Base isn't the biggest player in a crowded CRM market by any means, but it's not shying away from a fight with industry giants like Salesforce, Microsoft, Oracle and SAP. This week it introduced Apollo, a stand-alone artificial intelligence piece its CEO claims is well ahead of its well-heeled competitors -- and it chose to announce this new product in the middle of Dreamforce, Salesforce's enormous customer conference. Artificial intelligence is the watchword of the day in CRM with both Salesforce and Oracle announcing AI products/features in the same week a couple of weeks ago. The bigger companies are adding smarts to more than just CRM, but Base CEO Uzi Shmilovici thinks his company's singular focus on CRM may be an advantage. Shmilovici claims for instance that his company's predictive scoring is much better than Salesforce's because he has more data.

Today, CircleBack, Inc. announced the launch of 3 APIs based on their artificial intelligence-driven engine, SharedIQ, to revolutionize the state of B2B contact data productivity. Each of the APIs--Email Signature Capture, Contact Append & Company Append--offers enterprise customers the opportunity to build high-accuracy, high-value contact data flow into the hearts of their offerings and bolster the efficiency of the CRM, Marketing Automation Systems, and Account-Based Marketing tools currently on the market. Since the launch of the CircleBack app in early 2015, CircleBack, Inc. has worked to offer businesses the same access to its proprietary AI engine that it offers to over 2 million app users. The CircleBack engine uses artificial intelligence and billions of contact data points to discover, append, and update enterprise contact data. "CircleBack APIs will bring organizations much-needed control of their contact data," explains Manoj Ramnani, CircleBack, Inc. Founder and CEO.

The Spain-headquartered startup, which today is announcing 6 million in Series A funding, is using deep learning to help startups and larger companies recruit developers. Specifically, its AI tech is analysing the code of millions of developers via their open source contributions in order to match them to appropriate job openings. Meanwhile, the company, which is only in its second year of operation, say it's already close to being profitable and on track to close the year something approaching 1 million in revenue. It also plans to further develop its product, including applying its AI-driven analysis of code to other products aimed at developers, both free and paid-for.

San Francisco-based Demandbase, a business-to-business (B2B) marketing company, is putting its Spiderbook acquisition to work by adding a layer of artificial intelligence (AI) to its Account-Based Marketing (ABM) platform. Spiderbook, which Demandbase acquired in May, offers machine learning and data science to analyze web pages and target accounts. Demandbase capitalized on those capabilities to create DemandGraph, an AI-powered business graph designed to help marketing and sales teams collect business data like vendor, customer and partner relationships. Demandbase CEO Chris Golec told CMSWire the DemandGraph AI capabilities take Demandbase's digital footprint of information on companies and "exposes it to a much bigger set of data." "If you think about what Salesforce is doing with Einstein, that's all about applying AI to (customer relationship management) CRM data and the people you're doing business with," Golec said.

Financial markets, throughout their evolution, have always been susceptible to manipulations and various unscrupulous activities. The history can be traced back as far as 600 BC when a Greek merchant borrowed money from public to grow corn. The lenders were assured of repayment along with interest when the product was sold. In case of failure to repay on time, the lenders could acquire merchant's cargo along with the boat. The fraud designed by the borrower was to sink the empty boat and keep both the money and corn.

Artificial intelligence (AI) made the leap from science fiction to the corporate world some time ago. Amazon and Netflix use it routinely to make purchasing recommendations, iPhone users speak to Siri every day, and banks give investment advice or calculate risks thanks to these technologies. But these are only the first hesitant steps of AI, which is due to have a major impact on the financial sector, as revealed in this report in the magazine Euromoney, and featuring an interview with Marco Bressan, Chief Data Scientist at BBVA. Artificial Intelligence has been present in the financial industry for many years now, Marco Bressan, Chief Data Scientist at BBVA, tells Euromoney: "Currently it denotes a vision of the future; an aspect of the sci-fi imagination; something that you still can't do. But the truth is senior financial executives have been doing AI-related work, research and deployment of products for years", he notes. When we talk about Artificial Intelligence we refer to a set of technologies –many created decades ago– rather than one single product or system.

How many robots does it take to open a door? If the robots are trying to figure out how to do the task from scratch, then it helps to have as many as possible involved. In three separate research papers posted online Monday, researchers at Google and other Alphabet subsidiaries showed several ways in which robots can learn to perform simple tasks more quickly by sharing different types of learning experiences. The researchers are training teams of industrial robots to perform simple tasks using a technique called reinforcement learning, which combines trial and error with positive feedback. For the moment, these tasks are extremely simple, like opening doors or nudging objects around.

The world's first laundry sorting and folding robot will go on sale in 2017, its manufacturer said on Tuesday at the Ceatec electronics show just outside of Tokyo. Laundroid is the size of a large refrigerator and has a pull-out drawer near its base where unsorted clothes can be thrown in. A robot inside the device picks up each item of clothing and uses image analysis with artificial intelligence to figure out what kind of clothing it is so it knows the correct way to fold it. For humans, identifying and folding laundry is an easy albeit mundane task, but for a machine it's very difficult. That's reflected in the size of the device and its speed. During a demonstration on Tuesday it took about 10 minutes to pick out one garment, identify it and fold it.

In the summer of 2008, I noticed a mole on my arm that seemed to be getting bigger. It was hard to tell, though. I wasn't sure if it had actually grown -- or if I was just freaking out and being a hypochondriac for no good reason -- so I decided to have it checked out. Doing so required me to call a clinic, set up an appointment, wait for a few days, and then drive to the doctor's office. Once I was there, a woman with more than eight years of specialized medical education look a long, hard look at the mole and asked me a series of questions about it -- but when it was all said and done, she didn't have a definitive answer for me. Instead, she just referred me to different doctor who had more experience with melanoma, and the whole process started over again. It ended up being nothing, but the second doctor told me to keep an eye on it just to be safe. Fast-forward eight years, and I'm still keeping an eye on it -- but my methods have become a bit more sophisticated. Now, every few months, I pull a smartphone out of my pocket, fire up an application called SkinVision, and snap a picture of the mole.

Artificial intelligence is, of course, all the rage in tech circles, and the press is awash in tales of AI entrepreneurs striking it rich after being acquired by one of the giants, often early in the life of their startups. As always, the reality of building a startup is different, especially when one aims to build a self-standing company for the long term. The path to success in AI requires not just technical prowess but also careful thinking and execution through a range of strategic and tactical questions that are specific to this domain and market. One possible framework to think through these topics is this "5P"list: Positioning (finding blue ocean), Product, Petabytes (data), Process (social engineering) and People. This week at the excellent O'Reilly Artificial Intelligence Conference, I covered those topics (as much as a 40 minute presentation allows), with the help of Peter Brodsky, CEO of HyperScience.