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Deep Learning AI & Real-World Cases

| AI + T |

The field of artificial intelligence is fundamentally when machines can do tasks that typically require human intelligence. It incorporates machine learning, where machines can learn by experience and acquire skills without human contribution. Deep learning is a subset of machine learning where artificial neural networks, algorithms inspired by the human brain, learn from large amounts of data. Equally to how we learn from experience, the deep learning algorithm would make a task frequently, each time tuning it a little to improve the outcome. We refer to ‘deep learning’ because the neural networks have various deep layers which allow learning. Just about any problem that requires “thought” to figure out is a problem deep learning can learn to solve.

The amount of data we generate every day is astounding, it is currently estimated at 2.7 quintillion bytes; and it’s the resource that makes deep learning conceivable. Since deep-learning algorithms require a load of data to learn from, this increase in data creation is one reason that deep learning capabilities have grown in recent years. In addition to more data creation, deep learning algorithms benefit from the stronger computing power that’s available today as well as the proliferation of Artificial Intelligence (AI) as a Service. AI as a Service has given smaller organizations access to artificial intelligence technology and specifically the AI algorithms required for deep learning without a large initial investment.

Deep learning allows machines to solve complex problems even when using a data set that is very diverse, unstructured and inter-connected. The deeper learning algorithms learn, the better they perform.


As we are aware, we are in a time where machines can learn to solve complex problems without human intervention. A question worth asking is what exactly are the problems they are tackling? Below are a few of the tasks deep learning presently supports. The list will only continue to grow as the algorithms continue to learn via the infusion of data.

1. Virtual Assistants

Whether it’s Alexa or Cortana or Siri, the virtual assistants of online service providers use deep learning to help understand your speech and the language humans use when they interact with them.

2. Language Translations

In a similar way, deep learning algorithms can automatically translate between languages. This can be influential for travelers, business people and those in government.

3. Vision For Drones & Autonomous Vehicles

The way an autonomous vehicle understands the realities of the road and how to respond to them whether it’s a stop sign, a ball in the street or another vehicle is through deep learning algorithms. The more data the algorithms receive, the better they can act human-like in their information processing. For example, knowing a stop sign covered with snow is still a stop sign.

4. Chat-bots & Service Bots

Chat-bots and service bots that provide customer service for a lot of companies can respond in an intelligent and helpful way to an increasing amount of auditory and text questions thanks to deep learning.

5. Image Colorization

Transforming black-and-white images into color was formerly a task done meticulously by human hand. Currently, deep learning algorithms can use the context and objects in the images to color them to recreate the black-and-white image in color. The results are impressive and accurate.

6. Facial Recognition

Deep learning is being used for facial recognition not only for security purposes but for tagging people on social posts like Facebook and we might be able to pay for items in a store just by using our faces very soon. The challenges for deep-learning algorithms for facial recognition is knowing it’s the same person even when they have changed hairstyles, grown or shaved off a beard or if the image taken is poor due to bad lighting or an obstruction.

7. Medicine & Pharmaceuticals

From disease and tumor diagnoses to personalized medicines created specifically for an individual’s genome, deep learning in the medical field has the attention of many of the largest pharmaceutical and medical companies.

8. Personalized Shopping & Entertainment

Ever mused how Netflix provides suggestions for what one should watch next? Or when Amazon provides ideas for what one should buy next and those suggestions are exactly what one needs but didn't knew it before? That is deep-learning algorithms at its core.

The more experience deep-learning algorithms become, the more efficient they become. It will be an exciting few years as technology continues to evolve and develop.

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