Artificial Intelligence and Machine Learning explained
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.
Automated testing still lags
Automated testing initiatives still lag behind in many organizations as increasingly complex testing environments are met with a lack of skilled personnel to set up tests. Recent research conducted by Forrester and commissioned by Keysight found that while only 11% of respondents had fully automated testing, 84% percent of respondents said that the majority of … continue reading
Report: Fully automated testing remains elusive for organizations
Despite the growing complexity of the software that drives organizations, few companies have fully automated testing or are using AI, according to new research conducted by Forrester and commissioned by Keysight. For the study, Forrester conducted an online survey in December 2021 that involved 406 test operations decision-makers at organizations in North America, EMEA, and … continue reading
TigerGraph launches new version of TigerGraph Cloud
TigerGraph today announced new features for TigerGraph Cloud, including the addition of multi-user collaboration, integrated login, private networking options, and extended global cloud coverage. The Enterprise IAM (Identity and Access Management) feature allows for a single enterprise account to manage multiple users and their role-based access with a holistic view of all solutions in one … continue reading
TIBCO updates WebFOCUS platform
With the release of TIBCO WebFOCUS 9.0.0, the company announced stronger data access, AI/ML capabilities, and developer tools. “Valuable data and AI/ML algorithms flow through absolutely every corner of an organization. The use of visual discovery and improved data management enables users and developers to turn that raw data into truly meaningful insights,” said Mark … continue reading
Indico 5 launched to accelerate automation on unstructured data
Indico Data, the unstructured data company, unveiled Indico 5 to allow companies to better make use of automation and intelligent document processing (IDP) on unstructured data. Despite 90% of enterprise data being unstructured according to a December 2021 IDC report, only 2% of it is being utilized based on additional Google research. The platform uses … continue reading
Olive and NTT DATA Join Forces to Accelerate the Global Development and Deployment of AI Solutions
U.S.A., March 14, 2021 — Olive, the automation company creating the Internet of Healthcare, today announced an alliance with NTT DATA, a global digital business and IT services leader. The collaboration will fast track the creation of new healthcare solutions to transform the health experience for humans — both in the traditional healthcare setting and … continue reading
The power of AI in data integration
While the amount of data in the world is infinite, our attention span is not. That’s why AI is becoming a valuable tool for data integration to create concise analysis from data and to make it more accessible to everyone throughout an organization. According to SnapLogic’s Ultimate Guide to Data Integration, AI and ML capabilities … continue reading
SD Times Open-Source Project of the Week: TorchRec
Earlier this week, at an event on AI in the Metaverse, Meta (previously Facebook) announced that it was open-sourcing TorchRec. TorchRec is a PyTorch library for recommendation systems. According to PyTorch, the TorchRec library includes modeling primitives, optimized recommendation system kernels, a sharder for partitioning tables, a planner than can generate sharding plans, GPU inference … continue reading
Azure focuses on feature abundance and integrations to become the all-inclusive cloud experience
Microsoft Azure has been showing faster growth than any other cloud provider over the last few years, and its vast ecosystem of partnerships and integrations continually make it an appealing platform for existing and prospective customers. The platform currently stands as the second largest cloud offering in the world with 21% market share, following AWS’s … continue reading
SD Times Open-Source Project of the Week: Elyra
Elyra is a set of AI-centric extensions to JupyterLab Notebooks that includes features like an AI pipelines visual editor; the ability to run a notebook, Python, or R script as a batch job; reusable code snippets, and more. To create pipelines using the Visual Pipeline Editor, users need to open the JupyterLab Launcher and select … continue reading
As applications drive business, iPaaS comes of age
If the adage “data is the new oil” stands true, then Integration Platform as as Service (iPaaS) is the machinery you need to drill and tap it. The allure of iPaaS is that it offers the integration needed to knit and integrate the data and processes of multiple business applications and the actual applications themselves. … continue reading
SD Times Open-Source Project of the Week: ZenML
ZenML is an extensible open-source MLOps framework designed to create reproducible pipelines. The framework enables data scientists to write their code as automated pipelines from day one. It is built to encourage the iterative and experimental nature of machine learning work, but also to provide a path to an automated, production-ready software base that can … continue reading
