In recent years, the digital world has been faced with more buzzwords than ever before, and as technology grows more complex, so does the way we describe it.
The problem is, ideas and software are developing at a rapid pace. One that is very hard to keep up with unless you’re investing time in just about every day in order to do so. That leaves the general public mostly unaware and lacking understanding about the important technological progress we’re in the midst of making.
If you’ve just tuned back into the world of tech and you find yourself confused by the latest buzzwords, with understanding of their explanation quickly becoming an expectation of common knowledge, you’re far from alone. Hopefully, this overview will help you grasp the meaning of each one of these buzzwords.
Artificial Intelligence / AI
Although you’ve definitely been hearing a lot more about it lately, artificial intelligence actually dates all the way back to 1956. John McCarthy, a researcher at Stanford, was actually the first person to coin the term. He defined it as a sub-field of computer science. AI, in short, is the ability for a machine/computer program to learn and think.
Many people consider AI a type of technology, but it’s more accurate to see it as a broad concept. A concept where machines can handle tasks in a way we’d consider smart or intelligence. In order to call a machine or program artificially intelligent, it must be capable of doing certain things.
Number one, it needs to be capable of mimicking the human thought process and human behavior. Number two, it needs to act in a human-like way, which generally means acting in an intelligent, rational, and ethical manner.
Although the terms AI and Machine Learning are sometimes used interchangeably, they aren’t the same. AI is a broad concept. ML is AI’s most common application. AI includes machine learning and other technology, like neural networks and natural language processing, within it.
In science fiction, AI is generally what fuels robot takeovers and machines that can outsmart their human counterparts. In reality, it has a very practical application, and its widespread use isn’t as far off as many people think. AI is actually already being used to affect many parts of your daily life.
Voice assistants, like Alexa and Siri, for example, are capable of recognizing our speech, analyzing the information, and then providing an answer or solution—or, at least, trying to do so. These assistances are continuously learning about the people they are interacting with, so much so that they’ll eventually be able to accurately anticipate what you need.
Other touch points that connect you with AI include Apple Music, Pandora, and Spotify. These services help you find music you may like based on the things you express interest in. They monitor your choices and insert them into a computer learning algorithm so they can customize suggestions just for you. This is among the simplest uses of AI, but it’s definitely a feature you’re likely to find yourself appreciating.
In addition to the above, AI is quickly making progress in some other areas that you might not expect. For instance, many of the short news stories you read on Yahoo! or the Associated Press are likely written by artificial intelligence. That’s right, AI is already capable of basic robot-fuels writing. Although it’s not yet capable of composing creative novels or an in-depth article, it can write a simple and short article, like a financial summary or sports recap.
Self-driving cars, online games like Alien: Isolation, and smart home devices such as Google’s Nest are other examples of artificial intelligence being used in your daily life.
Machine Learning / ML
Machine learning should be understood as an application of AI. Machine learning, or ML, is focused on developing programs capable of accessing data and automatically learning from it. By doing so, they don’t require the assistance or intervention of a human in order to grow their intelligence. The concept is based n the belief that humans should give data to machines and let them learn from it own their own.
As such, machine learning is quickly taking off as one of the primary applications for AI. The reason being is that, in recent years, it has been widely recognized that our data is growing at a pace that far surpasses our ability to process it on our own. This means that companies and organizations of all kinds are missing out on crucial data that could be helping them do things better.
Everything an AI program learns through machine learning comes directly from the data it processes. It learns how to best behave in the future based on information from the past. “Programming by example” is a good way to describe machine learning. If a company wants to filter spam from users’ inboxes, for instance, they could just program a system that picks up on certain words or senders and sends it to spam. But, for a more accurate spam filter, they could use machine learning in which the computer will be able to create its own filter based on thousands (or even hundreds of thousands) of spam emails used as an example database.
With these examples, the computer will be able to pick up on patterns that a human may never see. And, of course, a program can do so much more quickly and efficiently than a human could. When it comes to processing large droves of data, a machine learning program is obviously the way to go. Basically all AI programs depend on or incorporating ML since we wouldn’t really consider an artificially intelligent program “intelligent” if it couldn’t learn through experience.
Like AI, machine learning has been around for a long time, but the ability to apply complex mathematical calculations automatically, over and over again to big datasets, gradually getting faster and faster, is a new development in the field that has given it massive momentum.
Supervised and unsupervised learning are the two most common learning methods for a machine learning program to use, but there are other methods, such as semi-supervised learning and reinforcement learning.
With supervised learning, the program is basically given a set of questions to which the programmer already knows the answer. The program will learn by comparing the outputs it came up with to the correct outputs the programmer had from the real world. The model will then be modified accordingly. In situations where historical data is likely to predict future events, supervised learning is most often used. For instance, it can be used to anticipate an insurance customer most likely to file a claim or a fraudulent credit card transaction.
On the other hand, unsupervised learning is used when data lacks historical labels. The system isn’t given the correct answer so it must figure out what’s being given to it. It has to explore the data provided and find some structure to it. It works best with transactional data. For instance, this method could be used to identify customer segments that share attributes or to find the main attribute that separates one segment from another.
Somewhere in-between, semi-supervised learning is used for similar applications as supervised learning, but it uses both labeled and unlabeled data. Usually, are unlabeled is used than labeled since it takes less effort to acquire unlabeled data. Reinforcement learning uses a trial and error approach so the program can figure out which action gets the best reward. This is often used for gaming, robotics, and navigation.
Neural Networks / NN
A neural network (NN) would be more properly called an artificial neural network so as not to confuse the subject with a real human’s nervous system. However, whether you call it the NN or the ANN, the real difficulty is in understanding its relevance to AI.
In the simplest way possible, a neural network is a computing system. Multiple highly interconnected processing elements make up the NN, allowing it to review and sort external inputs. The idea of an artificial neural network is to mimic how a human’s brain works.
A human’s brain is constantly categorizing and processing information. An artificial neural network works to do the same. A NN is a processing device, whether actual hardware or a computer algorithm, that loosely models itself after the human brain, yet on a much smaller scale. A large NN may have hundreds or maybe even thousands of neurons (processor units), but a real human brain has billions of neurons.
Typically, a NN will be organized into layers. Each layer is made up of interconnected nodes, each of which contains an activation function. The input layer is where patterns are introduced to the neural network. This layer communicates with the hidden layers of the network. That’s where the actual processing takes place.
The neural network processes inputs using a system of weighted connections. When finished processing, the hidden layers link to the output layer. That’s where the answer/output is shown. Typically, the NN will contain a learning rule that will modify the weight of each connection according to the input patterns being introduced to it. In a way, a neural network is learning by example just as a child would. For instance, just like a child, an NN learns to recognize cats by looking at examples of cats.
In general, neural networks do a great job when it comes to matching up patterns and then pinpointing subtle trends within a set of very diverse data. That’s what’s great about a neural network: it’s able to make progress towards a goal even if the development team is not sure how to find a solution to the problem. Complex or poorly understood problems are, therefore, a good candidate for neural network application.
Image classification is a fantastic example because the development team probably won’t be capable of writing out all the rules that should be used to determine whether or not an image contains a cat. However, if you give it enough examples, the neural network will be able to figure out for itself what the primary features are when an image does contain a cat.
More scientifically, a neural network could be used to identify the signature of a planetary transit even when the development team hasn’t told it what features are significant. To do so, it would just need a set of light curves that don’t correspond to planetary transits and another set that does.
Overall, a neural network is very flexible and can be used to make predictions, clarify data, and design systems of all sorts.
Deep learning is a technique within machine learning that enables a computer program to share a natural human trait: the ability to learn from example. This key piece of technology is behind revolutionary equipment, such as autonomous (self-driving) cars. With deep learning, a car is able to distinguish a tree from a pedestrian or recognize a stop sign on the side of the road. Deep learning is also behind voice control within TVs, phones, and other devices.
For these reasons, deep learning has also been receiving extra attention lately because it is allowing us to work towards achievements that, before now, were only part of science fiction.
With deep learning, a computer will be set up to classify images, text, or sounds. When given the opportunity, a deep learning model is capable of achieving incredibly accuracy that can even exceed the performance of a human. Using a tremendous amount of labeled data, along with multi-layer neural networks, models can be effectively trained.
Using deep learning, a machine learning program is able to achieve accuracy ratings exceeding anything previously possible. This is enabling consumer electronics to finally begin to meet user expectations and it’s also very critical for the safety of things such as driverless vehicles. When it comes to tasks such as classifying an object within an image, deep learning has recently begun outperforming humans.
The theory behind deep learning first became recognized in the 1980s, but due to lackluster computer technology, it has only become useful—or, more so, feasible—in the recent past.
Firstly, deep learning requires significant amounts of labeled data. Not only is this difficult and expensive to acquire, it also is expensive to store and process. A driverless car, for instance, requires millions upon millions of images along with thousands of hours worth of video.
Secondly, the deep learning model in itself can only run using substantial amounts of computing power. Today’s extra high performing GPUs are finally capable of producing enough power to get these programs running efficiently. Cloud computing/clusters were really the breakthrough allowing development teams to make deep learning happen in a matter of hours instead of weeks.
If a development team is being faced with a complex problem like natural language processing or image classification, deep learning will likely be utilized.
Internet of Things / IoT
The Internet of Things, or IoT, can be among the most difficult concepts to understand. In simplistic terms, an Internet of Things system will integrate four components together: a user interface, data processing, connectivity, and sensors/devices.
It all starts with the sensors or devices, which are responsible for collecting data from the environment around them. That might mean taking in a complete video feed from a security camera or simply reading the temperature within the room. In some instances, more than one sensor can be bundled together and the device may be responsible for more than one task. Sort of like how a smartphone has multiple sensors built into it (a GPs, accelerometer, camera, and so on).
The next part of the equation is connectivity. The data needs to be sent into the cloud, but there needs to be a way for it to get there. The sensors and devices can be directly connected to the cloud using WiFi, Bluetooth, satellite, cellular, or a variety of other methods. Every option has its pros and cons, considering range, bandwidth, and power consumption. But, they all serve the same purpose, which is loading the data into the cloud.
The third component is data processing. Once the data has made it into the cloud, software is utilized to process the data in some way. Maybe it’s making sure a room’s temperature reading is within the right range or it might be using computer vision to process a video feed and identify an object, such as an intruder inside your home.
So, what happens if the processing reveals that there is an issue, like the temperature is too hot or there is an intruder inside the house? The user interface is used to display the output in a user-friendly way. That might mean emailing you, texting you, or somehow notifying you that the data has been processed and an issue has been identified.
The user interface may be able to allow you to check on the system even before being alerted, letting you be proactive with your security or climate control. For example, you might be able to see the video feed through an app or browser.
The more interesting aspect, however, is that the IoT isn’t always one-way. In some cases, you might be able to affect the system through the interface. For example, you might be able to adjust the temperature of the room using a phone app or you might be able to arm the alarm with the swipe of a finger. Other actions can also be performed automatically based on rules you have defined, like turning on the air conditioner when the temperature rises past a certain point.
To recap, an IoT system is made up of four aspects. The sensors or devices “talk” to a cloud where the data is then processed. Based on the results of the processing, an action may be taken which a user can then review through an interface. Sensors or devices may be adjusted automatically or possibly can be reviewed and adjusted proactively by the user.
Industrial Internet of Things / IIOT
IIoT, or the Industrial Internet of Things, is the application of IoT to industrial and manufacturing processes. Often called the “industrial internet”, IIoT works to incorporate machine learning to process data collected by sensors while also integrating M2M (Machine-to-Machine) communication and different types of automation that have separately existed for years.
When it comes to implementing IIoT, development teams and those funding them are working on the philosophy that a smart machine is more effective at accurately and consistently working with real-time data than humans are. By capturing and communicating this data in an effective way, companies can find issues and inefficiencies sooner than they could if they waited for a human to pick up on them. This saves money and helps support BI, Business Intelligence, efforts.
Especially when it comes to the realm of manufacturing, IIoT has a lot of potential for sustainable practices, supply chain traceability, quality control, and the overall efficiency of a supply chain. When it comes to an industrial type setting, the IIoT can be used to enhance field service, energy management, predictive maintenance (PdM), and even asset tracking.
The IIoT is driven by a network of sensors and devices. Like with IoT, these sensors and devices are responsible for communicating the data they collect into the cloud, where software will process the information. The analysis will bring about valuable insights that companies are able to use to make better decisions for their business in a much faster manner.
An Industrial IoT system will have multiple components within it. The first set of components is known as intelligent assets, this includes sensors, controllers, security components, applications, and so on. They collect, store, and communicate data.
The next layer is the data communications infrastructure, better known as the cloud. This is where analysis takes place and business information is generated from the raw data being processed. The intelligent assets will transmit data into the communications infrastructure. It is then converted into information the company can take action on, like whether or not a machine is performing at high efficiency.
Predictive maintenance is certainly among one of the top benefits that the industrial internet of things brings with it. With predictive maintenance, an organization is able to use the real-time data their IIoT system is generating to predict potential defects in their machinery before they cause issues. This enables a company to address the problem before a machine breaks or a part goes bad.
The potential to improve field service is another significant benefit of the IIoT. With the IIoT, technicians are assisted in identifying issues that customers may face with their equipment before they have the chance to become a major problem, thus saving customers a lot of inconvenience.
Yet another benefit that comes along with the IIoT is asset tracking. Customers, manufacturers, and suppliers alike are able to use an asset tracking system to check the location, status, and condition of a product as it moves through the supply chain. Stakeholders can even setup to receive an alert in the event that the asset becomes damaged or is at risk of being damaged. This lets them take preventative action or find a fast solution to remedy the problem.
All of this has the potential to vastly improve a customer’s experience. Using the industrial IoT, a manufacturer is capable of capturing and analyzing important data about how their customers are using the products they purchase. This lets manufacturers and product designers build better roadmaps and customer-centric IoT devices so they can continuously improve their predictions and operations.
Finally, the industrial IoT is able to improve the management of a facility. All equipment is susceptible to problems overtime and factories may face changing conditions, such as temperature and humidity fluctuations. These things can lead to less than optimal conditions for the operation of the facilities, which is where the IIoT can be utilized to improve efficiency yet again.
There are many similarities between IIoT and IoT, like the use of cloud computing, sensors, connectivity, data analysis, and so on. But, they’re used for different applications. The IoT is able to connect multiple devices across different sectors, like agriculture, consumer utilities, governments, and even entire cities. Smart appliances, smartphones, and other devices all fit within the IoT–things that don’t generally cause an emergency ordeal if things go awry.
In contrast, IIoT applications include machines, oil, gas, industrial utilities, and manufacturing facilities. Downtime and system failures within the IIoT can lead to a high risk and potentially life-threatening situation. An IIoT network is also more focused on improving health, safety, and/or efficiency while IoT is more user-centric.
Industry 4.0 / i4.0
The ever-growing world of technology in combination with traditional manufacturing practices have blended together to make Industry 4.0. IIoT deployments and large-scale M2M are all included in Industry 4.0, which marks the use of increased automation to improve communication, monitoring, and overall output. With Industry 4.0, companies who embrace it will use technologies such as elf-diagnosis and all new levels of data analysis to improve productivity by unprecedented amounts.
With Industry 4.0, the goal is for factories to become increasingly more and more automated and more able to self-monitor as machines are given a way to communicate with both each other and their human counterparts. This frees up human workers for tasks that require their attention while resulting in smoother, more efficient operations overall.
The first industrial revolution came when the world moved from farming practices to be more focused on factory productions in the 1800s. The second industrial revolution began in the 1850s and lasted through World War I when steel was introduced, factories are electrified, and mass production began. The third industrial revolution came in the 1950s when the world made the change from analogue, electronic, and mechanical technology to digital technology.
Finally, we entered into the fourth industrial revolution when things shifted once again, now moving full force in the direction towards digitization. The Internet of Things and cyber-physical systems are only the beginning of an industry where companies of all sizes look towards automation and computing power to give them massive insight and greater efficiency.
Now, although scholars argue about when exactly we should consider Industry 4.0 to have begun, the first time the term Industry 4.0 was actually used was in 2013, in a German government memo. Since then, “Industrie 4.0” has been used worldwide to describe the applications of IoT and other technologies within the industrial and manufacturing sectors.
The memo that initially coined the term laid out a high-tech strategy in which plans to nearly completely automate the manufacturing industry were explained, almost negating the need for any human involvement whatsoever. Although we haven’t reached such a point yet, Industry 4.0 is upon us and German Chancellor, Angela Merkel, urged the world to embrace it and find ways to maximize it when speaking at the World Economic Forum, suggesting we need to be working more quickly towards an integrated i4.0 driven world.
Since the memo’s release, Germany has invested more than $215 million USD into research across academic scholars, businesses, and the government, and other countries are not trailing far behind. Globally, the Industry 4.0 market is estimated to be worth around $4 Trillion USD by 2020, encompassing the value of IoT and all the worthy technologies that fit within the ideal version of i4.0.
And, although many businesses are nowhere need prepared for the full adoption of i4.0 technologies, it is thought that every business on every level could benefit from doing so. One government report, led by Siemens UK, even claimed that fully utilizing this advanced tech could create around 175,000 jobs and boost the manufacturing sector by £445 billion in the United Kingdom.
In recent years, we have seen many new technologies develop that we’re just now starting to fully embrace and understand. While already being implemented actively into our daily lives–whether you realize it or not–these technologies are truly only being utilized on a very small, incomplete scale compared to their full potential.
In the coming years, it’s expected that computing power will continue to improve, these technologies will grow more and more accessible, and their use will grow more and more widespread as they continue to help us work, live, and play more efficiently.
So, you might want to dig into some of these buzzwords.