For the past year, AI has taken root in just about every aspect of the online business sphere. The vast majority of businesses have experimented with it by using tools such as ChatGPT and Midjourney.
However, few are truly familiar with the basics of this technology, and the definition of common terms. This can be problematic, especially if you’re an entrepreneur planning to leverage AI for your business.
We’ve got you covered.
Here’s a comprehensive glossary of 15 AI terms you need to know to make an informed decision about which tools to use.
AI
To start off with the very basics, there’s AI itself – short for artificial intelligence. It’s generally used as an umbrella term to denote a huge range of technologies. AIs are computer programs that process staggering amounts of data to imitate the cognitive function of a human brain, including creating language and art.
Computer algorithm
Next up, there’s the term ‘algorithm’ or ‘computer algorithm’. This describes a computer program following a set of instructions, such as carrying out a certain sequence of tasks to process and analyze data.
Some of the most influential algorithms are those used by social media platforms like Facebook and TikTok. The platforms use them to analyze user behavior and determine which content to recommend.
Machine Learning
Another commonly-used term in the AI sphere is Machine Learning (ML). This describes a particular type of algorithm that processes a large dataset to identify invisible patterns and connections. Facial recognition is a prime example of ML – with sufficient data, it can identify individuals even in a crowd.
Model
Not talking about Gigi Hadid here. In AI, a model is a computer program that an ML algorithm has trained to carry out one specific task. It’s specialized and not able to branch out by itself. For instance, Midjourney is a model trained to generate images. While it’s amazing at that, it can’t suddenly start generating text.
Not all of them are huge – smartphone camera apps today use AI editing tools that run on the device itself. A growing number of smartphones now include dedicated AI chips just for this kind of usage.
Generative AI
Talking about generating, ‘generative AI’ is another frequently used term that needs some explanation. While many AI algorithms are used to process and analyze data, the mission of generative AI is to create ‘new’ content. From DALL-E to ChatGPT, these models have received massive media attention recently.
However, it’s important to keep in mind that generative AI is not capable of true creativity. The ‘original’ content it creates is based on patterns identified from a massive training dataset. It regurgitates and reshuffles these patterns to create ‘new’ content, but often misses contextual nuance.
Training Data
Training data is a term that we’ve already used several times throughout this article. It’s a summary word for all the data that developers feed an AI to help it learn patterns. This data has a critical impact on the performance of the resulting model – it’ll only be capable of working with patterns that exist in its training data. Similarly, if the training data is biased (in terms of pronouns, for instance), the AI will adopt that bias.
Supervised Learning
We’ve mentioned training an algorithm before. It’s important to know that there are two ways of doing so – supervised and unsupervised learning.
In supervised learning, you give the algorithm a clearly labeled training dataset.
For instance, say you want it to be able to identify crops in satellite images. To train it, you give it a set of GPS points that are labelled with the crop types you want it to recognize, such as rice or mango trees. It will then take this data, look at the satellite images, and identify the characteristics that correspond to each type. That way, it will be able to re-identify the same crops in other images.
Unsupervised Learning
In contrast, unsupervised learning trains AI by having it identify patterns in input data without any human-generated training sets.
Staying with the satellite image example, you can also train an algorithm by having it cluster together pixels with similar characteristics – for instance, to separate flooded areas from those that aren’t during an inundation event.
Neural Networks / Deep Learning
Next up, there’s artificial neural networks (ANNs), which are used in an ML technique called deep learning. Neural networks imitate the structure of neurons in the brain. An ANN is constructed of several interconnected layers of nodes, each representing a neuron. Every layer processes data in its own right and then outputs the results to be taken up by the next neuron. The largest models, like the one underlying ChatGPT, have billions of these nodes.
Parameters
When training AI algorithms, parameters are factors that you can modify. Taking up the example of unsupervised learning, for instance, you can tell the algorithm you’re training how many types of clusters you want to sort your data into. Similarly, you can identify weights for characteristics and thresholds for sorting.
Natural Language Processing (NLP)
One of the tricky tasks that AI has mastered over the past few years is natural language processing (NLP). This term is used to refer to a particular kind of AI that can understand and imitate written or spoken language. NLP is responsible, for example, for voice-activated devices.
Transformer
Introduced by Google in 2017, transformers are a kind of AI architecture that relies on ‘tokenization’. This process turn a string of symbols into data, then analyze it for patterns. This approach is the basis for the majority of today’s prominent AI models. The “GPT” in ChatGPT, for instance, stands for Generative Pre-trained Transformer.
Tokens
Tokens are elements that have been transformed to serve as input data for an AI. For instance, any ChatGPT prompt is turned into tokens before it is processed.
Hallucinations
A hallucination is an AI output that seems perfectly plausible but is, in fact, utter nonsense. For instance, students have submitted papers generated by ChatGPT – only for their professors to find that some of the papers cited in the work are completely made-up.
Application Program Interface (API)
Finally, one common term that’s often used in connection with AI is API – application program interface. The ChatGPT website, for instance, talks to the API of the actual model in the background. This gives users a friendly graphic interface they can harness to submit prompts without understanding any of the underlying code.
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