Everybody’s been raving about how artificial intelligence and machine learning are saving us. It’s quickly becoming a dogma to live by. Machines are under the spotlight and they seem to be our solution to live our best lives. Just when you think you’ve gotten your head around these terms, someone starts chattering about deep learning.
Yet another term to get us all confused. It seems impossible to keep updated with the tech industry. They’re constantly innovating new devices and using fancy techniques to improve our lives. This is obviously awesome but since it is such an elaborate and fast-paced industry we can’t genuinely comprehendhow technology is improving our lives.
This frustration can make people unmotivated to investigate further about what deep learning is. We need to take this frustration to look at how people are able to intricately improve machines in outstanding ways because of deep learning. Let’s take the spotlight off the machines and humanize technology.
What makes Deep Learning stand out in comparison to Machine Learning
When I first heard the term deep learning, I originally thought it was a study method for us to learn more efficiently. Silly right? Well maybe not. As it turns out that is basically what deep learning is. But not for us, for machines.
Deep learning is defined as a subtype of machine learning that uses algorithms from neural networks. Deep learning models can go deeper than machine learning models because there is a way bigger database thanks to the neural networks.
Neural networks are like a simulation of the human brain in that it has interconnected parts so the machine can make decisions and predictions like a human brain would. Neural networks are why we are able to get machines to learn on their own and is why we have achieved such a complex method as deep learning.
With all these neural network layers deep learning doesn’t plateau like machine learning. It can be more independent so having more data means that we can have better algorithms and bigger models giving machines more accuracy. Unlike in machine learning where we have to manually choose features and a classifier to arrange images, deep learning can do that automatically with feature extraction.
So you may be wondering why we even bother to use machine learning and instead just focus on deep learning. It all comes down to the data you have and what you’re trying to solve.
With deep learning, you require:
– A high-performance GPU (computing power)
– Lots of labelled data
If you don’t have these things you should use machine learning to manually decide which classifiers will work with your data. Since machine learninguses less data it’s also a faster approach. But, you’re looking for more accuracy and have the resources to support the large database necessary for deep learning then this would be the ideal choice.
Where Deep learning is making a significant impact
1. Self-driving cars always seemed like a futuristic idea that we’d only see in movies, yet we’ve almost made it a reality. While we do have self-driving cars such as the famous Tesla, we still haven’t gotten to the point where cars can be totally independent without a human being.
With deep learning, we will definitely get there. Telsa has been the company to make the furthest advancements due to its extraordinary amounts of data. Three key components for auto-pilot to be accurate are:
– Computer vision
– Route planning
These are incredibly difficult things for a machine to master because they have to depend on humans actions on the road and make snap-decisions just like a person would. Telsa’s advanced achievements with deep learning have allowed them to have the most accuracy in the three key components that all self-driving cars need.
2. Voice control has been a commonly used form of AI with Siri and Alexa. But what about in translation? Speech to Text has been the go-to method for translation apps, but there have been lots of problems with these apps, such as latency, robotic translations and miscommunication. What people are talking about lately is the Speech to Speech method.
Google’s Translatron app has used deep learning to develop the most natural translation tool we’ve seen. Google used a neural network model to build a sequence-to-sequence model. This means that Translatron can directly translate speech from one language to another without losing the nuances and the natural demeanour of the person’s voice. Making the translation a more human experience.
3. The advancement of medical research has been tremendous thanks to deep learning. One area that has made a lot of progress is Oncology. Oncology is a branch of medicine that handles the prevention, diagnosis and treatment of cancer. Deep learning’s robust algorithms have proven to be more accurate in diagnosing cancer than pathologists (a scientist who studies the causes and effects of diseases).
Cancer is the second largest cause of death worldwide and is constantly being researched. Deep learning’s diagnosis accuracy has greatly changed how we handle cancer. We are able to give more answers faster and be more confident in how to treat patients. With deep learning, the future of cancer research can achieve wonders for us to finally conquer cancer.
How Jaya uses Deep learning to improve performance
We believe that deep learning has given us the tools to truly give our clients the best experience possible. By investing in deep learning strategizes we are investing in you! Here is one example of how we use deep learning at Jaya.
At Jaya we use speech analytics to empathize with our clients. We do that by interpreting peoples moods in recorded conversations to know how they really feel. This means that with the accuracy of deep learning we can get to know our client’s reactions to our services over the phone to improve our interaction with them.
What does Deep learning really mean for our future
When we fully digest the possibilities deep learning can gift to us we can quickly change our perspective of technology. The development of deep learning has opened up countless doors for us to grow. The more we acknowledge that the more we can prevent it from causing havoc.
Deep learning has made it possible to have machines work even better than humans. With that knowledge, we must educate people to befriend AI rather than compete with it. It’s inevitable that artificial intelligence will replace many jobs. So for a sustainable future, we need to understand AI and train people to work in areas that are irreplaceable by machines. This gives us a more fulfilling lifestyle.
The average person spends 90,000 hours of their life working. Why spend it doing an impersonal job that a machine could do? When we embrace the future of deep learning and learn how to manage it, we can create a space where more people can creatively and individually be themselves.