Salesforce Inc., a global computing company, announced on Sunday, an initiative that integrates Artificial Intelligence (AI) technology into its software for enterprises. Called Salesforce Einstein, the company came up with such an initiative to streamline the sales, services and marketing processes of a workplace. Artificial intelligence has the capabilities to function with human-like abilities and hence its implementation for CRM will make the sales and marketing tasks automatic. This new offering has a set of online AI services that will ease the sales tasks and improve productivity. It will help sales people to predict market behavior and focus on the relevant information.

Improving customer service, acting as a personal assistant or providing a new, user-friendly interface: chatbots are employable in many ways. Belgian start-up company Craftworkz is building machines that speak with people in natural language. In the world of e-commerce, providing an outstanding level of customer support can be quite a challenge, especially in terms of scalability. Customers usually go online at night, when there's no customer support available. Furthermore, when they all get in touch with the service desk at the same time they are clogging it up, and often the support they need is related to a frequently asked question.

The App ecosystem is broken. There are millions of apps out there, and it is hard to find that one that suits you. Platforms do not seem to be investing enough in improving their algorithms (or even applying AI at all) to make them more discoverable or relevant to individual users. For users, the experience with apps has become laborious: you open the app store, search for an app, scroll to find what you want, download it, install it, open it, sign up, go through a tutorial and voila! After a lot of clicks, you finally get to the real deal.

Salesforce is really laying on the jargon when it comes to Einstein, an artificial-intelligence addition to the CRM giant's entire product line set to be announced Monday. "Einstein's models will be powered by advanced machine learning, deep learning, predictive analytics, natural language processing and smart data discovery," according to a Salesforce press release. But AI is red-hot right now, and it must be said that Salesforce has paid dearly for the right to use such language. It has spent 650 million buying a dozen companies to acquire their staff and technology, chairman and CEO Marc Benioff said last week. The word "Einstein" will be appended to each of Salesforce's eight product offerings. Details will be fully shared at the annual Dreamforce users' conference Oct. 4-7 in San Francisco, but for now, here's a partial list of what Salesforce says that will mean in practice.

Artificial intelligence is changing the world in many ways and the medical field is one that has been revolutioanised by AI. While some reports have been arguing that it may take over from human doctors soon, it's already proving helpful in detection and diagnosis of several conditions. A recent study has suggested that Artificial Intelligence can be instrumental in improving accuracy of diagnosis for lung diseases. It does so by using algorithms that can perform predictive analysis of data and learn from that. Researchers developed an algorithm process in addition to clinical variables of smoking history and body mass.

Bazillion Beings, a creator of bots driven by artificial technology, is out to prove that bots can actually replace simple applications. Through the creation of an "independent online life form" -- or "Lifo" (life form) -- artificially intelligent bots can deliver services to users. TechCrunch reported that the bots are a combination of public APIs and other microservices that are algorithmically generated to create brand new services. Bazillion Beings' bots also have an incentive to deliver -- if they can make enough money to cover server costs, then they survive. The most successful bots -- those that make excess revenue -- are cloned by the Bazillion Beings community with the addition of possible improvements.

Here comes the newest hobby horse among the greatest high-tech giants: chatbots are on their way to revolutionizing our ways of communicating and consuming (yes, again!). Chatbots, powered by artificial intelligence, are able to chat with consumers, especially via instant messaging. This is not the first time we say so in this blog: conversations are undoubtedly out in fashion! May it be via applications such as WhatsApp, Facebook Messenger, or even through the telephone where while consumers enjoy exchanging views with those around them, they would also prefer to do so with trademarks as well. It is no coincidence that the world's leading tech sector is drawn into a relentless race for Artificial intelligence, aiming at, among others, enabling communication between humans and machines without going through any subtlety whatsoever.

Big data is a powerful resource to measure and get insights about customers. The problem is, a lot of this data is in silos, uncategorized or disconnected from the marketing and customer success teams that need them the most. Artificial intelligence coupled with predictive analytics could unlock this data, This means you can go beyond metrics like daily active users, monthly active users, users active 7 days after sign up and so on and view an even larger pools of metrics to understand where you need to make intervention.

Around midnight one Saturday in January, Sarah Jeong was on her couch, browsing Twitter, when she spontane ously wrote what she now bitterly refers to as "the tweet that launched a thousand ships." The 28-year-old journalist and author of The Internet of Garbage, a book on spam and online harassment, had been watching Bernie Sanders boosters attacking feminists and supporters of the Black Lives Matter movement. In what was meant to be a hyper bolic joke, she tweeted out a list of political carica tures, one of which called the typical Sanders fan a "vitriolic crypto racist who spends 20 hours a day on the Internet yelling at women." The ill-advised late-night tweet was, Jeong admits, provocative and absurd--she even supported Sanders. But what happened next was the kind of backlash that's all too familiar to women, minorities, and anyone who has a strong opinion online. By the time Jeong went to sleep, a swarm of Sanders supporters were calling her a neoliberal shill. By sunrise, a broader, darker wave of abuse had begun. She received nude photos and links to disturbing videos. One troll promised to "rip each one of [her] hairs out" and "twist her tits clear off." The attacks continued for weeks. "I was in crisis mode," she recalls.

In this second article on adversarial validation we get to the meat of the matter: what we can do when train and test sets differ. Will we be able to make a better validation set? The problem with training examples being different from test examples is that validation won't be any good for comparing models. That's because validation examples originate in the training set. We can see this effect when using Numerai data, which comes from financial time series.