Machine Learning, Artificial Intelligence, Internet of Things (IoT), NLP, Deep Learning, Big Data Analytics and Blockchain
What is Machine Learning?
Machine learning is a field of study that applies the principles of computer science and statistics to create statistical models, which are used for future predictions (based on past data or Big Data) and identifying (discovering) patterns in data. Machine learning is itself a type of artificial intelligence that allows software applications to become more accurate in predicting outcomes without being explicitly programmed.
The basic objective of machine learning is to build algorithms that can receive input data and use statistics for prediction of an output value within an acceptable range. It provides the ability to automatically obtain deep insights, recognize unknown patterns, and create high performing predictive models from data, all without requiring explicit programming. Machine learning can be applied to detect fraudulent credit card transactions or to predict pricing.
Machine learning algorithms can be categorized as being supervised, semi-supervised or unsupervised. Supervised algorithms require humans to provide feedback about the accuracy of predictions along with input and desired output. Unsupervised algorithms do not need any training or human involvement. They use an iterative approach called deep learning (explained later in this post) to review data and making conclusions.
What is Artificial Intelligence (AI)?
Artificial Intelligence is the field of study by which a computer (and its systems) develops the ability for successfully accomplishing complex tasks that usually require human intelligence such as visual perception, speech recognition, decision-making, and translation between languages. In other words, artificial intelligence is concerned with solving tasks that are easy for humans but hard for computers.
While artificial intelligence typically concentrates on programming computers to make decisions, machine learning emphasizes on making predictions about the future. If you use an intelligent program that involves human-like behavior, it can be artificial intelligence. However, if the parameters are not automatically learned (or derived) from data, it’s not machine learning.
AI and ML are often seemed to be used interchangeably. But, they are not quite the same. Artificial Intelligence is the broader concept of machines being able to carry out tasks in a way that we would consider “smart”. Whereas, Machine Learning is a current application of AI based on the idea that we should really just be able to give machines access to data and let them learn for themselves.
What is Natural Language Processing (NLP)?
One of the core goals of artificial intelligence is natural language processing (NLP).
NLP is a field of computer science that is at the intersection of artificial intelligence and computational linguistics. NLP deals with programming computers to process large natural language corpora. In simple words, NLP involves intelligent analysis of written language.
For example, you have got a lot of data written in plain text. NLP techniques can reveal the insights from it for you. These insights typically include sentiment analysis, information extraction, information retrieval, search etc. NLP usually deal with research papers, blogs, social media feed text messages (including smileys); it doesn’t deal with images.
What is Deep Learning?
Deep learning is another aspect of artificial intelligence that is concerned with matching the learning approach used by humans to gain certain types of knowledge. In other words, deep learning is a way to automate predictive analytics. Unlike NLP, Deep Learning algorithms do not exclusively deal with text. Deep learning involves mathematical modeling, which can be thought of as a composition of simple blocks of a certain type, and where some of these blocks can be adjusted to better predict the final outcome.
The word “deep” means that the composition has many of these blocks stacked on top of each other – in a hierarchy of increasing complexity. The output gets generated via something called Back propagation inside of a larger process called Gradient descent which lets you change the parameters in a way that improves your model.
Let’s go a little deep now. Traditional machine learning algorithms are linear. Deep learning algorithms are stacked in a hierarchy of increasing complexity. Imagine a baby is trying to learn what a dog is by pointing the finger to objects. The parents will either say “Yes, that is a dog” or “No, that is not a dog”. As the baby continues to point to objects, s/he becomes more aware of the features and characteristics that all dogs possess. In this case, the baby is clarifying a complex abstraction (the concept of dog) by building a hierarchy of increasing complexity created. In each step, the baby applies the knowledge gained from preceding layer of hierarchy. Software programs use the deep learning approach in a similar manner. The only difference is that the baby might take weeks to learn something new and complex; a computer program could do that in few minutes.
Data Science, Big Data & Big Data Analytics
In order to achieve a certain level of accuracy and speed, deep learning programs require access to immense amounts of training data and processing power. Now, this is very much possible in today’s age of big data (and big data analytics) and the internet of things. Big data is a broad and evolving term for a large number of datasets. The data could be structured, semi-structured or unstructured (non-structured).
Big data analytics is the process of analyzing big data to identify hidden patterns, popular trends, unique correlations and other critical and useful information. For example, an e-commerce company will apply big data analytics to investigate customer or consumer behavior & mindset, and buying patterns. While big data is all about data, patterns (or trends) insights & impacts, internet of things is about data, devices, and connectivity.
What is the Internet of Things (IoT)?
The Internet of Things (IoT) is the inter-networking of physical devices (also termed as connected devices or smart devices), vehicles, buildings and other objects (which could be smart wearable, diagnostic device, kitchen appliances etc.) embedded with electronics, software, sensors, actuators, and network connectivity that enables these “smart objects” to collect and exchange data. In other words, Internet of things is a global infrastructure for the information society. IoT allows advanced services by interconnecting (physical and virtual) things based on existing and evolving interoperable information and communication technologies.
For example, the smart refrigerator in your kitchen (at home) can send you an alert (or notification) on your Smartphone (while you are leaving office) when you’re out of milk or gas. Your wearable or smart watch can warn you if there is something wrong with your pulse or heart-rate. Additionally, all this information gets recorded. Later, the software after looking at the data can provide you information like: you are likely to run of milk on Wednesday, run out of gas in two weeks, or likely to get a heart attack in three months (so, time for a check-up and take precautions).
Since the idea of networking appliances and other objects is personalized and confidential, security is a major concern. IoT security comes into play here. IoT security is the area of endeavor concerned with safeguarding connected devices and networks in the Internet of things. IoT is expanding at an exponential rate. Like Big Data, IoT is creating new opportunities and providing a competitive advantage for businesses in current and new markets. The Internet of Things (IoT) is an ecosystem of ever-increasing complexity. It’s the next wave of innovation that is bound to humanize every object in our life, and it is the next level of automation for every object we use. It keeps adding more and more devices to the digital fold every day to improve process and growth. It touches everything—not just the data, but how, when, where and why you collect it. One of the ways to look at IoT is as multiple blocks – such as connected objects, gateways, network services, and cloud services. As mentioned earlier, security is of paramount importance.
Block chain Technology
The current IoT ecosystems rely on centralized communication models. All devices are identified, authenticated and connected to cloud servers that sport huge processing and storage capacities. The connection between devices needs to go through the internet. A decentralized approach to IoT networking would solve many of the security issues.
Here arrives the Blockchain Technology. The blockchain is a database that maintains a continuously growing set of data records. It is distributed in nature; there is no master computer holding the entire chain. Instead, the participating nodes have a copy of the chain. It’s also ever-growing — data records are only being added to the chain. Blockchain is public. So, everyone participating can see the blocks and the transactions stored in the database. However, it’s protected by a private key.
Blockchain technology is considered as the missing link to deal with scalability, security and reliability issues of IoT. Blockchain technology can be used in tracking billions of connected devices, enable the processing of transactions and coordination between devices; allow for significant savings to IoT industry. According to the experts, the decentralized approach would eliminate single points of failure, creating a more resilient ecosystem for devices to run on. The cryptographic algorithms used by block chain technology could make consumer data more private. One of the popular applications of block chain technology is Bitcoin and Crypto currencies. However, there are several other applications of the block chain technology beyond crypto currencies and financial services.