Two systems of thinking – when AI leverages human psychology

This includes actions like identifying someone in a crowd, doing long calculations in your head, playing chess, and so on.

Recently, the concept of two systems is being used by Bard (the AI ​​chatbot by Google) to improve its mathematical and string operations, making its response more dynamic and accurate.

But how Bard uses this psychological concept for enhancing its own AI system?

How the Principles of Thinking Help AI

Before we dive right in, let us understand the main advantages and disadvantages of each system.

The book points out that System 1 thinking is responsible for 98% of all our thinking, while System 2 thinking is responsible for the remaining 2% and is a slave to System 1.

But both systems have its advantages and disadvantages and it influences heavily on our decision-making capabilities.

Disadvantages of Each System

Relying too heavily on System 1 thinking can lead to biases and mistakes. Some of the caveats of System 1 thinking are the following:

• Heavy indulgent of Confirmation bias
• Tendency to Overlook concrete and important details
• Ignoring evidence we dislike, that leads to ignorance
• Overthinking seemingly simple or irrelevant decisions
• Producing questionable justifications for bad decisions

and so on.

On the other hand, relying too much on System 2 thinking can also lead to mistakes and negative consequences. These include:

• Overthinking simple decisions and wasting huge amount of time
• Inability to make quick decisions
• Being too skeptical and withholding judgment too much
• Experiencing decision fatigue and cognitive overload
• Making decisions that are too logical and not taking into account emotions

Two Systems of Thinking: Applied to AI

While in the human domain, this is highly psychological, things get pretty interesting when this concept is applied to AI and Computing.

LLMs (AI model that powers chatbots like Bard and CHatGPT) can be thought of as running in System 1.

How?

LLMs (the AI ​​models that run these chatbots) work by finding patterns in the billions of training data that it was trained on before, and it generates a response that matches the common pattern. For example, when you say a chatbot to “write an essay on Climate Change”, the following is the process in the backend –

• Find matching queries in its vast training database. The chatbot tries to find a common query that includes the keywords “climate change” and “essays”.
• Finding a trend or pattern. The chatbot then tries to find a common trend or pattern among all selected data. For example, the pattern could be that almost all of the data has to mention ‘carbon emissions’, ‘carbon footprinting’, ‘plastic pollution’, ‘global warming’, and so on. Moreover, the title-and-paragraph formats of essays are also a pattern in itself (in contrast to other formats like poems, blogs, etc.).
• Generating a text according to the pattern. This is the fun part. Think of this process as solving a jigsaw puzzle.
  The bot tries to generate the text with using the bits of data (the puzzle pieces) and tries to make it resemble the pattern of a similar essay (the final pictures), which in this case is an essay on climate change. It creates several iterations (ie outputs) of the prompt that you gave and compares it to reference data, which could be an already-written essay on climate change.
• Giving the output. The iteration that closely matches the desired result is chosen, and printed on the screen.

This process may look long, but it takes only a couple of seconds to perform in traditional LLMs. The 1st step is done way before in the developing and training phase of an LLMs, which consists of the AI ​​model being trained on datasets containing billions of data. After learning from this massive dataset, and finding the pattern in all of them, the chunky and hard part of the LLM process is done.

The rest of the step is rather quick, owing largely to the quality of the data on which the model was trained. Generally, the better the training data supplied, the better the predictions and generation.

Thus LLM generates texts effortlessly without ‘thinking’ much. It just finds the pattern and compares the output to the reference.

Therefore, LLMs lie in System 1 – which is quick and efficient. However, the drawback to this is that LLMs can generate incorrect and biased outputs, and even make up their own facts and figures (AI hallucination).

This is the reason for the following case, where sometimes the Bard shows effortless result for hard prompts, but fail miserably in the easy tasks like the one below –

This is because solving a certain math problem is efficient when you follow a specific sequence of steps, rather than relying on ‘patterns’ of similar math problems.

This is where Traditional computing works better. For instance, the way how the calculators in your computer work.

Traditional computing follows a sequence or a structure, which is in the form of code or a simple algorithm. With this respect, traditional computing is preferred to do tasks such as doing mathematical problems, manipulating string operations, doing conversions and so on. The drawback is that since it follows a specified format, it may not be necessarily fast, or efficient most of the time. The traditional computer can find the answer to the questions like 12*24 = 288 but takes a longer time to do calculus-related questions.

However, the plus point here is that it is almost certain to get the right answer most of the time.

Observe that Traditional computing is rather slow, more logical and structured when compared to LLMs

Thus Traditional computation falls under system 2. It’s relatively slow, much more systematic and logical. It consists of an algorithm, code or any other hard-coded execution system.

Google’s Bard quite interestingly is trying to use both systems to make their chatbot’s response more optimum.

How Bard uses it

Bard had a tough start when it was launched. The initial promotional video that showcased Bard’s capability faced huge criticism after the response consisted of misinformation.