One of the hottest trends across all industries today, machine learning is driving an explosion in the capabilities of artificial intelligence. Gartner, in its most recent Hype Cycle for Emerging Technology, put machine learning at the peak. The firm predicted that by 2020, artificial intelligence technologies, including machine learning, will be virtually pervasive in almost every new software product and service.
Moreover, according to the analysts at IDC, machine learning will continue to grow at more than 50% rate per year through 2020, when the total AI revenue could top $46 million. David Schubmehl, research director, cognitive systems and content analytics at IDC, said, “Cognitive/AI systems are quickly becoming a key part of IT infrastructure and all enterprises need to understand and plan for the adoption and use of these technologies in their organizations.”
All that being said, what exactly is machine learning? Does it deserve all the hype it is receiving? What is its relation to AI and what technologies should you know about to make the most of machine learning?
Here are the above and beyond answers to all the questions, you have related to machine learning!
What is machine learning & why it matters
When it comes to the history, the first person ever to use the phrase “machine learning” was likely Arthur Samuel, who developed one of the first computer programs for playing checkers. In 1959, he defined machine learning as the technology that gives “computers the ability to learn without being explicitly programmed.” Other computer scientists have proposed more mathematical definitions for machine learning, but Samuel’s definition remains one of the most accurate and easiest to understand.
Machine learning is a subfield of artificial intelligence (AI). The goal of machine learning generally is to understand the structure of data and fit that data into models that can be understood and utilized by people.
Although machine learning is a field within computer science, it differs from traditional computational approaches. In traditional computing, algorithms are sets of explicitly programmed instructions used by computers to calculate or problem solve. Machine learning algorithms instead allow for computers to train on data inputs and use statistical analysis in order to output values that fall within a specific range. Because of this, machine learning facilitates computers in building models from sample data in order to automate decision-making processes based on data inputs.
Machine learning examples for beginners
Machine learning can achieve some pretty impressive feats in AI, but it’s also responsible for simpler, but still incredibly useful applications.
One good illustration of machine learning in action is the so-called “spam” filter that your email system most likely uses to distinguish between useful emails and unsolicited junk mail. To do this, such filters will include rules entered by the programmer, to which it can add numbers that — when added up — will give a good indication of whether or not the software thinks the email is good to show you.
The problem is that rules are subjective. A rule that filters out emails with a low ratio of the image to text isn’t so useful if you’re a graphic designer, who is likely to receive lots of useful emails that meet these parameters. As a result, machine learning allows the software to adapt to each user based on his or her own requirements. When the system flags some emails as spam, the user’s response to these emails (either reading or deleting them) will help train the AI agent to better deal with this kind of email in the future.
One of the most obvious demonstrations of the power of machine learning are virtual assistants, such as Apple’s Siri, Amazon’s Alexa, the Google Assistant, and Microsoft Cortana. Each relies heavily on machine learning to support their voice recognition and ability to understand natural language, as well as needing an immense corpus to draw upon to answer queries.
Additionally, any technology user today has benefitted from machine learning. Facial recognition technology allows social media platforms to help users tag and share photos of friends. Optical character recognition (OCR) technology converts images of text into movable type. Recommendation engines, powered by machine learning, suggest what movies or television shows to watch next based on user preferences. Self-driving cars that rely on machine learning to navigate may soon be available to consumers.
Introduction to the different types of machine learning
In machine learning, tasks are generally classified into broad categories. These categories are based on how learning is received or how feedback on the learning is given to the system developed.
Two of the most widely adopted machine learning methods are supervised learning which trains algorithms based on example input and output data that is labeled by humans, and unsupervised learning which provides the algorithm with no labeled data in order to allow it to find structure within its input data. However, there are categories involved as well. Here is a brief introduction to all the types of machine learning:
This type requires a programmer or teacher who offers examples of which inputs line up with which outputs. For example, if you wanted to use supervised learning to teach a computer to recognize pictures of cats, you would provide it with a whole bunch of images, some which were labeled as “cats” and some of which were labeled as “not cats.” The machine learning algorithms would help the system learn to generalize the concepts so that it could identify cats in images it hadn’t encountered before.
In unsupervised learning, data is unlabelled, so the learning algorithm is left to find commonalities among its input data. As unlabelled data are more abundant than labeled data, machine learning methods that facilitate unsupervised learning are particularly valuable.
The goal of unsupervised learning may be as straightforward as discovering hidden patterns within a dataset, but it may also have a goal of feature learning, which allows the computational machine to automatically discover the representations that are needed to classify raw data.
Without being told a “correct” answer, unsupervised learning methods can look at complex data that is more expansive and seemingly unrelated in order to organize it in potentially meaningful ways. Unsupervised learning is often used for anomaly detection including for fraudulent credit card purchases, and recommender systems that recommend what products to buy next. In unsupervised learning, untagged photos of dogs can be used as input data for the algorithm to find likenesses and classify dog photos together.
The importance of huge sets of labeled data for training machine-learning systems may diminish over time, due to the rise of semi-supervised learning.
As the name suggests, the approach mixes supervised and unsupervised learning. The technique relies upon using a small amount of labeled data and a large amount of unlabeled data to train systems. The labeled data is used to partially train a machine-learning model, and then that partially trained model is used to label the unlabelled data, a process called pseudo-labeling. The model is then trained on the resulting mix of the labeled and pseudo-labeled data.
The viability of semi-supervised learning has been boosted recently by Generative Adversarial Networks ( GANs), machine-learning systems that can use labeled data to generate completely new data, for example creating new images of Pokemon from existing images, which in turn can be used to help train a machine-learning model.
This involves a system receiving feedback analogous to punishments and rewards. A classic example of reinforcement learning (as it applies to machine learning) is a gambler sitting in front of a row of slot machines. At first, the gambler doesn’t know which slots will pay off or how well, so he tries them all. Over time, he discovers that some of the machines are set “looser,” so that they pay off more frequently and in higher amounts. As time passed, the gambler — or in this case, the computer program — would increase his earnings by playing the looser machines more often.
The Difference Between AI & Machine Learning
Machine learning may have enjoyed the enormous success of late, but it is just one method for achieving artificial intelligence.
Machine learning and AI are not interchangeable terms, but they are related to each other. You could have AI without machine learning but AI machines would take forever to develop and cost an enormous amount of time and money. Machine learning allows for the machine to learn on its own and complete tasks. In order for AIs to rapidly develop and accurately complete tasks, the machine must be able to learn for any and all situations.
The easiest way to think about the difference between AI and machine learning is that AI is the broader concept of machines with the capability of completing tasks in a human-like manner. Machine learning makes it easier for machines to access and interpret data on their own.
The Role of Neural Networks in Machine Learning
A very important group of algorithms for both supervised and unsupervised machine learning are neural networks. These underlie much of machine learning, and while simple models like linear regression used can be used to make predictions based on a small number of data features, as in the Google example with beer and wine, neural networks are useful when dealing with large sets of data with many features.
Neural networks, whose structure is loosely inspired by that of the brain, are interconnected layers of algorithms, called neurons, which feed data into each other, with the output of the preceding layer being the input of the subsequent layer. Each layer can be thought of as recognizing different features of the overall data.
There are various types of neural networks, with different strengths and weaknesses. Recurrent neural networks are a type of neural net particularly well suited to language processing and speech recognition, while convolutional neural networks are more commonly used in image recognition. The design of neural networks is also evolving, with researchers recently devising a more efficient design for an effective type of deep neural network called long short-term memory or LSTM, allowing it to operate fast enough to be used in on-demand systems like Google Translate.
The Most Popular Machine Learning Languages
While not used for consumers, if you are looking to take up a course in machine learnings, you will have to learn the various languages associated with this phenomenon. Here are some of the popular languages you’d come across:
The Python machine learning language is a data science book that has been in use with the manufacturing industry for a while now and is routinely being used to bring production systems into operations. Python is a class-leading machine learning system that gives users direct access to predictive analytics, therefore proving itself to be the world’s foremost data science languages. It has become the machine language of choice for developers who are looking to frame better questions or expand the capabilities of their existing machine learning systems.
Python is a comprehensive language that covers a range of libraries, including those of Teano, Keras and sci-kit-learn. It also features easy to comprehend walkthroughs and even useful tips from opinion analysis to neural networks, allowing users to find answers to complicated issues. Python is one of the increasingly popular scientific languages, and its user-friendliness only adds to its appeal. Python is also a useful communication tool that takes us one step closer to a future of reproducibility. On the downside, Python is comparatively more fragmented than other machine languages, which can drastically reduce productivity.
Machine learning is a sequence of complex algorithms and not mere black magic, and the C-family machine learning language is the perfect example of how good design and user-centric features can automate sequences. For consequential production implementations, C offers users a robust library that allows them to customize implementations of project-specific algorithms.
The Java/C-family machine learning language is a haven for the seasoned developer who has the time to make minor tweaks using comprehensive libraries. It is no surprise then that most current and older machine learning algorithms are written in Java. Even deep learning implementations for LIBLINEAR and LIBSVM are written in C-family language, with Python and other tools used as leverage. Java is a functional programming language that will allow future machine learning systems with speed, accuracy, and precision.
This is an open-source programming language used primarily for statistical computing. It has grown in popularity over recent years and is favored by many in academia. R is not typically used in industrial production environments but has risen in industrial applications due to increased interest in data science. Popular packages for machine learning in R include caret (short for Classification And REgression Training) for creating predictive models, randomForest for classification and regression, and e1071 which includes functions for statistics and probability theory.
C++ language of choice for machine learning and artificial intelligence in game or robot applications (including robot locomotion). Embedded computing hardware developers and electronics engineers are more likely to favor C++ or C in machine learning applications due to their proficiency and level of control in the language. Some machine learning libraries you can use with C++ include the scalable mlpack, Dlib offering wide-ranging machine learning algorithms, and the modular and open-source Shark.
Machine Learning Tools to Make Your Software Intelligent
Google, Facebook, Apple, Microsoft and other tech giants standing on the bleeding edge of AI/ML innovation are actively investing in the democratization of artificial intelligence. In the recent years, these companies have open-sourced many AI/ML libraries, tools or started offering these solutions as a part of their commercial offerings and cloud services. Here are the top tools you could use to make your software intelligent!
TensorFlow Object Detection API
Object Detection API is a new feature integrated into TensorFlow, Google’s state-of-the-art software library for machine learning. The API provides a convenient way for ML developers and researchers to identify objects in images using optimized computer vision algorithms developed at Google. Object Detection API functionality comes with the MobileNets single shot detector optimized to run on mobile devices. Designed for the limited computational and power resources of smartphones, MobileNets makes it easier for mobile developers to integrate ML functionality into their mobile applications. If you want to use AI/ML functionality in your desktop software, Object Detection API provides a heavy-duty Inception-based CNN (Convolutional Neural Network) that is optimized for heavy data processing. In both cases, with Object Detection API, it becomes easier to integrate image recognition functionality into your software, which offers a great alternative to using cloud-based ML services.
Google’s Cloud Video Intelligence API
Video Intelligence API is part of Google Cloud Platform (GCP) ML services along with Google Natural Language API and Google Speech API. In a nutshell, Video Intelligence API is a suite of REST API functions that help users identify objects in videos, make videos searchable and make them discoverable. This functionality can be used to detect changes in scenes and objects and identify contexts to power video marketing, introduce interactivity into video content, detect pornographic content in the social networks or video streaming apps, label videos to generate meta-information and more. Since Video Intelligence API is provided as a REST service, there is no need to download any library or software. All you need to do is to register on the Google Cloud Platform and begin using Video Intelligence API via the standard cloud pay-as-you-go scheme.
Apple’s Core ML
In June 2017 Apple released its Core ML API designed to make AI faster on its iPhone, iPad, and Apple Watch products. The API covers all sorts of ML operations such as image and face recognition, object detection, NLP (natural language processing) and NLG (natural language generation). Core ML supports popular ML tools and models, including neural networks (deep, convolutional, recurrent), linear models and decision trees. It may be easily integrated into an Xcode development environment and become a part of your iOS app functionality. By making pre-trained ML models available for iOS developers, Apple’s Core ML promises to increase the scope of iOS applications with core AI/ML functionality available to users of Apple products. In addition, since Core ML is designed for on-device processing, it secures the privacy of user data and ensures that your app is running even if a network connection is broken. In pair with the efficient on-device performance that saves memory and power consumption, Core ML strongly establishes AI/ML as a part of Apple’s ecosystem.
Machine learning is a continuously developing field. Because of this, there are some considerations to keep in mind as you work with machine learning methodologies or analyze the impact of machine learning processes.
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