MIT's Nightmare Machine puts Halloween scares in hands of AI

Since the inception of networked video surveillance, many companies have worked to develop a variety of different analytics to enhance the value of the systems to end-users. Some vendors have been more successful than others in being able to provide reliable video analytics to their customers and, after a period in which the technology was greeted with a healthy amount of skepticism, it has now become commonplace in many surveillance installations across a wide range of vertical markets. While the use cases for analytics have changed, the technology itself has remained relatively the same – algorithms are created to search for certain pre-defined actions within a camera's field-of-view. However, the evolution of artificial intelligence (AI) means that the future of analytics will lie not in the creation of static algorithms but on the ability of machines to learn what operators should and should not be alerted to. For example, for those installations that use virtual trip wires for notification of perimeter breaches, many analytics cannot decipher between a human coming onto the property, which would obviously be the primary concern, vs. an animal, which would be of little interest.

At the inaugural O'Reilly AI conference, 66 artificial intelligence practitioners and researchers from 39 organizations presented the current state-of-AI: From chatbots and deep learning to self-driving cars and emotion recognition to automating jobs and obstacles to AI progress to saving lives and new business opportunities. There is no better place to imbibe the most up-to-date tech zeitgeist than at an O'Reilly Media event as has been proven again and again ever since the company put together the first Web-related meeting (WWW Wizards Workshop in July 1993). The conference was organized by Ben Lorica and Roger Chen, with Peter Norvig and Tim O'Reilly acting as honorary program chairs. Here's a summary of what I heard there, embellished with a few references to recent AI news and commentary: In contrast to traditional software, explained Peter Norvig, Director of Research at Google, "what is produced [by machine learning] is not code but more or less a black box--you can peak in a little bit, we have some idea of what's going on, but not a complete idea." Tim O'Reilly recently wrote in "The great question of the 21st century: Whose black box do you trust?": Because many of the algorithms that shape our society are black boxes… because they are, in the world of deep learning, inscrutable even to their creators – [the] question of trust is key.

This was the subject of a question asked on Quora: What are the top 10 data mining or machine learning algorithms? Some modern algorithms such as collaborative filtering, recommendation engine, segmentation, or attribution modeling, are missing from the lists below. Algorithms from graph theory (to find the shortest path in a graph, or to detect connected components), from operations research (the simplex, to optimize the supply chain), or from time series, are not listed either. And I could not find MCM (Markov Chain Monte Carlo) and related algorithms used to process hierarchical, spatio-temporal and other Bayesian models. My point of view is of course biased, but I would like to also add some algorithms developed or re-developed at the Data Science Central's research lab: These algorithms are described in the article What you wont learn in statistics classes.

GPUs are highly specialized computer chips that were originally designed to accelerate the processing of video information in a computer. The conversation we had led to a discussion about the future of computer hardware, and I became more excited than ever. Our new friend had us over to the Silicon Valley headquarters of his company, where we walked into a room that had the very latest demonstrations for their GPU processors. We saw video games with graphics so realistic and impressive that it made everything I had seen previously look simplistic and cartoonish. Just as impressively, the games reacted to my control button pushes instantaneously.

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.