Computers have come a long way from being a calculator to bearing artificial intelligence capacity, giving incredible results. Sometimes, those results are better than the average human outcomes, in highly specialized situations.
Recent developments and technological breakthroughs have allowed computers to develop highly optimized analytical frameworks and predict situations with high degrees of accuracy.
Thus, Artificial Intelligence and technologies like Deep Learning have extensive applications in nearly all sectors. In many scenarios, these technologies are making work possible without any human intervention.
What is AI?
Artificial Intelligence is possessed by machines or computers that try to mimic human intelligence or human cognitive functions like learning and problem-solving. Humans artificially type of intelligence is artificially created for machines by humans.
Also, AI is something that hasn’t yet been achieved in its true sense, which is also referred to as Artificial General Intelligence (AGI) sometimes. What people get fascinated by, are Machine Learning and Deep Learning technologies.
Innovations in AI include technologies like speech recognition - as seen in the case of Alexa/Siri devices, structured prediction, anomaly detection, and Artificial Neural Network (ANN), relevant recommendations in Amazon & Netflix, and yeah, let’s not forget Self Driving Cars.
But why create something with an intelligence that could replace us, humans?
A straight answer to this question is, to automate.
We either cannot perform some tasks or eventually get bored of doing the same task every day. But this isn’t applicable in the case of machines.
They can work more efficiently without getting bored. With recent technological developments, we just need to feed some data and use relevant computer algorithms, and the computers will automatically learn how to do some specific work for us!
For fields that demand high task execution, higher accuracy, or involve monotonous tasks, AI is the solution.
AI decides by analyzing previous events and establishing connections between them for predicting the future, so it’s more accurate.
Applications of AI
There are countless applications of AI in almost every industry that has some monotonous tasks or deal with data and security. Let’s discuss some of the applications of AI:
1. Video Analytics for Surveillance
The most prominent application of AI lies in the field of Surveillance.
Video Analytics refers to using real-time videos to detect or track objects, particularly vehicles or humans. It is being widely used for developing Self Driving Cars, License Plate Recognition [LCR] and Smart Parking technologies.
With AI in Video Analytics, we can innovate traditional security and surveillance systems to meet modern security needs.
Now, we don’t need a group of security personnel to supervise a network of cameras. We just need one operator to manage alerts being sent by video analytics software using AI to detect anomalies based on pre-fed data.
2. Machine Learning and Data Analytics
In machine learning, machines learn from pre-existing data under supervised training by humans and apply that knowledge to make accurate decisions.
This technology is used to target advertisements to people who are most likely going to click on them.
It uses personalized data of individuals from their digital footprints and predicts or generalizes the behavior of customers to target them with personalized promotions.
What is Deep Learning?
Deep Learning is a technology used by Computers to gain knowledge as we humans do.
It’s a specialized form of machine learning wherein we provide a dataset based upon which a decision is made.
But the important aspect of deep learning lies in the capability it provides the machines, i.e., learn from their own mistakes, hence the term “Deep learning”. It is perceived as a way to automate predictive analysis, thus making the job of data scientists of interpreting and analyze data faster and easier.
How does Deep Learning Work?
Machine Learning is a type of Artificial Intelligence that uses historical data to predict decisions, but its learning is human-supervised.
The accuracy of a Machine Learning algorithm to detect a dog depends on how well the programmers declared the feature set.
Telling the machine how a dog would look and explaining its features is a tedious and laborious job. That’s why computer scientists developed Deep Learning.
Unlike Machine Learning, Deep Learning does not require human supervision to learn.
The programmer does not have to define a feature set. The Deep Learning program builds the feature set itself and learns from its mistakes.
This gives it an edge as unsupervised learning is faster and usually more accurate.
Applications of Deep Learning
Deep Learning, being a subset of Machine Learning, trains computers to learn by examples or historical data to produce a final output using abilities similar to some cognitive aspects of the human brain.
It has applications in all sectors concerned with data analytics. Let’s get to know some of its applications:
1. Computer Vision
One of the fastest-growing subdomains of deep learning, Computer Vision focuses on empowering computers to gain a high-level understanding of digital images or videos.
Computer Vision is applied in a wide range of use cases from self-driving cars to video surveillance and security. It is used by autonomous cars to identify and evaluate their environment for safe driving and heighten their perception and control.
With computer vision, self-driving cars make use of image segmentation technology wherein a digital image is broken into pieces to recognize objects and avoid crashing in real-time.
2. Natural Language Processing (NLP)
One of the most popular segments of deep learning, NLP or Natural Language Processing merges Artificial Intelligence with human language.
Since language has its nuances and intricacies, NLP is the most complex deep learning algorithm to create.
Like, for instance, one single word can have different meanings in different languages, and thus, NLP is designed to recognize the correct meaning of a word associated with the context by reading and understanding previous sentences.
3. Audio Signal Processing (ASP)
The next big thing in the digital world is voice search.
Deep Learning’s rapidly growing field of Audio Signal Processing or ASP is used to create voice-activated programs in association with NLP. The futuristic technologies that convert voice messages into a transcribed script or text use the audio recognition aspect of ASP.
Businesses are also investing in AI and Deep Learning as they make processing and analyzing data faster.
As data is gold for companies, they need an algorithm to manage and evaluate it to predict situations.
Many companies like Tooliqa and Orbital Insight are developing futuristic technologies involving Computer Vision, Deep Learning to innovate security systems and develop self-driving cars.
Tooliqa is working towards merging 3D and Deep Learning technologies to revolutionize the interior design industry where algorithms are being developed to predict design choices
The applications of AI and Deep Learning are increasingly being expanded with new use cases being discovered very frequently.
We, humans, prefer simplicity in our lives. Thus, we are developing ways to make Computers faster and better every day. These developments should help us progress towards a promising future.
Read more: The Next Future Powered By Deep Learning | Insights - Tooliqa
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