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Deep Learning: Groundbreaking Developments in Artificial Intelligence

Deep Learning: Groundbreaking Developments in Artificial Intelligence

Deep learning has been here for a while. Yet became an overnight sensation only in 2016, when AlphaGo, an artificially intelligent system earned victory over Lee Sedol, one of the world’s best Go players. 

Deep learning which is a subset of AI and machine learning have changed the heart of intelligent systems. 

Intelligent systems taking over the jobs of many workers have painted a scary picture but perhaps it may be broken free from our control. Most of the deep learning methodologies were widely recognized and acknowledge in humanizing machines due to the extensive growth of machine learning and deep learning. 

The past decade has shown groundbreaking developments in AI, but what is this technology everybody is talking about? And why it is crucial in the AI sector?

Well, let’s first define what is deep learning. Simply said, it can also be termed as deep structured learning through which it employs artificial neural networks to further help process information.  As inspired by biological nodes present inside our body, so does deep learning quickly helps in recognizing and processing images and speeches. Then computers learn what images look like or what does the sound represents, once all these have been recognized, a large database is created of this stored knowledge for future reference. In simple words, deep learning helps these intelligent systems enable the computer to learn things and do things exactly the way a human can do. 

According to Geoffrey Hinton, the Godfather of deep learning made the functions of it cool once again. It was then defined that only a few of these systems were intelligent enough to improve word prediction and improved shape recognition. 

However, not long ago, perhaps in 2012, dep learning started to be useful in everything – from consumer applications such as Siri to pharmaceutical research. 

How deep learning functions?

Let’s define by representing it through self-driving cars. AI specialists can easily create a high definition map to boost onboarding capabilities or develop a navigating system for future purposes. 

While some may also admit to the fact stating they can live even without HD maps by simply fixing a camera on the car. Even despite having the best camera, you will still find a failed case. Despite having the best algorithms set in place, there is always a probability of missing something or the other. Thus, your sensors have broken the connection, all you have right now is the map. 

Therefore, if it important by having human labeling as the first process. Radar like detection system and street-view imagery that utilizes laser light to recognize 3D shapes in combination with lane marking information is the first code that is fed manually into the deep learning engine. This is further reiterated, improved, and retrained until it can automatically detect lane markings. 

It is also said that multiple vehicle manufacturers will likely be comfortable in sharing their sensor data but no personally identifiable information that can raise privacy concerns in an attempt to help autonomous vehicles learn from each other and vice versa gain the best of benefits from the updates. 

What is the significance of deep learning?

Deep learning is raging like a wildfire and is gaining supremacy in terms of accuracy. You will find the majority of tech companies are looking forward to investing in deep learning as machines are gaining more traction in becoming intelligent systems. 

Google’s AI program, AlphaGo is just the beginning of what deep learning can do. Most of Google’s functions such as Google search engine or voice recognition systems heavily rely on deep learning. 

For example, Google’s Smart Reply know what exactly needs to go in your short email responses, Facebook can identify your friend’s face via a digital photo, and Netflix just knows exactly what your next preferred movie might be. 

Deep learning might be powerful for the upcoming future, let us dive further and check out the companies that have been making rapid use of deep learning.

Prevention of frauds 

Fraud prevention such as money laundering can easily be detected by relying on a model having parameters built around the transaction trail. However, with the help of deep learning, parameters such as time, IP address, geographic location, and the type of retailer can be tracked minimizing the area from where the activity took place. 

Drug discovery 

With AI’s expertise in drug discovery, the market value has doubled. According to new researches, the global drug discovery informatics market rose to USD 800 million in 2018 and is anticipated to grow to an annual growth rate of 12 percent by 2025. 

Image recognition 

Deep learning plays a huge role in image or video processing. It is majorly used to identify a single image from a cluster of images with the help of resemblances along with object recognition.  A company called ViSENZE developed commercial applications that can easily detect images and tagging by simply using their deep learning networks. This facilitates customers to use pictures rather than using keywords to search for any product of the company.

Automatic text generation 

With the help of deep learning, the system can now build a relationship between the movement of the finger and the new text that is to be generated. This model is even capable of learning how to form sentences and even capture the style of writing. 

Xiang Ma, a machine learning expert, and veteran research manager at HERE Technologies says, “Deep learning is solving a problem and is a useful tool for us. And we know it’s working. But it could just be a stop-gap technology. We don’t know what’s next.”

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