Original article was published by Davide Camera on Artificial Intelligence on Medium
Comparison between AI/ML/Deep Learning
Now a day’s people are very much confused with the terms called artificial intelligence, machine learning, and deep learning.
For them, it seems to be that all these three concepts are exactly the same, but in reality, they are interrelated to each other but are not same.
Deep learning is a subpart of machine learning. It works in the same manner as that of machine learning does just the fact that it differs in terms of its ability.
In the case of Machine learning, the model becomes better while growing. If an error occurs, the programmers have to solve the issue by itself as the machine learning models require some supervision, but deep learning differs in this case as the model fixes itself. It did not require any help from the outside. A good example would be an automatic car driving system.
“Deep learning is a subset of machine learning used to decode complicated issues by learning from its own methods of computation.”
Machine learning and deep learning are further the subsets of artificial intelligence. AI is entirely different from ML and Deep learning. AI is hiking up so fast these days due to its concept that the machine has to imitate exactly like a human brain while solving the problems and learning. It has good potential to outperform for the company’s growth reinventing some new ideas by changing its way of work.
“An ability of a computer or machine to work the same as the human brain is called Artificial Intelligence.”
AI means to copy the human brain in such a way that it performs exactly how a human brain would in any particular situation by thinking and functioning just like a human. AI is still under growth but has achieved a lot by so far, and the best example to take into the account would be the Sophia an advancement of AI, Siri, etc. It does not need to be preprogrammed, and rather it uses algorithms which works for its intelligence. It incorporates Reinforcement learning, deep learning neural networks.
Applications of Machine Learning
Machine learning is growing so rapidly that in the golden era of AI, where ML is incorporated in our day to day life, and we did not even acknowledge it such as; Alexa, Google maps, Google Assistant and Google maps.
Image recognition is one of the most common applications of machine learning. It is used to identify objects, persons, places, digital images, etc. The popular use case of image recognition and face detection is, Automatic friend tagging suggestion (for instance Facebook provides us a feature of auto friend tagging suggestion).
While using Google, we get an option of “Search by voice,” it comes under speech recognition, and it’s a popular application of machine learning. Speech recognition is a process of converting voice instructions into text, and it is also known as “Speech to text”, or “Computer speech recognition.” At present, machine learning algorithms are widely used by various applications of speech recognition. Google assistant, Siri, Cortana, and Alexa are using speech recognition technology to follow the voice instructions.
Machine learning is widely used by various e-commerce and entertainment companies such as Amazon, Netflix, etc., for product recommendation to the user. Whenever we search for some product on Amazon, then we started getting an advertisement for the same product while internet surfing on the same browser and this is because of machine learning.
Virtual Personal Assistant
We have various virtual personal assistants such as Google assistant, Alexa, Cortana, Siri. As the name suggests, they help us in finding the information using our voice instruction. These assistants can help us in various ways just by our voice instructions such as Play music, call someone, Open an email, Scheduling an appointment, etc.
Automatic Language Translation
Nowadays, if we visit a new place and we are not aware of the language then it is not a problem at all, as for this also machine learning helps us by converting the text into our known languages. Google’s GNMT (Google Neural Machine Translation) provide this feature, which is a Neural Machine Learning that translates the text into our familiar language, and it called as automatic translation. The technology behind the automatic translation is a sequence to sequence learning algorithm, which is used with image recognition and translates the text from one language to another language.
Sentiment analysis is another real-time machine learning application. It also refers to opinion mining, sentiment classification, etc. It’s a process of determining the attitude or opinion of the speaker or the writer. In other words, it’s the process of finding out the emotion from the text.
The main concern of sentiment analysis is “ what other people think?”. Assume that someone writes ‘the movie is not so good.’ To find out the actual thought or opinion from the text (is it good or bad) is the task of sentiment analysis. This sentiment analysis application can also apply to the further application such as in review based website, decision-making application.
Online Fraud Detection
Machine learning is making our online transaction safe and secure by detecting fraud transaction. Whenever we perform some online transaction, there may be various ways that a fraudulent transaction can take place such as fake accounts, fake ids, and steal money in the middle of a transaction. So to detect this, Feed Forward Neural network helps us by checking whether it is a genuine transaction or a fraud transaction. For each genuine transaction, the output is converted into some hash values, and these values become the input for the next round. For each genuine transaction, there is a specific pattern which gets change for the fraud transaction hence, it detects it and makes our online transactions more secure.
If we want to visit a new place, we take help of Google Maps, which shows us the correct path with the shortest route and predicts the traffic conditions.
It predicts the traffic conditions such as whether traffic is cleared, slow-moving, or heavily congested with the help of two ways:
- Real Time location of the vehicle form Google Map app and sensors
- Average time has taken on past days at the same time.
Everyone who is using Google Map is helping this app to make it better. It takes information from the user and sends back to its database to improve the performance.
News classification is another benchmark application of a machine learning approach. Why or How? As a matter of fact that now the volume of information has grown tremendously on the web. However, every person has his individual interest or choice. So, to pick or gather a piece of appropriate information becomes a challenge to the users from the ocean of this web.
Stock Market trading
Machine learning is widely used in stock market trading. In the stock market, there is always a risk of up and downs in shares, so for this machine learning’s long short term memory neural network is used for the prediction of stock market trends.
In medical science, machine learning is used for diseases diagnoses. With this, medical technology is growing very fast and able to build 3D models that can predict the exact position of lesions in the brain. It helps in finding brain tumors and other brain-related diseases easily.
Prediction is the process of saying something based on previous history. It can be weather prediction, traffic prediction, and may more. All sort of forecasts can be done using a machine learning approach. There are several methods like Hidden Markov model can be used for prediction.
Online Customer Supports
Recently almost all websites allow the customer to chat with the website representative. However, not website has an executive. Basically, they develop a chat-bot to chat with the customer to know their opinion. This is possible only for the machine learning approach. It’s just a beauty of machine learning algorithm.
A small video file contains more information compared to text documents and other media files such as audio, images. For this reason, extracting useful information from video, i.e., the automated video surveillance system has become a hot research issue. With this regard, video surveillance is one of the advanced application of a machine learning approach.
Email Spam and Malware Filtering
Whenever we receive a new email, it is filtered automatically as important, normal, and spam. We always receive an important mail in our inbox with the important symbol and spam emails in our spam box, and the technology behind this is Machine learning. Below are some spam filters used by Gmail:
- Content Filter
- Header filter
- General blacklists filter
- Rules-based filters
- Permission filters
Some machine learning algorithms such as Multi-Layer Perceptron, Decision tree, and Naïve Bayes classifier are used for email spam filtering and malware detection.
One of the most exciting applications of machine learning is self-driving cars. Machine learning plays a significant role in self-driving cars. Tesla, the most popular car manufacturing company is working on self-driving car. It is using unsupervised learning method to train the car models to detect people and objects while driving.
Different types of Machine Learning
A machine learns from a trained data set to create a model. Whenever there is a new input to the algorithm, it predicts on the basis of the model. The evaluation is made in terms of accuracy, and the algorithm is deployed only if the accuracy is accepted by the algorithm of machine learning, else the model is trained repeatedly with a large data set.
The machine learning algorithm can be broadly classified into 3 main categories:
- Supervised learning
- Unsupervised learning
- Reinforcement learning
Supervised learning is the one done under the supervision of a teacher or a supervisor. Basically, the model is trained over a labeled dataset. A labeled database is one which contains both inputs as well as the output parameters. Here the trained dataset act a teacher, and its primary role is to train the model. The prediction is made once the model is built. It works in the same manner as the student learns under the supervision of the teacher.
It is a kind of learning in which the output target is not given to the model while performing the training. It only has the input variables. The model has to lean itself. The trained data that is fed to the system can be unlabeled as well as unstructured in nature.
The unstructured data is the one where the noise or some irrelevant information is present, whereas in case of unlabeled data it does not contain any target value other than the input data and is easy to collect as compared to labeled one in the supervised learning.
Reinforcement based Learning
In this type of learning, the agent connects with the environment and searches for the best outcome. It is a hit and trial method. Based on the result, the author may be either rewarded or penalized for every wrong and correct answer. The more the positive rewards points gained, the more the model can train itself. And the prediction is made after getting trained entirely.