At the Plaza at 300 Ashland Place in downtown Brooklyn, patrons mill around a large yellow shipping container with black triangles painted on its side. A nod to the flying geese quilt pattern, which may have served as a coded message for enslaved people escaping to freedom along the Underground Railroad, the design and container serve as a bridge between the past and the future of the African diaspora. At the center of the art project by the Brooklyn-based transmedia artist Stephanie Dinkins, a large screen displays artificial intelligence (AI) generated images that showcase the diversity of the city. Commissioned by the New York-based art non-profit More Art and designed in collaboration with the architects LOT-EK, the AI laboratory If We Don't, Who Will? will be on display until 28 September. It seeks to challenge a white-dominated generative-AI space by highlighting Black ethos and cultural cornerstones.

Governments made a "lack of concrete progress" toward regulating artificial intelligence this year even as the question of the technology's safety rocketed up the global agenda, according to the 2023 "State of AI" report, published Thursday. The field of AI safety "shed its status as the unloved cousin of the AI research world and took center-stage [in 2023] for the first time," the report said. But amid a lack of global consensus on the way forward for regulation, the developers of cutting-edge AI systems were "making a push to shape norms" by proposing their own regulatory models. While last year it seemed that open-source efforts were taking the lead in AI, Big Tech reasserted its hold over the sector in 2023, the report said. This year, amid an ongoing shortage of powerful computer chips, the largest tech companies gained leverage both from their existing computing infrastructure and their large capital reserves, as the cash required to train large AI models continues to escalate.

In the old days there used to be a saying that "what we call'artificial intelligence' is basically what computers can't do yet"[1] -- so as things that were thought to take intelligence -- like playing chess -- were mastered by a computer they ceased to be things that needed "real" intelligence. Today, it's almost as though the situation has reversed, and to read most press-releases and media stories it now appears to be that "what we call'artificial intelligence'" is basically anything that a computer can do today". So in order to get a better handle on what we (should) mean by "artificial intelligence" I use the landscape chart below. Almost any computer programme can be plotted on it -- and so can the "space" that we might reasonably call "AI" -- so we should be able to get a better sense of whether something has a right to be called AI or not. The bottom axis shows complexity (which I'll also take as being synonymous with sophistication).

It takes a well-designed information architecture -- IA -- to ensure good AI. The challenge is getting both people and data on the same page when it comes to AI work. And there's much work to be done on both fronts. That's the word from Seth Dobrin, global chief AI officer at IBM. "Data is the food for AI, yet few organizations sit down at the table to design an AI strategy with a full accounting of where all their data resides and how organized it is," he says. "IT professionals are drawing from at least 20 data sources to inform their AI, and some have to draw from hundreds, so this is a big data infrastructure issue."

The pandemic has taught a similar lesson about artificial intelligence (AI): Organizations are either on the right track with their AI strategies or, if anything, need to dramatically step up the pace of investment. Children's Hospital chief information officer Dan Nigrin points out that AI applications that promote telehealth, for example, "are not necessarily covid-related, but certainly the pandemic has accelerated the consideration and use of these kinds of tools." In a recent MIT Technology Review Insights survey of 301 business and technology leaders, 38% report their AI investment plans are unchanged as a result of the pandemic, and 32% indicate the crisis has accelerated their plans. The percentages of unchanged and revved-up AI plans are greater at organizations that had an AI strategy already in place. Consumers and business decision-makers are realizing there are many ways that AI augments human effort and experience.

As AI tools become more commonplace, many businesses find themselves playing catch up when it comes to incorporating these new systems into their existing infrastructure. And that's more than understandable -- these tools are highly varied, often poorly-understood, and they're constantly evolving. To start making sense of the AI landscape and determine how your business will need to adapt, the first thing to understand is that the term "AI" in fact covers a huge spectrum of different things. In a study presented in the forthcoming book Artificial Intelligence for Sustainable Value Creation, we mapped out how more than 800 different AI systems were being used across 14 industries. Based on our analysis, we found that these systems fell into four distinct categories: systems that complete rote tasks with limited ethical implications, systems that complete rote tasks that do have an ethical component, systems that complete creative tasks with limited ethical implications, and systems that require both creativity and ethical decision-making.

The big data advancement, facilitated by the deployment of numerous sensors, internet connectivity and hardware and software improvement in computational power, communication abilities and digital storage, have enabled AI to scale from small academic research projects to large enterprise production applications. Essentially, big data required sophisticated AI models to analyze and derive knowledge and insights, while the AI models needed the critical mass of big data for training and optimization. Hence, at present, data is often perceived as a sufficient strategic moat for AI startups. As venture capital investors, we see this phenomenon routinely. In recent years, we have seen many startups that place data acquisition at the heart of their business strategy.

The European Commission's Joint Research Center released a report that explores the European perspective on artificial intelligence (AI), along with the global AI landscape's state of play. The report recognizes the value of AI across industry, but while acknowledging the fierce competition on AI taking place between the EU, China, and the United States, it ultimately dismisses the need for Europe to win this global race, arguing instead that for the EU, the more important goal is focusing on developing values and ethics in AI. This is a naive perspective, especially given that China is not only fiercely competing on developing AI, but also aspiring to dominate in AI so as to compete in industries where Europe is leading today. Ironically, even if the EU's first priority is to shape the values and ethics of AI, it will be severely limited in its ability to do so if it is not leading the development and adoption of this technology. Europe would be wrong to forget that any competition involves winner and losers--and more often than not, the winners are those who compete with gusto.

Ask your resident experts, Why does AI work? Readily, they'll explain How it works, methods emptying in a mesmerizing jargonfall of gradient descent. Why will an expensive and inscrutable machine create the knowledge I need to solve my problem? A glossary of technical terms, an architectural drawing, or a binder full of credentials will do little to insulate you from the fallout if you can't stand up and explain Why. The purpose of AI is to create machines that create good knowledge. Just as a theory of flight is essential to the success of flying machines, a theory of knowledge is essential to AI. And a theoretical basis for understanding AI has greater reach and explanatory power than the applied or technical discussions that dominate this subject. As we'll discover, there's a deep problem at the center of the AI landscape. Two opposing perspectives on the problem give a simple yet far-reaching account of why AI works, the magnitude of the achievement, and where it might be headed. Many overlook the question because it's obvious how knowledge is created: We learn from observation. This is called inductive reasoning, or induction for short.

Experts are divided about whether enterprises need a Chief Artificial Intelligence Officer (CAIO) and how the role relates to data scientists and CIOs. The argument against the role is that you don't want a C-level position focused on a technology. In this view AI is a tool and it makes no more sense to hire someone at that level just to implement AI than for other tools. Over the next few weeks I hope to demonstrate how far reaching AI is. I also will argue that the winners and losers in most industries will be determined by AI more than any technology since the PC revolution. The term "artificial intelligence" has morphed away from referring to artificial general intelligence (AGI).