We may not even notice, but AI presents in our everyday life. We are facing this intelligence from the first minutes after we wake up. From what we see in the newsfeed when checking socials. From the music that appears in our doing-breakfast playlist. Or when you ask your voice assistant what’s the weather today and what’s the quickest route we can take to get to work. The luckiest of us wake up in smart houses, in which AI, knowing our habits, can adjust the most comfortable lighting in the bedroom, set up the temperature and even turn on the coffee machine to make our mornings really great.
Thus, AI, being an indivisible part of our life, has been in the public eye for quite a long time. Lots of different buzzwords get thrown around in conversation here and there. But do we really know it? For instance, when it comes to deep learning vs. machine learning, can we see the difference? However, these two subsets have much more application in the modern world. Many people struggle to differentiate between them, so let’s try to figure it out.
Many organizations claim to incorporate Artificial Intelligence (AI) in their products or services. However, it is important to understand what they mean by that.
While there is some overlap between AI, Machine Learning, and Deep Learning, the words themselves are not interchangeable.
Many industry veterans and business tycoons have varying views of Artificial Intelligence and its step-children, Machine Learning and Deep Learning.
Some quotes point to its future impact and clearly indicate the benefits of gaining knowledge in the discipline:
Undoubtedly, Artificial Intelligence, Machine Learning, and Deep Learning will have an impact on society and automation. Jobs around the world are bound to be affected in the years ahead, but this does not have to be a bad thing. With the right controls in place, it is more likely that the technology will help us grow and advance to new levels.
Artificial Intelligence (AI) was launched in the early 1960s as a means of empowering computers to think and act more like humans. AI is generally divided into 3 different categories based on its capabilities.
Weak AI is when a computer is good only at a particular task. And this task computer performs better than humans. Some examples of weak AI include image and voice recognition algorithms, and chess programs. So while Deep Blue’s accomplishments in 1997 of being the first program to beat a grandmaster in chess are impressive, no one would ever confuse Deep Blue for a human.
Weak AI is used extensively in healthcare. Weak AI can help diagnose patients with greater accuracy, improving overall patient health. For example, Enlitic company uses deep learning to automatically label digital medical images and takes medical diagnostics to a completely another level. It helps doctors to prescribe treatment and predict patient outcomes. In detecting rare or hardly distinguishing signs of disease computers show better results than most trained doctors.
Even complex programs like voice assistants or self-driving cars, recently gaining popularity, are still examples of weak AI.
Strong AI, also sometimes called General AI, has the ability to mimic human behavior more precisely. It is self-aware and it can perform not just one precise task, but all human tasks. It can find logical reasoning, draw conclusions and create associations. Unlike Weak AI, Strong AI adjusts to a constantly changing environment and teaches itself to solve any emerging problem.
There are no current examples of Strong AI in the real world, although the work being conducted in Machine Learning and Deep Learning is a start.
If this type of AI is ever developed, it would be significantly more capable than humans. It could help propel us up to the next stage of evolution or could decide that we’re simply not worth the effort.
It is a subset of AI that aims to teach machines. Machine Learning is supposed to mark out data, learn from it, and apply the received experience to make further predictions. In fact, over time, receiving more data, machine learning algorithms are supposed to become more accurate.
Ability to learn like human vs. ability to replicate human behavior correlate as Machine Learning vs AI concepts.
Supervised Machine Learning uses previously labeled human data to quickly identify one feature from another and perform the recognition on a huge amount of data, off-limits for human recognition.
Among the most comprehensible examples of this type of ML is the spam filter. By pointing which letter is spam and which is not you teach the algorithm to move relevant content letters to the spam folder automatically.
Unsupervised machine learning is a little less intuitive. It is useful in situations where the final, expected outcome is unknown. With unsupervised ML, the data is not labeled. This data is fed into the algorithm and the algorithm sorts the data into clusters based on different criteria.
For example, such an algorithm is used in Shazam, first, it helps to detect desirable song sounds from other noises in the user’s environment. Then it creates a song fingerprint and compares it to another fingerprint in its base.
Reinforcement learning is perhaps the closest parallel to the real world. Here, the algorithm learns through trial-and-error. Based on the outcome, they are either rewarded when they make the correct choice or punished for an incorrect decision.
Such learning is frequently used in the game industry. Game designers traditionally perform numerous gameplay-testing sessions with test users in order to improve the gamer experience. It takes a lot of time and effort to find all the game bugs, in complex games, it becomes almost impossible for human resources. That’s where reinforcement learning comes in handy. Avoid complicated test systems produced by a team of engineering experts that use decision-tree algorithms. Reinforcement learning can check an infinite number of variants and all you need is just to set up the goal of the game.
The capability of modern computers has grown significantly during recent years, and the number of the generated data by all the planet users grows day by day. Those factors made it possible to train large neural networks. Deep Learning uses neural networks, making it the closest analog to the human brain in how it learns and handles data.
When comparing Deep Learning vs. Machine Learning, the former can extract features automatically from raw data, without any human help. Deep Learning establishes feature hierarchies. It starts from the highest level of simplest features and goes deeper layer by layer of artificial neuron network determining the most complicated ones. That’s why this concept is called Deep Learning.
Just like Machine Learning is a subset of AI, Deep Learning is a subset of Machine Learning. Think about it as like a set of nested Russian dolls with deep learning being the centermost doll.
Deep Learning uses neural networks, making it the closest analog to humans in how it learns and handles data. These neural networks let deep learning networks work with massive amounts of data. When comparing deep learning vs. machine learning, the former not only become better in analyzing data but can correct itself when high-precision of prediction is at stake.
The Artificial Neural Network (ANN) is modeled on a human brain, with each neuron connected to all the others that are downstream. A Deep Learning neural network has an input layer and an output layer. In between these two are multiple “hidden layers” where the calculations are performed. The more hidden layers there are, the “deeper” and more capable the network is.
NIX shares its experiment in converting text to naturally sounding speech, which required implementing Deep Learning in its architecture.
Many ask Neural Network vs Deep Learning; is it the same or have any difference? Nural network is a core element of Deep Learning architecture. There are several types of deep neural networks:
Convolutional Neural Network (CNN)
This type of neural network is best suited for image analysis.
Recurrent Neural Network (RNN)
Recurrent neural networks are used to build models with sequential data.
Generative Adversarial Network (GAN)
A GAN uses two different neural networks to create a new one. An example of a GAN in practice is when new photographs are generated from data that look real to the human eye.
Deep Belief Network (DBN)
DBNs are not as popular as the other types of neural networks. They were invented as an alternative solution to help train neural networks that were becoming stuck.
While deep learning is much more powerful, it has only recently become popular as an alternative solution for researchers due to the need for massive data sets and computing power. With access today to copious amounts of data, which was previously not so available, and cloud computing services, deep learning is going to continue to grow in popularity.
As we’ve already discussed, deep learning is just a facet of machine learning. So, a better question to ask is what makes deep learning special in comparison to the rest of Machine Learning and AI as a whole. The simplest answer to that question is the neural network that Deep Learning uses.
This neural network helps reduce the need for human intervention, but it increases the demand for data exponentially in comparison. In addition to the volume of data, deep learning also requires more powerful computers capable of handling much more complex data.
There are many different areas where ML algorithms can be used and they are particularly popular in the financial sector. With a narrow focus, ML models can look for patterns in data and flag inconsistencies for a human to intervene and fix them.
Machine Learning vs Neural Network suits business cases that can gather thousands of data points for the training datasets, while Deep Learning artificial neural networks can learn only after operating millions of data points.
Nevertheless, maintenance of Machine Learning requires a team of experts that can manually choose features, classify them and adjust algorithms in case they deviate. Deep Learning requires much less human intervention, it can correct itself when high-precision of prediction is at stake.
So the business that needs Deep Learning has an unstructured and colossal range of data. In other cases, you can save time by implementing Machine Learning algorithms.
Thanks to Deep Learning, we have chatbots and personal assistants as well as self-driving cars. Deep Learning even impacts entertainment, helping services like Netflix personalize and curate a shortlist of selections based on our likes and dislikes.
Here are some examples of the real-life application of artificial intelligence. Though the use of it is not limited to those provided, and also includes game development, space exploration, automatic cars, and more.
Machine Learning can help even the most experienced doctors to diagnose medical problems, before and after complex surgeries and while prescribing more effective ways of treatment. It can predict and diagnose illnesses several times faster than medical experts and has already saved thousands of lives worldwide.
The world population is constantly increasing, which impacts world hunger. That’s why improving food production has fundamental importance. Machine Learning can help to receive necessary insights by monitoring and analyzing data on weather, adequate sunshine and water, animals, birds, and insects migrations, and how fertilization impacts yield. Machine Learning uses data from real-team surveillance cameras, drones, satellites, and in-ground sensors to detect crop field intruders pick the best routes to get the crop to the market as fresh as possible, and optimizes pesticide formulas.
One of the most common uses of AI science is fraud prediction. It can analyze the behavior of transactions, detect anomalies and signal credit card fraud in such a way saving billions of dollars annually. It is also used for providing customer support, as millions of bank clients run numerous queries to bank operators, which humans can’t process as quickly as artificial intelligence does. Such chatbots not only generate quick automated responses but also can find the necessary answer in bank policies and answer the client adopting it to natural language.
By analyzing customer input data such as location, demographic, and use of devices and combining it with customer behavior on the site, any brand can enhance customer experience by offering the most suitable content. Gathering historical data about the customer can help to create a personalized list of suggestions. Netflix recommends to the customer which movie he or she may like, based on the patterns of previous choices of film and reaction to the film of customers with similar data points. AI is frequently used for content creation, optimizing PPC campaigns, and image recognition.
Depending on the needs of your business, there could be a place for AI, Machine Learning (ML), Deep Learning (DL), or all three. Each of these models has a part to play within business but it can be difficult to know which is best in any given situation. This is where an expert like NIX comes into the equation.
Understanding and using AI within your business can help dramatically improve the overall customer experience and simultaneously help to make companies more efficient and profitable. Knowing the right strategy is essential in ensuring your implementation is successful.
For companies looking to work with large volumes of unstructured data, a deep learning strategy might be the best solution. The NIX team understands how neural networks and deep learning work and can help you implement a solution based on your needs and requirements.
Alternatively, if your needs are narrower, a machine learning strategy might be most suitable to help you realize quick progress. Reach out to the NIX team for help to understand what solution is best for your business.
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