Data is becoming an increasingly important commodity in today’s world as nations and large corporations are all after a piece of big data to drive profit and progress. Data scientists are increasingly needed to make sense of big data, and one of the tools they have at their disposal is an artificial intelligence technology known as deep learning. Is deep learning important, and do data scientists need to know about it?
Data scientists do need to know about deep learning as it is the most cutting edge technology used in data analysis today. Deep learning is deployed in a whole host of different disciplines and represents the starting point of the future of artificial intelligence.
To understand why deep learning as an artificial intelligence technology is important to data scientists, we first need to define what artificial intelligence is and look at how it is used in machine learning. We can then proceed to look at deep learning, which is a subset of machine learning.
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What Is Artificial Intelligence?
Artificial intelligence has long been a buzz phrase among science fiction enthusiasts and doomsday soothsayers who predict the takeover of artificial intelligence and the end of human civilization. It is indeed a field of technology that inspires much curiosity and speculation.
Artificial intelligence in simple terms can be defined as the intelligence programmed into machines and robots to enable them to mimic human learning. Intelligence speaks of the mimicking of human processes of learning to acquire information and make meaningful decisions, while artificial refers to that it is man-made and not naturally occurring.
A great everyday example of artificial intelligence is Apple’s virtual assistant ‘Siri’, Amazon’s home virtual assistant ‘Alexa’, Google’s virtual assistant ‘Google Assistant’, and many more. Artificial intelligence technology allows these virtual assistants to receive information, process the information, and subsequently make smart decisions based on that information.
Many of us would have asked Siri to tell us about the current date or time, or even the weather forecast. These are simple tasks. However, Siri can also be asked to complete remarkably human-like tasks, such as telling a joke or engaging in casual conversation.
How does an impersonal non-human software learn how to do these things? How does it learn to interact with humans in a human-like way without an actual person behind the screen?
The answer is artificial intelligence. With artificial intelligence, machines and software can learn, store, process, and make better decisions from new information.
Artificial intelligence is used in many other sectors. Some might even come as a surprise to us. Artificial intelligence is increasingly embedded in our digital lives and is set to play a larger role in the coming decades.
What Is Machine Learning?
Machine learning is a subset in the world of artificial intelligence. It is a system that provides machines with a way of learning that results in its ability to make specific predictions or decisions independently. Let’s take a closer look at how this works.
A machine is fed data. The machine then processes this data and produces its own data modeling or algorithm based on the data available. This allows it to make educated guesses or decisions based on new data that is fed into it. Let’s look at a few real-world examples of the application of machine learning to appreciate better how it works.
Suppose we are designing a machine that can predict the weather on a given day, we provide the machine with as much data about weather trends over different seasons and years. With machine learning, this will process all the data faster than it would take an average person to do, and make predictions about the likely weather for, say, next Tuesday.
Another example of using machine learning is the detection of fraudulent online transactions by a bank. The artificial intelligence software is fed huge amounts of data about previously discovered fraudulent online activities, likely including the amount of money transacted, the time or place of the transaction, and any relevant data.
The software then processes this data, and a system of algorithms is developed. This software can now predict when an online transaction appears fraudulent, and flag it up for closer human inspection.
Machines are not perfect, and they can get things wrong. We want machines to make predictions that are as close to reality as possible. And there are ways to improve its learning process by supervising or reinforcing their learning.
Unsupervised machine learning refers to feeding a machine raw data and allowing its artificial intelligence process to take over to create its data modeling and make its predictions.
Supervised machine learning refers to the same process, except it receives additional input regarding the desired outcome based on the data given to it. This means that humans are involved in creating and fine-tuning the machine’s data modeling program to create better outcomes desired.
Reinforced machine learning allows machines to develop their data modeling and algorithms. Human feedback is given time to time so that the outcome generated is closer to what is desired. This human feedback provides a sort of positive reward system that drives the machine to refine its algorithm to produce better outcomes.
What Is Deep Learning?
Deep learning is a subset and a more advanced form of machine learning. Deep learning takes a step further in mimicking how the human brain processes information. To understand how this works, we first need to look at how the human brain works.
At any given time, the human brain is inundated with different stimuli, triggers, and experiences. It is processing all these different pieces of information with the help of different neural circuits.
In other words, the human brain does not process just one information at a time to produce a single decision. For example, you’re hungry and decide to eat a banana. You’re not just thinking about whether the banana is food. At a more complex level, you’re also thinking about your dietary preferences, what you last ate, whether the food is fresh, and so on.
The human brain can process inter-connected information with many layers all at once, just like a spider web of data. When it encounters new information, the web grows bigger and more complex. Data is constantly being analyzed at multiple levels.
Deep learning seeks to mimic this layered thinking process. It does so by creating deep neural layers, which essentially means that new and old data are analyzed, linked, and compared to each other like a web, instead of just a simple decision tree, like how it typically is for less-advanced machine learning.
This means that machines equipped with deep learning can process data in a way that is even more human-like. However, because of the incredible speed at which it can do so, it is also in this sense more advanced than human thinking and data processing.
An example of deep learning in action is its usage in Twitter sentiment analysis. Twitter sentiment analysis is a software that can analyze the tone of speech made through tweets and can make predictions on whether a tweet can be construed as hate or racist speech.
How does a non-human software analyze the highly subjective tone of human speech and decide on whether it constitutes hate or racist speech? After all, speech is constantly changing, with new slangs and new shades of meaning added to existing words happening all the time.
A machine equipped with deep learning software is given a huge amount of data about speech that can be labeled as positive or neutral speech, as well as speech that can be labeled as hate or racist speech. The deep learning machine then processes and labels the data and absorbs new data as they become available. The new data is linked with existing data at multiple points, and a web begins to form.
With these multiple layers of data, also known as deep neural networks, this software can make decisions regarding whether a new tweet likely contains hate or racist speech.
To improve their outcome, humans can supervise their learning and correct wrong decisions. The deep learning software then adds that input into future decision-making. Thus, deep learning software can learn and mimic human brain processes in doing so.
What Is a Data Scientist?
Data scientists are people who mine data, analyze data, and interpret data. They collect data through various means and create a model in which that data can be analyzed and interpreted to be useful for human progress.
Today, data is available in a way that was unthinkable in the past. Let’s look at social media, taking Facebook as an example. Every comment, like, and post on your Facebook profile becomes data. Every information you volunteer about yourself, like your birthday, political preferences, socioeconomic status, becomes a rich source of data that companies would look to tap into.
This is why you sometimes get the eerie feeling that an advertisement that pops up on Facebook matches too well what you’ve just been searching for. Data scientists process all the data on Facebook and create algorithms to decide what a client might be interested in buying. This is big data at work, and it is big business – Facebook is currently worth over $700 billion.
However, data scientists are also crucial in other sectors, such as in geology and meteorology. They are used to predict where the next earthquake might strike, when a volcano might erupt, and where the next superstorm might occur. Data is everywhere and can tell us very important information about ourselves if we know how to harness it well.
As data collection becomes more sophisticated, data scientists do an important job of translating that data into actionable policies. However, it simply does not work in today’s world to sit with a stack of papers and sort through them manually. Data processing requires far greater sophistry, which involves creating artificial intelligence software that can do the job well.
This is why it is so important for data scientists to know deep learning. Deep learning is the most advanced mechanism of machine learning available today. It is also the mechanism advanced enough to process huge amounts of data and to do it with nuance.
Without deep learning technology, the work of data scientists would be greatly hindered. Deep learning technology is so important that every effort should be taken to advance it to the next level.
Other Applications of Deep Learning
In this article, we have seen a few examples of the use of deep learning software in real life. Let’s look at a few more.
Data has been a huge feature of modern political life, and data scientists are extensively involved in helping political parties and their candidates make campaigning decisions.
Deep learning allows for an added depth of political data analysis by analyzing voting behavior and policy making. Deep learning allows for the analysis of which political message makes the biggest impact on a certain demographic group, allowing for increasingly targeted political advertising.
Data has driven cancer research for years. Much of cancer research is about generating data – data on the efficacy of certain medications and the response of cancer cells to various treatments. The data gathered paints a picture – a mosaic that allows researchers to see the bigger picture and achieve the goal of finding a cancer cure.
A team at UCLA has developed an advanced microscope that can teach deep learning software to identify cancer cells accurately. By being able to identify cancer cells quickly and accurately, doctors can take faster action.
Self-driving cars have started to become available in the market and are already commonly used in certain countries, such as the UAE. Self-driving cars can bring someone from one place to another while only requiring minimum input from the driver.
How is this achieved? As any driver would be familiar, there are multiple things to watch out for when driving: a stop sign, a traffic light, or a person crossing the road.
Self-driving cars can achieve their independence largely from deep learning artificial intelligence technology. Deep learning technology allows self-driving cars to notice all of the potential dangers while on the road and teaches the self-driving cars to take quick action when necessary, such as stopping when a pedestrian suddenly crosses the road. Thus, deep learning increases human comfort, speed, and safety.
Customer service is an industry that is focused on customer ratings and satisfaction, and deep learning is playing a bigger and bigger role in helping companies achieve just that. Deep learning allows for algorithms to be developed to help companies understand a client’s wants and preferences and make decisions that will improve the customer experience.
Deep learning artificial technology is also changing how companies communicate with their customers. Chatbots, or chat robots, are artificial intelligence software that enables a company to communicate with its customers in a very human-like manner. Chatbots are sometimes even given human names so that the customer experience in interacting with them is as lifelike as possible.
One example of chatbots being used is in online stores. When a client clicks onto the page of a digital store, the client might want to know details such as shipping times and prices and the range of products on offer. Instead of having the hassle of needing to search around for information, the client can choose to communicate with a chatbot, if one is in use.
The chatbot would greet the client in a friendly way and guide the client to the answers that one needs. With deep learning, the chatbot would also be able to learn from any interaction it has with a client, thus improving its services as time goes on.
This kind of artificial intelligence software helps companies put on a friendly front to new clients, and reduces cost, especially if companies receive a lot of client questions and requests. Clients who can get fast answers from a friendly voice, even if it is just artificial intelligence, are more likely to rate the company more highly and use its services, creating a win-win scenario.
Author’s Recommendations: Top Data Science Resources To Consider
Before concluding this article, I wanted to share few top data science resources that I have personally vetted for you. I am confident that you can greatly benefit in your data science journey by considering one or more of these resources.
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- Roadmap To Becoming a Data Scientist: If you have decided to become a data science professional but not fully sure how to get started: read my article – 6 Proven Ways To Becoming a Data Scientist. In this article, I share my findings from interviewing 100+ data science professionals at top companies (including – Google, Meta, Amazon, etc.) and give you a full roadmap to becoming a data scientist.
The uses of deep learning in artificial technology are wide and varied. From our smartphones to large corporations to governmental bodies, the role of deep learning is increasingly indispensable.
Data scientists need to know about deep learning because it represents the most advanced form of artificial technology today. Advancements in it will be the foundation for a future in which artificial technology truly drives human progress.
All efforts must go toward adopting deep learning in more areas of life and making artificial intelligence a part of our bright future.
Here’s a quick recap of the post:
- Deep learning is an integral part of data science, making it an invaluable asset.
- Data scientists need to constantly adapt to new programs and ways of working.
- Deep learning allows data scientists to improve customer relations for optimal data analysis.
- Machine learning through deep thinking makes data science much easier these days.
BEFORE YOU GO: Don’t forget to check out my latest article – 6 Proven Steps To Becoming a Data Scientist [Complete Guide]. We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and created this comprehensive guide to help you land that perfect data science job.
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