All-natural and organic are familiar terms to consumers, and anything artificial has become anathema to many. Unless we're talking artificial intelligence – or AI – then investors should be hungry to learn as much as possible about a technology that is becoming as ubiquitous as organic tofu. The vast majority of nearly 2,000 experts polled by the Pew Research Center in 2014 said they anticipate robotics and artificial intelligence will permeate wide segments of daily life by 2025. A 2015 study covering 17 countries found that artificial intelligence and related technologies added an estimated 0.4 percentage point on average to those countries' annual GDP growth between 1993 and 2007, accounting for just over one-tenth of those countries' overall GDP growth during that time. Interesting numbers – but just what is artificial intelligence?

In 2013 a British artificial-intelligence startup called DeepMind surprised computer scientists by showing off software that could learn to play classic Atari games better than an expert human player. DeepMind was soon acquired by Google, and the technique that beat the Atari games, reinforcement learning, has become a hot topic in the field of AI and robotics. Google used reinforcement learning to create software that beat a champion Go player last year. Now OpenAI, a nonprofit research institute cofounded and funded by Elon Musk, says it has discovered that an easier-to-use alternative to reinforcement learning can get rival results when it plays games and performs other tasks. At MIT Technology Review's EmTech Digital conference in San Francisco on Monday, OpenAI's research director, Ilya Sutskever, said that could allow researchers to make progress in machine learning faster.

Computer-based artificial intelligence (AI) has been around since the 1940s, but the current innovation boom around everything from virtual personal assistants and visual search engines to real-time translation and driverless cars has led to new milestones in the field. And ever since IBM's Deep Blue beat Russian chess champion Garry Kasparov in 1997, machine versus human milestones inevitably bring up the question of whether or not AI can do things better than humans (it's the the inevitable fear around Ray Kurzweil's singularity). As image recognition experiments have shown, computers can easily and accurately identify hundreds of breeds of cats and dogs faster and more accurately than humans, but does that mean that machines are better than us at recognizing what's in a picture? As with most comparisons of this sort, at least for now, the answer is little bit yes and plenty of no. Less than a decade ago, image recognition was a relatively sleepy subset of computer vision and AI, found mostly in photo organization apps, search engines and assembly line inspection.

Alice Lloyd George is an investor at RRE Ventures and the host of Flux, a series of podcast conversations with leaders in frontier technology. It's hard to visit a tech site these days without seeing a headline about deep learning for X, and that AI is on the verge of solving all our problems. Marcus, a best-selling author, entrepreneur, and professor of psychology at NYU, has spent decades studying how children learn and believes that throwing more data at problems won't necessarily lead to progress in areas such as understanding language, not to speak of getting us to AGI – artificial general intelligence. Marcus is the voice of anti-hype at a time when AI is all the hype, and in 2015 he translated his thinking into a startup, Geometric Intelligence, which uses insights from cognitive psychology to build better performing, less data-hungry machine learning systems. The team was acquired by Uber in December to run Uber's AI labs, where his cofounder Zoubin Ghahramani has now been appointed chief scientist.

Recent years have seen great progress for AI. In particular, artificial agents have learned to classify images and recognize speech at near-human level. However, for artificial agents to reach their full potential, they should not only observe, but also act and learn from the consequences of their actions. Learning how to behave is especially important when an agent interacts with humans through natural language, because of the complexity of language and because each person has a different communication style. Reinforcement learning (RL) is the area of research that is concerned with learning effective behavior in a data-driven way.

When I tell people that I work at an AI company, they often follow up with "So what kind of machine learning/deep learning do you do?" This isn't surprising, as most of the market attention (and hype) in and around AI has been centered around Machine Learning, and its high profile subset Deep Learning, and around Natural Language Processing, with the rise of the chatbot and virtual assistants. But while machine learning is a core component for artificial intelligence, AI is in fact more than just ML. So what does it really mean for an application to be "intelligent"? What does it take to create a system that is "artificially intelligent? In the real world, the late and great Alan Turing came up a test to measure whether a machine is able to exhibit behaviour is that equivalent to that of a human, aptly known as the Turing Test.

Lucas Carlson is VP of strategy at Automic Software. Are you getting tired of hearing about artificial intelligence? It seems we must be reaching peak hype cycle around AI when almost every article written about it rehashes the same tropes around self-driving cars, the latest game that has been mastered by computers, or the next house appliance to get speech recognition. There is so much noise around AI that it's hard to find a signal. And the real work of AI is happening behind the scenes of mainstream press coverage.

Corporate giants like Google, IBM, Yahoo, Intel, Apple and Salesforce are competing in the race to acquire private AI companies, with Ford, Samsung, GE, and Uber emerging as new entrants. Over 200 private companies using AI algorithms across different verticals have been acquired since 2012, with over 30 acquisitions taking place in Q1'17 alone (as of 3/24/17). This quarter also saw one of the largest M&A deals: Ford's acquisition of Argo AI for $1B. In 2013, Google picked up deep learning and neural network startup DNNresearch from the computer science department at the University of Toronto. This acquisition reportedly helped Google make major upgrades to its image search feature.

When Ray Kurzweil met with Google CEO Larry Page last July, he wasn't looking for a job. A respected inventor who's become a machine-intelligence futurist, Kurzweil wanted to discuss his upcoming book How to Create a Mind. He told Page, who had read an early draft, that he wanted to start a company to develop his ideas about how to build a truly intelligent computer: one that could understand language and then make inferences and decisions on its own. It quickly became obvious that such an effort would require nothing less than Google-scale data and computing power. "I could try to give you some access to it," Page told Kurzweil.