We've been overselling current capabilities of AI for years, but that doesn't mean it doesn't have a bright future. That's perhaps why Stanford University researchers conceived of a "One Hundred Year Study on Artificial Intelligence" (100 years!) back in 2016, with plans to update the report every five years through 2116, charting the progress of AI along the way. Five years after the inaugural report, the study authors recently released the second report. The TL;DR? We've made "remarkable progress" in just five years, on the back of ever-improving data infrastructure, yet we still fall "far short of the field's founding aspiration of recreating full human-like intelligence in machines." What we are discovering, however, is the importance of meshing human and machine to achieve better outcomes.

Excelling at a test often does not translate into excelling at the skills the test purports to measure. This is true not only of humans but also of AI systems, and the more so the greater the claims of the test's significance. This became evident less than a decade after the introduction of the Winograd Schema Challenge (WSC),3 a test designed to measure an AI system's commonsense reasoning (CSR) ability by answering simple questions. An example would be, given the information: The sculpture rolled off the shelf because it wasn't anchored, answering: What wasn't anchored? There are multiple AI systems2 that achieve human performance on the WSC but are not capable of performing CSR.

When Elon Musk and Jack Ma famously sat down for a chat about AI, their thoughts were inspiring for some and excruciating for others. They discussed AI in an all-too-common display of fantastical forecasts and philosophical musings. The status quo of conversations about AI shoots us into the future, where we're allowed to get far ahead of ourselves simply because such discussions are admittedly more fun. We need to get back to the present, so that we may actually solve the problems standing in the way of our projected future. For that, I recommend a new book, Rebooting AI: Building Artificial Intelligence We Can Trust, which has set off a buzz in the land of AI enthusiasts and skeptics alike.

The Annual Tech Survey of the International Legal Technology Association (ILTA) is being released, and the US-based organisation has very kindly allowed Artificial Lawyer to publish one of the more AI-related snapshots from its survey across 537 law firms. The key finding is that 54% of those who responded to this part of the survey said they were not pursuing some type of AI / machine learning option. And that is real progress. As seen in the table below, which ILTA has allowed to be shared today, the reality is that it's a clear minority of just 10% that have'one or more AI/ML tools in production'. WHAT IS YOUR FIRM'S CURRENT STRATEGY ON ARTIFICIAL INTELLIGENCE/MACHINE LEARNING (AI/ML)?

With all the attention Artificial Intelligence (AI) attracts these days, a backlash is inevitable – and could even be constructive. Any technology advancing at a fast pace and with such breathless enthusiasm could use a reality check. But for a corrective to be useful, it must be fair and accurate. The industry has been hit with a wave of AI hype remediation in recent weeks. Opinions are surfacing that label recent AI examples so mundane that they render the term AI practically "meaningless" while others are claiming AI to be an "empty buzzword."

As part of the BBC's Intelligent Machines season, Google's Eric Schmidt has penned an exclusive article on how he sees artificial intelligence developing, why it is experiencing such a renaissance and where it will go next. Until recently, AI seemed firmly stuck in the realm of science fiction. The term "artificial intelligence" was coined 60 years ago - on August 31 1955, John McCarthy proposed a "summer research project" to work out how to create thinking machines. It's turned out to take a bit longer than one summer. We're now entering the seventh decade, and just starting to see real progress.