The following is a guest post authored by David Pitman (@edavepitman), co-founder of Converge (@converge_co). I was told Fred was the man to talk to about inspecting buildings. But I don't know that he's ever used email," my reference said, advising that I call his phone. Surprisingly though, Fred has a website. The sole image on the site shows him standing on a roof in a ten-gallon hat staring jauntily down at the steep angle.

Artificial Intelligence is a fascinating topic for many people nowadays, no matter if they are a consumer or an influencer. Today, I'm happy to be joined by Ilya Gelfenbeyn, CEO and co-founder of Api.ai, a conversational UX platform used to embed natural language understanding capabilities into connected devices, apps and services. Regular readers of SitePoint may recognize the service, as we have covered Api.ai in the past with a series earlier this year on getting started with the platform. Ilya has a background in machine learning, natural language processing and conversational interfaces. Elio: We have covered Api.ai in the past, but could you briefly explain the concept behind it?

The White House wound up a nationwide series of workshops on artificial intelligence today on a cautionary note: Yes, AI promises to ease many of humanity's ills, but humanity needs to make sure that flesh-and-blood policymakers are firmly in charge. Latanya Sweeney, director of the Data Privacy Lab at Harvard's Institute of Quantitative Social Science, said AI programs should be made to reflect the norms agreed upon by human society. "I want the people we elect controlling those norms, not the technology itself. Those norms should include supporting social equity and diversity, said Alicia Glen, New York City's deputy mayor for housing and urban development. "At its best, artificial intelligence can be a tool to promote equity, and it obviously can create huge economic opportunity for a lot of people," she said. "But it can also have discriminatory effects, whether they're intended or unintended.

Google is adding to its already substantial set of machine learning technologies with the acquisition of a French company called Moodstocks, a visual recognition machine learning technology company. Object recognition is one of the more difficult problems for machine learning, and it's a problem that Google has been working on for a while. In a blog post announcing the deal, Google noted that many of its services including Google Translate and Smart Reply Inbox already rely on machine learning technologies. The addition of Moodstock will help with visual recognition. Vincent Simonet, head of the R&D Center of Google France wrote in the blog post that Google has made great strides in terms of visual recognition technology -- for instance, if you search the word "party" or "beach" you'll get a good image match.

The machine learning algorithms that are being created to solve important business problems for fleets use data to answer complex problems ranging from fraud to fuel spend benchmarking and optimization, WEX Inc.'s Kurt Thearling says. The old computer adage garbage in, garbage out (GIGO) is becoming true in the industry as trucking begins to rely heavily on "machine learning algorithms for everything from managing fuel spend to lifecycle costs. Fleets that don't understand GIGO will not see the efficiencies promised from technologies, and that is because the data inputs are not always "clean", says Kurt Thearling, vice president of analytics for WEX Inc. Fleet Owner recently had a chance to ask … read more at fleetowner.com

In the third episode of RoboticsLIVE, my guest discusses robotics, artificial intelligence, chatbots, and startups; including two fascinating companies in which his VC is invested. Guest David Vandegrift has a passion for artificial intelligence. Interview conducted by Nick Rishwain, JD.

A few years ago, the general public thought artificial intelligence (AI) was but a futuristic technology exclusive to science fiction. That is until DeepMind was created in 2010, an artificial intelligence (AI) company that was later bought by Google in 2014, and is now making big strides in the industry. DeepMind currently boasts fully functioning artificial agents capable of doing human tasks like learning how to play video games as well as performing similar cognitive functions like accessing key pieces of information from a short-term memory. It sounds surreal, like something out of an Isaac Asimov novel. These artificial agents, or programs, are using what's called reinforcement learning (RL).

Tomorrow's networks are driving price and performance requirements that call for custom silicon, according to a senior manager for a company using ASICs. The volume of data, applications and transactions hitting data centers is increasing at an exponential pace. Add in predictions that by 2020 users will own as many as 25 connected devices and, according to Cisco, the Internet of Things will account for as many as 50 billion new IP-enabled devices and you can see a tsunami of traffic headed our way. Networking systems built around multi-purpose processors are about to slam into a price/performance wall that will either choke traffic or break networking budgets. Recently, Google engineers blogged about a new ASIC they developed, called a Tensor Processing Unit designed to accelerate machine-learning applications.

Nasdaq Inc. is trying to identify would-be white-collar criminals by using artificial intelligence systems originally built to track terrorists and sex traffickers. The exchange is testing systems that analyze data about trading activity against what traders say on their corporate chat and email accounts, in an effort to spot potential insider trading, market manipulation and other crimes faster and more accurately than current surveillance systems can. Parsing the chatter of traders in the time before, during and after transactions -- and matching those findings with trading data -- provides "holistic surveillance," said Bill Nosal, vice president of business development for market technology at Nasdaq NDAQ, 0.28% . "This can show what was happening in the trader's head," Nosal said. The work comes as New York Attorney General Preet Bharara and other state and federal officials step up efforts to prosecute financial crimes.

At the International Symposium on Biomedical Imaging in Prague this past April, a Harvard-based artificial intelligence system won the Camelyon16 challenge, a competition comprised of participants introducing their individual AI system and its ability to facilitate automated lymph node metastasis diagnosis. Referred to as PathAl, the computing system identifies cancerous cells through deep learning--an algorithmic technique that accumulates copious amounts of unstructured data and organizes it into clusters before analyzing it for patterns. Deep learning is predominately used in speech recognition systems like Apple's Siri and Microsoft's Cortana. According to one of the challenge's organizers, Jeroen van der Laak of Radboud University Medical Center in Netherlands, the technology featured in the competition went "way beyond" his expectations, as the AI's accuracy proved strikingly close to that of human beings. In addition, van der Laak said AI technology has the propensity to intrinsically redefine the way histopathological images are handled in the medical community.