The algorithms adaptively improve their performance as the number of samples available for learning increases. Deep learning for example, is a specialized form of machine learning.
When should machine learning be used?
Consider using machine learning when you have a complex task or problem involving a large amount of data and lots of variables, but no existing formula or equation. For example, machine learning is a good option if you need to handle situations like these:
Machine learning algorithms are used in the applications of email filtering, detection of network intruders, and computer vision, where it is infeasible to develop an algorithm of specific instructions for performing the task. Machine learning is closely related to computational statistics, which focuses on making predictions using computers.
The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a field of study within machine learning, and focuses on exploratory data analysis through unsupervised learning. In its application across business problems, machine learning is also referred to as predictive analytics.
Machine Learning applies Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. It focuses on the development of computer programs that can access data and use it to learn for themselves.
The process of learning begins with observations or data. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.
There are many different types of machine learning algorithms, with hundreds published each day, and they’re typically grouped by either learning style (i.e. supervised learning, unsupervised learning, semi-supervised learning) or by similarity in form or function (i.e. classification, regression, decision tree, clustering, deep learning, etc.).
Regardless of learning style or function, all combinations of machine learning algorithms consist of the following:
Representation (a set of classifiers or the language that a computer understands)
Evaluation (aka objective/scoring function)
Optimization (search method; often the highest-scoring classifier, for example; there are both off-the-shelf and custom optimization methods used).
Machine learning enables analysis of massive quantities of data. While it generally delivers faster and more accurate results in other to identify profitable opportunities or dangerous risks, it may also require additional time and resources to train it properly.
Combining machine learning with AI and cognitive technologies can make it even more effective in processing large volumes of information.
How Do Machines Learn?
Well, the simpler answer is, just like humans do. First, we receive the knowledge about a certain thing and then keeping this knowledge in mind, we are able to identify the thing in the future. Also, past experiences help us in taking decisions accordingly in the future.
Our brain trains itself by identifying the features and patterns in knowledge/data received, thus enabling itself to successfully identify or distinguish between various things. Similarly, we feed knowledge/data to the machine, this data is divided into two parts namely, training data and testing data.
The machine learns the patterns and features from the training data and trains itself to take decisions like identifying, classifying or predicting new data. To check how accurately the machine is able to take these decisions, the predictions are tested on the testing data.
There are different approaches to getting machines to learn, from using basic decision trees to clustering to layers of artificial neural networks (the latter of which has given way to deep learning), depending on what task you’re trying to accomplish and the type and amount of data that you have available.
This dynamic sees itself played out in applications as varying as medical diagnostics or self-driving cars. While emphasis is often placed on choosing the best learning algorithm, researchers have found that some of the most interesting questions arise out of none of the available machine learning algorithms performing to par.
Most of the time this is a problem with training data, but this also occurs when working with machine learning in new domains.
Research done when working on real applications often drives progress in the field, and reasons are twofold:
Research done when working on real applications often drives progress in the field, and reasons are twofold:
Tendency to discover boundaries and limitations of existing methods.
Researchers and developers working with domain experts and leveraging time and expertise to improve system performance.
Let’s understand this with the help of a basic machine learning example:
Consider that you want to predict whether the next day is going to be rainy or sunny. Generally, we will do this by looking at a combination of data like the weather conditions of the past few days and present data such as wind direction, cloud formation etc. Had it been raining for the past few days, we would predict that it would rain for the next day too based on the pattern and vice versa.
Similarly, we feed the past few days’ weather data along with the present data such as wind direction, cloud formation etc. to the machine, and based on the data provided, the machine will analyze the patterns and eventually predict the weather for the next day.
Machines that learn are useful to humans because, with all of their processing power, they’re able to more quickly highlight or find patterns in big (or other) data that would have otherwise been missed by human beings. Machine learning is a tool that can be used to enhance humans’ abilities to solve problems and make informed inferences on a wide range of problems, from helping diagnose diseases to coming up with solutions for global climate change.
Machine Learning is an application of AI. It is seen as a subset of artificial intelligence. One definition of AI is that “It is the study of how to train the computers so that computers can do things which at present human can do better.”
Therefore It is an intelligence where we want to add all the capabilities to machine that human contain.
Machine learning algorithms build a mathematical model of sample data, thus "training data", in order to make predictions or decisions without being explicitly programmed to perform the task.
Machine learning algorithms build a mathematical model of sample data, thus "training data", in order to make predictions or decisions without being explicitly programmed to perform the task.
It uses two types of techniques: supervised learning, which trains a model on known input and output data so that it can predict future outputs, and unsupervised learning, which finds hidden patterns or intrinsic structures in input data. Supervised learning uses classification and regression techniques to develop predictive models.
Classification techniques predict discrete responses—for example, whether an email is genuine or spam, or whether a tumor is cancerous or benign. Classification models classify input data into categories. Typical applications include medical imaging, speech recognition, and credit scoring.
Use classification if your data can be tagged, categorized, or separated into specific groups or classes. For example, applications for hand-writing recognition use classification to recognize letters and numbers. In image processing and computer vision, unsupervised pattern recognition techniques are used for object detection and image segmentation.
Common algorithms for performing classification include support vector machine (SVM), boosted and bagged decision trees, k-nearest neighbor, Naïve Bayes, discriminant analysis, logistic regression, and neural networks.
Regression techniques predict continuous responses—for example, changes in temperature or fluctuations in power demand. Typical applications include electricity load forecasting and algorithmic trading.
Use regression techniques if you are working with a data range or if the nature of your response is a real number, such as temperature or the time until failure for a piece of equipment.
Common regression algorithms include linear model, nonlinear model, regularization, stepwise regression, boosted and bagged decision trees, neural networks, and adaptive neuro-fuzzy learning.
Choosing the right algorithm is key—there are dozens of supervised and unsupervised machine learning algorithms, and each takes a different approach to learning.
There is no best method or one size fits all. Finding the right algorithm is partly about using trial versions—even highly experienced data scientists can’t tell whether an algorithm will work without trying it out. But algorithm selection also depends on the size and type of data you’re working with, the insights you want to get from the data, and how those insights will be used.
Here are some guidelines on choosing between supervised and unsupervised machine learning:
Choose supervised learning if you need to train a model to make a prediction--for example, the future value of a continuous variable, such as temperature or a stock price, or a classification—for example, identify makes of cars from webcam video footage.
Choose unsupervised learning if you need to explore your data and want to train a model to find a good internal representation, such as splitting data up into clusters.
Business Benefits of Machine Learning
With all the buzz around big data, artificial intelligence, and machine learning (ML), enterprises are now becoming curious about the applications and benefits of machine learning in business.
With Google, Amazon, and Microsoft Azure launching their Cloud Machine learning platforms, we have seen artificial intelligence and ML gaining prominence in the recent years. Some of the most common instances are 'Spam' detection by your email provider, and 'Image' or 'Face' tagging done by Facebook.
While Gmail recognizes the selected words or the pattern to filter out spam, Facebook automatically tags uploaded images using image (face) recognition technique. Business benefits of AI and ML are numerous. Let us look at some of the most significant ML and artificial business benefits, starting with the sales and marketing sector.
Simplifies Product Marketing and Assists in Accurate Sales Forecasts
ML helps enterprises in multiple ways to promote their products better and make accurate sales forecasts. ML offers huge advantages to sales and marketing sector, with the major ones being -
Massive Data Consumption from Unlimited Sources.
ML virtually consumes unlimited amount of comprehensive data. The consumed data can then be used to constantly review and modify your sales and marketing strategies based on the customer behavioral patterns. Once your model is trained, it will be able to identify highly relevant variables. Consequently, you will be able to get focused data feeds by foregoing long and complicated integrations.
Rapid Analysis Prediction And Processing
The rate at which ML consumes data and identifies relevant data makes it possible to take appropriate actions at the right time. For instance, ML will optimize the best subsequent offer for your customer. Consequently, the customer will be able to see the right offer at a given point of time, without you actually investing time to plan and make the right ad visible for your customers.
Interpret Past Customer Behaviors
Data which are related to past behaviors or outcomes can be analyzed and interpreted with ML. Therefore, based on the new and different data you will be able make better predictions of customer behaviors.
Facilitates Accurate Medical Predictions and Diagnoses
In healthcare industry, ML helps in easy identification of high-risk patients, make near perfect diagnoses, recommend best possible medicines, and predict read missions. These are predominantly based on the available datasets of anonymous patient records as well as the symptoms exhibited by them.
Near accurate diagnoses and better medicine recommendations will facilitate faster patient recovery without the need for extraneous medications. In this way, ML makes it possible to improve patient health at minimal costs in the medical sector.
All these applications make machine learning a top value-producing digital innovation trend. Furthermore, ML and Artificial Intelligence enables businesses to effortlessly discover new trends and patterns from large and diverse data sets. Businesses can now automate analysis to interpret business interactions, which were traditionally done by humans, to take evidence-based actions.
This empowers enterprises to deliver new, personalized or differentiated products and services.
When working on an artificial intelligence project it is always ideal to program with the any of the following:
Python - It is considered to be in the first place in the list of all AI development languages due to the simplicity. The syntaxes belonging to python are very simple and can be easily learnt. Therefore, many AI algorithms can be easily implemented in it. Python takes short development time in comparison to other languages like Java,
C++ or Ruby. Python supports object oriented, functional as well as procedure oriented styles of programming. There are plenty of libraries in python, which make the tasks easier. For example: Numpy is a library for python that helps to solve many scientific computations.
R - R is one of the most effective language and environment for analyzing and manipulating the data for statistical purposes. Using R, you can easily produce well-designed publication-quality plot, including mathematical symbols and formulae where needed. Apart from being a general purpose language, R has numerous of packages like RODBC, Gmodels, Class and Tm which are used in the field of machine learning.
These packages make the implementation of machine learning algorithms easy, for cracking the business associated problems.
Lisp - It is one of the oldest and the most suited languages for development in AI. It was invented by John McCarthy, the father of Artificial Intelligence in 1958. It has the capability of processing the symbolic information effectively.
Lisp - It is one of the oldest and the most suited languages for development in AI. It was invented by John McCarthy, the father of Artificial Intelligence in 1958. It has the capability of processing the symbolic information effectively.
It is also known for its excellent prototyping capabilities and easy dynamic creation of new objects, with automatic garbage collection. Its development cycle allows interactive evaluation of expressions and recompilation of functions or file while the program is still running. Over the years, due to advancement, many of these features have migrated into many other languages thereby affecting the uniqueness of Lisp.
Prolog - This language stays alongside Lisp when we talk about development in AI field. The features provided by it include efficient pattern matching, tree-based data structuring and automatic backtracking. All these features provide a surprisingly powerful and flexible programming framework. Prolog is widely used for working on medical projects and also for designing expert AI systems.
Java - Java can also be considered as a good choice for AI development. Artificial intelligence has lot to do with search algorithms, artificial neural networks and genetic programming. Java provides many benefits: easy use, debugging ease, package services, simplified work with large-scale projects, graphical representation of data and better user interaction.
It also has the incorporation of Swing and SWT (the Standard Widget Toolkit). These tools make graphics and interfaces look appealing and sophisticated.
Artificial intelligence (AI) is simply an area of computer science that emphasizes the creation of intelligent machines that work and react like humans.
Robotics is also a major field related to AI. Robots require intelligence to handle tasks such as object manipulation and navigation, along with sub-problems of localization, motion planning and mapping.
Problem Solving in games such as “Sudoku” can be an example. It can be done by building an artificially intelligent system to solve that particular problem. To do this, one needs to define the problem statements first and then generating the solution by keeping the conditions in mind.
Borrowing from the management literature, Kaplan and Haenlein classify artificial intelligence into three different types of AI systems:
- Analytical
- Human-inspired
- Humanized artificial intelligence.
Analytical AI has only characteristics consistent with cognitive intelligence; generating a cognitive representation of the world and using learning based on past experience to inform future decisions. Human-inspired AI has elements from cognitive and emotional intelligence; understanding human emotions, in addition to cognitive elements, and considering them in their decision making.
Humanized AI shows characteristics of all types of competencies (i.e., cognitive, emotional, and social intelligence), is able to be self-conscious and is self-aware in interactions with others.
In the twenty-first century, AI techniques have experienced a resurgence following concurrent advances in computer power, large amounts of data, and theoretical understanding; and AI techniques have become an essential part of the technology industry, helping to solve many challenging problems in computer science, software engineering and operations research.
Factors Accelerating The Growth Of Artificial Intelligence (AI)
The innovation and research directed by the best tech companies are affecting industry verticals. Though technology has ever been a very important element for all these industries, AI is bringing technology to the center of every company. AI is going to be infused to virtually every program and apparatus.
Platform companies are investing in the study and creation of AI. They're working towards creating AI more accessible for companies.
Three essential facets are accelerating the rate of innovation within the area of Machine Learning and Artificial Intelligence.
Advanced computing architecture: Conventional microprocessors and CPUs aren't meant to take care of Machine Learning. The fastest CPU might not be the perfect selection for training an intricate ML version. CPUs have to be complemented with a new breed of chips.
Because of the rise of AI, Graphics Processing Unit (GPU) have been in demand. What was once regarded as part of high-end gambling PCs and workstations has become the very sought after processor from the general cloud. Contrary to CPUs, GPUs arrive with tens of thousands of cores that hasten the ML training procedure.
Even for conducting a trained version for inferencing, GPUs are getting to be essential. Moving ahead, some kind of a GPU will probably be there where there's a CPU. From customer devices to virtual machines from the cloud, GPUs would be the key to AI.
The following innovation comes in the Kind of Field Programmable Gate Array or FPGA.
The following innovation comes in the Kind of Field Programmable Gate Array or FPGA.
These chips are customizable and programmable to get a particular sort of workload. Conventional CPUs are developed for general-purpose calculating whereas FPGAs can be programmed from the area once they are fabricated. FPGA devices are selected for market computing tasks like training ML versions. Public cloud sellers are harnessing FPGAs to provide highly optimized and optimized infrastructure for AI.
Last, the access to bare metal servers from the cloud is bringing scientists and researchers to conduct high-performance computing work in the cloud. These committed, single-tenant servers provide best in class functionality. Virtual machines suffer in the noisy neighbor issues because of this common and multi-tenant infrastructure.
Cloud infrastructure providers such as Amazon EC2 along with IBM Cloud are supplying bare metal servers.
These inventions will fuel the adoption of AI in areas like Aerospace and picture processing.
These inventions will fuel the adoption of AI in areas like Aerospace and picture processing.
Progress In Deep Neural Networks : Artificial Neural Networks (ANN) are substituting conventional Machine Learning versions to evolve accurate and precise versions.
Convolutional Neural Networks (CNN) provides the ability of profound learning to monitor vision. A number of the current improvements in computer vision like solitary Shot Multibox Detector (SSD) and Generative Adversarial Networks (GAN) are discovering image processing. )
By way of instance, employing a few of those techniques, videos, and images which are taken in low light and very low resolution could be improved into HD quality. The continuing research in computer vision is now the foundation for picture processing in health care, protection, transport and other domain names.
A number of these emerging ML methods like Capsule Neural Networks (CapsNet) will basically alter the manner ML versions are deployed and trained. They'll have the ability to create models that forecast with precision even if trained using restricted data.
Accessibility To Historical Data Sets: Ahead of Cloud became mainstream, saving and accessing information was pricey. As a result of the cloud-based companies, academia and authorities are unlocking the information which was once restricted to the cassette cartridges and magnetic discs.
Data scientists want access to large historical datasets to educate ML models which may predict with greater precision. The efficacy of an ML version is directly determined by the quality and dimensions of this dataset. To address complicated problems.
With information storage and recovery becoming more economical, government agencies, healthcare institutions, and institutions are creating unstructured information accessible to the research area.
One can intuitively surmise artificial intelligence (AI) is today's hot commodity, gaining traction in businesses, academia and government in recent years.
Now, there is data -- all in one place -- that documents growth across many indicators, including startups, venture capital, job openings and academic programs.
One key measure of AI development is startups and venture capital funding. From January 2015 to January 2018, active AI startups increased 2.1x, while all active startups increased 1.3x, report states.
The AI Index also cited McKinsey data that demonstrated the types of AI solutions being deployed in organizations.
In North American organizations, the main forms of AI include the following:
- Robotic process automation 23%
- Machine learning 23%
- Conversational interfaces 20%
- Computer vision 20%
- Natural language text understanding 17%
- Natural language speech understanding 16%
- Natural language generation 11%
The AI and machine learning practices can be seen everywhere in the area of computer science. It gives you an idea of the possible number of ways how can design a computer system. It is made to perform the cognitive functions as described by the humans.
By 2035, it has been projected that AI will add $814 billion to the economy of UK with increasing rates from 2.5 to 3.5%. The capabilities of AI are immense. For instance, there is a machine that has mastered the complex game Go, that was thought to be a difficult challenge if at all artificial processing was applied to the game.
Vehicles are now operating independently, including trucks that are remotely driven by one operator. There is a proliferation of automated parts and robotic devices when it comes to completing tasks with ease; these accomplishments are giving new rise to the revolution of AI.
