Up until the last 10 years or so, artificial intelligence was the stuff of science fiction: machines that could learn from a variety of interactions to make decisions and take actions that would normally require a human to execute. Because of that science fiction, there are those who fear AI as the beginning of the rise of intelligent robots. So, ethical development of AI has become an important issue in the IT community.
Artificial intelligence is having a big impact on application development. Today, we see AI in many different computing environments. It is also popping up in customer service call centers, in dialog boxes on websites, in the Industrial Internet of Things, as well as in our children’s toys, our homes and businesses. When coupled with automated processes, machines can take over many of the more mundane tasks businesses have to complete on a daily basis.
Of course, applications of AI are much broader and more sophisticated. AI can be found in automotive controls, such as applying the brake when your car is quickly approaching the one ahead. It’s found in data analytics, processing and management, where AI can learn to spot anomalies in data and trigger alerts and actions to remediate the issue.
OpenAI says it is backlogged with a waitlist of prospective testers seeking to assess if the first private beta of its GPT-3 natural language programming (NLP) tool really can push the boundaries of artificial intelligence (AI). Since making the GPT-3 beta available in June as an API to those who go through OpenAI’s vetting process, … continue reading
Microsoft has revealed it is teaming up with OpenAI to exclusively license GPT-3. According to the company, this will further Microsoft’s goals to develop and deliver advanced AI solutions for customers, as well as create new solutions that harness the power of advanced natural language generation. GPT-3 is an autoregressive language model that outputs human-like … continue reading
Snyk is looking to bolster its security platform with the acquisition of DeepCode, a provider of real-time semantic code analysis. Through its AI-powered platform, DeepCode is able to assist developers with app quality and security. According to Snyk, the addition of DeepCode will add to its existing open-source security, container security and infrastructure as code … continue reading
Google has announced a new set of services aimed at simplifying Machine Learning Operations (MLOps) for data scientists and machine learning (ML) engineers. According to Google, companies are using machine learning to solve challenging problems, but machine learning systems can create unwanted technical debt if it is not managed well. Google noted that machine learning … continue reading
The latest integration of traces allows Instana users to drill down into profiles related to individual requests and user traces which require investigation. “Instana’s single-click direct path from individual traces directly to applicable process profiles makes it both easy and quick for users to get the information they need to identify CPU, Memory and Concurrency … continue reading
Process change is more about people than process. At least until processes can be fully automated. During the past decade, I have championed technology-led process improvement initiatives at over a dozen large companies. I have seen process improvement implemented successfully primarily when such initiatives were pursued with a healthy mix of technology and human understanding … continue reading
Artificial intelligence (AI) is a broad field that spans academic research with ambitions to create an artificial human brain (general AI) through to practical applications of deep learning (DL), a branch of machine learning (ML, itself the part of AI concerned with learning systems built on data rather than prepared rules). DL has many real-world … continue reading
A couple of years ago, there was a lot of hype about using AI and machine learning (ML) in testing, but not a lot to show for it. Today, there are many options that deliver important benefits, not the least of which are reducing the time and costs associated with testing. However, a hands-on evaluation … continue reading
AI and machine learning (ML) are finding their way into more applications and use cases. The software testing vendors are increasingly offering “autonomous” capabilities to help customers become yet more efficient. Those capabilities are especially important for Agile and DevOps teams that need to deliver quality at speed. However, autonomous testing capabilities are relatively new, … continue reading
Longtime application performance monitoring provider New Relic is shifting gears, announcing its product focus has shifted to observability with new updates to its New Relic One platform. According to the company, New Relic One has become an expanded observability platform comprised of three products: the Telemetry Data Platform, Full-Stack Observability and Applied Intelligence. The telemetry … continue reading
Airship launched free feature flags by Apptimize, enabling app developers to control the scope and timing of feature launches to validate success and reduce risk prior to full rollout. Developers can use Apptimize Feature Flags to choose when and to whom new features are accessible, and easily turn them on or off. In addition to … continue reading
Planview announced the acquisition of Aptage, which focuses on the application of AI/ML to portfolio management and work management. “For Planview, the acquisition of Aptage brings data science and domain expertise in AI/ML. Not only will that accelerate the company’s current roadmap in AI/ML – a growing area of focus for the market and our … continue reading