What will be the impact of machine learning on economics? NEW YORK, NY - MAY 05: Susan Athey speaks at TechCrunch Disrupt NY 2014 - Day 1 on May 5, 2014 in New York City. The short answer is that I think it will have an enormous impact; in the early days, as used "off the shelf," but in the longer run econometricians will modify the methods and tailor them so that they meet the needs of social scientists primarily interested in conducting inference about causal effects and estimating the impact of counterfactual policies (that is, things that haven't been tried yet, or what would have happened if a different policy had been used). Examples of questions economists often study are things like the effects of changing prices, or introducing price discrimination, or changing the minimum wage, or evaluating advertising effectiveness. We want to estimate what would happen in the event of a change, or what would have happened if the change hadn't taken place.

Our analysis includes companies applying AI algorithms to verticals like healthcare, security, advertising, and finance as well as those developing general-purpose AI tech. Our analysis includes all equity funding rounds and convertible notes. In addition, auto tech company and unicorn Zoox raised $200M in Series A in Q2'16 and cybersecurity startup StackPath raised a $180M private equity round in Q3'16. Last quarter also saw 4 mega-rounds: $130M Series B round raised by life science startup Zymergen, $120M Series B round raised by computer vision startup SenseTime, $100M Series C round raised by facial recognition startup Face, and a $100M round raised by Israel-based Voyager Labs.

Machine learning has been a constant on tech trend lists for years. This year, it's time to embrace what humans can learn by interacting with machine learning. As Google's head of Machine Intelligence, Blaise Aguera y Arcas, noted in a recent Medium article: "Machine intelligence will expand our understanding of both external reality and our perceptual and cognitive processes." In the spring of 2016, Google's AlphaGo software, fueled by machine learning, beat the world's greatest human Go player, Lee Sedol. The victory was a major milestone for a specific type of AI, called deep neural networks, more closely modeled on the way humans think.

Kristen Stewart is quickly becoming one of my favourite people. She's gone from Twilight to indie darling, picking some amazing films that show us she has way more talent than Bella Swan gave her credit for. Stewart is gearing up to debut her directorial debut, a 17-minute short film called Come Swim, which was inspired by a painting Stewart had previously completed. The film integrates the painting using a technique called "style transfer," which uses convolutional neural networks to have an algorithm change a video in real time. Stewart, along with her film's producer and an engineer, drafted a three-page research paper, called "Bringing Impressionism to Life with Neural Style Transfer in Come Swim."

When Facebook (NASDAQ: FB) suggests you "tag" a friend in a photo, it generally suggests that friend's name. That small interaction provides a glimpse into the world of an emerging and powerful aspect of artificial intelligence (AI) in action -- image recognition. With its treasure trove of words and pictures from 1.79 billion monthly active users, it is using that data, combined with recent advancements in AI, to propel this and other technological advances. Facebook may well have the lead in facial recognition, even extending a step further into the realm of facial verification. It released a research paper in 2014 in which it reported 97.35% accuracy, which approaches human levels of recognition.

One of the disadvantages of machine learning as a discipline is the lack of reasonable confidence intervals on a given prediction. There are all kinds of reasons you might want such a thing, but I think machine learning and data science practitioners are so drunk with newfound powers, they forget where such a thing might be useful. If you're really confident, for example, that someone will click on an ad, you probably want to serve one that pays a nice click through rate. If you have some kind of gambling engine, you want to bet more money on the predictions you are more confident of. Or if you're diagnosing an illness in a patient, it would be awfully nice to be able to tell the patient how certain you are of the diagnosis and what the confidence in the prognosis is. There are various ad hoc ways that people do this sort of thing.

Machine learning, a kind of artificial intelligence that enables computers to learn without being explicitly programmed, will be a driving force in smartphone innovation in the coming year, according to Deloitte's newly released report on digital trends, Technology, Media & Telecommunications (TMT) Predictions. This year, over 300 million smartphones will be equipped with neural network machine-learning capabilities, the report predicts. Such functionality could enhance a range of functions, including image classification, navigation and speech recognition. The rise of machine learning on smartphones could spark a similar dynamic in the fast-growing arena of wearables. For mobile users in disconnected spaces such as underground or in a plane, machine learning could enable new capabilities, while those operating in a connected environment may see tasks performed more quickly, or with enhanced privacy.

Self-learning, machine learning, and AI are all buzzwords in the tech field today. They all represent the next generation in software development and management. In this brave new world, programmers will often set up the application -- and the software will do the rest. Driven by big data, deep learning systems, and consumer demand, you may be investing in self-learning programs sooner than you think. Self-learning, often referred to as machine learning, is a form of AI.

The latest Gartner Hype Cycle for Emerging Technologies illustrates how quickly technology innovations have the potential to redefine buyer, supplier and customer relationships for any business. Gartner added 16 new technologies to the Hype Cycle this year, including blockchain, machine learning, general purpose machine intelligence, smart workspace in addition to many others. Gartner identifies transparently immersive experiences, the perceptual smart machine age, and the platform revolution as three overarching trends that have the most potential to reshape business models, and provide enterprises with access to emerging markets and ecosystems. The Hype Cycle is based on an assessment of the market hype, maturity, business benefit and future direction of more than 2,000 technologies, grouped into 11 topic areas.

Let's say you are a highly-experienced professional with little to no experience in analytics. You find yourself paired with a data analyst or data scientist and are presented with some fancy modelling that makes about as much sense as quantum physics or Ancient Greek. You are clearly out of your depth but need to be able to decide whether you can back the findings and present it to senior management, or send it back for fine-tuning. You just need to be able to speak enough of their language to let them use their expertise to help you. The two common mistakes are to either entirely distrust the numbers or be possessed of a blind faith in analytics.