IBM has launched a unit designed for human resources to better find talent and recruit using artificial intelligence. The company's HR effort, dubbed IBM Talent & Transformation, includes select Watson AI-based services that can help HR become a growth engine to enable digital transformation. AI can be used to revamp workflow, employee engagement, recruitment and retention while providing a more diverse workforce, the company says. The Watson Talent Suite rolls up behavioral science, AI, and psychology and applies it to HR. Components include Watson Career Coach, a virtual coach that provides advice for career paths, and Watson Candidate Assistant, which looks through the history of job seekers and matches them with openings. These services were developed for IBM's internal HR team and the company claims it drove $107 billion in benefits in 2017 with better employee satisfaction.

Content provided by IBM with Insider Studios. In the summer of 1956, 10 scientists and mathematicians gathered at New Hampshire's Dartmouth College to brainstorm a new concept Assistant Professor John McCarthy called "artificial intelligence." According to the original proposal for the research project, McCarthy--along with fellow organizers from Harvard, Bell Labs and IBM--wanted to explore the idea of programming machines to use language and solve problems for humans while improving over time. It would be years before these lofty objectives were met, but the summer workshop is credited with launching the field of artificial intelligence (AI). Sixty years later, cognitive scientists, data analysts, UX designers and countless others are doing everything those pioneering scientists hoped for--and more.

Computers aren't going to replace creative pros -- but machine learning and artificial intelligence can be powerful tools in the storytelling process. The 60-second spot was directed by Oscar-winner Kevin Macdonald, working from a script that was developed by IBM's Watson AI system. To produce the spot for the Lexus ES executive sedan launching in Europe, the automaker enlisted its creative agency, The&Partnership London, along with technical partner Visual Voice. The agencies collaborated with the IBM Watson team to use AI to analyze 15 years' worth of footage, text and audio for car and luxury brand campaigns that have won Cannes Lions awards for creativity, as well as a range of other external data. Watson identified elements common to award-worthy commercials that were "both emotionally intelligent and entertaining," according to IBM.

The elite team of engineers and medical specialists assembled by IBM's Watson Health division had the innocuous code name "Project Josephine," but its mission could not have been more urgent: to fix the artificial intelligence software at the core of the company's campaign to tackle the $7 trillion global health care market. The predicament faced by IBM officials, STAT has found, was that it could not get its software to reliably understand and analyze language in patient medical records. That was critical for the company to deliver on multimillion-dollar contracts with hospitals and drug companies. Unlock this article by subscribing to STAT Plus and enjoy your first 30 days free! STAT Plus is a premium subscription that delivers daily market-moving biopharma coverage and in-depth science reporting from a team with decades of industry experience.

In my experience as a C-level executive and long-time AI professional, I've learned that people who want to utilize artificial Intelligence find getting started to be the most difficult part. Even the more confident practitioners could easily become intimidated by the array and complexity of tools to navigate. But this problem is now a thing of the past. With IBM Watson Studio, you and your project can now hit the ground running. IBM Watson Studio's integrated environment makes AI significantly easier, by allowing users to quickly and easily build visually appealing projects and models. I don't have the luxury to get bogged down in inefficient and slow processes.

For most businesses, machine learning seems close to rocket science, appearing expensive and talent demanding. And, if you're aiming at building another Netflix recommendation system, it really is. But the trend of making everything-as-a-service has affected this sophisticated sphere, too. You can jump-start an ML initiative without much investment, which would be the right move if you are new to data science and just want to grab the low hanging fruit. One of ML's most inspiring stories is the one about a Japanese farmer who decided to sort cucumbers automatically to help his parents with this painstaking operation. Unlike the stories that abound about large enterprises, the guy had neither expertise in machine learning, nor a big budget. But he did manage to get familiar with TensorFlow and employed deep learning to recognize different classes of cucumbers. By using machine learning cloud services, you can start building your first working models, yielding valuable insights from predictions with a relatively small team. We've already discussed machine learning strategy. Now let's have a look at the best machine learning platforms on the market and consider some of the infrastructural decisions to be made.

After logging into Watson Studio, select New Modeler Flow. Enter a name, keep the default settings, and then click Create. Next expand the Import menu, drag the Data Asset node onto the stream canvas and select Titanic training data file (train.csv) in the node settings to load data into the project. Right-click the node and select Preview to see your detailed dataset. To build a modeler stream look under Record Operations.

IBM's Watson supercomputer has beat Jeopardy champions, reconstituted recipes, and even helped create highlight reels for the World Cup. Now it's taking on a new tech challenge; changing how the construction industry operates. A new partnership between IBM and Fluor, a global engineering and construction company, will put the supercomputer's computational skills to work on making building more efficient. The new Watson-based system, in development since 2015 and now in use on select projects, will be able to analyze a job site "like a doctor diagnoses a patient," according to Leslie Lindgren, Fluor's vice president of Information Management. That degree of risk analysis, predictive logistics, and comprehension is no small challenge given the complexity of today's construction megaprojects.

Many developers, including myself, want to use AI in their applications. Building Machine Learning models, however, often requires a lot of expertise and time. This article describes a technique called AutoML, which can be used by developers to build models without having to be data scientists. While developers only have to provide the data and define the goals, AutoML figures out the best model automatically. Cognitive services are provided by most cloud providers these days.

Marketers get excited about data, artificial intelligence and the internet of things because of their combined power to potentially impact consumers' everyday lives. Across the commerce landscape, the potential applications may be limitless: Farmers are now using satellite data to help increase crop yields and improve the quality of the food we eat. Shippers are deploying blockchain technology to modernize the supply chain and get products into stores more safely and quickly. Banks are relying on encrypted mainframe computers to help protect consumers' personal data and prevent cybercrime. One of the areas in which marketers have only just begun to tap the exponentially increasing unstructured data of the internet is the weather.