ME: Alexa! Call the fire department! The roof is on fire!
ALEXA: Ok, playing “The Roof Is On Fire” by Rockmaster Scott and the Dynamic Three.
One of the biggest gripes consumers have about 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 work the way they’re supposed to? Well, thanks to the exploration and implementation of the discipline of Deep Learning (DL), it’s not a heavy lift at all. In fact, the advent of DL is taking artificial intelligence into a far more, well, intelligent place.
Deep learning is a learning method for machines, inspired by the structure of the human brain and how we learn. It’s the technology that makes autonomous vehicles a reality and allows your smartphone voice assistant to get better at assisting you over time. Deep learning allows artificial intelligence systems to imitate the manner in which humans acquire certain kinds of knowledge. DL algorithms try to draw conclusions – similar to how humans do it – by continually analyzing data.
Deep Learning is a sub-field of Machine Learning (ML) that mimics the functioning of the human brain in processing data. DL enables machines to learn without human supervision and grants them the ability to recognize speech, translate languages, detect objects, and even make data-driven decisions. DL is a type of ML that is an imitation of the neurons of the human brain and tries to mimic their functions. DL systems can learn and improve their performance with access to larger volumes of data.
With the help of Deep Learning, an AI system can learn and improve without any human supervision. DL also enables machines to learn from data that is unlabeled or 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 because of DL. The term “deep” is used to specify the number of hidden layers the neural networks have. While traditional neural networks contain two to three hidden layers, deep networks can have as many as 150 layers. For instance, the spam filtering algorithm in your email account is an example of a machine learning algorithm. ML makes computers more human as it grants the ability to learn and progress, and it also keeps car warranty spam off your desktop.
Another way ML and DL are different is in how they learn. If you have to teach a machine to categorize images of dogs and cats, you’ll have to provide structured data – in this instance, the labeled images of dogs and cats – for the ML algorithm to learn the specific features that differentiate the images of both animals. The algorithm gets better with each labeled image exposed to it.
Moreover, DL applications are already in the marketplace, having an impact on our daily lives, including Uber, AirBnB, online dating apps, and more. Of course, autonomous vehicles use DL to process millions of datasets to learn how to navigate the road safely. With DL models, driverless cars can handle unprecedented scenarios without causing harm to the riders or pedestrians.
DL algorithms are also used in recommendation systems to suggest content streaming companies like Netflix and products e-commerce platforms like Amazon, but one of the most groundbreaking implementations of DL is in the healthcare sector, led by Marpai, an AI-driven health tech company transforming third party administration in the self-funded market by deploying DL to radically reduce costs and improve lives.
In healthcare, DL can match patients with providers to ensure the right care for the right condition, help patients find high-quality providers in any market, and can even identify providers that meet personal preferences e.g. language, gender, and zip code. Marpai uses DL to predict near-term health events for health plan members to prevent costly developments, guide members to top quality providers for best outcomes, and is building SMART automation to create claims processing cost reductions by driving down fraud, waste, and abuse.
With DL, healthcare organizations can find new means to enhance patient experience and satisfaction and even identify costly, replaceable processes. DL can help providers and companies plan ahead by predicting disease states and seasonal demands. A DL system can effortlessly find the correlation between factors that cause seasonal illness and predict future illness by analyzing past data. DL models can also help companies build strategies for compliance and health engagement.
Armed with DL, Marpai is already serving more than 60 self-funded companies and 40,000 members, Marpai works with world-class provider networks including Aetna and Cigna, and partners with brokers and consultants across the U.S.
And, not for nothing, AI that’s improved via Deep Learning won’t mistake a cry for help for a song request.
