The World Intellectual Property Organization has developed a ground-breaking new "artificial intelligence"-based translation tool for patent documents, handing innovators around the world the highest-quality service yet available for accessing information on new technologies. WIPO Translate now incorporates cutting-edge neural machine translation technology to render highly technical patent documents into a second language in a style and syntax that more closely mirrors common usage, out-performing other translation tools built on previous technologies. WIPO has initially "trained" the new technology to translate Chinese, Japanese and Korean patent documents into English. Patent applications in those languages accounted for some 55% of worldwide filings in 20141. Users can already try out the Chinese-English translation facility on the public beta test platform.

His relevant research work also includes: multimodal traffic control (assisted with machine learning and computer vision techniques), integration with decentralized route choice models and dynamic congestion pricing protocols, vehicle-to-infrastructure (V2I) communication with connected vehicles, energy efficiency optimization, and data-driven self-learning and active congestion management based on performance measurement.

The scientific community has made tremendous strides in developing neural networks, computer systems that are built to operate like the human brain. Researchers have managed to get these systems to beat the world's best Go players, identify images and shrink their file sizes. Heck, we've even taught them to write like Philip K Dick. Most incredibly, Google recently taught two nets to design their own encryption algorithm. The problem, however, is that even the researchers that designed these systems aren't particularly sure how they actually work.

Today, Microsoft Research is publicly releasing its effort to tackle just one of the problems plaguing natural language understanding -- knowledge. The company believes that background knowledge is one of the key separators between the way humans and machines understand language. Probase, a knowledge database Microsoft has been working on for quite some time, is serving as the base for a new public tool called Microsoft Concept Graph. Probase brings 5.4 million concepts to the table, beating other knowledge databases like Cyc, which offers 120,000 concepts. The goal of all the connected information is to support text analysis by mixing interpretations with probabilities -- this is very similar to the way humans use rapid process of elimination to accomplish the same task.

Turns out, the inner workings of neural networks really aren't any easier to understand than those of the human brain. But thanks to research coming out of MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), that could soon change. They've devised a means of making these digital minds not just provide the correct answer, classification or prediction, but also explain the rationale behind its choice. And with this ability, researchers hope to bring a new weapon to bear in the fight against breast cancer. The scientific community has made tremendous strides in developing neural networks, computer systems that are built to operate like the human brain.

But, this story is real. About 15 years ago, in Walmart, a sales guy made efforts to boost sales in his store.

Data is everywhere – masses of it. And it's helping businesses to make better decisions across departments. Marketing can utilise data to discover the effectiveness of email campaigns, finance can analyse past trends to make predictions and projections for the future, and sales can target their follow-up with detailed information on prospective customers. But data is only useful when business tools transform it into valuable information. Data intelligence through algorithms and analytics make business data relatable. The most advanced solutions require enormous amounts of data to be able to offer accurate insight to users.

We had teams running the gamut from PhD researchers to regular business consultants that simply knew how to use Tableau. In my time there I saw that for complex problems, there are common scenarios requiring specific teams. There are many views on the subject, and lots of crossover or hybrid teams, but this model has held true for me. Think of implementing an IT support ticket system to prevent SLA breaches. High problem uncertainty, low data complexity - You really just need a BA team with technical experience in your business.

I have a little secret: I don't like the terminology, notation, and style of writing in statistics. I find it unnecessarily complicated. This shows up when trying to read about Markov Chain Monte Carlo methods. Take, for example, the abstract to the Markov Chain Monte Carlo article in the Encyclopedia of Biostatistics. Markov chain Monte Carlo (MCMC) is a technique for estimating by simulation the expectation of a statistic in a complex model. Successive random selections form a Markov chain, the stationary distribution of which is the target distribution. It is particularly useful for the evaluation of posterior distributions in complex Bayesian models. In the Metropolis–Hastings algorithm, items are selected from an arbitrary "proposal" distribution and are retained or not according to an acceptance rule. The Gibbs sampler is a special case in which the proposal distributions are conditional distributions of single components of a vector parameter. Various special cases and applications are considered. I can only vaguely understand what the author is saying here (and really only because I know ahead of time what MCMC is). There are certainly references to more advanced things than what I'm going to cover in this post.

She paints and writes poetry. She's also an artificial intelligence from the movie Her, which imagines how a juiced-up Siri will change our lives. Now, tech companies large and small are racing to make this a reality. You've heard the jargon: AI, machine learning, deep learning, neural networks, natural language processing. What is artificial intelligence, or AI? AI, simply put, is an attempt to make computers as smart, or even smarter than human beings.