10 AI Terms Microsoft Wants Everyone to Know in the AI Era
Artificial Intelligence (AI) which is no longer a new concept, nevertheless breakthroughs in machine learning have radically changed the way we can build AI and recognize its potential. It received more attention in 2022 when OpenAI released the ChatGPT chatbot. That, combined with major corporations such as Microsoft and Google making serious AI products available to the masses drove it into popular culture. A whole host of AI companies are paving the way for this next wave, and new terminology is starting to pop up in order to help define this vibrant landscape. I have listed 10 AI terms that you need to know.
- Artificial Intelligence (AI)
- AI or artificial intelligence is defined as the capacity of a computerized machine to replicate human tasks such as perceiving and recognizing language, decision making; converting languages analyze emotions and learn without experience. These models are built using AI algorithms that process large amounts of data and automate tasks which would usually necessitate human intelligence.
- Machine Learning (ML)
- Machine Learning (ML) is a type of AI, and it is how you get there. In machine learning practice, systems learn how to identify patterns (data) and make predictions by running data through the algorithms repeatedly with changing inputs and feedback. Just like playing piano scales millions of times until you can finally sight-read a music sheet! ML is well suited to solving more complex problems, such as image and language recognition.
- Large Language Model (LLM)
- Chatbots are powered by Large Language Models (LLMs), AI models which use machine learning in order to replicate human communication. These transformers are trained on tons of text to implicitly learn the structure and grammar from context primarily based neural networks (NNs), inspired by how human language works.
- Generative AI
- Unlike other GPTX, generative AI is used to create new things and they harness the power of LLMs to achieve this- their main work is not about re-writing or providing information on topics. The generator “learns” patterns, either from the dataset or by being pointed out to that structure and generates an output that resembles in some way with something new. Content can be in the form of images, music, text (including code) and video; using generative AI.
- Hallucinations
- This type of coding could be used to build AIs that can write stories, poems, or lyrics and such an AI will most likely come with “generative” capabilities: it produces text when you prompt a question but unfortunately not always truthfully informed. This is also known as hallucinations or fabrications since the AI could not discern whether this information was true or false.
- Responsible AI
- AI responsible policies must be in place including at the level of adventuring unit (machine learning model, software channel UI) and accessibility rules for applications. This is important for systems that have a real impact on decisions in education and healthcare, where they can mirror human biases of their training data.
- Multimodal Models
- A multimodal model is capable of dealing with different data types at the same time, for example, images and sounds or text. It adds this with each other to do things like addressing questions about photos, making it the utmost multi-tasked.
- Prompts
- A prompt is a type of instruction – in the form of language, image, or code (which tells the AI what task it needs to perform). Prompts must be thoughtfully designed by both engineers and users so that large language model writes the desired output.
- Copilots
- The AI models of Microsoft embedded in products meant to assist users with their work (so-called copilots). Digital assistants such as Copilots exist to help with writing, coding, summarizing, or even searching for information in multiple applications.
- Plugins AI
- plugins work like apps on a smartphone where it allows specific requirements of an application to be met without changing the core model. They enable interaction with other software, provide access to new data, complete complex calculations, and connect it more deeply into the digital ecosystem thus extending the potential of an AI.
Conclusion The evolving technology space needs us to know them and hence understanding these AI terms is important. Deep learning is now a thing-of-the-present and not the future, transforming how we interact with everything. Learning these terms will allow you to remain aware of how AI is reshaping different elements of life and work. The meaning of these terms is only going to grow in importance with bigger companies like Microsoft continuing to innovate and add a lot more AI into their products.