In this special guest feature, Gary Baum, Vice President of Marketing at MyScript, talks about how handwriting recognition is enhancing machine (and human) learning. As an input method, handwriting recognition teaches machines to adapt to the user, adding in another layer to their evolving skill set. Those users can program systems simply by jotting down notes and in turn, build platforms most reflective of the human experience. Gary is a tech industry veteran with more than 20 years of executive marketing and product management experience. At MyScript, he oversees global marketing activities and educational efforts to build MyScript brand awareness and drive technology adoption, expand and commercialize digital ink technology offerings, and nurture strategic collaborations and partnerships within the digital writing ecosystem.

Boosting is an ensemble technique that attempts to create a strong classifier from a number of weak classifiers. In this post you will discover the AdaBoost Ensemble method for machine learning. This post was written for developers and assumes no background in statistics or mathematics. The post focuses on how the algorithm works and how to use it for predictive modeling problems. If you have any questions, leave a comment and I will do my best to answer.

Where marketers used to rely on a mix of broad demographic data and personal intuition to engage audiences, data analytics and predictive algorithms are now helping them tailor messaging right down to the word. Machine learning lets you say: I want to target people that have posted a picture of a guitar in the last three months, or: I want to target people with the INTP personality type that posted something angry about Bernie Sanders recently. Markers have really managed to crack the code and find ways to innovate using machine learning. Join us and hear indico CEO Slater Victoroff explore the science behind today's stronger, data-driven marketing campaigns, and find out how you can start applying these strategies to your business!

When sailors speak of "blue water," they anticipate taking a boat offshore, out of sight of land, and usually for an extended cruise – an adventure underwritten by a frisson of risk. Much of the chatter about artificial intelligence has the patina of blue-water thinking about it. Its promise is expansive – as broad as an ocean. There are unplumbed depths, certainly treasure and possibly danger. At the risk of overworking the metaphor, today Artificial Intelligence (AI) sits in the matrix of a perfect storm of coincidence.

Technology has a long and uneasy relationship with gender and identity. On one hand, technologies like social media allow people to connect across large distances to share their personal stories, build communities and find support. Yet there have also been troubling cases, such as Facebook's'Real Names' policy where users have been locked out of their accounts and left without recourse. As technology gets more complex and ubiquitous, these cases will continue to get worse. Widespread implementation of seemingly "objective" technology means less accessibility for those who do not fit developers' conceptions of an "ideal user" – usually white, male and affluent.

To resolve the issue, the industry need to understand why users are implementing ad blockers. The main reasons usually cited by the industry are normally data consumption, load times and battery use. These are all valid, but when looked at closely, they don't seem to be the main cause of ad blocking. Research by Global Web Index found that only one third of users state page load times are the reason for using an ad blocker, while just three in ten said battery life was the motive for installing the software. The real key to the problem lies in irrelevant advertising.

If a neural network can write Shakespeare, could it write a resume for you? Inspired by the remarkable results of Recurrent Neural Networks and using thousands of anonymized resumes from untapt, I've been experimenting with applying deep learning techniques to the CV. There's a seminal blog post called The Unreasonable Effectiveness of Recurrent Neural Networks by Andrej Karpathy, a PhD student and instructor at Stanford. Andrej is taken by surprise by the magical results from Recurrent Neural Networks, a particular type of neural network that can process arbitrary sequences of inputs. In one example, he feeds the Complete Works of Shakespeare into an RNN.

One of the most frustrating elements of the current healthcare environment is the administrative burden of prior authorizations for medications and procedures. It is a frustration for providers, for patients, and for payers. Is there any way to solve this dilemma? For physicians, an estimated 20 hours per week is spent in prior authorization activities, costing an average of 83,000 in excess annual overhead per physician. Is there an actual benefit for this effort? Most physicians say that payers (commercial, Medicare, Medicaid, and pharmacy benefit managers (PBMs)) use prior authorizations to keep costs down.

When Zinedine Zidane, the then French captain, headbutted Italy's Marco Materazzi during the 2006 World Cup final, the clash quickly became one of the most infamous incidents in football history. What was not clear was what sparked the Frenchman's ire – Zidane said his mother had been insulted, a charge that Materazzi vigorously denied. The head-butt got Zidane sent off and Italy won the game. However, had there been technology there to identify what was said, the result could have been very different, Helen Bear believes. "If a machine lip-reader was in existence, the other player [could] have got sent off too so it would have been 10 men against each other in a World Cup final," she argues.

KPMG's recent announcement was particularly noteworthy from my perspective, because it indicated that the audit firm would be deploying IBM's Watson "cognitive computing technology" to KPMG's professional services offerings.--According "One current initiative is focused on employing supervised cognitive capabilities to analyze much larger volumes of structured and unstructured data related to a company's financial information, as auditors'teach' the technology how to fine-tune assessments over time. This enables audit teams to have faster access to increasingly precise measurements that help them analyze anomalies and assess whether additional steps are necessary." IBM is, of course, one of KPMG's biggest software-auditing clients. All of these recent reports mention that the AI technologies currently are being contemplated for use in connection with financial audits.