ME: Alexa! Call the fire department! The roof is on fire!
ALEXA: Okay, I’ll play The Roof Is On Fire by Rockmaster Scott and the Dynamic Three.
One of the biggest criticisms consumers have of their smart devices is that they wish they were just a little bit smarter. After all, is it too much to ask that our smart devices at least function as they should? Well, thanks to the research and implementation of the deep learning (DL) discipline, this is not a difficult task at all. In fact, the advent of DL is leading artificial intelligence to a far smarter place.
Deep learning is a learning method for machines, inspired by the structure of the human brain and how we learn. It is the technology that makes autonomous vehicles a reality and enables your smartphone voice assistant to better serve you over time. Deep learning enables artificial intelligence systems to mimic the way people acquire certain types of knowledge. Similar to humans, DL algorithms try to draw conclusions by continuously analyzing data.
Deep learning is a branch of machine learning (ML) that mimics how the human brain works when processing data. DL enables machines to learn without human supervision, enabling them to recognize language, translate languages, recognize objects, and even make data-driven decisions. DL is a type of ML that mimics the neurons in the human brain and tries to mimic their functions. DL systems can learn and improve their performance by accessing larger amounts of data.
With the help of deep learning, an AI system can learn and improve without human supervision. DL also enables machines to learn from data that is untagged, unstructured, or both. However, the learning process can be unsupervised, semi-supervised, or supervised.
Branches of artificial intelligence such as computer vision and natural language processing are possible through DL. The term “deep” is used to indicate the number of hidden layers of the neural networks. While conventional neural networks contain two to three hidden layers, deep networks can have up to 150 layers. For example, the spam filtering algorithm in your email account is an example of a machine learning algorithm. ML makes computers more human by providing an opportunity to learn and progress, and it also keeps auto warranty spam off your desktop.
Another difference between ML and DL is how they learn. If you need to train a machine to categorize pictures of dogs and cats, you need to provide structured data – in this case the labeled pictures of dogs and cats – so that the ML algorithm learns the specific characteristics that the pictures of both animals have. The algorithm gets better with every labeled image that is exposed to it.
In addition, DL applications are already out in the market and making an impact on our daily lives, including Uber, AirBnB, online dating apps, and more. Of course, autonomous vehicles process DL to process millions of records in order to learn how to move safely on the road. With DL models, driverless cars can tackle unprecedented scenarios without harming drivers or pedestrians.
DL algorithms are also used in recommendation systems to suggest content streaming companies like Netflix and ecommerce platforms like Amazon, but one of the most groundbreaking implementations of DL is in the healthcare sector, led by Marpai, an AI-driven healthcare technology company Transforming Third Party Management in the self-financed market through the use of DL to radically cut costs and improve life.
In healthcare, DL can match patients with providers to ensure the right care for the right condition, help patients find quality providers in each market, and can even identify providers who match personal preferences such as language, gender, and zip code. Marpai uses DL to predict short-term health events for health insurance members to prevent costly developments, guide members to top-notch providers for best results, and builds SMART automation to achieve cost reductions in claims handling by eliminating fraud, waste and Abuse will be reduced.
With DL, healthcare organizations can find new ways to improve patient experience and satisfaction, and even identify costly, interchangeable processes. DL can help vendors and businesses plan ahead by predicting disease states and seasonal demands. A DL system can easily find the correlation between factors that cause seasonal illness and predict future illness by analyzing past data. DL models can also help companies develop compliance and health engagement strategies.
Equipped with DL, Marpai already looks after more than 60 self-financed companies and 40,000 members, works with first-class provider networks such as Aetna and Cigna and works with brokers and consultants in the USA
And it’s not for nothing that AI, enhanced through deep learning, won’t confuse a call for help with a song request.
Mark Anthony is a former Silicon Valley executive at Forrester Research, Inc. (Nasdaq: FORR). He is now the host of the nationwide syndicated radio called The Patriot and The Preacher Show. Learn more at patriotandpreachershow.com.