It used to be that even sophisticated mobile robots could be easily defeated by using (say) a table to block its way. The robot would sense the table, categorize it as an obstacle, try to plan a path around it, and then give up when its planner fails. This works because robots generally don't know what most objects are, or how they work, or what you can do with them: They just get turned into obstacles to be avoided, because in most cases, that's the easiest and safest thing to do. You can't normally use a table across a hallway to deter a human, because humans understand that tables are physical objects that can be moved, and the human will just pull the table out of the way and keep on going. Even if the table doesn't behave exactly the way we'd expect it to (like, one of the wheels is stuck), we can adapt, and figure it out.

The purpose of text classification is to give conceptual organization to large collection of documents.An interesting application of text classification is to categorize research papers by most suitable conferences. Finding and selecting a suitable academic conference has always been a challenging task especially for young researchers. We can define a'suitable academic conference' as a conference, which is aligned with the researcher's work and have a good academic ranking. Usually researcher have to consult to their supervisors and search extensively to find a suitable conference. Among many conferences, few are considered to be relevant to send a research work.

Artificial intelligence just keeps getting smarter and smarter. Now Microsoft researchers say they've developed speech recognition technology that can grasp a human conversation as well as people do. The work out of the Microsoft Artificial Intelligence and Research department was published in a scientific paper this week. It shows that when the speech recognition software "listened" to people talking, it was able to transcribe the conversation with the same or fewer errors than professional – human – transcriptionists. The technology delivered a word error rate (WER) of 5.9 percent, which is roughly the same as that of people who were asked to transcribe the same conversation.

For consumer companies, large-scale leveraging of customer and behavioral data to drive personalized customer experiences is turning into a virtual arms race. Marketing technology platforms of the last 10 years were built around campaign process that were still highly manual, requiring marketing execs to do all the testing, optimization and which makes the cycle time for learning and actually influencing marketing very slow. Now more and more marketers recognize the need to deploy advanced personalization capabilities that make the use of machine-learned optimization. And, Matt Fleckenstein, Chief Product Officer at Amplero, helps marketers achieve just that. With a track record for conceiving, building, and launching martech products and services it comes easy to him.

Next-generation cognitive computing is redefining how we live and work as more businesses are using all the data available to them to improve performance and customer service, and drive innovation and revenue. Today's business challenges have never been more complex, and the critical insights that can help address these challenges are often buried in an avalanche of data. Previously, these insights were beyond the capabilities of conventional computing solutions – programmable systems based on mathematical principles that harken back to the 1940s. But IBM Watson has changed the game. IBM Watson is built upon a new foundation called cognitive computing – a system that learns and reasons from interactions with humans, files, online interactions and its environment.

As a venture capitalist, my team and I try to foresee the major disruptive trends ahead of most and it has become clear to us that we are moving from a world of machine automation to a world of autonomous machines. Sometimes some of the biggest disruptions are happening in plain sight of everyone but go unnoticed for years and can only be fully appreciated with a rear view mirror perspective! At BootstrapLabs we feel that the Autonomous Corporation is one of these mega trends silently awakening in the networks of every corporations around the world... For this reason we felt it was important to bring this topic at the forefront and bring our our AI community together on November 15th. We stand in front of the 4th and largest wave of the industrial revolution, powered by AI and Data.

These days there's no shortage of things to keep you awake at night, wherever you stand on the political spectrum. For others it's the prospect of Brexit being thwarted. For others still, it's whether the Chinese economy will hold up, what the outcome of the US presidential election will be or the risk of artificial intelligence taking over your job. So what's the best way to handle the inevitable anxiety that goes hand-in-hand with all that uncertainty? Will Borrell studied that anxiety up close after the Brexit vote in the UK earlier this year.

LONDON--(BUSINESS WIRE)--MediaGamma has announced the launch of a new Audience Prediction product, which is set to make a major impact on the ad tech market. By applying deep learning to unique data sets, coupled with MediaGamma's unique AI Decision Support Engine, the product is set to provide players in the ecosystem with over 90% certainty about a user's interests and demographic profile. The new product will help people to navigate uncertainty to make better decisions, and a major telecoms company has already signed up. The Audience Prediction product is the latest in a broad portfolio of products created by MediaGamma (http://www.mediagamma.com/), The start-up's world-renown team of data scientists deliver bespoke real time, prediction-based data science solutions focusing on online user behaviour.

Check out how three Galvanize students put together an IBM-inspired chess AI, and get the files for free on Github. This project focuses on computer science concepts such as data structures and algorithms. Chessnut is the chess engine we are using for all the moves and chess logic. Currently trying to implement multiprocessing as our recursive function uses a lot of computing power so calculating heuristics on board states more than 4 levels deep takes a lot of time. With a depth of 3 levels, our AI makes pretty good moves but also makes a lot of ill-advised ones as well. The AI's chess intelligence is estimated to be at a level 3 out of 9.

At its core, machine learning is a sub-field of artificial intelligence. Machines are being designed and developed in a way that they can not only process information but also mine the data being fed to come up with patterns and trends that facilitate decision making. Some machines have progressed to a point where they can make decisions without requiring human input. However, this is a basic definition of what machine learning is and how it works. To understand the concept better, here's a 5-point cheat sheet you can refer to: To sum it up, machine learning is a journey that enables organizations to be more data driven by using data to proactively make decisions.