Machine Learning is not something that comes to the arena just last year or few. There are so many Machine Learning applications, we take only five to show you that how important it is to follow this trend, and it is not something new.

Google debuted its take on the smart display earlier this year with a slew of Echo Show rivals. This is, after all, the Google way. As it did with Android, Google created the ecosystem and then partnered with third-party companies like Lenovo and JBL to make the actual products. However, as with the Pixel and the first run of Google Home products, Google likes to dabble in hardware, too. That's why it wasn't much of a surprise when Google announced the Home Hub -- its very own spin on the smart display.

Imagine if we could call out to our living room TV to access a show or movie, get step-by-step cooking directions, watch YouTube clips on demand, have it double as a digital photo frame, look up anything in the world, make phone calls and run your home automation system. How cool would that be? That's the promise of Google's new Home Hub, a $149 talking speaker that is taking on Amazon's $229 Echo Show in what's expected to be one of the biggest marketing battles of the holidays. How small is the Google Home Hub? So tiny that the Amazon Echo dwarfs it, and Mr. Jinx the cat towers over it.

Google brings video to the talking speaker category with the new Google Home Hub. USA TODAY's Jefferson Graham explains why the device has potential. A link has been sent to your friend's email address. A link has been posted to your Facebook feed. Google brings video to the talking speaker category with the new Google Home Hub.

Facebook's ad targeting and the subsequent data collection strategies have already enraged users. Recently, we learned how Facebook stretches out its authority over phone numbers used for two-factor authentication by the users, for ad targeting. And now, Facebook seems geared up to collect users' data through a brand new method. This time, it may use its latest launch Facebook Portal, for the purpose. Facebook Portal is an amalgamation of smart camera and home assistant technologies. The camera repositions itself and zooms automatically, using AI, to keep a moving person in focus.

Artificial Intelligence (AI) may seem like a sci-fi but AI machines can do some pretty amazing and impossible stuff which humans will never be able to. Today, Artificial Intelligence is changing every aspect of technology. Nowadays, technology is not just about creating tools for practical purposes, it is much more advanced. AI is the latest example of it. From self-driving cars to playing chess, AI has outperformed humans in each and every task with its high tech new, time-tested tools.

Actually, most people have very little knowledge of how artificial intelligence works, or for that matter, how broadly it is used in everything from daily financial transactions to determining your credit score. Take the stock market, for example. Only a tiny amount of trading on Wall Street is carried out by human beings. The overwhelming majority of trading is algorithmic in nature. It's preprogrammed so that if the price of soybeans or oil goes down, all kinds of additional steps will take place.

"Alexa, what's the weather going to be like today." It's taken decades for scientists to understand natural human speech to the point where voice-activated interfaces such as Alexa, the natural language processing system by Amazon, are sufficiently enabled to be successfully accepted by consumers. Alexa is who talks to users of Amazon's Echo products including the Echo, Dot and Tap, as well as Amazon Fire TV and other third-party products. Even since 2012, when the patent was filed for what would ultimately become Amazon's artificial intelligence system Alexa, there has been tremendous growth in capabilities and the credit for that growth goes to machine learning. For something that we do every day without giving it any thought, conversation between machines and humans is complex.

This analysis explores the temporal sequencing of objects in a movie trailer. Temporal sequencing of objects in a movie trailer (e.g., a long shot of an object vs intermittent short shots) can convey information about the type of movie, plot of the movie, role of the main characters, and the filmmakers cinematographic choices. When combined with historical customer data, sequencing analysis can be used to improve predictions of customer behavior. E.g., a customer buys tickets to a new movie and maybe the customer has seen movies in the past that contained similar sequences. To explore object sequencing in movie trailers, we propose a video convolutional network to capture actions and scenes that are predictive of customers' preferences. The model learns the specific nature of sequences for different types of objects (e.g., cars vs faces), and the role of sequences in predicting customer future behavior. We show how such a temporal-aware model outperforms simple feature pooling methods proposed in our previous works and, importantly, demonstrate the additional model explain-ability allowed by such a model.

We consider the problem of online collaborative filtering in the online setting, where items are recommended to the users over time. At each time step, the user (selected by the environment) consumes an item (selected by the agent) and provides a rating of the selected item. In this paper, we propose a novel algorithm for online matrix factorization recommendation that combines linear bandits and alternating least squares. In this formulation, the bandit feedback is equal to the difference between the ratings of the best and selected items. We evaluate the performance of the proposed algorithm over time using both cumulative regret and average cumulative NDCG. Simulation results over three synthetic datasets as well as three real-world datasets for online collaborative filtering indicate the superior performance of the proposed algorithm over two state-of-the-art online algorithms.