R/GA Ventures, with partner Westfield Labs, has concluded its Connected Commerce Accelerator program with a demo event in San Francisco. The accelerator is a three-month, immersive, mentor driven program designed for startups developing connected hardware products and software services with the goal of helping them to build businesses and brands that can scale. The program taps into the emerging class of products that combine hardware, data, and digital services in compelling ways for consumers and businesses. Following 12 weeks of mentorship, pilots, and ongoing work with brand, technology, and business consultants from the R/GA Services team, Westfield Labs, and program partners, the ten participating companies presented their commerce and retail-focused businesses to investors and business partners. The participants represent the next wave of innovation in the commerce and retail space, ranging from innovative takes on fulfillment, delivery, and returns to retail workforce optimization and training solutions, adaptive messaging powered by machine learning, a new platform for the connected store, and companies using artificial intelligence (AI) for customer service, bot management, and image recognition.

In part one of this series last week, we discussed the emerging ecosystem of machine learning applications and what promise those portend. But of course, as with any emerging application area (although to be fair, machine learning is not new), there are bound to be some barriers. Even in analytically sophisticated organizations, machine learning often operates in "silos of expertise." For example, the financial crimes unit in a bank may use advanced techniques to catch anti-money laundering; the credit risk team uses completely different and incompatible tools to predict loan defaults and set risk-based pricing; while treasury uses still other tools to predict cash flow. Meanwhile, customer service and branch operations do not use machine learning at all because they lack the critical mass of specialists and software.

In fact this one is very special. Every now and then there comes a field of technology that strikes us as being especially exciting. With all the latest accomplishments in the field of artificial intelligence it's really hard not to get excited about AI. Companies such as Google, NVIDIA or Comma.ai are using neural networks to train cars that know how to drive themselves. Apps such as PRISMA are using AI to create artwork from photography that is inspired by real artists.

Google Vision is a new API that enables users to identify the contents of an image using Google's Machine Learning technology. If you've uploaded a photo to Google Image search lately, you may have notice it's got a lot smart Or if you've used Google Photos on your smartphone, it seems to know what's in each image. You may have noticed "Best Guess for this image…" followed by the name of what's in the photo. Upload a dog or cat to Google, and it can spot it. It does this by examing the photograph using machine learning technology.

The research was conducted to find out what price maximises profit without sacrificing the high demand for the product due to the price being too high nor sacrificing the margins on the product due to the price being too low. The goal is to experiment with different price levels for the same product in one market place and country to see how sales volumes change with prices and which volume level of products we can be sold for that optimal price range. As a data scientist it is my responsibility to identify the optimum prices of products so the items can be sold for maximum profit. Sales managers and small business owners are faced with the decision of at what price to sell each of their products in each marketplace or country in order to be able to maximize profit. With each line of product being added and a lot of products to monitor, it is very difficult to determine the optimum price for each product.

Cognitive computing has the potential to revolutionize your business. Imagine an IT system that can understand, learn and reason. You speak to the system and it understands natural language, context and nuance. It can read and recall millions of pages of text. It learns and synthesizes data to provide expert recommendations on a range of topics. A cognitive business uses this technology to create business insights.

Platform-based talent markets help put the emphasis in human-capital management back where it belongs--on humans. The best way to organize corporations--it's a perennial debate. But the discussion is becoming more urgent as digital technology begins to penetrate the labor force. Although consumers have largely gone digital, the digitization of jobs, and of the tasks and activities within them, is still in the early stages, according to a recent study by McKinsey Global Institute (MGI). Even companies and industries at the forefront of digital spending and usage have yet to digitize the workforce fully (Exhibit 1).1 1.See McKinsey Global Institute, "Digital America: A tale of the haves and have-mores," December 2015. The stage is set for sweeping change as artificial intelligence, after years of hype and debate, brings workplace automation not just to physically intensive roles and repetitive routines but also to a wide range of other tasks. MGI estimates that roughly up to 45 percent of the activities employees perform can be automated by adapting currently demonstrated technologies.

It's at the peak of the famous technology hype cycle, but don't let that make you cynical. Artificial intelligence is already making a difference in public service delivery. And unlike other technologies – it's designed to be a quick learner, so it doesn't rely on humans to figure everything out. The tool has potential in a huge number of areas, and will change how many of us live and work. Here are five ways that public servants can already use AI to make a difference.

How to get people interested in art? How to expose permanent-collection works that sit in storage? These are questions art museums constantly ponder. Recently, Tate Britain asked another one: How can artificial intelligence help? It put the question to anyone who wanted to compete for the 2016 IK Prize, which promotes the use of digital technology in the exploration of art at Tate Britain or on the Tate website.

Bruce Ratner, The Significant StatisticianTM, is President and Founder of DM STAT-1 Consulting, the ensample for Statistical Modeling, Analysis and Data Mining, and Machine-learning Data Mining in the DM Space. DM STAT-1 specializes in all standard statistical techniques, and methods using machine-learning/statistics algorithms, such as its patented GenIQ Model, to achieve its clients' goals – across industries including Direct and Database Marketing, Banking, Insurance, Finance, Retail, Telecommunications, Healthcare, Pharmaceutical, Publication & Circulation, Mass & Direct Advertising, Catalog Marketing, e-Commerce, Web-mining, B2B, Human Capital Management, Risk Management, and Nonprofit Fundraising. Bruce holds a doctorate in mathematics and statistics, with a concentration in multivariate statistics and response model simulation. His research interests include developing hybrid-modeling techniques, which combine traditional statistics and machine learning methods. He holds a patent for a unique application in solving the two-group classification problem with genetic programming.