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Understanding the Major Differences between Deep Learning and Machine Learning

By gswardman September 14, 2020

In the tech world and data science, the current buzzwords, which often seem interchangeable, are deep learning and machine learning. In computer science, there are, however, subtle differences between the two.

The easiest way to understand the difference between deep learning and machine learning is to understand that deep learning is machine learning. Deep learning is simply evolved machine learning. It utilizes a programmable neural network that allows a machine to make decisions accurately without human intervention.

What is Deep Learning?

Deep learning is an AI (Artificial Intelligence) function that apes the human brain in speech recognition, natural language processing, object detection, data processing, and pattern creation, which is used in making decisions and mimic how the human brain works.

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Deep learning is a subset of Machine Learning in AI that contains networks that can learn through unlabeled or unstructured data points without human supervision. Deep learning is also known as Artificial Neural Network (ANN) or deep neural learning. It simulates the human brain in the output layer. Deep learning can be used in the detection of money laundering and other functions.

How Deep Learning in Artificial Intelligence Works

The evolution of the digital era, which has led to all sorts of data explosions worldwide, has led to the development of deep learning. This data, or big data, is obtained from different sources like internet search engines, social media, online cinemas, and e-commerce platforms. This significant amount of data is sharable via fintech apps such as cloud computing and is easily accessible.

This big data is typically unstructured, and its vast nature means it could take years to understand it, or even extract the information they need. Most companies recognize the data’s potential, and they are turning to AI systems for automation.

Machine Learning

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The definition of machine learning is the ability of a computer program that learns and adapts to new data without human intervention. Machine learning is a subset of Artificial Intelligence that keeps your computer’s in-built algorithms current, whether or not the world’s economy changes.

Complex source code or algorithm is built into a computer, allowing it to identify data and make predictions around the said data. Machine learning parses the colossal amount of consistent and readily accessible data, which helps the machine, make decisions. Machine learning helps us in many ways, such as lending, news organizing, fraud detection, advertising, and investing.

How Machine Learning Works

Different sectors deal with big data in varying formats and from various sources. This data, just like in deep learning, is readily accessible due to using technology. Most governments and companies recognize the data potential of tapping into the data but run short of time and resources needed to go through the data and pick what is useful. AI measures are used by various sectors to gather process and share relevant data from the data sets. Machine learning is one AI method that is used to process big data.

Machine learning’s different data applications are created via complex source code or algorithm built into the computer. The code builds a model that identifies new layers of data and makes predictions around it. This model makes use of parameters built into the code to create patterns that form its decision-making process.

When input data is made available, the ML algorithm automatically adjusts the parameters, checking if there is a pattern change, but the model stays the same.

Types of Machine Learning

The four types of machine learning are:

· Supervised machine learning

This is a scenario or learning process where you provide the computer with labeled data to differentiate pictures using algorithms for image sorting. If the assigned task were to separate cats from dogs, each would have a label. This is the training data set and remains intact until the program can sort out images.

· Semi-supervised machine learning

In this scenario, only some images have labels. The program uses algorithms and computer vision to guess the unlabeled pictures. The data is entered into the program as its training data. New images are provided, with a few labels, a repetitive process that goes on until the program gains image recognition and can differentiate between cats and dogs at a reasonably good rate.

· Unsupervised machine learning

Unsupervised learning involves no labels, and the program is blindly tasked with image classification of the cats and dogs' into two batches using two algorithms. One is clustering, which groups the same objects depending on color, size, etc. The other is called association. The program uses a decision tree or creates rules if-when, depending on any similarities, or a common pattern in the image, grouping them appropriately.

· Reinforcement machine learning

Chess is an excellent example of this algorithm, or AlphaGo, which we shall discuss in a while. The program learns the rules of chess, how to play, going through each step to complete around. The only data fed to the program is if it won or lost the previous match. It keeps playing and keeps track of its moves until it wins a game.

Deep Learning vs. Machine Learning

1. Human Intervention

With ML systems, a human has to identify and code by hand the applied features, based on the type of data, such as shape, orientation, pixel value, etc.) A deep learning system, on the other hand, attempts to learn the features without the need for human intervention.

The program learns to detect and acknowledge lines and edges of facial structures, then other parts of the face, and the full facial representation. This involves a vast amount of data, and as time elapses, the program self-trains and the probability of it making accurate answers such as accurate facial recognition increases. This training uses neural networks, which mimics the human brain, without needing a human to recalibrate the program’s code.

2. Hardware

The amount of processed data and the complex mathematical calculations used in the algorithms are vast, making the deep learning system need compelling hardware compared to machine learning systems. Graphical Processing Units (GPUS) are a type of hardware used in deep learning techniques. Lower-end machines with little computing power can run machine-learning programs.

3. Time

A deep learning system requires substantial data sets, complex mathematical formulae, and various parameters, which mean a deep learning system, can take a long period to self-train. Deep learning can be several hours or weeks, while a machine-learning model takes less time, from several seconds or several hours!

4. Approach

The source code used in machine learning parses data in bits, and the bits merged to make up a solution. Deep learning systems view scenarios in one instance. For example, if you need a program that can identify objects in an image, such as different animals, you have to use two machine-learning steps. The first is object detection and then recognition.

With deep learning programs, you would have to input the image first. With training, the deep learning program returns both the objects and where they are located in the picture in a single result.

5. Application

Machine learning and deep learning systems are used for varying applications. Basic ML applications integrate predictive programs like the forecast of stock market prices, or weather patterns or identifiers for email scams. Amazon uses deep learning to aggregate and analyzes purchasing data, accurately predict demand, analyze purchase patterns, and identify fraudulent purchases.

Deep learning applications are associated with future strong AI, although in theory, while machine-learning applications are related to narrow AI. Strong Artificial Intelligence and computer systems are equal to our human Intelligence.

Other than facial recognition, another application for deep learning is autonomous or self-driving cars. The programs utilize a number of layers of neural networks to perform specific tasks like determining objects to stay clear of, how to read traffic lights when to slow or speed up.

Strong AI vs. Narrow AI

Strong AI's primary features include the ability to solve puzzles, make judgments, reason, communicate, learn, and plan. It should have objective thoughts, be conscious and self-aware, perceptive, and sentient.

Strong AI is currently theoretical and is predicted to develop by 2030 or 2045, while others expect it might happen in the next century, or never. However, AlphaGo and its successors almost defy this theory. AlphaGo is the first deep learning computer program to beat a professional human Go, player, a feat well beyond its time. DeepMind Technologies was the first to develop AlphaGo, and later, Google acquired it.

AlphaGo used reinforcement learning to teach itself how to play go by playing numerous games against itself by combining a neural network and a search algorithm. As the program plays, the neural network updates itself and predicts moves and the winner. The system begins on a clean slate, on a neural network that does not know how to play the game Go.

Weak/narrow AI only mimics human recognition. Strong AI would theoretically have human cognition and solve problems as a human would. Apple’s Siri is an example of a narrow artificial intelligence algorithm that brings machine learning functionality to iPhone’s mobile platform. Siri helps in the completion of specific tasks, but cannot express self-awareness.

Another example of deep learning is something we all take for granted. TensorFlow by Google is a deep learning library. Google uses machine learning in all products, which improves its search engine, image captioning, recommendations, or translations. When you search a keyword in Google’s search bar, its AI provides you with a proposal on the next word

Google intends to utilize its colossal dataset to give its users the best user experience. The three groups that make use of machine learning are:

    • Data scientists
    • Researchers
    • Programmers

These groups use a similar toolset for collaboration and efficiency improvement. Google has the world’s most gigantic computer, to which TensorFlow was designed to scale. TensorFlow is a vast library built by the Google Brain team to hasten deep neural network research and machine learning.

TensorFlow was designed to operate on mobile OS (operating systems) and multiple GPUs or CPUs. It has several hidden layers in different languages, like C++, Java, or Python.

How Machine Learning Is Used in Customer service

In customer services today, most applications use machine-learning algorithms. They make workflow more dependable, increase agents’ productivity, and drive self-service. The data these algorithms are fed originates from a steady flow of customer queries, which includes their current issues. When these issues are aggregated into an AI application, it leads to faster and accurate predictions. Machine learning is useful in customer service in different ways, such as:

· Chatbots

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Chatbots are the first item that comes to mind for most people when AI technology in customer service is mentioned. Chatbots’ ability to stimulate customer care representatives' interactions and answer simple customer inquiries is an effective and efficient self-service solution. Machine learning supports Chatbots’ ability to learn when to use specific responses, when to gather data from users and when to hand the customer over to human agents

· Virtual Assistants

Virtual assistants differ from Chatbots in that they do not attempt to simulate interactions with human agents. They focus on specific areas where they can assist the customers along the customer journey. When they have machine learning capabilities, they learn about the type of information they can relay to the agents, or save the information for use in an analytic program.

6. Predictive Analysis

For continual optimization, customer service requires measurable analytics. Machine learning helps to add predictive elements for support analytics. Predictive analysis uses data from previous customer interactions to predict future quantitative results. It can operate in real-time to capture insights that might escape an agent. To deliver good customer care experiences, these insights are of great help.

Conclusion

Going by the vast amounts of data being generated by the big data era, we will experience innovations. These innovations will happen as soon as the next decade, and according to researchers and experts, most of these will be in deep learning applications.

Deep learning models are in use in many aspects of our lives. We think nothing of it when we converse with Chatbots on Facebook, Instagram, Amazon Alexa, etc. On our social media pages, deep learning algorithms are what offer page suggestions. We also think nothing of it when giving Apple’s Siri commands, but this is one example among many narrow AI. We are headed towards an even more advanced big data era, and we might soon see cognitive robots in our midst. Only time will tell.