Needless to say, artificial intelligence and machine learning are at the forefront of technical innovation. From autonomous driving vehicles to predictive analytics, robotic manufacturing to smart homes, how we live, work and play will be impacted in profound ways.
At CloudFactory, we specialize in combining brilliant tech with a talented global workforce. We use our own machine learning algorithms to augment worker productivity with the vision of creating 10X workers. And, we’ve worked with some of the most innovative companies to develop ML data and algorithms that improve business processes and user experiences.
As talk of AI and machine learning permeates our world, the benefits for businesses are getting lost in a sea of technical jargon and prognostication by experts from all walks of life. At the end of the day, data is the fuel for any machine learning algorithm and it’s common for the terms used to describe how that data is used to become confusing. As folks who love data, machines, and especially people, we decided to put together a helpful guide to machine learning and the terms that businesses should know as they look to navigate this exciting and complex frontier of technological advancement.
What is Machine Learning vs. Artificial Intelligence?
Some confuse machine learning (ML) and artificial intelligence (AI), or use the terms interchangeably. They are similar, but it's important to know that they're not the same.
In basic terms, artificial intelligence is building technology that behaves like a human. Self-driving cars, Siri, smart homes, and many other emerging technologies are examples of AI.
Machine learning is a subset of artificial intelligence that uses algorithms to learn from data sets. Algorithms are essentially a series of steps that lead to the completion of a task. Using data and algorithms, ML technologies make intelligent predictions or perform actions.
Make sense? We hope so.
How Machine Learning Works
In traditional computing, a human programmer builds in every command to instruct a machine what to do in different situations. There is no action that wasn’t intentionally created by a human. In machine learning, the framework is developed and data is provided, so that machines can teach themselves what they need to know and understand what to do.
Why Accurate Data is Crucial to ML
Machine learning is modeled to work in a similar fashion to the human brain. However, there are still limitations to what technology can do. To create sophisticated and valuable machines, large data sets are critical.
Think about it, according to scientists, the human brain has around 90 billion nerve cells that are linked together by trillions of synapses, or pathways that allow signals to travel through. This forms our neural network and is what makes it possible for us to absorb data, think creatively, move, feel emotion and do everything that, well, makes us human.
In comparison to the human brain, even the most sophisticated supercomputers haven’t been able to match the processing speed. The closest attempt was by the K computer in Japan, and it took 705,024 processor cores and 40 minutes to mimic just one second of human brain activity.
Technology may still lack the power, intuition and social nuances that humans have, but it can also perform some tasks as well as, or even better than us. For instance, some may argue that for investing, it is better to rely on what the data tells us than our human instincts, or we may be able to dramatically reduce the number of human-caused car accidents year over year with smarter cars.
Key Machine Learning Data Terms
Now that we’ve established how machine learning and data work together, you may be able to imagine the applications that it could have in your business or life. For instance, investors use ML to make more intelligent and profitable investment decisions. At CloudFactory, we use machine learning in our WorkStream Flow platform to automate repeatable tasks and use predictive analytics to improve work results.
But, when it comes to data, there are some important terms that businesses should know.
Classification is when a set of data trains a machine to identify certain inputs or observations, and based on those, assign them to certain outputs or categories. It is an example of supervised learning, because it uses a set of labeled data examples and classifies any new data into those sets.
Classification should not be confused with clustering, which is an example of unsupervised learning, in which different sets of data are grouped together based on some relationship that the machine finds between them.
Data classification for machine learning has a number of real-life applications. For instance, in healthcare, if a patient has certain conditions, a computer may predict certain classes as the diagnosis. It has also been used in banking and for credit approval. Certain criteria, such as payment history, length of accounts and income data sets may be used to determine an individual’s approval.
In the traditional sense, an annotation is when we reference a piece of information back to the original source or to additional, relevant data. In machine learning, it’s very similar.
In order to be able to perform sophisticated functions like understanding and responding to human language and verbal commands, computers need to be able to find patterns and make inferences from the data. This is done by creating annotated examples that are in the background of datasets. Google tested this out with DeepMind and by linking articles written by CNN and Daily Mail by developing an AI machine that can read and reference other related materials.
3. Image Tagging
Image tagging is a fairly common practice in preparing and organizing image data for machine learning algorithms. Image tagging is the simple act of identifying objects, categories, or even human gestures to help organize data for ground truth data sets and ongoing machine learning applications.
However, manually identifying and processing image data is a tedious task that consumes large amounts of time and resources, particularly for data scientists. A recent survey indicates that some data scientists spend over 60% of their time organizing and preparing data for their algorithms. This is why many growing companies choose to outsource these tasks to CloudFactory. With the help of automation, CloudFactory has tagged over 15 million images and transcribed over 200 million, with over 99% accuracy.
4. Facial Recognition
One of the earliest things that we’re able to do as children is recognize faces, and technology is now able to do the same. Facial recognition has primarily been used for security purposes, but new possibilities in marketing and enhanced UX have made facial recognition an important aspect of machine learning. At CloudFactory, we work with companies to improve their facial recognition technologies by honing and developing ML algorithms.
5. Natural Language Processing (NLP)
Natural language processing or NLP is the ability for computers to interact in dialogue with humans, either audibly or through text. NLP machine learning technologies are built to understand and communicate with human languages. Technologies like Siri, Amazon Echo, and Google Home operate using NLP-based algorithms.
Communication is one of the most natural functions for humans, so it’s crucial to develop natural language processing in AI technologies. Many innovative companies are realizing the benefits that NLP holds and look to CloudFactory to develop it with speech recognition, text analysis and other solutions.
6. Sentiment Analysis
What if you could know what your customers, users or even investors felt about your company? While the ability to read minds is still the stuff of science fiction, technology has made it easier to discern and track what people think about you.
Sentiment analysis, also called opinion mining, is the process of studying words and determining their emotional meaning. In other words, you track what people say about you online and determine if those statements are positive, negative or neutral.
Now that people post almost everything on social media, review sites, forums and blogs, those opinions can be analyzed and tracked. CloudFactory’s does sentiment analysis by combining automation with our on-demand workforce to help brands track online feedback, gain insights and improve their experiences.
As apps like Pokémon GO have seemingly taken over the world (and caused temporary insanity), the use of virtual and augmented reality with mobile technologies is becoming more mainstream. But, bridging the gap between physical and digital spaces is still a major challenge. One of the keys to overcoming it is in developing better algorithms for landmark recognition.
Landmark recognition is the ability for technology to identify a building, physical object or landmark. To do this, technology relies on large amounts of image data and smart algorithms.
Machine learning is revolutionizing how we live, work, play and interact with the world around us. It holds the potential to augment human capabilities to make us smarter and more productive than ever. But, machine learning technologies are only as good as the data powering them. CloudFactory is helping companies by providing human intelligence to create accurate training data sets that improve ML algorithms. Find out more about how CloudFactory can help power your machine learning innovations.