Understand key machine learning terms in AI

With reports flying about the latest artificial intelligence developments on a daily basis, there is a lot going on. AI-related terminology, not all of them are easy to understand. Computer science jargon is particularly complex when it comes to machine learning. This article covers the most commonly used terminology around machine learning that every working professional should have a basic understanding of.

What is Machine Learning?

Machine learning is a fundamental aspect of AI focused on algorithmic research. It allows computers to build flexible models from data and relationships, allowing systems to perform specific tasks and make predictions.

Machine learning powers the technology that ultimately makes decisions and recommendations by using multiple data points to identify patterns over time. This is in contrast to traditional computers, which require explicit instructions for every aspect of a task. For decades, machines had to be taught everything. With artificial intelligence, they learn.

What you need to know about machine learning

Your phone knows you. Your phone learns that you always get home around 5:15 PM. Based on that information, the phone can predict how long it will take to get home by analyzing factors such as the time of day and actual traffic. It learns from a combination of historical traffic patterns and real-time data about the day’s traffic. Machine learning is also being used for personalized recommendations, face and voice recognition, and many other applications.

What are Neural Networks?

A neural network is a way of teaching a computer to process data like the human brain. A neural network is a machine learning process that uses interconnected nodes (or artificial neurons) to extract meaning from large amounts of data. Applications of neural networks include:

• Computer vision (facial recognition, content moderation, etc.)
• Speech recognition (e.g. create transcript from speech)
• Financial forecast

What is Deep Learning?

Deep learning is a subset of machine learning that uses artificial neural networks with multiple layers. Deep learning models are the result of his AI researchers working to code the equivalent of the human brain with extensive interconnections, rather than the “this then that” model of a traditional linear computer. think there is. Deep learning accomplishes this by creating parameters and applying weights to them. That is, build a model of what is important and how the important elements interact, and change them over time and iteration.

Through these weighted parameters, successful deep learning models find and use patterns in data that are not readily visible or intuitive to humans.

What you need to know about deep learning

Deep learning models are already powering machine translation and are at the heart of self-driving car technology. Modern chatbots rely on deep learning, and the medical industry in particular strongly leverages these models for computer-aided diagnosis.

In other specialties, deep learning enables well-trained AI systems to assemble documents based on user input and established guidelines. For example, you can output a draft document based on an input form.

What is Foundation Model?

A foundation model is a large AI model trained on very large amounts of data, often by self-supervised or semi-supervised learning. In other words, the model starts with a “corpus” (the dataset it is training on) and iteratively produces output while matching the output against the original data. The underlying model, once trained, is capable of outputting complex, structured responses that mimic human responses to prompts.

The advantages of the underlying model over previous deep learning models are: Universalwhich can be adapted to a wide range of downstream tasks.

What you need to know about the underlying model

A foundation model can start with very simple data (even with huge amounts of very simple data) to build and learn very complex things. Consider how your profession is made up of many complex and nuanced concepts and jargon. A good underlying model uses a vast body of knowledge to provide answers in easy-to-understand language, offering the possibility of answering questions quickly and accurately.

Here’s what the foundation model is good at:

• translation (from one language to another)
• classification (sorting items into the correct category)
• clustering (Group similar)
• Ranking (determination of relative importance)
• wrap up (generates concise summaries of long texts)
• Anomaly detection (Find something rare or unusual)

These features will greatly benefit professionals in their daily work, such as reviewing large numbers of documents to find similarities and differences and decide which ones are most important.

What is Large Language Model?

A Large Language Model (LLM) is a subset of the underlying model, usually more specialized and fine-tuned for a particular task or domain. LLMs are trained on various downstream tasks such as text classification, question answering, translation, and summarization. This fine-tuning process helps the model adapt its language understanding to the specific requirements of a particular task or application.

large scale language modelis commonly used for various natural language processing applications and is known for producing coherent and contextually relevant text based on the input provided. However, LLMs are also subject to: Hallucination, the output confidently asserts factual claims that are not actually true or justified by the training data. This is not necessarily a bad thing in all cases. While the ability of LLMs to mimic human creativity can be advantageous (such as asking an LLM to write lyrics in the style of Taylor Swift), it is a serious concern when it comes to quoting. Resources in a professional context. Hallucinations associated with quoting facts tend to decrease as LLMs are trained more carefully on both large and diverse data and on specific specific tasks and human reviewers flag those errors.

What you need to know about large language models

From Microsoft Excel to VBA to more complex databases, we already knew that computers were great at manipulating data based on numbers. With LLM, you can apply more powerful analysis and manipulation to unstructured word data, such as legal and accounting articles and regulations, entire corpora of organizational documents, and larger datasets. .

LLM promises that for professionals who work with words, risks and decisions, Excel is a power doubling for those who work with numbers.

What is Cognitive Computing?

Cognitive computing is a combination of machine learning, language processing, and data mining designed to help humans make decisions. Cognitive computing differs from AI in that instead of the AI ​​choosing the best algorithm, it collaborates with humans to find the best answer. The medical deep learning example applies here as well. Doctors use cognitive computing to aid diagnosis. They leverage their expertise, but they also leverage machine learning.

The future of machine learning

Machine learning is one of the most exciting and fastest growing areas of computer science in recent years, and it continues to do so. As the world generates more and more data every day, the need for intelligent algorithms that can make sense of this data and extract insights is only increasing.

However, like any technology, it also has potential. Risks and ethical considerations It should be carefully considered and dealt with. As we move forward, it is important to approach machine learning with a balanced perspective, be aware of both its potential and limitations, and strive to ensure that it is developed and deployed responsibly.

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