Demandbase, a leader in Account-Based Marketing (ABM), announced DemandGraph, the B2B marketing industry's artificial intelligence driven business graph that captures and monitors in-depth business behavior and relationships across businesses worldwide. According to Forrester Research, the B2B sector represents more than $1 trillion in digital commerce every year, more than double the size of the B2C economy. DemandGraph helps companies unlock the value of this trillion dollar market by reading and understanding billions of web pages on the Internet through Machine Learning and Artificial Intelligence. DemandGraph enables B2B marketers to understand the entire business network of a company--customers, partners, suppliers, competitors, investments, regulatory filings and more to identify which companies and buying committees are in-market for particular solutions. It also provides a complete 360-degree view of potential and existing customers, which allows companies to better predict potential buyers, personalize campaigns and ultimately close more deals.

We expected to see artificial intelligence (AI), home and vehicle automation, smarter devices, augmented and virtual reality, and IoT to dominate at CES 2017. And truth be told it most certainly is not failing to deliver. In addition to new products in the usual consumer device categories like video game tech, TVs, and audio equipment we're seeing loads of the new trending technologies being showcased and it's clear that Christmas is not over at CES 2017. Everything from smart home bots, hacker-fighting routers and wallpaper TVs to super cool Lego Boost robotic kits that teach kids to code, is being shown off at the popular tech event making it super difficult to choose the coolest of them all. That said, here's a look at some of the top AI enabled tech and gadgets at CES 2017.

As part of my research on the year ahead, I embarked on a series, 20.17 Big Ideas for 2017, to ask a number of my favorite award-winning marketing experts, authors, and other thought leaders-- as well as some of Firebrand Group's own digital strategy and branding experts--to recommend one "Big Idea" that companies can take advantage of to get ahead in 2017. One of the individuals I was fortunate enough to interview for this series was Abby Whitmer, Director of Global Digital Marketing for Mary Kay. Whitmer, a longtime beauty insider, was added to the beauty giant to enhance its digital innovation, so it's no surprise that her big idea for 2017 involves Machine Learning. How are you using machine learning in your day to day? I'm trying to focus on how machine learning can be used now - as opposed to a very pie-in-the-sky, long-term way.

Why work in finance when you could be learning to replicate the brain? If you're a managing director at Goldman Sachs with a reasonable chance of making partner, why would you leave? Most people in finance would be intensely thrilled to get a sniff at a Goldman partnership. Unless, of course, something even better was on offer. For David Ha, former co-head of Japanese rates trading at Goldman Sachs, and a managing director at 32, the better alternative was a residency at Google Brain, Google's 12 month programme in California to jump-start careers in machine learning.

Networked Insights, a Chicago-based company whose tools crawl the social web and analyze data for marketers, has raised $30 million in Series E funding, the company announced Friday. The company sifts through 560 million social media posts from 30 million sources daily, it said on its website. It sorts and analyzes that information for marketers and agencies, and pitches itself as a way for them to create better-targeted and more effective campaigns. Its clients include NBC Universal, media-buying agency GroupM, and American Family Insurance, whose venture arm is an investor. The funding will help Networked Insights invest in deep learning and artificial intelligence capabilities on its platform, Kairos, the company said. It said it would also use the infusion to enter partnerships and pursue acquisitions.

You may have heard the term before, and may have asked yourself either "what is metadata" or "why is it as important as data?" This article will be an attempt to clear up those two subjects. As this can often be quite dense, let's jump right in! In short, metadata are important. I like to answer this "what is metadata" question as such: metadata are a shorthand representation of the data to which they refer.

Computers aren't blind, but they can't see. They can detect objects, but must be trained to understand what those objects are. That takes two things: a ton of example data collected and organized by artificial intelligence and humans to accurately label and verify that data. Done right, this process can teach self-driving cars to avoid pedestrians, detect parking lot usage by counting cars in satellite photos or assess roof damage from drone footage. Businesses send CrowdAI their autonomous vehicle, satellite or drone imagery.

Every year at Frost & Sullivan, the Transformational Health team brainstorm top predictions for the New Year to come. Public and political pressure on the control of surging drug prices, globally, will compel health authorities to bring transparency measures around drugs pricing, especially for some of the diabetes and cholesterol medicines where more low-cost generic competition is gaining market acceptance. With the potential to change how healthcare information is stored, shared, secured and paid for, blockchain technologies have immense potential to tackle some of the biggest challenges in healthcare information management. Companies like Gem Health are among the few companies currently advocating the use and benefits of such a platform. As more and more experts and healthcare professionals find the usability of these AI systems as decision support tools and not decision makers, uptake of AI-enabled clinical decision support tools is expected to increase in the coming years.

This video examines two of the main problems with machine learning, regression, and classification. Regression is a combination of multidimensional fitting and function interpolation. With the regression problem, you are trying to find function approximation with the minimal error deviation or cost function. That means that if you have multiple events characterized by input parameters which can be labeled differently, and you want your system to predict which label should be used – this is the classical classification problem.

We are living in a new social era of living and with that there is a need for smarter solutions and assistants that can recognise and effectively identify friends, from intruders. Like with FLARE by BuddyGuard, an indoor security camera that protects homes all by itself using artificial intelligence and machine learning technology on a small custom-made IoT hardware device. At the second annual Virtual Assistant Summit taking place on the 26 - 27 January, George Platon, CTO and Founder of BuddyGuard, will be sharing the company's approach to AI assistants in the job of home security. I asked him a few questions ahead of the summit to learn more about his role, and how AI can be utilised within home security. What do you feel are the leading factors enabling recent advancements and uptake of virtual assistants?