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?

CES 2017 was set to be the biggest so far, with more exhibitors, products, and visitors than ever before in its 50 year history. But one signal sparked my interest above everything else: increasing focus on how technology brings value over technology per se. It was hard to find anything not labeled "smart" or "intelligent" this year. While just a year ago AI seemed futuristic, people now expect it … and expect it to work straight out of the box. This is because we've reached sufficient maturity in what I call the 4Cs of useful AI: computing, connectivity, cognition, and convergence.

As has become an unwelcome tradition, as Friday wound down and the weekend was so close we could nearly taste it, breaking news hit. The biggest Friday night bombshell came in the form of an indictment of a Russian national engaged in a massive conspiracy to influence the upcoming midterm elections. With millions of dollars at her disposal, she and her co-conspirators have allegedly been engaging in a coordinated effort to use Americans' weaknesses and divisions against us, to amp up racial discord, and generally sow chaos and discontent. Of course, it wasn't like the week had been drama free up until that point. The fun, if you can call it that, began last Saturday, when Robert Mueller expert Garrett Graff explained what he expected to see next from the investigation into Russia's attack on the 2016 election.

Machine Learning (ML) is currently the verge that has the biggest impact on the banking industry. Take a look at how 5 largest banks of the US are using ML in their workflows. The Federal Reserve of the US has recently published an official report on the largest banks in the US. It lists quite a ton of banks, yet we are not surprised by the fact 5 largest and most influential banks of the US are investing heavily into imbuing their services with Artificial Intelligence (AI) and ML. Just to illustrate the efficiency of this approach -- these banks have closed more than 400 of local branches in 2016 and still met their margin thresholds, as mobile banking combined with the ML helped them meet and exceed their customer's expectations.

Attribute-aware CF models aims at rating prediction given not only the historical rating from users to items, but also the information associated with users (e.g. age), items (e.g. price), or even ratings (e.g. rating time). This paper surveys works in the past decade developing attribute-aware CF systems, and discovered that mathematically they can be classified into four different categories. We provide the readers not only the high level mathematical interpretation of the existing works in this area but also the mathematical insight for each category of models. Finally we provide in-depth experiment results comparing the effectiveness of the major works in each category.