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.Coursera’s CEO, Jeff Maggioncalda, says leveraging AI in online learning is key to a more accessible, flexible education experience. Coursera is a major platform for free and paid, non-credit and credit learning opportunities. Remember MOOCs? The term isn't in as wide usage as it was a decade ago but Coursera was an early serious player in that space and still offers short-form training and master’s degrees from Ivy League institutions like the University of Pennsylvania.

While many in education have been worrying about how AI is and will impact teaching and learning, online providers and course designers have been more likely to embrace AI tools.

Generative AI is good at language translations and Coursera who now has 4,200 courses translated into 17 languages as AI has made the translations easier and more affordable. They have also experimented with using AI for a personalized learning companion (chatbot) named Coach where students can ask for help on a concept, to create practice problems, or summarize activities. It won’t give users the answer, especially during testing.

For course designers, it can create outlines, write learning objectives, and compile lessons into new courses.

Coursera works with partners who can make content available for free.

Natural Language Processing (NLP) is a field of artificial intelligence focused on enabling computers to understand, interpret, and generate human language. The "natural" part is that the goal is that this AI language use is meaningful and contextually relevant. This might be used for tasks such as language translation, sentiment analysis, and speech recognition.

Search engines leverage NLP to improve various aspects of search. Understanding what a user means when searching for a search string and understanding what the different pages on the web are about and what questions they answer are all vital aspects of a successful search engine.

According to AWS, companies commonly use NLP for these automated tasks:
•    Process, analyze, and archive large documents
•    Analyze customer feedback or call center recordings
•    Run chatbots for automated customer service
•    Answer who-what-when-where questions
•    Classify and extract text

NLP crosses over into other fields. Here are three.

Computational linguistics is the science of understanding and constructing human language models with computers and software tools. Researchers use computational linguistics methods, such as syntactic and semantic analysis, to create frameworks that help machines understand conversational human language. Tools like language translators, text-to-speech synthesizers, and speech recognition software are based on computational linguistics. 

Machine learning is a technology that trains a computer with sample data to improve its efficiency. Human language has several features like sarcasm, metaphors, variations in sentence structure, plus grammar and usage exceptions that take humans years to learn. Programmers use machine learning methods to teach NLP applications to recognize and accurately understand these features from the start.

Deep learning is a specific field of machine learning which teaches computers to learn and think like humans. It involves a neural network that consists of data processing nodes structured to resemble the human brain. With deep learning, computers recognize, classify, and co-relate complex patterns in the input data.

Overview of NLP

A neural network is a type of deep learning model within the broader field of machine learning (ML) that simulates the human brain.

It was long thought that the way to add "intelligence" to computers was to try to imitate or model the way the brain works. That turned out to be a very difficult - some might say impossible - goal.

Neural networks process data through interconnected nodes or neurons arranged in layers—input, hidden, and output. Each node performs simple computations, contributing to the model’s ability to recognize patterns and make predictions.

These deep learning neural networks are effective in handling tasks such as image and speech recognition, which makes them a key component of many AI applications.

When neural networks are being "trained," they make random guesses. A node on the input layer randomly decides which of the nodes in the first hidden layer to activate, and then those nodes randomly activate nodes in the next layer, and so on, until this random process reaches the output layer. If you know of any of the large language models (LLM) then you have seen this at work. GPT-4 has around 100 layers, with tens or hundreds of thousands of nodes in each layer.

Have you ever clicked thumbs-up or thumbs-down to a computer’s suggestion? Then you have contributed to the reinforcement learning of that network.

I have found that predicting the future of technology is rarely accurate, and predictions on AI have generally been wrong. In 1970, one of the top AI researchers predicted that “in three to eight years, we will have a machine with the general intelligence of an average human being.” Well, that did not happen.

Most current AI systems are "narrow AI" which means they are specialized to perform specific tasks very well (such as recognizing faces) but lack the ability to generalize across different tasks. Human intelligence involves a complex interplay of reasoning, learning, perception, intuition, and social skills, which are challenging to replicate in machines.

That idea of reaching artificial general intelligence (AGI) has its own set of predictions with experts having varying opinions on when AGI might be achieved,. I have seen optimistic estimates of a few decades to more conservative views spanning centuries or even beyond. It is hard to predict but breakthroughs in AI research, particularly in areas like reinforcement learning, neural architecture search, and computational neuroscience, could accelerate progress towards AGI.