That was the experience of Tech Elevator coding bootcamp graduate Kyle Pierson, who recently began his career as a software developer for LMI. He calls video games a "stepping stone" toward his coding career. As a child, he remembers asking his dad to look up cheat codes for Twisted Metal 2 online. "When he found a webpage that contained those codes, I realized that the computer was like a library where I could get anything I wanted," he says. This innate curiosity led to him wondering how computer programs worked.

In just a few short weeks, we'll be electing our next American President. Among myriad other duties, whoever gets the job will be tasked with overseeing one of the most significant technological expansions the world has ever seen. He or she will need to understand these new technologies to help the country reap the most benefits from them. Of all the innovations just on the horizon, the one with the most game-changing potential is 5G wireless technology. For the last 30 years, the technology industry mostly focused on connecting people to other people.

An international team of researchers has trained a robot to imitate natural human actions, in the hope that humans and robots can coordinate their actions during critical events such as surgeries. Researchers from Italy's Polytechnic University of Milan led an international team that trained a robot to imitate natural human actions. The work demonstrates humans and robots can effectively coordinate their actions during high-stakes events such as surgeries. Over time, the research could lead to improvements in safety during medical procedures because robots do not tire and can complete an endless series of precise movements. Robotic co-workers "will just allow us to decrease workload and achieve better performances in several tasks, from medicine to industrial applications," says Polytechnic University of Milan's Elena De Momi.

As the interest in data science, predictive analytics and machine learning has grown in direct correlation to the amount of data that is now being captured by everyone from start ups to enterprise organisations, endjin are spending increasing amounts of time working with businesses who are looking for deeper and more valuable insights into their data. As such, we've evolved a pragmatic approach to the machine learning process, based on a series of iterative experiments and relying on evidence-based decision making to answer the most important business questions. In this series of posts, we're going to look at what machine learning really is (and isn't), the endjin process and some examples of how and where we've put it to use. So what do machine learning and data science actually mean? My previous post argued that there's no mad science or dark art at play, just a pragmatic process based around trial and error with statistics.

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.