Artificial Intelligence

Researchers have classified AI into seven categories; you may be disappointed to learn that we've only realized three of them so far! In this video, Master Inventor Martin Keen lays them out, from narrow AI we know and enjoy today to the other extreme, super AI, which may have superior emotional and intellectual intelligence than humans... someday (?).

The recent interest in AI as meant a lot of people have been encountering new vocabulary. Martin Keen is to help you sort it out. This video runs through key terms like machine learning, deep learning, foundation models, and large language models and how they're related to each other.

What is Generative AI and how does it work? What are common applications for Generative AI? Watch this video to learn all about Generative AI, including common applications, model types, and the fundamentals for how to use it.

Over the past five years, Transformers, a neural network architecture, have completely transformed state-of-the-art natural language processing. Want to translate text with machine learning? Curious how an ML model could write a poem or an op ed? Transformers can do it all. In this episode of Making with ML, Dale Markowitz explains what transformers are, how they work, and why they’re so impactful. Watch to learn how you can start using transformers in your app!

• What are foundation models for AI? (6m read)

Generative AI has stunned the world with its ability to create realistic images, code, and dialogue. Here, IBM expert Kate Soule explains how a popular form of generative AI, large language models, works and what it can do for enterprise.

Learn about Large Language Models (LLMs), a powerful neural network that enables computers to process and generate language better than ever before. Dale and Nikita share how LLMs work and how you can interact with them via prompts.

Large language models-- or LLMs --are a type of generative pretrained transformer (GPT) that can create human-like text and code. There's a lot of talk about GPTs and LLMs lately, but they've actually been around for years! In this video, Martin Keen briefly explains what a LLM is, how they relate to foundation models, and then covers how they work and how they can be used to address various business problems.

Large Language Models (LLMs) and Generative AI intersect and they are both part of deep learning. Watch this video to learn about LLMs, including use cases, Prompt Tuning, and GenAI development tools.

• What is generative AI? (10m read)
• What are large language models? (5m read)

With all the excitement around chatGPT, it’s easy to lose sight of the unique risks of generative AI. Large language models (LLMs) -- a form of generative AI -- are really good at creating prose that sounds like a native speaker. But because they’re so good at it, large language models may give a false impression they possess actual understanding. They don't! In this video, Phaedra Boinodiris explains the potential risks of relying on large language models to your business, brand, or even society. She also presents mitigation strategies for reducing these risks.

Large language models (LLMs) like chatGPT can generate authoritative-sounding prose on many topics and domains, they are also prone to just "make stuff up". Literally plausible sounding nonsense! In this video, Martin Keen explains the different types of "LLMs hallucinations", why they happen, and ends with recommending steps that you, as a LLM user, can take to minimize their occurrence.

Large language models usually give great answers, but because they're limited to the training data used to create the model, over time they can become incomplete--or worse, generate answers that are just plain wrong. One way of improving the LLM results is called "retrieval-augmented generation" or RAG. In this video, IBM Senior Research Scientist Marina Danilevsky explains the LLM/RAG framework and how this combination delivers two big advantages, namely: the model gets the most up-to-date and trustworthy facts, and you can see where the model got its info, lending more credibility to what it generates.

Large Language Models (LLMs) aren't just for generating text; they're also very good at translating languages. Internet users and customers alike prefer to interact with businesses in their native languages. In this video, IBM Distinguished Engineer Suj Perepa explains how LLMs approach translation differently than past solutions based solely on machine learning, linguistic rules and dictionaries. Bottom line: This new LLM-based approach makes it easier for you to address your customers in their preferred language.

When you create a prompt for a large language model, are the answers sometimes wrong or just plain weird? It may be you! Or more accurately, the way you are formulating your question. In the video, Martin Keen explains why LLMs are led astray and offers suggestions on prompting techniques to reduce these mishaps.

Have you heard of these AI prompt engineering methods?

• Retrieval Augmented Generation (RAG)
• Chain-of-Thought (COT)
• ReACT (Reason + Act)
• Directional Stimulus Prompting (DSP)
  Wondering what the differences and values of each are?
  In this video, IBM Distinguished Engineer Suj Perepa explains those differences and values, provides an example of each method, and tells how they can be best used and even combined.