Artificial Intelligence (AI) and Machine Learning(ML) Explained
Artificial Intelligence (AI) and Machine learning (ML) are the base of almost every technology available today, be it the virtual assistant on your phone or the supercomputers that process the most difficult physics simulations. Most people nowadays get confused between Artificial Intelligence and Machine Learning. They consider both of them to be the same while they have quite a lot of differences. Both are generally used in reference to robots. So, let's check out what Artificial Intelligence (AI) and Machine Learning (ML) exactly are.
How do we even begin to predict where artificial intelligence is going? With all the data that is being collected about us in the form of IoT devices, there is so much information to mine and make sense of. Even if you are not a consumer, your information is most likely being taken by a company and used to better profit from your information. If you are a developer with an interest in applying artificial intelligence in your projects, you must first understand the basics of AI.
AI has become so ubiquitous that the simplest of us don’t really think about it anymore. But just how is this artificial intelligence technology being used? Let’s have a look at some of the most important uses of artificial intelligence:
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Artificial Intelligence (AI)
1. What is AI?
Artificial intelligence is an area of computer science and artificial intelligence research. The name speaks very well for itself. According to Wikipedia, Artificial intelligence (AI) is intelligence demonstrated by machines, as opposed to the natural intelligence displayed by humans or animals. Artificial Intelligence is the heart and core of almost every self-sustained computer system on the planet. Artificial Intelligence is a way of mimicking human intelligence using machines. Leading AI textbooks define the field as the study of "intelligent agents": any system that perceives its environment and takes actions that maximize its chance of achieving its goals. We are all surrounded by AI systems every time. We have AI in our computers, in our phones and virtual assistants. In fact, AI has become an integral part of our lives.
2. The History of AI:
The search for a technique that can instill human intelligence in machines started in 1950. A researcher called Alan Turning published a research paper: Computing Machinery and Intelligence. He asked a small yet revolutionary question, "Can machines think?" This research along with concurrent research in neurobiology, information technology, and cybernetics led researchers to the possibility of building electronic brains. Thus started the quest to make machines intelligent like humans.
The term 'Artificial Intelligence' was coined by John McCarthy, whom we know as the father of Artificial Intelligence. The first use of AI was mostly to run simulations. Later, as technology grew and along with the introduction of the 'Internet of Things', the scope of AI rapidly expanded. Between 1961 and 1972, IBM launched the IBM Shoebox, the first speech recognition tool. Fast forward to 2011, Apple launched Siri, the first-ever virtual assistant. And then started a surge of AI systems like Amazon Echo, Google Assistant, Humanoid robots, and much more.
3. Important terms about AI:
a) Algorithm:
An algorithm is a set of rules provided to a computer system, which guides the computer through the steps it should undertake to complete a task. An algorithm consists of variables, which are bonded to each other by some formulae. An algorithm is one of the most crucial aspects of any AI system since it controls how the system behaves under certain conditions. An algorithm may also consist of 'if this-then that' condition to tweak the system such that it will provide a specific output corresponding to the input. Most of the time, an algorithm is considered the same as a program, but that is not the case. An algorithm is the set of rules, the skeleton of the program. Program is a medium to feed the algorithm to a computer. The program is written according to the steps provided in the algorithm.
b) Data Science:
Data Science is a term highly associated with AI. Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data and apply knowledge and actionable insights from data across a broad range of application domains. Modern AI systems have to deal with a huge amount of data. Data Science allows them to organize their data properly and use it efficiently. There are 4 main steps in working of any algorithm using data science:
1) Data acquisition: This step involves gathering of raw structured and unstructured data by either manual input or automatic input.
2) Preparing and maintaining: The data acquired from step 1 is now arranged and sorted using algorithms in a format that would be useful for analysis. This may include steps from cleansing, deduplicating, formatting the data to using ETL(extract, transform, load) and data integration technologies.
3) Processing the data: The data is examined by data scientists for biases, range, patterns and distribution of value to determine the data's compatibility for deep learning algorithms.
4) Analysis: Data scientists perform predictive and statistical analysis, regression and deep learning to extract insights from the data.
The data is then used to train the AI system using training algorithms.
c) Computer Vision (CV):
Computer Vision allows a computer to interpret images. It is a field of AI that enables computers and systems to derive meaningful information from images, videos, and other visual inputs and take actions or make predictions based on the data. If AI enables computers to think, Computer Vision helps it to see. Computer Vision is very much similar to human vision. While we humans use our eyes for vision, Computer Vision employs a series of cameras. The image processed by our eye is analyzed by the brain, whereas in CV, the image is interpreted by algorithms. Computer Vision is widely used in facial detection and recognition software, robotics, and drones. It is also used in self-driving vehicles.
How AI technology is being used in the real world
1. IBM Watson:
One of the most well-known projects of artificial intelligence is IBM Watson. The computer is an IBM supercomputer built for answering human language questions and analyzing vast amounts of text. Watson is the first major AI system that was built on a physical computer and therefore it is rather expensive. Watson can process approximately 140 billion lines of data each day, which makes it one of the most powerful computers of its kind. Watson has been used in a number of different industries. Watson uses a cluster of 90 IBM Power 750 servers, each with a 3.5 GHz processor and 16 terabytes of RAM! Watson is used in Business Automation, IT Services, Advertising, Healthcare, and other fields.
2. Virtual Assistants:
Virtual Assistants are everywhere, be it Cortana on your PC or Google Assistant and Siri on your phones. All these run on AI. AI is used in these assistants to keep a track of your activities, and accordingly decide what you need. But, this is not the only use of AI in them. These assistants are made to replicate human behavior as much as possible. One key aspect of human behavior is speech. Virtual Assistants use AI to learn languages and talk like humans. Apart from your phones, virtual assistants are used by companies for customer service, automation, dealing with databases, and much more.
3. Automation:
AI is widely used in automation. In fact, it is the backbone of most automation systems. Automation systems have to make proper decisions based on their present condition. They use AI to determine what steps have to be taken to achieve the desired output. Smart home devices are connected to each other and have to maintain the room condition as per the user's needs. They use AI to gather information about their surrounding conditions and make the necessary changes.
Machine Learning
What is Machine Learning?
Machine Learning is a subset of AI.
A machine learning algorithm is a computer program that uses the structure and regularities in data to teach itself how to process the data in a predictable way. It tries to predict or explain the behavior of data based on past experience. Essentially, as humans, we do not have access to the secret code, so we try to define a relationship between different parts of data by human logic. In machine learning, we try to predict and explain the data based on the structure and regularities of the data itself. Even today, we often cannot explain the hidden structure of data. If we ask "how to use the genome in a genomic analysis?" we don't really know the answer, we just try to understand the data and work with it based on common logic, trying to identify the relationship.Important terms in Machine Learning:
1) Natural Language Processing (NLP):
Natural Language Processing is a way by which a machine understands human language. NLP mainly involves speech recognition, natural language understanding, and natural language generation. It allows computer to understand language as humans do. You can refer to a detailed article on NLP here.
2) Supervised and Unsupervised learning:
AI systems are constantly evolving and upgrading for better usability and features. To make them compatible with new upgrades, these systems need to be trained so that they can perform the tasks accurately. Training an AI system includes collecting data from various sources and teaching the system to manage it using algorithms so as to get the desired output. Now, there are two types of learning in AI. These are supervised learning and unsupervised learning. In supervised learning, the AI system is corrected by a human if it takes a wrong decision. The inputs are provided manually. Unsupervised learning is like artificial consciousness. When the system is left into an environment on its own, it analyzes the environment, takes the inputs, and accordingly makes decisions. It does this all by itself, or with very minimum human interference.
3)Artificial Neural Networks:
Artificial Neural Networks are inspired by biological neural networks. The human brain has a network of neurons, each connected to generate nodes. These networks are used for processing information and help us learn stuff. A similar principle is used in Artificial Neural Networks. They can be defined as statistical learning models that are used to estimate or approximate functions that depend on a large number of inputs. Neural networks are usually used when the volume of inputs is far too large for standard machine learning approaches previously discussed.
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