Article | August 17, 2020
A few years ago, the team at Gartner came up with a useful framework designed to help IT asset managers compare different Software Asset Management tools based on “six critical activities” of SAM. Known as the DINROS framework, it outlined the following activities: Discovery: the act of interrogating TCP/IP networks to identify network-attached physical and virtualized platforms upon which software executes Inventory: the process of capturing platform configuration information and extracting a complete list of all its software. Normalize: the consolidation of discovered inventory datasets to remove duplicated or conflicting information
Article | March 29, 2020
Governments all around the globe are using artificial intelligence (AI) to help fight against the ongoing COVID-19 pandemic. The technology is being used for various different things, including speeding up the development of testing kits and treatments, giving citizens access to real-time data, and tracking the spread of the virus. South Korea’s government, one that is being touted as an example for how to combat the virus, pushed their private sector to start developing testing kits right away, immediately after the reports began to arrive out of China. One of those companies was Seoul-based molecular biotech company Seegene, which used AI to help quicken the process of developing testing kits. The company was able to submit its solution to the Korea Centers for Disease Control and Prevention (KCDC) just three weeks after the scientists began their work. According to Chun Jong-Yoon, founder and chief executive of the company, the process would have taken at least two to three months without the use of AI.
Article | March 2, 2020
Yet, Java persists. Easy to learn, highly adaptable and a conveniently jumping-off point to learn lots of other coding protocols, Java remains a very in-demand skill set. And with Java developers still average salaries in the six-figure range, it’s still an easy call to look into adding Java to any coder’s bag of tricks. The 2020 Complete Java Master Class Bundle is assembled to help students of any skill level, even first time Java users, get comfortable with the basics and start creating with this heritage language almost immediately. Across seven courses and more than 62 hours of training, this package covers everything, from foundational learning to all the specialized Java uses that can make your resume truly sing.
Article | December 21, 2020
Machine learning — a branch of artificial intelligence that gives computer systems the ability to automatically improve and learn from experience — has been making serious waves for the last few years. More recently, though, the applications for smartphones and other small screen experiences have started to take shape, driving the way millions interact with their mobile devices.
Yes, Your Mobile Devices are Becoming Smarter
So what do these innovations means for your business? Machine learning can, essentially, make your smartphone “smarter” by improving a host of functions and processes instantly. In fact, most smartphones are already using some type of machine learning or intelligent automation application that aids mobile devices in becoming more efficient and effective. Predictive text messaging, for example, is one such application that’s already become part of the mobile vernacular — chances are, you use it daily without thinking twice.
As a whole, businesses are ramping up their machine learning investment, meaning we’ll be seeing more of this technology — and more accessible versions of this technology — in the coming months and years. For each generation, there’s an added level of intuitiveness when it comes to mobile technology — your current smartphone is smarter than the computers that helped bring man to the moon, in many ways. From that end, how advanced will our mobile devices be in another 10 or 20 years? Smartphones could be paving the way for Robotic Process Automation (RPA) and evolving the very way many industries work.
What’s Next for Mobile Machine Learning
Historically, machine learning requires a tremendous amount of power — power mobile devices simply didn’t have. However, businesses can now install special chips in drones, automobiles and smartphones enabling them to consume 90 percent less power. As a result, mobile devices — even without an internet connection — can perform a variety of once-complex tasks, including:
Virtual / Augmented Reality
Smarter Camera Functionalities
Improved Device Security
Going forward, envelope-pushers are driving towards even bigger, better, more sophisticated applications — think motion control and navigation, diagnosing and analyzing sensory data and more. Interactivity or perceptual interfaces are also capabilities that the new applications are expected to be equipped with, giving mobile devices seemingly endless capabilities.
Due to these unique benefits, machine learning on small devices is clearly becoming a priority for businesses.