While artificial intelligence increases in popularity, it can be a bit of a challenge to understand some of the technical terms and concepts surrounding this complex technology.
This fascinating new approach to life has come to affect us not only on a personal level, but is also transforming our economic and political systems for the better; enhancing, improving and altering our reality into something straight out of a sci-fi novel.
Knowing exactly what algorithms are, what big data means and how machine learning and natural language processing will come to affect pretty much everything that we do from a business and personal perspective will help provide you with a better understanding of how AI acts as a friend, rather than a foe.
Here are some of the most common terms that surround this field:
9. Artificial Intelligence (AI)
Artificial intelligence is considered a branch of computer science that focuses on giving computers and other ‘smart machines’ the ability to imitate and replicate human intelligence. And usually at a much higher level.
Humans are able to make decisions based on data/information, experience and intuition. AI is the science behind creating and implementing an artificial version of that decision-making process. Mainly for the use of computers and other electronic devices.
We have the ability to identify, distinguish and engage with objects, actions and processes in our surroundings. This is based on our intelligence. AI takes those abilities and digitizes them, providing technology with the same intellectual capacity and capabilities. Usually in a far more accurate, effective and efficient way.
8. Algorithm
An algorithm is basically a set of rules or steps that a computer follows in order to provide specific outcomes. This outcome is usually in the form of a solution to a problem, or an answer to a calculation.
It basically tells a computer, or device what steps to take in order to complete a given task.
For example, a programmer can create a simple code that can act in the same way a calculator does; you input a sum, press enter and the result is an answer. The algorithm would be the steps that the programmer tells the code to follow in order for the calculation to occur.
Similar to the process of following a recipe to make that tasty stew. An algorithm is considered the steps that you follow to complete your gourmet masterpiece.
7. Machine Learning (ML)
Machine learning (ML) is taking algorithms and programming them to learn from processes, outcome(s) and experience. This is in order to produce a solution without the need of a programmer to manually input new algorithms.
It automatically learns how to perform tasks and provide insights based on the data that it has access to. Essentially acting in the way that humans do, i.e. learning from experience and taking action based on data, ML aims to learn, predict and act based on AI.
ML also calculates, learns and processes information way faster than we’re able to. And with accuracy that can’t be replicated by human intelligence and intuition.
If the algorithm is considered the steps required to make your stew, then ML could: make the stew for you, offer you different methods to cook it and even provide alternative ingredients or better pricing when you plan to make the next one.
6. Big Data
Big data refers to vast amounts of data that can’t be processed by traditional computing. This data is usually found in enterprises that constantly collect and store information from customers, sensors, employees, etc.
With so much data frequently being stored and collected, it goes without saying that it needs to be processed and utilised in effective ways.
That’s where modern technology and infrastructures come into play. It takes new practices and approaches to extract and organise all of that data. Data used for analysis, predictions, insights and the necessary actions for better profits, enhanced customer experiences and increased productivity.
5. Robotics
Robotics is concerned with combining AI and engineering. This is to produce intelligent machines that are capable of handling tasks and processes more efficiently than humans do.
The purpose and intent of designing, producing and using these robots is to assist in some of the activities that we do on a daily basis. For example, packaging, transportation, cooking, welding, etc.
Although it seems like a replacement for traditional roles filled by humans, robotics will help with dangerous, high-risk jobs.
4. API
An Application Programming Interface (API) allows for the interaction and exchange of information between different services and applications.
In simpler terms, it takes a user query or request to a system that extracts information from various points and produces an answer, result or solution based on that request.
For example, when you need to book a plane ticket and want to go through pricing options, but know how long it takes to go through each airlines’ website, you can make use of a website like Cheap Flights. Which makes use of APIs to take your request, send it through to different airlines and produce results that show you each airline and their corresponding prices, dates, etc.
3. Data Mining
While it might be confused with simply gathering data for particular uses, data mining refers to the actual process of sorting through large amounts of raw data. The aim is to extract useful information and discover patterns to find correlations that will aid in producing specific outcomes.
This process involves using software to turn raw (and chaotic) data into information that can be used for analytical purposes.
For example, businesses want to discover insights about their customers (marketing, sales) or employees (retention, morale and productivity). So they can use data mining methods to gain valuable insight and information to then act upon.
2. Neural Networks (Artificial Neural Networks)
Artificial neural networks (ANN) are a set of algorithms that replicate or mimic the neural networks within the human brain.
Both humans and animals have neural networks that work to identify, distinguish and learn patterns in our environments. This is so that we’re able to make effective decisions, avoid risks and function symbiotically within society or nature.
Through AI, technology is able to replicate the way in which our brains operate. This is done by applying algorithms that have the ability to both learn and predict outcomes. Some of the primary uses for artificial neural networks include sales forecasting, risk management, anomaly detection and natural language processing.
1. Natural Language Processing (NLP)
Natural language processing (NLP) is a combination of sciences including linguistics and AI to read and use language like humans.
It allows computers to analyse and process human language and make use of it in effective ways; software and smart devices are able to identify, interpret and read text, recognise speech and determine meaning.
A great use for this is in recognising and translating text from different languages. This allows us to understand what might be said or written. For example, an app called Waygo is able to read Chinese characters and directly translate them into English through your smartphone camera.
It also helps chatbots interpret and understand language, so that it outputs useful and relevant information to the user. NLP can also help identify customer pain points through text analysis, and it’s even the process behind auto-correct and grammar correction in things like Grammarly.
Indeed, AI might have brought us a whole new set of terminology, but we’re already adapting to and making use of it in more ways than one…
That’s why it’s far more beneficial to know these terms, what they entail and how we already engage with this new technology, instead of being kept in the dark and having a panic attack every time we hear things like big data, machine learning and natural language processing.
