Data Science, Artificial Intelligence, Analytics, and Machine Learning at the Enterprise scale are terms you've probably heard before. But what do they mean? We break it down for you in this blog. So, What Is Data Science? Data Science is a series of disciplines, technology, skills, expertise, and knowledge that encompass one thing: obtaining and preparing data for analysis.
Networking plays a critical role in one's professional life, particularly for people who depend on experts' opinions and hold curiosity about lived experiences.LinkedIn lets users connect to people of their choice, exchange ideas, or collaborate on projects. However, communicating directly with them comes at a price. Email messages for LinkedIn where users can message users directly is a premium feature. LinkedIn groups play a vital role in bringing together the tech enthusiasts to collaborate and brainstorm ideas; and it works particularly well in areas like artificial intelligence and machine learning, where you can get inputs from people who are experienced enough to instill a sense of direction and provide significant insights. It is a collaborative group of AI researchers who work on next-generation machine intelligence.
There's no lack of startups around the world trying to make industrial activities more efficient with artificial intelligence. Some invent robots to assist or replace manual labor, while others use machine learning to help businesses discover insights. Synergies Intelligent Systems falls into the second category. Michael Chang founded Synergies in 2016 in Boston to provide easy-to-use AI-powered analytics tools to medium-sized manufacturers. Having worked at Foxconn in Shenzhen in the late 2000s helping the Apple supplier improve yield rate, or reduce the percentage of defective products, using data analysis, Chang realized that not every factory has the financial prowess to spend tens of thousands of dollars on digitization.
Managing data has always been a challenge for businesses. With new sources and higher volumes of data coming in all the time, it's more important than ever to have the right tools in place. Predictive analytics tools and software are the best way to accomplish this task. Data scientists and business leaders must be able to organize data and clean it to get the process started. The next step is analyzing it and sharing the results with colleagues.
Wish your network could predict its own problems and fix them automatically? Cisco believes it has the technology you need. The networking tech giant announced today what it said is the culmination of two years of work: an analytics engine that can predict network issues before they happen, and with enough integration and training even fix problems itself, Cisco said. Citing data from an in-house study, Cisco said that 45 percent of IT leaders it surveyed cited responding to disruptions as their biggest networking challenge of 2021. Predictive analytics technology, coupled with "enormous amounts of historical [networking] data," is a potential solution, Cisco said.
Researchers have developed a new method that uses artificial intelligence to analyze animal behavior. This opens the door to longer-term in-depth studies in the field of behavioral science--while also helping to improve animal welfare. The method is already being tested at Zurich Zoo. Researchers engaged in animal behavior studies often rely on hours upon hours of video footage which they manually analyze. Usually, this requires researchers to work their way through recordings spanning several weeks or months, laboriously noting down observations on the animals' behavior.
Multiple times over the last decade, this column has covered the issue of the importance of data quality in decision making, both by executives as well as machines. Back in 2014, when the "big data" craze was mesmerizing the C-Suite, the warning was issued in Big Data and the Madness of Crowds. More recently in How Bad Data Is Undermining Big Data Analytics from December 2020. Since then, more and more news has emerged regarding the failures of AI and Machine Learning initiatives with the blame given to faulty data as the reason. The recent demise of IBM Watson Health is the latest example.