How Deep Learning Is Used Everyday
There are countless applications of “deep learning” in everyday life – and not just by cyber security gurus. From Siri to self-driving cars – deep learning is behind the algorithms that train machines to “think”. Data extrapolation is repeated and improved as the machine “learns” patterns of behavior. From there, it makes predictions and decisions in a similar way to our brains – no human required!
How Machines Learn
Deep learning mimics the workings of a human brain by processing data for use in applications like speech recognition, decision making, language interpretation, and more. By learning statistics and using predictive modeling, deep learning disentangles the complications of everyday life with simple data extrapolation.
The more data that is fed into a machine, the better the machine is at intuitively understanding that data and its potential application. As a result, human intelligence is not required to help it understand the new information. Instead, machines draw information from data that is unstructured & unlabeled – raw data that hasn’t been determined to be malicious or benign. Artificial Intelligence then makes a determination on whether the file is good or bad using deep learning.
This “learning” refers to an AI function that imitates the way humans gain certain types of knowledge. Using statistics and predictive modeling – much like we humans would – deep learning continues learning from each new tbit of information it receives. It’s the next step after machine learning, since it enables computers to subsequently think and act on that information, all with less human intervention.
The Future Of Deep Learning
Artificial Intelligence has universally validated a machine’s ability to think and act with less human mediation for decades. Deep Learning has specifically facilitated this “thinking” and “acting” with the human brain as its model. Absorbing, deciphering, computing, and analyzing data is now done in minutes by a qualified machine.
What does this mean for the future? As hackers and bad theater actors are getting smarter, the evolution of this Deep Learning technology can step up its game.
The Starpoint Difference
Starpoint Technology detects otherwise unknown threats within seconds, even if the software itself has never recognized that exact threat before. It’s deep learning has allowed it to identify what might be malware, and then determine its percentage of risk. This technology is trained so effectively before testing that its intelligence is unmatched in today’s cyber security industry.
Think of it this way. The more languages you expose a child to, the more languages it will learn to speak. Likewise, the greater information and data sets you provide to an intelligent person, the more accurately they will learn. Just like human learning, a machine’s deep learning gets better over time and with more information. As a result, its margin of error decreases exponentially.
That’s what sets Starpoint apart from the rest – it’s continually being trained and tested like the human brain but without human assumptions.
Whether it’s a Windows or Linux executable, Starpoint detects the pieces of malicious code that most other software wouldn’t have learned about yet.That’s because Starpoint investigates the fundamental level of files – binary data.
Starting at the lowest level of any code is what allows for machine learning to evolve into deep learning. Just like in our brains, the more the information travels through neurological pathways, the more that information is retained. This deep learning brain power is what enables Starpoint to conquer unseen and unknown threats with unparalleled speed and accuracy.