Over the past year, AI has been rooted in almost every aspect of the online business field. The majority of companies experimented with tools such as ChatGpt and Midjourney.
However, few people are familiar with the basics of this technology and the definition of general terminology. This can be a problem, especially if you are an entrepreneur who is planning to use AI in your business.
We have you covered.
Below is a comprehensive glossary of 15 AI terms you need to know to make an informed decision about the tools you use.
ai
To start with the very basics, there is AI itself – short for artificial intelligence. It is commonly used as an umbrella term for indicating a vast range of techniques. AIS is a computer program that processes an incredible amount of data to mimic cognitive functions in the human brain, such as creating language and art.
Computer Algorithms
Next is the term “algorithm” or “computer algorithm.” It explains the computer program according to a series of instructions, including performing a series of tasks to process and analyze data.
Some of the most influential algorithms are those used on social media platforms. Facebook and Tiktok. The platform uses them to analyze user behavior and determine what content it recommends.
Machine Learning
Another commonly used term in the AI field is machine learning (ML). This describes a particular type of algorithm that handles large data sets to identify invisible patterns and connections. Face recognition is a typical example of ML. With sufficient data, we can identify individuals within the crowd.
Model
We're not talking about Gigi Hadid here. In AI, a model is a computer program that an ML algorithm trains to perform one specific task. It is specialized and cannot be diverted by itself. For example, Midjourney is a model trained to generate images. That's surprising, but you can't suddenly start generating text.
Not all of them are huge. Today, smartphone camera apps use AI editing tools that run on the device itself. More and more smartphones include dedicated AI chips for this type of use.
Generation AI
Speaking of generation, “generating AI” is another frequently used term that needs explanation. While many AI algorithms are used to process and analyze data, the mission of generating AI is to create “new” content. From Dall-E to ChatGpt, these models have recently attracted media attention.
However, it is important to note that generative AI does not provide true creativity. The “original” content you create is based on patterns identified from a large training dataset. You reflux and modify these patterns to create “new” content, but often you miss the contextual nuance.
Training data
Training data is a term that I have already used several times throughout this article. This is a summary word for all data that helps developers feed AI and learn patterns. This data has a significant impact on the performance of the resulting model. You can manipulate patterns that exist in the training data. Similarly, if the training data is biased (for example, from a pronoun perspective), AI will employ that bias.
Monitored learning
I mentioned algorithm training before. It is important to know that there are two ways to do so. It is a supervised supervised and teacherless learning.
Teacher learning gives the algorithm a clearly labeled training dataset.
For example, let's say you want to be able to identify crops in satellite images. To train, give them a set of GPS points labeled with the type of crop you want to recognize, such as rice or mango trees. This data is then taken, looked at the satellite image to identify the characteristics corresponding to each type. This will allow you to re-identify the same crop in other images.
Unsupervised learning
In contrast, unsupervised learning trains AI by identifying patterns of input data without human-generated training sets.
By staying with the example satellite imagery, you can also train the algorithm. For example, pixels with similar characteristics can be grouped together to separate flooded areas from areas during flooded events, for example.
Neural Networks/Deep Learning
Next is the artificial neural network (ANNS) used in an ML technology called Deep Learning. Neural networks mimic the structure of neurons in the brain. An ANN consists of several layers of interconnected nodes, each representing a neuron. Every layer processes the data on its own and outputs the results taken up by the next neuron. The biggest models have billions of these nodes, like the underlying ChatGpt.
parameter
When training an AI algorithm, parameters are factors that can be changed. Taking an example of unsupervised learning, for example, you could convey the algorithms that are training the type of cluster that sorts the data. Similarly, you can identify sort characteristics and threshold weights.
Natural Language Processing (NLP)
One of the tricky tasks AI has mastered over the past few years is Natural Language Processing (NLP). This term is used to refer to a specific type of AI that can understand and imitate written or spoken language. NLP is responsible for voice-activated devices, for example.
transformer
Introduced by Google in 2017, Transformers is a kind of AI architecture that relies on “tokenization.” This process converts a set of symbols into data and analyzes the patterns. This approach is the basis of most of today's well-known AI models. For example, “GPT” in ChatGPT stands for Generation Pretraining Transformer.
token
A token is an element that has been converted to act as input data for AI. For example, the CHATGPT prompt is converted to a token before it is processed.
Hallucinations
Hallucinations are AI outputs that seem completely plausible, but they are actually completely nonsense. For example, students submitted papers generated by ChatGpt. It is only to find that some of the papers cited in the work are fully constructed.
Application Program Interface (API)
Finally, one common term commonly used in connection with AI is the API-Application Programming Interface. For example, the ChatGpt website describes the API for the actual model in the background. This provides a friendly graphics interface that users can use to send prompts without understanding the underlying code.
