Chatbots look likely to be a cornerstone of many brands' marketing campaigns this year with the programs capable of generating automated engagement with consumers. Able to sit within messaging apps and communicate when prompted, chatbots can serve a wide variety of roles; the Guardian is distributing news; Activision engaged fans with the Call of Duty Series, Domino's Pizza is accepting food orders, and KLM's flight assistant houses boarding pass and flight times. For creative brands with on tap digital talent, the sky's the limit. The Drum has touched down with Paul Walsh, the founder of bCRM, a chatbot campaign management tool, which is now integrated with BotKit, one of the most widely used bot creation tools, to outline the basics for prospective brands. The Drum: What benefits can chatbots bring to consumers?

IBM has announced the launch of IBM Machine Learning, a cognitive platform for the z System mainframe. The new platform uses the core technology from IBM Watson to perform big data analysis for clients using z System for cloud data storage. The new Machine Learning platform automates the creation and deployment of analytic models, allowing data scientists to perform analysis in-house. Models can be written in any language (Scala, Java, Python), using any framework and data type required for best results. IBM Machine Learning also includes a function called Cognitive Automation for Data Scientists, which helps data scientists to choose the correct algorithm for their needs.

First you do not always need to normalize (standardize) the input vectors (feature vectors), sometimes is good, sometimes is bad. In general you scale your feature vector when the magnitude of a feature dominates the others, so the model cannot pick up the contribution of the smaller magnitude features. Read here for a detailed explanation. Second there are two general classes of machine learning problems: classification and regression. In a classification type problem the output (dependent variable) is discrete, so you do not need to normalize it.

Microsoft Corp. is trying again in health care, betting its prowess in cloud services and artificial-intelligence can help it expand in a market that's been notoriously hard for technology companies. A new initiative called Healthcare NExT will combine work from existing industry players and Microsoft's Research and AI units to help doctors reduce data entry tasks, triage sick patients more efficiently and ease outpatient care. "I want to bring our research capabilities and our hyper-scale cloud to bear so our partners can have huge success in the health-care world," said Peter Lee, a Microsoft Research vice president who heads Healthcare NExT. Microsoft has tried to expand in health care before, with mixed results. It had a Health Solutions Group for many years, but combined that into a joint venture with General Electric Co.

As consumer devices become more capable, with voice control assistants such as Apple's Siri, Amazon's Alexa, Microsoft's Cortana and Google's Assistant, it is only natural to expect these artificial intelligence (AI) applications to move into more business settings. These capabilities could emerge in a number of areas, such as voice control-style interfaces on common productivity applications and voice output to warn IT managers of potential infrastructure faults or breaches. This makes sense -- after all, there is already an AI-enabled toothbrush that learns exactly how you clean your teeth and offers ways to improve your dental hygiene. It is just a matter of time before major industry players develop B2B apps with similar smart features. Today, voice-enabled products can be used for mundane tasks, such as dictating emails or text messages, querying internet searches or playing music and TV shows.

NEW YORK--Are you ready to turn your car, washing machine or running shoes into a point of sale? Visa is teaming up with IBM Watson to bring secure payment experiences to all sorts of connected products and services, embracing the ecosystem techies inelegantly refer to as the Internet of Things. IBM and Visa announced the collaboration Thursday at an event in Munich, Germany, where IBM is opening up a $200 million Watson Internet of Things headquarters. Via the partnership, the companies say they can support payments and commerce on virtually any of the 20 billion connected devices that Gartner estimates to be part of the global economy by 2020. "What we've seen over the last 12 months is serious companies committing serious business to IoT," says Brett Greenstein, vice president for IBM Watson IoT. Watson is IBM's cognitive computing platform, which leverages natural language processing and machine learning to learn from and extract patterns and meaning from mounds of unstructured data, from health care to sports.

For decades, data and analytics have played an important role in our economy. The process of analyzing data, however, remains labor intensive. Even with the most advanced techniques, data scientists spend countless hours developing, testing and retooling analytic models one step at a time. Worse yet, most organizations cannot find enough data scientists to complete this labor-intensive work. The impact is that we have not yet fully realized the promise of continuous intelligence; until now.

As machine learning becomes more accessible through avenues such as Intel's BigDL and IBM opening Watson's core machine learning components up to businesses, some developers and industry insiders are cautioning against getting too dazzled by the potential without considering the human role. However much data those programs can process, in the end, "what you do with the results of algorithms is key," said Jean-Francois Puget, Ph.D. (pictured), distinguished engineer, machine learning and optimization, IBM Analytics, at IBM. Puget spoke with Dave Vellante (@dvellante) and Stu Miniman (@stu), co-hosts of theCUBE, SiliconANGLE Media's mobile live streaming studio, at the IBM Machine Learning Launch Event in New York, NY. He offered his perspective on machine learning and its applications. "For most people, machine learning equals machine learning algorithms," Puget said. "When you look at newspapers or blogs, social media, it's all about algorithms. Our view [is] that sure, you need algorithms for machine learning, but you need steps before you run algorithms, and after."

New machine learning platform enables data scientists to automate the creation, training and deployment of operational analytic models. Chris Preimesberger is Editor of Features and Analysis at eWEEK. Join us for our next #eWEEKchat on March 8: "App Economy or API Economy: Which Is It Going to Be?"

This article was originally posted here, by Mubashir Qasim. In my last article, I stated that for practitioners (as opposed to theorists), the real prerequisite for machine learning is data analysis, not math. One of the main reasons for making this statement, is that data scientists spend an inordinate amount of time on data analysis. The traditional statement is that data scientists "spend 80% of their time on data preparation." While I think that this statement is essentially correct, a more precise statement is that you'll spend 80% of your time on getting data, cleaning data, aggregating data, reshaping data, and exploring data using exploratory data analysis and data visualization.