Applications of Data Science, Deep Learning, and Artificial Intelligence
Data science, deep learning, and Artificial Intelligence (AI) have all been subject to much research and consideration in the last decade. The current use of these three forms and an expected increase in their future applicability means that they will correlate with each other to form the basis of a smart society.
To understand the differences between the three and how they correlate with each other, it’s imperative to comprehend what they are and how they work towards creating a more technologically advanced society.
The term “AI” is used so often nowadays that we have a basic understanding of what it means: a computer’s ability to perform tasks such as visual perception, speech recognition, decision-making, and language translation. AI has progressed rapidly over the last few years, but it is still nowhere near matching the vast dimensions of human intelligence. Humans make quick use of all the data around them and can use what they have stored in their minds to make decisions. However, AI does not yet boast such abilities, instead, it using huge chunks of data to clear its objectives. This ultimately means that AI might require huge chunks of data for doing something as simple as editing text.
Data science is much more than just simple machine learning. Data here may not have been obtained through a machine, and it may not even be for learning purposes. Simply put, data science tends to cover the whole spectrum of data processing as we know it. Data science is not just related to the statistical aspect of the process, but it feeds the process and derives benefits out of it through data engineering. Data engineers and data scientists have a huge role to play in propelling AI forward.
Deep learning is machine learning’s most powerful technique for making the future happen. Much like the neurons in our brains, deep learning is the connection or the powerhouse present between data science and AI. Both machine learning and its subtype, deep learning, incorporate the process of learning from the data over time. While it is not the only thing connecting the two, deep learning is a type of machine learning that works best to strengthen the process of AI and data science. Deep learning can simply be defined as a machine learning technique that endeavors to teach computer systems things that come naturally to humans. For example, we can naturally interpret what a stop sign on the road means, but for a machine to locate this sign and interpret it, it needs a lot of learning and implementation. This stage of learning is called deep learning. Once we understand the concepts behind deep learning, we'll understand that it is indeed deep learning that shapes the reality behind driverless cars and voice control that we have become accustomed to.
Broad Applications of Deep Learning, Data Science, and AI
The use of deep learning, data science, and AI in tandem has opened the door for myriad opportunities. AI has a major role to play in shaping the benefits that we may enjoy in the future.
Here are some of today’s technologies and services that use deep learning, data science, and AI.
- Expert Systems: Watson by IBM is a perfect example of how expert systems can benefit from the collaboration between deep learning, data science, and AI. The computer, which is powered by AI can collect, absorb, and process data much quicker than humans. Watson can not only display a solution really quickly, it can also diagnose cancer with an unbelievable accuracy of 90 percent due to its vast knowledge, whereas well-trained doctors know only around 20 percent of the updates present in the diagnosis.
- Speech Recognition: Thanks to the use of AI and numerous endeavors by smartphone manufacturers, you can ask speech recognition software to locate the nearest ice-cream shop or order pizza, without typing a word. It is the creation of artificial neural networks that enforces the understanding computers have of what you say. It takes exhaustive machine learning to do this through AI.
- Google: Google is pleased to have made use of enhanced deep learning and data science algorithms that make sure to provide users with content deemed relevant for them. The search engine uses machine learning algorithms to find out a plethora of data regarding what people are searching and combs through more than a billion pages to rank the ones that are best for you first. All of this is done within the matter of microseconds. Amazing, right?
- Robotics: A lettuce production company by the name of Spread has revealed their plans to equip robots for handling affairs within the farms. By harvesting 30,000 lettuce heads every day, robots will drastically increase efficiency. The processors within these robots have been fed a high plethora of data regarding the process it takes to harvest lettuce. Not only will this AI revolution increase efficiency, it will also open doors to new possibilities.
You can learn more about the implementation of AI, deep learning, and data science here.
Find our Deep Learning with TensorFlow Online Classroom training classes in top cities:
|Deep Learning with TensorFlow||23 Feb -24 Mar 2019, Weekend batch||Your City||View Details|
|Deep Learning with TensorFlow||23 Mar -21 Apr 2019, Weekend batch||New York City||View Details|
Recommended articles for you
AI and Deep Learning Put Big Data on SteroidsArticle
Webinar Wrap-up: How to Build a Career in AI and Machine Lea...Article
Top AI and Machine Learning Trends for 2019Article