The Salesforce announcement of Einstein this week -- impressive as it was -- reminded me that marketers sometimes use terms like machine learning, artificial intelligence (AI) and even automation interchangeably. Businesses have long been infatuated with the word intelligence – business intelligence, relationship intelligence, and media intelligence – are all overused terms from the last decade, for example. In my mind, all of these descriptors are a stretch. Intelligence means something very specific: it's the disposition, composition and strength of an enemy. Yet business executives love war analogies and here we are mashing these terms together again. We like to use the term AI because it sounds more sophisticated.

Google has just released the latest version of its image captioning system as an open source model in TensorFlow. The new iteration is capable of providing image captions that are 93.9 percent accurate. According to Google, the new release brings forth significant improvements. The system is much quicker to train and can produce more accurate and detailed image descriptions when compared to the original. "Today's code release initializes the image encoder using the Inception V3 model, which achieves 93.9 percent accuracy on the ImageNet classification task," says Chris Shallue, a Google Brain team software engineer.

The company's main product is an algorithm that provides a forecast for three thousand different investment instruments, including shares, commodities, interest rates, foreign currency, exchange traded funds (ETFs), and global indices. "We have institutional customers who receive broader access to information, including personalization of the algorithm according to the portfolio they manage, and also private customers looking for more advanced tools for spotting opportunities in the market. In today's world, there is a lot of information that has accumulated in various companies; the main challenge is to be able to spot future trends using the information - to identify the significant information, and to filter out irrelevant information. In principle, the algorithm performs initial filtering for all three thousand investment instruments, and selects the ones that can be predicted.

Most fintech companies offer better interfaces for handling money (bank accounts, loans, payments, etc.), but quite a few startups are also offering a solution for a much older need than how to make money. Israel company I Know First, managed by CEO Yaron Golgher, is one of these. The company's main product is an algorithm that provides a forecast for three thousand different investment instruments, including shares, commodities, interest rates, foreign currency, exchange traded funds (ETFs), and global indices. "The algorithm rates all the investment instruments, and singles out investment opportunities in the capital market on a daily basis, according to the pricing anomaly it finds," Golgher says in a "Globes" interview. Golgher: "The algorithm is self-learning. It is based on purely quantitative values, not reading news or any kind of analysis. There is no human factor here. The algorithm uses artificial intelligence, an area in which huge companies like Apple Computers, Google, and Facebook have recently been making massive investments. The algorithm was developed by our development team, headed by cofounder and CTO Dr. Lipa Roitman. Roitman is a scientist from the Weizmann Institute of Science with over 20 years of experience in the specific field of artificial intelligence, machine learning, and algorithms. The forecast is given for a period of time. What is interesting is that for every forecast, the algorithm also assigns a probability that the forecast will be fulfilled. Every customer can receive a forecast according to his investment preferences. For example, a person investing in technology shares can receive the best opportunities in this segment, a customer investing in commodities will receive the best opportunities in the commodities market, etc." "We have expanded this year to 12 new countries, including the US and Europe, with an emphasis on Italy and France, and Russia, too. We work with Latin America, especially Brazil. The business model is based on access to the algorithm to a varying extent, according to the customer's size."

Sony's Computer Science Laboratory in Paris has been working on the research and development of pioneering music technologies since 1997, and a blog post from one of the lab's teams this week unveiled an advancement that could reverberate throughout the music world. The team's Flow Machines project successfully created two entire pop songs composed by artificial intelligence, after learning musical styles from a massive database. After "exploiting unique combinations of style transfer, optimization and interaction techniques, it can compose in any style," the post reads. The project's success aligns with the team's goals, which, according to the lab's site, has the aim to "abstract'style' from concrete corpora (text, music, etc.), and turn it into a malleable substance that acts as a texture." Though the team has been successful in the past with constraint-based spatialization -- intelligent music scheduling using metadata and award-winning systems (MusicSpace, PathBuilder, Virtuoso, etc.) -- the work it showed off this week might take home the prize for being one of the most impressive.

Now we can use the function f f(x), which we call the target function, as the proxy for the conditional distribution.

Welcome to the future, where you can face search for a live sex webcam performer and be served real-life humans to your telescreen who vaguely resemble the object of your desire within, well, hours depending on how busy the site's servers are. On a'normal' day the wait time is, presumably, more likely to be minutes.) The Belgian company behind the live sex search site is not disclosing which tech giant's algorithms it is using to power the face search feature, given the adult use-case and the latter's evident lack of desire to be associated with porn. But TechCrunch understands the API in question belongs to Microsoft -- namely its Cognitive Services (née Project Oxford) visual image recognition APIs, and specifically its Face API which lets developers add the ability to detect human faces and compare similar ones, organize people into groups according to visual similarity, and identify previously tagged people in images. So, yes, if you build a facial recognition API the porn use-cases will come… Hey there, Developers!

First--try some of the introductory tutorial/competitions. Those get your feet wet. Then just jump head first into a competition. Try and be active on the forums. I have found that the best way to learn is just struggle with it (in most anything--I faked my way into a DB engineer once, 2 years later I was teaching the course on SQL at a Fortune 100 company--I had my share of run-ins with the Admin though--we were on a first name basis)).

A startup technology company, AppZen, has introduced new technology that is leveraging artificial intelligence to examine expense reports for signs of fraud. "We are an AI company and we've built a solution for back-office automation," AppZen CEO Anant Kale told me. "As part of that, our first focus area is back office expense processing, which is essentially how to find compliance issues within expenses. We are focusing on automating the research and reasoning that human auditors do today and putting it into machines." Kale noted that most companies don't really audit anywhere near all of their expense reports.

Despite the ongoing efforts of banks to attract millennials, they are still failing to make an impression. According to Gallup, only 23% of millennials are actively engaged with their bank, making millennials the least engaged generation. Considering that fully engaged customers bring considerable benefits and higher revenues – this is a big problem. If banks expect millennials to "simply grow up," they may be facing a tough road ahead. Financial institutions – banks and others -- that find a way to communicate with millennials on their terms and give them the financial tools they want are more likely to end up the winners.