Top Must-Know Terms of AI and Machine Learning for Everyone
Machine learning and artificial intelligence are emerging topics in the field of technological advancement. It’s through these concepts that we today witness quite a few fascinating innovations. It seems rather appropriate for one to be well-versed with such technical jargon and concepts behind these technologies as they rapidly become common in our lives.
Although the terms listed below are merely the cherry on top, they do, however, serve as a great starting point for learning the basics of AI and machine learning in general.
Here’s a List of Some Basic Terms of AI and Machine Learning:
Anthropomorphism
This concept revolves around how AI chatbots are frequently entrusted with human characteristics by users. But it’s imperative to factor in that such technology can only imitate the language and are not sentient beings.
Bias
Large language models are prone to errors if training data impacts the output of the model, with consequences resulting in incorrect predictions and inappropriate answers.
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ChatGPT
One of the characteristics of OpenAI’s artificial intelligence language model is that it can now reply to visuals and also pass the Uniform Bar Exam in addition to responding to a myriad of questions, writing code, drafting emails, planning trips, and translating languages.
Bing
The inbuilt ChatBot of Microsoft’s search engine can be a part of open-ended conversations on any given topic or subject. Still, it has caught the public’s eye for its frequent errors, misleading responses, and much on the same line.
BARD
The agenda for Google’s chatbot was to be an innovative tool to aid in drafting emails and writing poems. It is also capable of coming up with ideas, composing blog articles and essays, and giving objective or subjective responses.
Emergent Behaviour
Inferring from the concept of large language models, based on their learning patterns and training data, it can demonstrate fascinating abilities such as writing code, composing music, and authoring fictional stories.
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Generative AI
Through navigating via vast amounts of training data, this technology is responsible for generating original text, images, videos, and computer codes.
Hallucination
Owing to its restrictions in its training data and design, large language models are susceptible to delivering factually wrong, irrelevant, or bizarre replies.
Large Language Model
This neural network learns abilities like language creation and conversational skills by examining a sizable amount of content from the internet.
Natural Language Processing
Large language models tend to use methods along the lines of text categorization and sentiment analysis, which collaborate with machine learning algorithms, statistical models, and linguistic rules, to be able to comprehend and generate human language.
Neural Network
A mathematical model of the human brain that delivers predictions or categorizations after recognizing patterns in data using layers of artificial neurons.
Parameters
These are numerical values that are picked on during training which define the traits and ways of a language model. They are used to determine the output likelihood; the more the parameters, the more clear and accurate the calculation, but the more computing power is required.
Prompt
To generate text for natural languages processing activities like chatbots and question-answering systems, a language model must start with this context.
Reinforcement Learning
A concept, often improved upon by human review for games and other challenging activities, which therefore trains an AI model to determine the best result through trial and error and getting rewards or penalties based on its results.
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Transformer Model
Many natural language processing applications, including chatbots and sentiment analysis tools, utilize neural network architecture that uses self-attention to comprehend the context and long-term dependencies in language, similar to the lines of ‘Parameters’.
Supervised Learning
In this concept of machine learning, a computer learns a function that translates input to output while being equipped to make predictions based on labeled instances. Applications like audio and image identification, as well as natural language processing, are then utilized.
Conclusion
From the outside, AI seems like a super complex topic reserved for the best engineers and the brightest minds in the world, whilst the rest of us are lagging, playing catch up. But it doesn’t need to be complicated or overwhelming. Once you understand the key terms at a high level, you’ll know more than most people.
