Over the last decade, machine learning has become one of the primary elements for businesses and our lives. We have an immense flow of data which multiplies indefinitely every second, giving us tons of information to work with. This gives us a good reason to believe that smart data analysis will become the next big tool in giving a hand to technological progress.
So, ever wondered if you have had an encounter with machine learning in your daily life? The answer is Yes! We have plenty of machine learning applications - right from Netflix recommendations or word or sentence correction while using an online search engine to face detection used in smartphones. Machine learning is literally everywhere!
The big question that comes in one’s mind is what is machine learning and how will it revise the way all applications work today?
Machine learning, in simple terms, studies computer algorithms and learns to do stuff, like making accurate predictions or behaving intelligently, on its own. It is a form of data analysis that simply automated analytical model building.
The whole concept can be viewed as ‘programming by example’. We often have tasks in mind, for example, spam filtering. Rather than programming a computer to perform the task directly, what machine learning does is, it looks at new methods and algorithms by which the computer will come up with its own new program based on the task we want to get done with.
The entire emphasis here is on automatic methods where computers keep learning all by themselves without any human intervention. Few examples of machine learning problems are:
1. Face Detection: To locate or find faces in images eg. Facebook tagging
2. Optical Character Recognition: To categorize handwritten character images by letters represented
3. Language Recognition: Understand the meaning of words/sentences spoken in different language.
4. Spam Filtering: To categorize mails as spam or non-spam
5. Topic Categorization: To identify topics of articles eg. news, and categorize them into different segments like politics, entertainment, sports etc
This technology, today, has become a crucial aspect for several burgeoning and established organizations all over the world. Which is why, through this article, we bring to you a brief view of what machine learning actually is. Before we get into that, let us have a look as to how it all started!
Overview of Machine Learning
Machine learning is seen as a subset of artificial intelligence and has its roots dated around 1950’s. It was during that time when a scientist named Alan Turing created “Turing Test” that was used to test if computers have real intelligence.
In 1957, Frank Rosenblatt designed the first neural network for computers that stimulated the thought process similar to that of a human being. Similar invention continued till late 1980’s that included concepts like Explanation Based Learning (EBL), Stanford Cart and more.
It was in 1990’s when the work on machine learning shifted from a knowledge based to a data-driven approach. Later in 2006, the concept of “Deep Learning” was introduced that helped distinguish between objects, images and videos.
New platform and new ideas kept on coming ever since. Software like Deep Face by Facebook, distributed Machine Learning toolkit by Microsoft and some more by Google were adding to the diversity this technology could bring to the world.
At present, the concept of machine learning has taken a whole new dimension. New algorithms are being developed every passing day that is bringing us closer to artificial intelligence.
How does Machine Learning Work?
Machine Learning, a subset of artificial intelligence, is primarily made up of three parts-
• The computational algorithm that contains a set of codes and is the core of making determinations
• The different variables and features (labeled and unlabeled) that add on to the decision-making process
• Base knowledge for which the output or the answer is known. This can be used to train the system to self-learn and use for future references.
At first, the model is fed with parameters for which the answer is known. The algorithm then processes the data and make adjustments until the output (learning) agrees with the known answer. More the data, simpler it is for the systems to compute complex data and make decisions.
Types of Machine Learning Algorithms
The algorithms of Machine Learning are categorized on the basis of the desired outcome of the algorithm. The basic types include the following:
1. Supervised Learning: This form is used where the algorithm generates a function that maps inputs to the desired outputs. The main area where it’s used is for classification problems where a function maps a vector into several classes by analyzing various input-output examples.
2. Unsupervised Learning: This type of algorithm models a set of inputs where labeled examples are not available i.e data sets without historical data. Such algorithms explore surpassed data to find the structure.
3. Semi-Supervised Learning: This combines both labeled and unlabeled examples to provide the desired function.
4. Transduction: This is similar to supervised learning where instead of explicitly constructing a function, it predicts new outputs on the basis of training inputs and outputs or new inputs.
5. Reinforcement Learning: The algorithm is guided by the feedback that the environment provides after it is impacted by an action.
6. Learning to Learn: Similar to adaptive learning, here the algorithm learns its own inductive bias based on its previous experience.
Why is Machine Learning Important?
Machine learning has provided the world with many practical applications that are useful for businesses as well as daily human lives. It has now become an important element that drives profits for organizations and has the potential to dramatically impact its future as well. So, let us understand and analyze the various factors that prove that it is an important technical breakthrough for us.
• Machine Learning allows devices to be capable of categorizing, processing, and generating data and patterns based on customer’s buying and spending behavior.
• It also utilizes customer’s feedback, opinions and interactions, their peers, social groups and virtually everything that defines their behavior.
• Machine learning algorithms help enterprises to increase their flexibility of shop floors, supply chains, collaborative partnerships or even detect the favorable price points that customers will approve.
• Financial institutions are using Machine Learning technology for conducting a risk analysis and also for identifying potential cases of fraudulent claims.
• Today, machine learning holds the power to control applications such as real-time speech translation, gene mapping, biometric identification system, web-content curation, and many more.
• It can be also leveraged to be used as a smart assistant like Microsoft’s Cortana and Apple’s Siri
• Various mapping, pattern recognition and recommendations operations can be performed with the help of machine learning that can help in different kind of behavior analysis that can help in personalized customer service.
• Machine learning’s application for natural language processing (NLP) can help generate reports by establishing connections between concepts and analyzing texts.
• It is an important tool for image recognition, image tagging and image classification.
Machine Learning Use-cases
Machine learning is now entering every industry and is an integral part of reshaping, remolding and revolutionizing the way we see things around us. Let us now have a look as to how it is transforming industries in every sector.
Having a tool to perfectly predict stock markets is a dream of any investment firm, hedge funds and traders involved in this sector. Previously, individuals would study realms of data to get even the slightest hint of how the market is going to behave. Machine learning can help predict the performance of a stock more accurately and quickly than humans.
AI and Machine learning combined can save billions of lives in the future. They hold the capability of identifying life-threading diseases before they become fatal and serious. In combination with IoT, patient’s health data can be monitored on a regular basis and alerts can be triggered in case of an anomaly. They are a tool for humans to lead a healthy lifestyle.
Malware and ransomware attacks have reached a level like never before and so has the number of cases dealing with online frauds. Data security firms have now turned to machine learning to curb down the ill-effects. They can look into the patterns of how the attacks happened in the past and report in case a similar pattern is observed. They are also very useful in predicting any security breaches.
Customers are your biggest assets and the more you know them, the better you can serve them. Brands are now trying to pick up every tiny bit of customer information through any mode - social website, previous shopping history, sentiment analysis, feedback and more. They are then using it to provide a personalized experience for the customers. For example, Machine learning can be used to analyze the buying pattern of the individual and recommendations can be made on the basis of their past purchase. Cross-Selling and produce bundling are also being promoted through this.
Machine Learning and Artificial Intelligence are playing a huge role in the transition of vehicles to smart cars. They are being used to analyze the huge data coming from connected cars to optimize traffic patterns, identifying road hazards and other factors that play a role in passenger safety in self-driven vehicles.
Few Facts about Machine Learning
• Machine Learning and Artificial Intelligence are not the same. Machine learning is rather a subset of AI that provides a set of mathematical tools to imitate and mimic human behavior and develop a better man-machine relationship
• Machine Learning can only be as good as the data you feed it with. It can only be used to discover unique patterns that are present in the data you have presented. For supervised tasks like classification, you would require a robust collection of correctly labelled training data.
• Most of the work that machine learning is involved in is data transformation.
• Machine learning is the background of almost everything we do. It is the algorithms of this technology that detects an incorrectly spelt word in your word document, or the sequence of websites that occur after a search result.
• While machine learning is considered as the most advanced technology available today, a little involvement of humans might actually improve the accuracy and self-awareness brought in by it.
• As limitless as its potential is, it is being used in advanced technology like blockchain to understand the various patterns of transaction that happens and report in case of any anomaly present.
• As powerful as the technology may look, the learning systems are highly vulnerable to operator error and might require human assistance to make it more efficient
• Once the ML system embeds biases into its model, it then continues to generating new training data that reinforce those biases.
Machine Learning and IoT - The Next Step!
As we look into finding newer technologies and knowing in what different ways can they transform our lives, IoT being fueled by machine learning is something to look forward to.
• IoT can provide machine learning with a huge amount of data that can help make complex decisions in no time. Companies can perform various analytical processes like predictive analysis etc, that can be very useful to predict the future market trends that can help them lay down successful strategies for the time to come.
• One of the biggest advantages that machine learning and its algorithms have provided to IoT is its capability to easily integrate with the various IoT’s platforms. With the exponential increase in the number of IoT devices, especially with the proliferation of mobile devices across the globe, machine learning is the perfect fit in the process of device development, programming and maintenance.
A good deal of research is still in process to combine these two technologies and use them together to provide advanced results that can help make processes simpler and more accurate. We shall look further into the scope of these two technologies together in our further articles.
Future of Machine Learning
Data has a whole new meaning today owing to the transformation brought in by machine learning. Computers are no more required to be taught to perform complex tasks like text translation or image recognition. Instead, they are doing all by themselves, thus mitigating any previous pitfalls and impasses experienced during programming.
It has already entered various sectors from healthcare to transportation and even sales and marketing. It is also being used by governments to simplify the way processes and information is being handled. It just needs to be adapted by more and more organizations to bring a change.
It is predicted that with a combination of artificial intelligence and advanced machine learning, will deliver more systems that can understand – learn – adapt – predict and potentially operate autonomously. With cheap hardware, better storage technology, advanced algorithms and massive data streams available at this time, it promises to contribute to the success of Machine Learning powered sophisticated artificial intelligence devices.
With wars among the leading Machine Learning Platform providers like IBM, Microsoft, Google and Facebook getting aggressive with each passing day, it promises to give us more sophisticated smart apps, digital assistants and more.
This in addition with provisions of cloud-based IT services, machine learning can now reach out to anyone with a laptop and internet connection. This is a positive step towards democratization of machine learning that enables self-service business intelligence and analytics.
According to Gartner, advanced machine learning can help push embryonic technologies reach a new level, in the future. Few of them include:
1. People-Literate technology (PLT) – This new technology promises us the ability to interact with computers with the help of blank and open dialogue boxes that retains and re-uses the past conversation we have with the device. It is believed that by 2020, 40% of interaction humans have with computers will be done via this mode.
2. Brain-Computer Interface - This technology claims to provide brain patterns to computers that can help control a device or programs. It enables signals from the brains to direct a certain external activity like a prosthetic limb.
3. Bioacoustics – Bioacoustics combines biology and acoustics that connects humans with digital businesses and workplaces.
With all this in place, the following are few assured trends in the field of machine learning that is sure to be a part of the future.
- The demand and supply gap of data science and machine learning skills will be on the rise till more academic institutions and enterprises collaborate to train future experts in this field.
- Most businesses will adapt to algorithmic models to continue with their daily operations and customer facing functions.
- With increased dependency of humans on machines, they are supposed to find peace and work together in the future
What are the new possibilities in store for us? Machine learning is believed to provide exciting possibilities that include advanced personalized healthcare, self-detecting data security programs, computer assisted security in public places like airports, advanced fraud detection in financial domain, being a universal translator and more.
Machine learning is sure to give a new dimension to the world we live in. New ideas and applications are sure to develop in days to come, which were once just a thought.
Without a doubt, machine learning is following the path to be a technology with far-reaching transformative powers. It is entering different industries and revolutionizing the way they work or think and is definitely the key which has unlocked future applications giving unlimited opportunities for companies to grow.
The future certainly needs machine learning and with other technologies like artificial intelligence and IoT together, a smarter world is not a distant dream anymore.