In a real-time interactive environment like customer support--where any mistakes have real business costs--any technology affecting customer interactions must be transparent and controllable. Machine learning is not infallible; the decisions artificial intelligence makes will not be 100-percent correct, 100-percent of the time. Therefore, it's important for support leaders to have insights into these decisions, as well as the opportunity to regulate them on a case-by-case basis. Only then can they control the risk--and the cost--of a potential failure.

Machine learning is something that many business are starting to tack onto their existing processes. Yet, to add machine learning capabilities after the fact is often a fool's errand. Joshua argues that machine learning cannot be an afterthought, but rather must be custom developed to suit the specific problem or question that each company is trying to answer. His company, Wise.io, tackles this challenge of helping business build ground up machine learning applications that generate accurate predictions for use in an array of business processes. Joshua Bloom is the cofounder and CTO of Wise.io. He is also an astrophysicist, and a professor of astronomy at UC Berkeley.

Georgia Institute of Technology researchers have developed a new artificially intelligent system that crowdsources plots for interactive stories, which are popular in video games and let players choose different branching story options. With potentially limitless crowdsourced plot points, the system could allow for more creative stories and an easier method for interactive narrative generation. Current AI models for games have a limited number of scenarios, no matter what a player chooses. They depend on a dataset already programmed into a model by experts. Using the Georgia Tech approach, one might imagine a Star Wars game using online fan fiction to let the AI system generate countless paths for a player to take.

While 3D artist Moritz Reichartz was creating a virtual rendition of Ai Weiwei's Stools in Mashup Between the Clouds, he was also nearing completion of an automated 3D animation inspired by artificial intelligence. Titled Hands Off [A.I.], Reichartz created the animated video over a year of research and development. In describing the animations as "automated," Reichartz means that no keyframes were used whatsoever for any of the clips--he merely "designed and guided the self-driving movements." In this sense the animated virtual objects and spaces, which are equally surreal, beautiful and alien, are types of A.I. Reichartz considers Hands Off [A.I.] to be an artistic answer to Tim Urban's essay "The AI Revolution: The Road to Superintelligence." The artist says the article gave him "heavy mental vertigo" and might have even scared him.

SAN FRANCSICO--Neural networks--artificial intelligence processing systems inspired by the human brain--are a hot topic in technology, as large companies like Facebook, Google and Microsoft are developing them and putting them into use. Most neural network technology in place today runs on graphics processing units (GPUs) from Nvidia Corp. and others. EDA and intellectual property vendor Cadence Design Systems Inc. stepped into the fray on on Monday (May 2), rolling out a new version of its Tensilica Vision processing core optimized specifically for vision/deep learning applications. "Everybody is spending a lot of time developing a lot of research and producing a lot of technology," said Pulin Desai, director of product marketing for Cadence's Imaging/Vision Group, in an interview with EE Times. "The market is very hot. Maybe it's hot because everything is being run on GPUs."

Currently at this time, all participants are in a listen-only mode. Later, we will conduct the question-and-answer session and instructions will follow at that time. Also, as a reminder, this conference call is being recorded. I would now like to turn the call over to your host to Richard Morin. Thank you, and good evening, everyone. Earlier today, we issued a news release announcing Cognex's earnings for the first quarter of 2016, and we've also filed our quarterly report on Form 10-Q. For those of you who have not yet seen these materials, both are available on our website at www.cognex.com. They contain highly detailed information about our financial results. During tonight's call, we may use a non-GAAP financial measure, if we believe it is useful to investors, or if we believe it will help investors better understand our results or business trends. For your reference, you can see a reconciliation of certain items from GAAP to non-GAAP in Exhibit 2 of the earnings release. I'd like to emphasize that any forward-looking statements we made in the earnings release or any that we may make during this call are based upon information that we believe to be true as of today. Things often change and actual results may differ materially from those projected or anticipated. You should refer to the company's SEC filings, including our most recent Form 10-K, for a detailed list of these risk factors. Now, I'll turn the call over to Cognex's Chairman, Dr. Bob Shillman.

In this article, we'll describe the evolution of our recommendation engine, from the initial minimum viable product (MVP) built with Apache Mahout, to a hybrid offline online pipeline. We'll explore the impact these changes have had on product metrics and how we've addressed challenges by using incremental modifications to algorithms, system architecture, and model format. To close, we'll review some related lessons in system design that apply to any high-traffic machine learning application. Indeed's production applications run in many data centers around the world. Clickstream data, and other application events from every data center, are replicated into a central HDFS repository, based in our Austin data center.

Whether you're in-store or online, the benefits of a personalized customer experience are undeniable. We want salespeople who can help us find exactly what we're looking for as quickly as possible; we return to lunch spots where all we have to ask for is "the usual." For e-tailers, personalization has traditionally relied upon recommendation engines, algorithms that look to match prior consumer clicks and purchases with like-minded cohorts. And personalization is indeed effective for those e-commerce sites. According to a recent Infosys survey, 74% of sites who have implemented personalization technology reported an increase in sales, 58 percent noted an increase in traffic, and 55% observed an increase in customer loyalty. In other words, a consumer journey tailored to a buyer's individual wants and preferences is a smart goal.

Making sense of data is one of the great challenges of the information age we live in. While it is becoming easier to collect and store all kinds of data, from personal medical data, to scientific data, to public data, and commercial data, there are relatively few people trained in the statistical and machine learning methods required to test hypotheses, make predictions, and otherwise create interpretable knowledge from this data. The Automatic Statistician project aims to build an artificial intelligence for data science, helping people make sense of their data. The project is at an early stage, but please have a look at our example analyses and feel free to contact us or subscribe to our mailing list.

Earlier this year, the American Information Technology and Innovation Foundation (ITIF) awarded their facetious'annual Luddite award' to a lose coalition of AI sceptics, including Tesla CEO Elon Musk and renown physicist Stephen Hawking. The ITIF labelled the likes of Musk and Hawking'alarmists' engaged in and "feverish hand-wringing about a looming artificial intelligence apocalypse". Yet the sarcastic gesture did not go down well. This week Nature published a scathing critique of the ITIF's'fanciful futurism', defending the'legitimate concerns' of Musk and Hawking. Ironically, the risks of AI are already being felt indirectly as universities lose young talent to the corporate sector.