IT - AI

12 Dark Secrets Of AI
CIO, January 6th, 2021
With the drumbeat for AI across all industries only getting louder, IT leaders must come to grips with the dark secrets of working with artificial intelligence to glean business insights

"Humanity have always dreamed of some omniscient, omnipotent genie that can shoulder its workloads. Now, thanks to the hard work of computer scientists in the labs, we have our answer in artificial intelligence, which if you buy into the hype can do just about anything your company needs done - at least some of it, some of the time.

Yes, the AI innovations are amazing. Virtual helpers like Siri, Alexa, or Google Assistant would seem magical to a time traveler from as recently as 10 to 15 years ago. Your word is their command, and unlike voice recognition tools from the 1990s, they often come up with the right answer - if you avoid curveball questions like asking how many angels can dance on the head of a pin..."


2020's Biggest Stories In AI
insideBIGDATA, January 7th, 2021
2020 provided a glimpse of just how much AI is beginning to penetrate everyday life. It seems likely that in the next few years we'll regularly (and unknowingly) see AI-generated text in our social media feeds, advertisements, and news outlets

"The implications of AI being used in the real world raise important questions about the ethical use of AI as well.

So as we look forward to 2021, it is worth taking a moment to look back at the biggest stories in AI over the past year..."


With growing demand and wider adoption due to affordability and accessibility, 2021 will bring broader deployment of AI at the edge solutions across various industries

"AI and edge computing will play a crucial role for businesses in alleviating the growing strain of managing large amounts of data since processing data right at the edge versus needing to transfer it to the cloud, creating more powerful, versatile, responsive and secure solutions.

AI at the edge will make it more efficient to process large amounts of data and complete time and resource demanding tasks for industries that rely on dozens of cameras, such as smart retail and industry 4.0. Edge processing will help process video streams coming from multiple cameras in real time. Camera-attached processing in addition to collective, multiple stream processing will remove the need for huge, in-store servers while also reducing communication costs. Overall, various industries will benefit from edge processing due to its reduction in network bandwidth costs and improvement on issues related to privacy, latency, and efficiency. 2021 will be a transformative year for enterprises leveraging computer vision for automation, with fast and smart adopters of edge AI becoming industry leaders for years to come..."


4 Ways To Overcome AI Obstacles
InformationWeek, January 7th, 2021
Four out of five organizations haven't scaled their AI. Here are some ways to change that

"Even as the pandemic tightens technology budgets, there are plenty of companies eager to leverage the highly beneficial capabilities of AI. They hire data scientists, identify use cases, and build proofs of concept. Yet, according to a recent research report from Capgemini, four out of five organizations fail to successfully scale these AI programs from the pilot and initial production stages..."

'Responsible AI' is at the beginning of a long slog, while Cloud Data Lakehouses will supplement but not replace Data Warehouses or Data Lakes

"If there's one obvious prediction that bore out over the course of what was otherwise a very unpredictable year, it was the acceleration in the adoption of cloud computing. Just look at the continued very healthy double-digit growth rates of each of the major clouds. For enterprises, it was about adapting to the virtual environment and constrained supply chains of a suddenly locked-down world..."

Although it's still in the early stage of adoption, the use of artificial intelligence in the public sector has vast potential

"According to McKinsey & Company, AI can help to identify tax-evasion patterns, sort through infrastructure data to target bridge inspections, sift through health and social-service data to prioritize cases for child welfare and support or even predict the spread of infectious diseases.

Yet as the promises of AI grow increasingly obtainable, so do the risks associated with it.

Public-sector organizations, which house and protect sensitive data, must be even more alert and prepared for attacks than other businesses. Plus, as technology becomes more complex and integrated into users' personal and professional lives, agencies can't ignore the possibility of more sophisticated attacks, including those that leverage AI..."


What Is An AI Chip? Everything You Need To Know
techradar.pro, January 5th, 2021
All your questions about AI chips, answered

"Many of the smart/IoT devices you'll purchase are powered by some form of Artificial Intelligence (AI)-be it voice assistants, facial recognition cameras, or even your PC. These don't work via magic, however, and need something to power all of the data-processing they do. For some devices that could be done in the cloud, by vast datacentres. Other devices will do all their processing on the devices themselves, through an AI chip.

But what is an AI chip? And how does it differ from the various other chips you may find in a device? This article will highlight the importance of AI chips, the different kinds of AI chips that are used for different applications, and the benefits of using AI chips in devices..."


What Is Semi-Supervised Machine Learning?
TechTalks, January 4th, 2021
Machine learning has proven to be very efficient at classifying images and other unstructured data, a task that is very difficult to handle with classic rule-based software

"But before machine learning models can perform classification tasks, they need to be trained on a lot of annotated examples. Data annotation is a slow and manual process that requires humans reviewing training examples one by one and giving them their right label.

In fact, data annotation is such a vital part of machine learning that the growing popularity of the technology has given rise to a huge market for labeled data. From Amazon's Mechanical Turk to startups such as LabelBox, ScaleAI, and Samasource, there are dozens of platforms and companies whose job is to annotate data to train machine learning systems..."

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