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.
Amazon aims to democratize deep learning with new library AutoGluon
Amazon has launched a new open-source library that will make it easier for developers to deploy machine learning models. With AutoGluon, Amazon aims to help developers deploy their models using just a few lines of code. “We developed AutoGluon to truly democratize machine learning, and make the power of deep learning available to all developers,” … continue reading
SD Times news digest: LG announces new framework for advancing AI technology, Google Project Zero’s new disclosure policy for 2020, and AvePoint raises $200 million
LG announced a new structured framework for the development of AI across the industry at CES 2020 this week. The framework will consist of four levels. Efficiency is the first level in which AI automatically adjusts performance in relation to pre-established sensory input parameters. Next is personalization, which focuses on pattern learning to optimize and … continue reading
SD Times news digest: Mastercard acquires cybersecurity company, XR Association updates developers guide, and Nim’s gc:arc feature
Mastercard announced an agreement to acquire RiskRecon to help financial institutions, merchants and governments to secure their digital assets. With RiskRecon’s AI and data-driven technology is designed to help organizations proactively manage cyber risks and better safeguard intellectual property and consumer and payment data. “By becoming part of their team, we have an opportunity to … continue reading
SD Times news digest: Ruby 2.7 now available, PyPy 7.3 released, Sensory unveils domain specific assistants for smart appliances
The developers of Ruby announced version 2.7.0 which introduces new features and performance improvements such as pattern matching, REPL improvement, compaction GC and the separation of positional and keyword arguments. The new release also includes performance improvements such as JIT -ed code now being recompiled to less-optimized code when an optimization assumption is invalidated. The … continue reading
Continuous Deployment for ML: The new software development life cycle
The new software development life cycle means working out ways to adapt the SDLC for your machine learning workflow and teams. With data scientists currently spending large chunks of their time on infrastructure and process instead of building models, finding ways to enable the SDLC to work effectively with machine learning is critical for not … continue reading
Implications and practical applications for AI and ML in embedded systems
“Civilization advances by extending the number of important operations we can perform without thinking about them.” —Alfred North Whitehead, British mathematician, 1919 Hailed as a truly transformational technology, artificial intelligence (AI) is positioned to disrupt businesses either by enabling new approaches to solving complex problems, or threatening the status quo for whole business sectors or … continue reading
Software predictions for 2020 from around the industry
Thought leaders weigh in on what we can expect from the software development industry in 2020: Adam Scroggin, CEO of CardBoard DevOps will continue to be key as we move toward 2020. Software teams will notice more and more that once a product is released, it is not done. Software products are never done. We … continue reading
Report: Interest in AI continues to skyrocket
The Human-Centered Artificial Intelligence lab at Stanford University has just released their AI Index 2019 Report. According to the lab, this report is one of the most comprehensive AI studies ever performed. For the report, the researchers took an interdisciplinary approach in order to discover patterns about AI’s impact on every industry, from national economics … continue reading
Cloudera calls for open standards for MLOps
Cloudera is calling for industry-wide participation in setting open standards for machine learning operations (MLOps) and machine learning model governance. Cloudera believes that community participation in developing these standards will ensure that companies can leverage their machine learning investments and pave a path for the future. RELATED CONTENT: AI ethics: Early but formative days Ethical … continue reading
Analyst Watch: Evaluating the ethics of software
In recent years, technology analysts have devoted much attention to the topic of developers and how the demographics of developers are changing. For starters, the International Data Corporation (IDC) has noted the growth of developer populations in China, India, Brazil, Russia, Indonesia and Turkey, as well as select countries in East Africa. In addition, IDC … continue reading
Azure Cognitive Search brings knowledge mining to the enterprise
Microsoft Azure Search will now be known as Azure Cognitive Search to signify the next wave of artificial intelligence in the enterprise. According to Microsoft, the first wave of AI was built for narrow apps, or training a single mode for a specific task. “What’s been challenging, however, is that these models individually can’t capture … continue reading
SD Times news digest: Fugue’s free tier for developers, OutSystems announces AI assistant app dev capability, and Mozilla’s bug bounty security program
Fugue announced a free tier for Fugue Developer, which provides automation tools to visualize cloud infrastructure, identify misconfiguration risks, and demonstrate compliance “The cloud upended traditional IT security, forcing hard tradeoffs between agility and security,” said Phillip Merrick, CEO of Fugue. “Fugue Developer puts cloud security in the hands of the software developers building and … continue reading
