What Is The Difference Between AI, Machine Learning And Deep Learning
Artificial Intelligence is setting the stage for an advanced technology-abled era. The recent years saw the rise of AI and its subsets, being Machine Learning and Deep Learning through various applications in healthcare and speech/fake recognition systems.
In 2014, Google acquired DeepMind Technologies – a British artificial intelligence subsidiary of Alphabet Inc. and research laboratory; the merge reflected the interest this tech giant, as well as any other grand organization, might have for the potentials that AI poses. Ever since, the company has been making significant advances in the field, promising a bright future where such sophisticated machinery would become an integral part of our everyday lives.
1. What is Artificial Intelligence?
Artificial intelligence (AI) is a broad field of computer science that focuses on creating intelligent machines that can perform tasks that usually require human intelligence. While AI is a multidisciplinary science with many methods, advances in machine learning and deep learning, in particular, are creating a paradigm shift in nearly every field of the tech industry.
2. Machine Learning definition
“Machine learning” (ML) is the study of computer algorithms that improve automatically through experience. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to do so.” – Wikipedia
In short, machine learning is a branch of artificial intelligence (AI); it focuses on building applications that learn from data and tasks regularly to improve their accuracy over time.
2.1. Machine learning process
An algorithm in data science is a collection of statistical processing steps. Algorithms are “trained” in machine learning to identify patterns and features in vast quantities of data to make decisions based on new information. The more data is processed, the faster the algorithm becomes, and the more reliable the decisions and predictions are.
Statistics are used by machine learning algorithms to identify patterns in large volumes of data. Data includes a wide range of items such as numbers, words, pictures, clicks, etc. If said items can be digitally processed, then they can be fed into a machine-learning algorithm. The detailed process of machine learning can be broken down into core steps as below:
- Get Data
Data is the first ingredient of machine learning. This process depends on the project and data type. Info from internet repositories sites like Kaggle can be considered during this time.
- Clean, Prepare & Manipulate Data
Real-world data is often unorganized or missing. Therefore, after choosing our data for machine learning to be successful, we need to clean, prepare, and manipulate it. Having a clean data set helps greatly with the model’s accuracy in testing periods.
After getting the data sets to a preferable state, they need to be converted into valid formats for the chosen ML platform. For example, we may need to convert the data into a .CSV file and upload it to AWS S3.
Finally, said data is split into training and test sets. The training set is used to train the model in subsequent phases, while the test data is used to validate the model.
- Train Model
The data set is linked to algorithms, which learn and develop predictions using sophisticated mathematical modeling. These algorithms usually fall into one of these three categories:Binary, Classification, Regression
- Test Model
The time to validate the trained model, using the aforementioned test data to check the model’s accuracy.
If the test results prove unsatisfactory, users can retrain and improve the model by reviewing data sets with business stakeholders, reconsidering the algorithm choice. There are dozens of algorithm choices within each algorithm class. A different algorithm may perform better compared to another.
Also being an element to factor in is adjust the parameters of the chosen algorithm to improve performance. Sometimes all it takes is a small adjustment for a more significant impact on test results.
2.2. Machine learning example
The machine learning process is used in a wide variety of applications and many of the services that we’re already familiar with today, such as:
- Digital assistants: They search the web and play music in response to our voice commands. Recommendation systems from Netflix, YouTube, and Spotify, search engines like Google and Bing, social media feeds like Facebook and Instagram, or voice assistants like Siri and Alexa all make use of this technology.
- Email filtering: One basic function that helped greatly with the email browsing experience is the Spam detectors, which stop unwanted emails from reaching our inboxes. The same learning pattern can be noticed with Gmail folders like Trash and Promotional.
- Automated support systems: Many fields can benefit from the application of this smart model. Robots vacuum the floors while users go about doing something else. Medical image analysis systems help doctors spot severe symptoms they might have missed, then come the self-driving cars like the cutting edge Tesla, which has been discussed a lot lately.
3. What is deep learning?
Deep learning is a branch of machine learning and is referred to as deep learning because it employs deep neural networks, which simulates those of the human brain. A neural network comprises layers that stack on top of each other. To learn from the results, the computer employs various layers. The number of layers in the model represents the depth of the model and how well it can perform.
3.1. Deep learning process
Deep learning uses artificial neural networks to mimic the way humans think and learn, while machine learning uses simpler principles like predictive models.
Input is still fed into deep learning computer systems, much as it is with machine learning, but the information is also in the form of large data sets, as deep learning systems need a large amount of data to understand it and return accurate results. The artificial neural networks then use the data to ask a series of binary true/false questions, requiring highly complex mathematical calculations, then classify that data based on the answers received.
3.2. Deep learning examples
Deep learning is widely used in computer vision, conversational AI, and recommendation systems. Conversational AI apps assist computers in comprehending and communicating in natural language, while recommendation systems use photos, vocabulary, and a user’s preferences to provide accurate and appropriate search results and services.
Take NVIDIA’s study for example, researchers and data scientists can greatly speed up deep learning training with NVIDIA GPU-accelerated deep learning frameworks, which may otherwise take days or weeks, to just hours or days. Developers may rely on GPU-accelerated inference frameworks for the cloud, embedded devices, or self-driving cars to deliver high-performance, low-latency inference for the most computationally-intensive deep neural networks.
4. Difference between machine learning and deep learning
Although simple machine learning models improve over time at whatever task they are given, they still need guidance. Machine learning requires human interaction to learn, and labeled datasets are needed to understand the differences between data inputs. The feature extraction and modeling measures in deep learning, on the other hand, are automated and more advanced.
Deep learning is a form of machine learning that is highly specialized. The first step in a machine learning workflow is to extract relevant features from images manually. The features are then used to build a classification model for the objects in the picture. Related features are automatically extracted from images using a deep learning workflow. Furthermore, deep learning conducts “end-to-end learning” – learning how to perform tasks automatically based on given information.
Another significant distinction is that deep learning algorithms scale as data grows, while shallow learning algorithms converge. Machine learning approaches a “plateau” or cap at a certain performance level as more examples and training data are applied to the network. Yet, on the other hand, the benefit of deep learning networks is that they always improve themselves as the size of the data grows.
To summarize the distinctions between the two, we can consider the following: Machine learning is a technique for parsing data, learning from it, and making informed decisions based on what it has learned. Deep learning simulates an “artificial neural network” that can learn and make intelligent decisions on its own by layering algorithms.
5. When to use machine learning or deep learning?
Machine learning provides a number of techniques and models from which to choose depending on the program, the amount of data you’re processing, and the problem you’re trying to solve. An effective deep learning application necessitates a large amount of data (thousands of images) for training the model, as well as GPUs (graphics processing units) to process the data quickly.
Consider if we have a high-performance GPU and a lot of labeled data when deciding between machine learning and deep learning. Machine learning might be a better option than deep learning if the above conditions are not met. Deep learning is more complicated than traditional machine learning, so one will need at least a few thousand images to get accurate results. Having a high-performance GPU means this can be done more effectively.
6. AI technology towards the future
According to PatentSight , as of November 2020, IBM was the largest owner of active machine learning and artificial intelligence (AI) patent families worldwide with 5,538 families owned. In 2018, the company had claimed the leading position from Microsoft now ranked third with 5,052 active families owned. Samsung ranked second with slightly under 5,500 patent families. The three leading companies had a lead of more than a thousand patent families to the Chinese company Baidu who owned less than four thousand active patent families in this technology field.
All and all, this is a positive trend from which we can expect more, as big data keeps being expanded and computer technology is becoming more powerful and affordable, more capable algorithms are being developed constantly, AI in general and machine learning, deep learning in particular will promise wondrous breakthroughs across all fields.