Is Google’s Chatbot Sentient? “Logical” Reasons to Disagree

File:Chatbot.jpg - Wikimedia Commons
“Chatbot” from Wikimedia Commons

I have some strong reasons why I think it’s useful to weigh in on the recent drama around a Google Ethics Engineer’s declaration of the LaMDA product having reached sentience. Slate has a great article about this that I think may bring you up to speed if you’re interested.

Slate’s position on why the assignment of the sentient label to the chatbot was misguided revolves around LaMDA’s complete reliance on human inputs and foundational language models. My assessment extends their position in a different direction and I’ll explain why.

Deductive Logic

All of these foundational models rely on two different types of logic that are common to the software community. The most common is called deductive logic and describes the process where the software compares the truth (or lack) of multiple assertions to determine actions to take. This is a pretty high level explanation (forgive me) which summarizes a significant body of research and work in deduction, but in general, deduction describes the application of rules and logic.

Inductive Logic

Inductive logic is useful for drawing inferences or conclusions from historical observations. If you draw eight black marbles out of the bag in your first eight tries, you might infer from this data that the bag is full of black marbles. Induction has recently experienced a resurgence in software due to the recent interest in deep learning and other machine learning techniques. Machine learning is a form of inductive logic where historical data are trained into models which allow the machine to infer likely outcomes due to current sensed parameters. So in the example of my weather data systems, I have sampled parameters like temperature, pressure, humidity, luminosity, etc., for years. I have also captured when rain occurs (easy to do in Tucson… it doesn’t happen much) and can then label every example of weather data with “rain” or “not-rain”. THEN, if I want to predict whether it will rain at some time in the near future, I conduct inference into my trained weather-rain model using the current values of the weather sensors. This is a very simple description of how machine learning works.

Combinations of Logic

Much current software relies on combinations of traditional deductive logic that makes decisions on when to incorporate inductive logic inferences in order to solve problems most effectively. I always imagine traditional software logic that is evaluating and connecting hundreds and hundreds of small trained machine learning models. This is an example of the combination of these two kinds of logic. This, very simply stated, is what the large foundational models like LaMDA and GPT-3 are doing. The difference is that they are generally using deductive logic rules and VERY LARGE trained models. Most of these foundational models are so large and computationally expensive that most normal people don’t have much ability to use them in any other format than toy applications provided by Google or OpenAI. The very large body of language used by these foundational models allows them to do incredible inference based off of language created by real humans. All the text in Wikipedia is an example of some of the language used to train these models. Inferencing these models using questions from humans (such as the Google employee) can yield surprising, even spooky results. Deductive logic rules can eliminate ridiculous or meaningless responses.

What’s missing? (Who knows what lies in the heart of a machine?)

Despite the fact that these foundational models can be VERY useful, they’re missing something major that prevents them from truly understanding language. How can I say this with confidence?

Abductive Logic is What’s Missing!

It is easy to point out that machines do not (and will not in the near future) have the capacity for abductive logic. Abduction describes an ability that humans have to make an observation Q and conclude that some general principle P must be the reason that Q is true. Notice that this is quite different than deduction and induction. The complexity of the various principles in the world makes abduction very difficult to perform. Sherlock Holmes was a renowned expert in using abduction when he would see, for instance, a wedding ring that was more shiny on the inside than the outside and make the conclusion with no further information that if a person removed the ring frequently it might have that appearance. Machines are not able to make these kinds of intuitive “leaps”. Our current, modern view states that science itself is an example of abduction. We seek hidden principles or causes that we wish to use to actually deduce the observable facts, “Frequently removing a ring might explain why it is shiny and clean on the inside but not the outside”.

There is plenty of research out there telling us that machines can not perform abductive logic. Part of the reason is that in abduction, a likely hypothesis needs to be inferred from a nearly infinite set of explanations. Something in the human brain protects us from getting locked in the infinite loop required to evaluate all these explanations. It is likely to be some mashup of intuition and mental models of rules and value systems that we use to jump to the most likely causes to explain the data. To go deeper, Mindmatters has a great discussion of all these concepts here. They also have a three part series on “The Flawed Logic Behind Thinking Computers”. Part1, Part2, and Part3. There are many more articles out there that explain this gap of machine intelligence including this one from VentureBeat.

Abduction and Natural Language

There is a growing body of work that indicates that abductive reasoning is part of the reason why humans can understand language (Neurips Proceedings link). Some of this is due to the need to interpret to decode errors in language. A famous example comes from Don Quixote where Sancho Panza, Don Quixote’s assistant says: “Senor, I have educed my wife to let me go with your worship wherever you choose to take me.” Don Quixote, immediately identifying the improper usage replies, “INDUCED, you would say, Sancho. Not EDUCED.” By our definition of abduction, we can see that here, Don Quixote uses abductive logic when he adopts the hypothesis that “induced” is the intended word given the context and the similarity between the two words. According to Donald Davidson, This kind of abductive interpretation can occur in natural language understanding when:

  1. Applying a hypothesis to understand new names or labels
  2. Revising prior beliefs or interpretations about particular phrases
  3. Altering interpretations of predicates or other grammatical constructs to fit the context


In the light of the growing numbers of applications of Machine Learning, there has been much more discussion of deductive and inductive reasoning than there was even ten years ago. It’s likely you’ve seen some of this.

It does appear, however, that the understanding of abductive logic is lagging. Though there have been efforts to simulate machine abduction, it has still yet to have been accomplished and for legitimate processing tractability reasons is likely not to be accomplished on traditional (not quantum) computing. This severely limits a machine from true natural language understanding, which would be needed by any sentient being to understand language and communicate. This would also apply to chatbots and describes why they are just examples of the Chinese Room (or a human-language-speaking parrot), neither of which demonstrate understanding of the languages emanating from them.

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