From the series: A practical approach between myth and reality In the previous article, we presented two ways of categorizing conversational agents — more widely known as chatbots — along with their advantages, limitations and use case scenarios. In the…
If developers occasionally feel hamstrung by MISRA’s restrictions, those (thankfully) rare incidents of catastrophic embedded software failure in the news put this level of circumspection back into context. In the previous posts we took a look at the rise of…
In the last post, we recapped the emergence of C as the dominant programming language for embedded automotive systems, from its roots in Unix in the early 1970s up to gathering criticism around its relative eccentricities as the platform matured….
In the 1980s electronically controlled ignition and fuel injection systems were the first major embedded systems deployed for the automotive industry, as ignition and injection analogue units started to be progressively replaced by the more controllable digital alternatives facilitated by…
You’ve already read a little bit about what really makes us tick and click as a team, in the first part of the article. As we’ve promised, here comes the second part of our behind the scenes series!
One of the most rewarding aspects of developing innovative IoT systems at scale is the opportunity to define the process and make sense of a rapidly changing marketplace where no single methodology, platform or approach dominates yet.
This year, Stack Overflow asked 100,000 developers worldwide how they feel about the tools they use and about their working environment. The 2018 survey also covered some new topics ranging from artificial intelligence to ethics in coding.
Especially over the last few years, many businesses have been considering the idea of building or acquiring their own AI-enabled conversational agent. However, despite the interest, research and effort that are put in, there are still just a handful of successful attempts.
Natural language processing is a messy and complicated affair but modern advanced techniques are offering increasingly impressive results. Word embeddings are a modern machine learning technique that has taken the natural language processing world by storm.
An interview with Petrica Martinescu, Lead Architect Web Platforms, Tremend. Microservices are emerging as the preferred way to create enterprise applications, bringing a wide range of advantages such as isolated risk and faster innovation, flexibility and agility.
Over the past few years we’ve seen amazing improvements in machine learning applied to natural languages. New applications have emerged, and some of them are likely to change how humans communicate with each other and with their computers.