencies with very little mobility beyond the national borders could violate privacy and openness norms and yet yield a temporary advantage in training algorithms inside a "walled garden." If one were to take the point of view that the biggest and highest impact AI applications are the ones that serve the greatest public purpose, access to data is key. While the U.S. scores well on all three criteria – and this might seem counter-intuitive to prevailing wisdom -- China operates with a handicap if global accessibility of the data is considered essential for creating successful AI applications in the future.

Due to the most recent progresses in machine learning, big data and computational power, artificial intelligence (AI) is widely accepted as having the potential to transform every industry and to overcome the biggest challenges facing society. AI may well be a revolution in human affairs and become the single most influential innovation in history. As with so many technological breakthroughs, progress in AI technologies has moved faster than society. We need a better understanding of how AI transforms our societies, who is most affected, why, and the consequences. Social and behavioural sciences will be crucial to make sense of these shifts and help us navigate them.

Unlike many valuable resources, real-time data is both abundant and growing rapidly. But it also needs to be handled with great care. That was one of the key takeaways from an online workshop produced by Science Business' Data Rules group, which explored what the rapid growth in real-time data means for artificial intelligence (AI). Real-time data is increasingly feeding machine learning systems that then adjust the algorithms they use to make decisions, such as which news item to display on your screen or which product to recommend. "With AI, especially, you want to make sure that the data that you have is consistent, replicable and also valid," noted Chris Atherton, senior research engagement officer at GÉANT, who described how his organisation transmits data captured by the European Space Agency's satellites to researchers across the world.

This article is part of a Technology and Innovation Insights series paid for by Samsung. Similar to the relationship between an engine and oil, data and artificial intelligence (AI) are symbiotic. Data fuels AI, and AI helps us to understand the data available to us. Data and AI are two of the biggest topics in technology in recent years, as both work together to shape our lives on a daily basis. The sheer amount of data available right now is staggering and it doubles every two years.

Central and Eastern Europe is well positioned to take a leading role in the development of AI in healthcare, but the creation of a marketplace for data is crucial. Just how important a role will artificial intelligence (AI) have in medicine over the coming years? That it will revolutionise healthcare is now beyond doubt, particularly in early diagnosis. Even so, its importance – and the need to speed up its implementation – cannot be overstated. Ligia Kornowska, the managing director of the Polish Hospital Federation, and a leader of the AI Coalition in Healthcare, is clear: "not to make use of AI," she says, "will soon be viewed as medical malpractice."

Breakthroughs in AI and innovations in applying blockchain for personal data control and monetization enable new ways to make money off of personal information that most people currently give away for free. Here we highlight three data science and business model innovations, starting with breakthrough ML technology that learns on the fly. There's an emergent machine learning technology out there that offers a clever new way of finding and classifying unstructured content. In geek-speak, the technology is a vertical, personalized search engine that doesn't require expensive knowledge graphs. In human speak, it's a context-sensitive, human-in-the-loop search engine that uses search criteria and implicit user feedback to recommend high-quality results.

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."

Each innovation challenges the norms, codes, and values of the society in which it is embedded. The industrial revolution unleashed new forces of productivity but at the cost of inhumane working conditions, leading to the creation of unions, labor laws, and the foundations of the political party structures of modern democracies. Fossil fuels powered a special century of growth before pushing governments, companies, and civil society to phase them out to protect our health, ecology, and climate. When innovations lead to disaster, it says much about the societal context. The Chernobyl nuclear disaster embodied the flaws of Soviet planning.