Machine learning as a service market grow at a CAGR of 43.7% to reach USD 3755.0 million by 2021 Is Elon Musk Right And Will AI Replace Most Human Jobs? Stay up-to-date on the topics you care about. We'll send you an email alert whenever a news article matches your alert term. It's free, and you can add new alerts at any time.

Imagine one day walking into a gorgeous structure--like LA's famous Walt Disney Concert Hall--only to discover it was designed by a computer system and constructed by automated robotic arms. Ai Build, a London-based startup, aims to pave the way to 3D printing on large scales. The company is equipping industrial-grade Kuka robotic arms with artificial intelligence and "3D printing guns" to 3D print large structures that focus on maximizing efficiency with labor and materials. Founder and CEO Daghan Cam dreamed up the technology while considering traditional commercial construction and wondering what a more efficient and automated process might look like. In October, the company partnered with engineering consulting firm Arup Engineers to debut the 3D printed "Daedalus Pavilion" at the GPU Technology Conference in Amsterdam.

In machine learning, you may often wish to build predictors that allows to classify things into categories based on some set of associated values. For example, it is possible to provide a diagnosis to a patient based on data from previous patients. Many algorithms have been developed for automated classification, and common ones include random forests, support vector machines, Naïve Bayes classifiers, and many types of neural networks. To get a feel for how classification works, we take a simple example of a classification algorithm – k-Nearest Neighbours (kNN) – and build it from scratch in Python 2. You can use a mostly imperative style of coding, rather than a declarative/functional one with lambda functions and list comprehensions to keep things simple if you are starting with Python. Here, we will provide an introduction to the latter approach.

New drugs typically take 12-14 years to make it to market, with a 2014 report finding that the average cost of getting a new drug to market had ballooned to a whopping $2.6 billion. It's a topic I've covered before, with a study published earlier this year highlighting how automation could be used to reduce the cost of drug discovery by approximately 70%. It's an approach that a number of companies are taking to market. For instance, London-based start-up Benevolent.AI utilizes complex AI to look for patterns in the scientific literature. They have already managed to identify two potential drug targets for Alzheimer's that has already attracted the attention of pharmaceutical companies.

In 1997, a computer program codenamed Deep Blue, defeated Russian chess grandmaster, Garry Kasparov. In 2006, Deep Fritz dethroned the then World Champion Chess player, Vladamir Kramnik. In 2016, Google developed an artificially intelligent computing system named AlphaGo. It added another name on the "list of humans" defeated by a machine, the highly ranked South Korean Go player, Lee Sedol. Prior to the dethroning of Kasparov, Kramnik and Sedol, it was thought that artificial intelligence still had ways to go before it could outwit and outmatch gifted human players.

When crowdsourced labor company CrowdFlower recently raised funding from Microsoft, co-founder Lukas Biewald told me his team was focused on technology that allows businesses to supplement algorithms and artificial intelligence with human judgment from crowdsourced labor pools. Now CrowdFlower bringing on more experts to shape the development of that technology. Specifically it's formed a three-person scientific advisory board, made up of Barney Pell (founder/co-founder of startups including Powerset, LocoMobi and Moon Express, who also led an artificial intelligence team at NASA), Anthony Goldbloom (founder and CEO of Kaggle) and Pete Warden (a staff research engineer at Google, where he's the technical lead on the TensorFlow Mobile machine learning project). "With all these different customers and all these different applications, we wanted them to be confident that they're going to get a high-quality algorithm," said Biewald. He's also a friend of mine from college --although we really only talk about CrowdFlower now, which is kinda sad when you think about it.) "One way to make sure all the product decisions we make really reflect the cutting edge was to get some of the world leaders come in and look at our product."

Advances in machine learning and deep learning systems are bring us much closer to developing true artificial intelligence (AI) than ever before. One major limitation to these systems, though, is the effort required to teach them, with most requiring thousands or even hundreds of thousands of examples before they can "learn" something new. Self-driving car systems absorb miles of traffic data to learn basic driving lessons, and this scary image generator had to be fed 200,000 images for it to recognize a normal face. However, a new development from the team at Google DeepMind may be the start of leveling out that steep learning curve for AI systems. To speed up the learning process, Google DeepMind researcher Oriol Vinyals added a memory component to a deep-learning system.

AI and machine learning changes the software paradigm computers have been based on for many decades. In the traditional computing domain, providing an input, we feed it into an algorithm to produce the desired output. This is the rule-based frameworkthe majority of the systems around us still work with. We set up our thermostat to a desire temperature (input) and a rule based programming (algorithm) will take care of reading a sensor and activating heating or AC machines to get to the room temperature we want (output). The industry has been working relentlessly for many years developing better hardware, software and apps to solve a gazillion problems and use cases around us with programmable solutions.

Ever wanted to develop your own artificially-intelligent programs? Microsoft is empowering everyone with the capability to create huge, intelligent data-processing systems with the release of the Cognitive Toolkit. The Cognitive Toolkit--previously known as CNTK--is a superfast deep-learning toolkit that brings commercial-grade quality and processing accuracy together with programming languages and algorithms you already use. It's not just for developers with a farm of servers and GPUs, though--hobbyists and modest users can be equally competitive because the Toolkit is flexible enough to run on a single laptop. Developers can also integrate into the Toolkit their own Python or C code.

Alice and Bob can keep secrets -- well, at least from Eve. These three are the neural networks (or neural nets) that a team from Google Brain, Google's research division for machine deep learning, developed to see just how well artificial intelligence (AI) can keep secrets. It turns out, they can do it pretty well. In a study published on arXiv, researchers Martín Abadi and David Andersen feature how neural nets can develop their own simple encryption techniques in order to keep messages from eavesdroppers, even without being given special cryptographic algorithms. In theory, neural nets "are generally not meant to be great at cryptography," the researchers said.