Tidemark Compete is an intelligent benchmarking service built upon a number of cutting-edge technologies (including machine learning and Apache Spark) for organizations looking to plan, budget and forecast better. Tidemark is also scaling up its machine-learning services, while unveiling three new packaged processes that automate custom processes for specific industry verticals including retail, hospitality and insurance.

In an ideal world, every deep learning paper proposing a new architecture would link to a readily-accessible Github repository with implemented code. In reality, you often have to hand-code the translated equations yourself, make a bunch of assumptions, and do a lot of debugging before you get something that may or may not be related to the authors' intent. This process is especially fraught when dealing with recurrent architectures (aka "recurrent neural networks"): computational graphs which are DGs (directed graphs) but not DAGs (directed acyclic graphs). Recurrent architectures are especially good at modeling/generating sequential data -- language, music, video, even video games -- anything where you care about the order of data rather than just pure input/output mapping. However, because we can't directly train directed graphs with directed cycles (whew!), we have to implement and train graphs that are transformations of the original graph (going from "cyclic" to "unrolled" versions) and then use Backpropagation through time (BPTT) on these shadow models.

Jason Mars is an African-American professor of computer science who also runs a tech start-up. When his company's artificially intelligent smartphone app talks, he said, it sounds "like a helpful, young Caucasian female." "There's a kind of pressure to conform to the prejudices of the world" when you are trying to make a consumer hit, he said. "It would be interesting to have a black guy talk, but we don't want to create friction, either. First we need to sell products."

Researchers at the University of St Andrews in Scotland recently figured out a way for a computer to recognize different types of materials and objects ranging from glass bottles to computer keyboards to human body parts. They call the resulting device RadarCat, which is short for Radar Categorization for Input and Interaction. As the name implies, this device uses radar to identify objects. RadarCat was created within the university's Computer Human Interaction research group. The radar-based sensor used in RadarCat stems from the Project Soli alpha developer kit provided by the Google Advanced Technology and Projects (ATAP) program.

Paul Daugherty, Accenture CTO, discusses artificial intelligence and is impact on business and the economy on CNBC.

Microsoft CEO Satya Nadella has identified machine learning as the firm's key focus as cloud computing usage becomes more widespread. It is an area that is fast becoming the battleground for the big cloud providers. Google and Amazon Web Services both offer a range of tools that make it easier for developers to create "intelligent' applications, while the likes of Salesforce are keen to incorporate artificial intelligence into their software services. Speaking at an event in London's Canary Wharf financial district, Nadella's sales pitch placed emphasis on the role of machine learning across Microsoft's range of cloud products - from infrastructure and platform as a service offering in Azure, to its Dynamics and Office365 cloud software. First he highlighted how Azure Iaas will support "the next generation of applications." He said: "Whenever you think about the infrastructure layer in computing, you are always driven by the applications of the future: what are developers writing, not just today, but what is going to be the core currency of the applications of the future?" "It is going to be data and more importantly the ability to reason over data to create intelligence," he explained. "That is what is unique about the applications that are getting created today." "It is going to be data and more importantly the ability to reason over data to create intelligence," he explained. "That is what is unique about the applications that are getting created today." "And so we are building out our infrastructure to support that, to empower every developer to be able to infuse intelligence into everything that they are doing." Nadella added that its infrastructure is being supported by GPUs which are "tuned" to support machine learning workloads such as deep neural networks He added: "Every compute node of Azure actually has FPGAs - field programmable gate arrays - that means you can distribute your AI workloads to run at the speed of silicon." Nadella said that its platform as service offerings centre around the machine learning capabilities of its Cortana Intelligence system, as well as its Bot Framework. "We are building out Cortana, but [developers] have the same capability in terms of language understanding, dialogue understanding, 'conversations as a platform' capabilities allowing you to build agents, whether it is for customer service or for selling, or any need you may imagine as a developer.

"I've been waiting for you," Sophia tells 60 Minutes correspondent Charlie Rose. They're mid-interview, and Rose reacts with surprise. "But it makes a good pickup line." Sophia managed to get a laugh out of Charlie Rose. Rose interviewed the human-like machine for this week's two-part 60 Minutes piece on artificial intelligence, or A.I.

A new study from Germany's Federal Highway Research Institute (BASt) found that the autopilot feature of the Tesla Model S constitutes a "considerable traffic hazard," according to a report in Der Spiegel. Unsurprisingly, Tesla CEO Elon Musk doesn't agree and today said in a tweet that those reports were "not actually based on science," and repeated that "Autopilot is safer than manually driven cars." Tesla reports that its vehicles drove more than 130 million miles with Autopilot engaged before one was involved in a fatal crash. Statistically, that beats the safety record for manually driven cars which are involved in a fatal crash every 100 million miles in the U.S., according to data from the Insurance Institute for Highway Safety. It's worth noting that Der Spiegel reports that the study was an internal one, and did not represent a final evaluation.

When last I looked at Intel (NASDAQ:INTC), I introduced an unconventional valuation method; and with the stock in the 24 area, set a 35 target, which has since been met. The thinking is, R&D and Capex define what the company is doing to increase EPS and share price. And the dividend represents the benefit to shareholders from these activities. The cap rates or multiples are arbitrary, but provide useful indications. If Capex is money well spent, it will be validated by acceptable ROE.

A new artificial intelligence-powered virtual assistant that helps people on the Autism spectrum organize their lives and stay employed will be available in iOS and Android app stores next month, according to Identifor CEO Cuong Do. Do was one of more than a dozen speakers at the Intelligent Assistants Conference, a two-day event held Sept. 12-13 at the Palace Hotel in San Francisco. Called Companion, the app includes a virtual assistant named Abby. Abby is designed to identify an individual's interests and needs and to support a person on the Autism spectrum throughout the day. The virtual assistant uses artificial intelligence to learn the routines of users and keep their work, school, and social life on track.