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.

The Dow Jones Industrial Average shed more than 1,300 points earlier this month in the most dramatic drop since February. If you are a client of Morgan Stanley MS Wealth Management, you may have received a message from your financial advisor. The purpose of the message would be to tell you exactly what happened in the market and what the firm's investment professionals are saying about it. It would also tell you your portfolio's current probability of success in light of recent events. The email would come from your financial advisor.

For most businesses, machine learning seems close to rocket science, appearing expensive and talent demanding. And, if you're aiming at building another Netflix recommendation system, it really is. But the trend of making everything-as-a-service has affected this sophisticated sphere, too. You can jump-start an ML initiative without much investment, which would be the right move if you are new to data science and just want to grab the low hanging fruit. One of ML's most inspiring stories is the one about a Japanese farmer who decided to sort cucumbers automatically to help his parents with this painstaking operation. Unlike the stories that abound about large enterprises, the guy had neither expertise in machine learning, nor a big budget. But he did manage to get familiar with TensorFlow and employed deep learning to recognize different classes of cucumbers. By using machine learning cloud services, you can start building your first working models, yielding valuable insights from predictions with a relatively small team. We've already discussed machine learning strategy. Now let's have a look at the best machine learning platforms on the market and consider some of the infrastructural decisions to be made.

Salesforce recently conducted a survey on how small businesses use technology, and we were surprised to see that 61% of small business owners believe their businesses are not ready for artificial intelligence (AI). Because AI adds the type of seamless efficiency that gives small businesses a huge boost -- and because you probably use it already anyway. That's right -- We rely on intelligent offerings every time we order from Amazon, ask Siri for directions, chat with a customer service rep, or add a suggested friend on Facebook. And there's no reason smaller businesses can't emulate these powerful, time-saving features, too. Here are three ways to add artificial intelligence to your growing business.

The Internet of Things (IoT) is full of promises to transform everything from transportation to building maintenance to enterprise security. But no field may have more to gain than the healthcare industry. Healthcare providers and device makers are all looking to the IoT to revolutionize the gathering of healthcare data and the delivery of care itself. But while many of those benefits are already becoming reality, others are still on the drawing board. Two very different IoT healthcare stories crossed by desk this month -- taken together they provide a surprisingly nuanced picture of healthcare IoT.

But Mica, the company's artificial intelligence, really deserves attention. With "Mica", the augmented reality company Magic Leap has introduced the next evolution of virtual assistants. In contrast to Cortana, Alexa or Siri with their disembodied voices, Mica has an incredibly real avatar. She yawns, makes eye contact and interacts with the wearer using Magic Leap's augmented reality glasses. At the time of Alexa, Siri or Cortana we are used to having ubiquitous but disembodied vocal assistants.

Google Assistant might soon have its own list- and note-taking functions instead of leaning on third-party apps. The 9to5Google team has sifted through the Google search app's code to discover an unannounced "Lists and Notes" web app for Assistant that lets you jot down important information to sync across devices. It's extremely basic (you can't do much more than add titles), but there's a degree of polish that suggests it's not just an experiment. It's not clear if or when Google might put this app into service. Any such move might leave people scratching their head -- if you already use Google Keep or Google Tasks, why switch to this?