By the time IBM introduced its personal computer in 1981, the company's "Job Training Program" was 13 years old. Secretaries and other administrative professionals across the country--workers whose jobs were affected by IBM's new computer and software--could go to one of the company's 74 job centers and gain skills in areas including computer programming, data entry, and word processing. Jobs in industries such as food services, transportation, and retail trade are at high risk of being automated, forcing workers to gain new skills to compete for well-paying jobs. From Google's self-driving cars to Apple's communication technology to Amazon's retail model, automation is becoming more and more pervasive. As communities across the U.S. witness growing gaps between the skills that workers have and the ones that employers need, workers will need training.

Hoy traemos a este espacio al FOURTH GLOBAL MACHINE INTELLIGENCE SUMMIT, que tedrá lugar el 28 - 29 JUNE 2017 en Amsterdam Informar de un error de Maps Postillion Convention Centre Amsterdam Paul van Vlissingenstraat 8 The Postillion Convention Centre Amsterdam is very conveniently located between the city and the arterial roads and 20 minutes from Amsterdam Airport Schiphol. TOPICS WE COVER NATURAL LANGUAGE PROCESSING INDUSTRIAL AUTOMATION Where machine learning meets artificial intelligence. The rise of intelligent machines to make sense of data. The Machine Intelligence Summit: where machine learning meets artificial intelligence. The rise of intelligent machines to make sense of data in the real world.

We study fairness in collaborative-filtering recommender systems, which are sensitive to discrimination that exists in historical data. Biased data can lead collaborative filtering methods to make unfair predictions against minority groups of users. We identify the insufficiency of existing fairness metrics and propose four new metrics that address different forms of unfairness. These fairness metrics can be optimized by adding fairness terms to the learning objective. Experiments on synthetic and real data show that our new metrics can better measure fairness than the baseline, and that the fairness objectives effectively help reduce unfairness.

Artificial intelligence is already reshaping the labor market. Its impact will likely become even more disruptive. But experts have historically been bad at predicting which jobs and tasks will be lost to automation, and public officials have historically been slow to respond to technological advances with smart, effective regulations. That's the nutshell of a RAND Corporation report on "The Risks of Artificial Intelligence to Security and the Future of Work," released earlier this week. What can K-12 educators and policymakers take away from the work?

As a digital analyst or marketer, you know the importance of analytical decision making. Go to any industry conference, blog, meet up, or even just read the popular press, and you will hear and see topics like machine learning, artificial intelligence, and predictive analytics everywhere. Because many of us don't come from a technical/statistical background, this can be both a little confusing and intimidating. But don't sweat it, in this post, I will try to clear up a some of this confusion by introducing a simple, yet powerful framework – the intelligent agent – which will help link these new ideas with familiar tools and concepts like A/B Testing and Optimization. Note: the intelligent agent framework is used as the guiding principle in Russell and Norvig's excellent text Artificial Intelligence: A Modern Approach – it's an awesome book, and I recommend anyone who wants to learn more to go get a copy or check out their online AI course.

Slowly but surely, artificial intelligence (AI) has infiltrated every area of our lives, from clothes shopping to TV viewing to dating. But what is its impact on education? Will it help teachers, or make them obsolete? In fact, AI does not detract from classroom instruction but enhances it in many ways. Here are some of the benefits of AI in our educational systems.

Give us your feedback Thank you for your feedback. It is not often that a new profession springs up almost overnight. It is also unusual for many of the people who find their way into this new field to do it without the formal training provided by the normal institutions of higher education. Machine learning, as well as the allied field of data science, is developing in a way that looks unlike most other professional career paths that preceded it. It represents both one of the most promising employment opportunities of the next few years and a model for how people entering the workforce today adapt to changes in employment demands in future.

Welcome to the first post of my series Deep Learning for Rookies by me, a rookie. I'm writing as a reinforcement learning strategy to process and digest the knowledge better. But if you are a deep learning rookie, then this is for you as well because we can learn together as rookies!

Today, it is no longer a question of adopting AI or not. Instead, ask yourself if you and your sales team are ready for the inevitable. Artificial intelligence for business is a reality. If your goal is to forge ahead and lead in your field, then you need to adapt to a workplace where AI plays a crucial role. As J.J. Kardwell, founder and CEO of predictive marketing software company EverString, puts it: "Growth-focused sales organizations of every size and stage cannot afford to ignore the benefits of AI-assisted sales."

Are you using deep neural networks in the real world, solving real world problems? A number of weeks ago I asked my LinkedIn connections this very question, in the wake of Kaggle's "The State of Data Science and Machine Learning" 2017 report. The Kaggle report revealed that "neural networks" are being employed by 37% of respondents. The report's algorithm breakdown considers CNNs, RNNS, and GANs separately. It's a given that self-selecting surveys are difficult to get perfect, but I was surprised by the high percentage that neural networks garnered, to be honest.