Plus, several planets--including a transiting hot Jupiter in the Kepler field--do indeed reflect their star's light. So, using thousands of synthetic data sets, as well as observations from those known planets, Millholland and Laughlin trained an algorithm to sift through the massive Kepler data set and search for shiny planets. Millholland ran the program on 142,630 Kepler stars, looking for large, non-transiting worlds that orbit their stars in less than one Earth-week. One way to answer that question is to look for hot Jupiters using Millholland's method in systems where smaller worlds have been detected orbiting rather far from their stars.

Similar conveniences have made their way into customer service via machine learning. The difference is that while the aforementioned machine learning examples learn and adapt to your daily routines, AI tools for customer service focus more on the customer's journey and the workflows of support staffs. We've highlighted some of the potential innovations coming to customer service by way of machine learning: Effective machine learning tools could have the same impact as a personal assistant, one that's infallible when it comes to routine tasks. Most people think of chatbots when it comes to machine learning in customer service (as they should, since they're all the rage at the moment), but don't forget about your other support channels.

It's true a significant amount of labor will be displaced, but second order effects will create a net increase in new jobs. It's true that the auto did displace pre-auto transportation jobs, but it also paved the way for entirely new industries -- three big beneficiaries come to mind. Second, consumer industries; the actual businesses that lived in these newly constructed complexes needed labor after all. It's the precise reason why every tech company and auto company is strategizing on how they will navigate the space.

How can we anticipate and manage trends in AI, and will creative and analytical skills really be the key to job success in an AI world? The progression and extensive testing of these vehicles presents a problem that cannot be easily fixed, meaning there'll be a gap between the smart cruise control phase and the introduction of a truly autonomous vehicle. That's not to say that autonomous vehicles won't be amazing. This next generation will become amazing at controlling and training the bots that manage their life.

Opinions expressed by Forbes Contributors are their own. The author is a Forbes contributor. The truth is that it's impossible for traditional marketing technologies, processes, and organizations to keep pace with the volume, velocity, and complexity of cross channel, personalized customer engagement in 2017. We're in the very early stages of transforming marketing with AI.

In the realm of music, the company Jukedeck has advanced its technology to the point that most listeners assume that the short musical pieces created by its AI are, in fact, created the old-fashioned way. And defining creativity in objective terms turns out to be quite difficult. As an expedient oversimplification, we can speak of two types: generative creativity, and combinatorial creativity. Combinatorial Creativity is the novel combination of pre-existing ideas or objects.

While these moves point to AI one day becoming ubiquitous for business tasks, Google's release of an academic paper called "One Model to Learn Them All" shows another route for machine learning to become commonplace. The paper describes a single machine learning template -- dubbed the MultiModel -- that can perform multiple tasks exceptionally well. The company's product offers automated guidance so that people writing job descriptions are able to maximize their response from interested and qualified applicants. In the report, researchers at the Facebook Artificial Intelligence Research lab describe using machine learning to train their "dialog agents" to negotiate.

With major technology companies and startups seriously embracing Cloud strategies, now is the perfect time to attend 21st Cloud Expo, October 31 - November 2, 2017, at the Santa Clara Convention Center, CA, and June 12-14, 2018, at the Javits Center in New York City, NY, and learn what is going on, contribute to the discussions, and ensure that your enterprise is on the right path to Digital Transformation. With major technology companies and startups seriously embracing Cloud strategies, now is the perfect time to attend @CloudExpo @ThingsExpo, October 31 - November 2, 2017, at the Santa Clara Convention Center, CA, and June 12-4, 2018, at the Javits Center in New York City, NY, and learn what is going on, contribute to the discussions, and ensure that your enterprise is on the right path to Digital Transformation. Join Cloud Expo @ThingsExpo conference chair Roger Strukhoff (@IoT2040), October 31 - November 2, 2017, Santa Clara Convention Center, CA, and June 12-14, 2018, at the Javits Center in New York City, NY, for three days of intense Enterprise Cloud and'Digital Transformation' discussion and focus, including Big Data's indispensable role in IoT, Smart Grids and (IIoT) Industrial Internet of Things, Wearables and Consumer IoT, as well as (new) Digital Transformation in Vertical Markets. Accordingly, attendees at the upcoming 21st Cloud Expo @ThingsExpo October 31 - November 2, 2017, Santa Clara Convention Center, CA, and June 12-14, 2018, at the Javits Center in New York City, NY, will find fresh new content in a new track called FinTech, which will incorporate machine learning, artificial intelligence, deep learning, and blockchain into one track.

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The concept is simple: One camera is streaming a live stream from one of the stones in Saimaa and people all around the world can try to see one of the rarest seals in the world. Then I was already interested for applying modern computer vision to track when the seal is actually in the picture and tweet about that. When you are applying machine learning to some problem there is many ways to achieve the target -- in this case to detect if there is a seal or not. Naive approach could be for example to find out differences between the two frames from the video stream and so detect if there is a change (and possibly seal) but that might have problems with changing light conditions.