I Believe Chatbots Understand Part of What they say. Let me Explain.

Physicist Sabine Hossenfelder does a good job in comparing the understanding exhibited by chatbot  with our understanding of quantum Mechanics, along the way describes the 'Chinese Room' model used in early AI thoughts.    Once Again, some great thoughts.  She writes: 

I believe chatbots understand part of what they say. Let me explain. 

Youtube:  https://www.youtube.com/watch?v=cP5zGh2fui0

Do Chatbots Understand YOU?      By Sabine Hossenfelder

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Try out my quantum mechanics course (and many others on math and science) on Brilliant using the link https://brilliant.org/sabine. You can get started for free, and the first 200 will get 20% off the annual premium subscription.

I used to think that today's so-called "artificial intelligences" are actually pretty dumb. But I've recently changed my mind. In this video I want to explain why I think that they do understand some of what they do, if not very much. And since I was already freely speculating, I have added some thoughts about how the situation with AIs is going to develop.

  @sabinehossenfelder  

00:00 Intro

01:15 The Chinese Room

03:05 The Quantum Room

04:14 How Do Chatbots Learn?

07:15 What Does "Understanding" Mean?

15:46 Do We "Understand" Quantum Mechanics?

18:21 Where Will The AI Boom Lead Us?

20:30 Check Out My Quantum Mechanics Course

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Can Computers Understand Complex Words, Concepts?

Provocative questions on understanding concepts.

Can Computers Understand Complex Words, Concepts?

UCLA Newsroom

Stuart Wolpert, June 16, 2022

Researchers at the University of California, Los Angeles (UCLA), the Massachusetts Institute of Technology, and the National Institutes of Health found that artificial intelligence (AI) can learn complex word meanings and understand them like humans do. The researchers developed the "semantic projection" technique to study 52 word groups to determine whether an AI system could learn to sort meanings. The results of their research showed their method was very similar to human intuition. across many objects and contexts. Said UCLA's Idan Blank, "This machine learning system is much smarter than we thought; it contains very complex forms of knowledge, and this knowledge is organized in a very intuitive structure."

What is Practical AI Understanding?

 Looking forward to reading this, Quanta mag is usually good and medium level technically for information delivery ... Yes ...  understanding in context is the most important thing, and thus precise calibration according to need is important. 

What Does It Mean for AI to Understand?

By Quanta Magazine, December 20, 2021

Even simple chatbots, such as Joseph Weizenbaums 1960s ersatz psychotherapist Eliza, have fooled people into believing they were conversing with an understanding being, even when they knew that their conversation partner was a machine.

Remember IBM's Watson, the AI Jeopardy! champion? A 2010 promotion proclaimed, "Watson understands natural language with all its ambiguity and complexity." However, as we saw when Watson subsequently failed spectacularly in its quest to "revolutionize medicine with artificial intelligence," a veneer of linguistic facility is not the same as actually comprehending human language.

Natural language understanding has long been a major goal of AI research. At first, researchers tried to manually program everything a machine would need to make sense of news stories, fiction or anything else humans might write. This approach, as Watson showed, was futile — it's impossible to write down all the unwritten facts, rules and assumptions required for understanding text. More recently, a new paradigm has been established: Instead of building in explicit knowledge, we let machines learn to understand language on their own, simply by ingesting vast amounts of written text and learning to predict words. The result is what researchers call a language model. When based on large neural networks, like OpenAI's GPT-3, such models can generate uncannily humanlike prose (and poetry!) and seemingly perform sophisticated linguistic reasoning.

But has GPT-3 — trained on text from thousands of websites, books and encyclopedias — transcended Watson's veneer? Does it really understand the language it generates and ostensibly reasons about? This is a topic of stark disagreement in the AI research community. Such discussions used to be the purview of philosophers, but in the past decade AI has burst out of its academic bubble into the real world, and its lack of understanding of that world can have real and sometimes devastating consequences. In one study, IBM's Watson was found to propose "multiple examples of unsafe and incorrect treatment recommendations." Another study showed that Google's machine translation system made significant errors when used to translate medical instructions for non-English-speaking patients.  ... '   ( full article at link below) 

From Quanta Magazine

View Full Article

Conceptualization as a Basis for Cognition

What do we mean by 'understanding'?  Can we check that off if it means we can perform some specified tasks?

Conceptualization as a Basis for Cognition — Human and Machine

A missing link to Machine understanding and Cognitive AI

By Gadi Singer  in TowardsDatascience

 While most contemporary discussions and classifications of AI capabilities center around what a system can do, I believe the path to higher intelligence and machine cognition relies on what a system can know and understand. Using rich AI knowledge representation frameworks and comprehensive models of the world can increase an AI system’s ability to transform information into deep knowledge, understanding, and functionality. To pursue this path to better AI, it is essential to understand what “understanding” really means for the human brain. Doing so allows for implementing frameworks that enable machine learning to parallel human understanding by integrating modeling and conceptualization with data and task generalization.

Conceptualization: The Basis for Human Thought

“Concepts” are the most basic building block in human thinking. Concepts serve as ontological roots for objects that we think about. Concepts represent a persistent set of essential attributes of an object class, which can change and expand with experience. Existing concepts can be abstracted or linked through analogy to additional domains and object classes. Examples of concepts include |dog|, |democracy|, |white|, and |uncle|. Physical or mental objects can be stored as a concept and accrue more data and attributes over time (e.g., |my dog Lucky| and |snow white| versus |off-white|). Even if the referent is invisible or abstract, like |love|, it can still be stored as a concept. Our understanding of the world relies on concepts, attributes of concepts, and relationships between concepts. We use concepts and facts composed of concepts and the relations between them to construct our world model. ... '