Machine learning (ML) is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. It is a method of data analysis that automates analytical model building. The interactive aspect of ML is important because as models are exposed to new data, they are able to independently adapt. ML is born from specific task patterns and learns from previous computations to produce reliable, repeatable decisions and results.
Machine learning works by exploring data and identifying patterns that involve minimal human intervention. Nearly any task can be completed with a data-defined pattern, allowing companies to transform processes that were previously only possible for humans to perform, such as responding to customer calls, bookkeeping, or reviewing resumes.
Machine learning uses two main techniques in its processes, namely supervised learning and unsupervised learning. Supervised learning allows an engineer to collect data or produce data output from a previous ML deployment – just like how humans actually learn. Meanwhile, unsupervised learning helps the engineer find all kinds of unknown patterns in data to learn some inherent structure to the data with only unlabelled examples. Two common unsupervised learning tasks are clustering and dimensionality reduction.
Clustering = attempting to group data into meaningful clusters. It is useful for tasks such as market segmentation.
Dimensionality reduction = reducing the number of variables in a dataset by grouping similar or correlated attributes for better interpretation.
Deep learning (DL) is a subset of machine learning in which multilayered neural networks learn from vast amounts of data. Within each layer of the Neural Network, deep learning algorithms perform calculations and make predictions repeatedly, progressively and gradually improving the accuracy of the outcome over time. Deep learning enables systems that learn to identify objects and perform complex taste with increasing accuracy – all without human intervention.
AI’s brain has neurons. They are represented by circles and are inter-connected. These neurons are grouped into three types: input (receive input data), hidden (perform mathematical computations on inputs) and output (return the output data, giving predictions such as price or weight) layers. These neurons will then apply an Activation Function of data to standardised output coming out of the neuron – but in order to train a Neural Network, you need a large data set.
After the output is produced, the iterating process through the data set and comparing the outputs begin, producing a Cost Function, indicating how much the AI is off from the real outputs. After the iteration, the weights between neurons are adjusted using Gradient Descents to reduce the cost function.
Artificial intelligence (AI) refers to any human-like intelligence exhibited by a computer, robot, or another machine to mimic the capabilities of a human mind – learning from examples and experiences, recognising objects, understanding and responding to language, making decisions, and solving problems. This knowledge is then processed to perform functions a human might perform, like greeting a hotel guest or driving a car.
Parsing through mountains of data created by humans, AI’s iterative processing and intelligent algorithms allow the software to learn automatically from patterns or features in the data. AI can also interpret both text and images to discover patterns in complex data and then act on those learnings. See the following video to know better how AI works:
In simpler terms, AI is a border concept of intelligence demonstrated by machines. ML, which is a branch of AI, is a system or algorithm that is designed to learn structures to predict future outcomes. And DL, which is the subtype of ML, is suitable for self-training algorithms and feature extraction.
|Deep Learning||Machine Learning||Artificial Intelligence|
|Can be categorised into supervised, semi-supervised, and unsupervised labels;||Has the ability to perform automated data visualisation;||Needs deep learning to teach a computer to do what comes naturally to humans and learn by examples;|
|Needs a huge amount of resources like Big data in the form of structured or unstructured data;||Has the ability to automate repetitive tasks and increasing productivity;||Is able to recognise individual faces using biometric mapping;|
|Requires a large number of layers in the model, such as input and activation;||Plays a critical role in enabling a business to spark more value to its customer engagement;||Can automate simple and repetitive tasks;|
|Needs well-tuned hyperparameters like No of epochs, Batch size, No of layers, etc for successful Model accuracy;||Has the ability to take efficiency to the next level when merged with IoT;||Has an efficient feature extraction to reduce the degree of disorder;|
|Helps minimise the cost of iterations.||Has the ability to change the mortgage market;||Has zero correlation among features to achieve independence and minimality of feature-set;|
|Offers an accurate data analysis when analysing massive volumes of data;||Uses Artificial Neural Networks, such as Convolutional Neural Network and Recurrent Neural Network;|
|Can generate extreme levels of business intelligence, boosting business operations.||Can perform ethical gene editing;|
|Can effectively stimulate mock disaster drills to identify potentially vulnerable locations, plan precautionary actions, monitor and govern resource allocation seamlessly.|
Debunking misconceptions around Artificial Intelligence, Machine Learning, and Deep Learning
Now that you have learned better about AI, ML, and DL technology, let’s debunk some misconceptions surrounding these automatons.
Myth #1 Artificial Intelligence, Machine Learning, and Deep Learning is basically the same
While these three technology definitions are often used interchangeably, they are not synonymous. From the aforementioned definitions, AI solves a task that usually requires human intelligence. Ml solves a specific AI-task by learning from detailed data, making it a strict subset of AI, while DL solves ML problems by using neural networks as its algorithm.
Myth #2 neural networks are like a human brain
While you might have heard that neural networks in DL simulate human brains, this belief is completely wrong. Neural networks do not come close to the performance of the human brain, especially when it comes to efficiency. Our brains are light-years aways from anything human-made.
Myth #3 Artifical Intelligence learns from itself and might defeat humans
Many people think that an AI can magically get better and better automatically. The truth is the system has been trained on historical data and present data which makes it able to do task close to humans – but not completely the same as how humans do. When AI is running in production, it uses the mass data knowledge to make a judgment about new observations it has never seen before – and often, that is the end of it.
The second misconceptions about beating humans are like what you’ve seen in movies – that’s just a good imagination. The truth is it is impossible because the way living humans think cannot be emulated or faked.
Myth #4 You need a gigantic amount of data
It is true that more data will give AI and ML better performance. Training Neural Networks from scratch might also require millions of images before it can yield expected results. But you can also start with a network pre-trained on a large dataset to solve a similar problem. You can retrain the last parts of your network that are specific to your use-case. This way, you don’t need a thousand sets of images to prevent getting the results you want – you only need a few hundred.