Two systems of thinking – when AI leverages human psychology


Using Google Bard as an example, guest contributor Aditya Anil explains how Daniel Kahneman’s ideas can help build better chatbots.

“Thinking, Fast and Slow” is a New York Times best-seller book by psychologist and Nobel Laureate Daniel Kahneman. The book presented his hypothesis on how and what drives our thinking.

This hypothesis of his is currently being harnessed by AI chatbots like Google’s Bard to make themselves more efficient and accurate.

But how exactly does the hypothesis by Daniel Kahneman covered in the book, help develop AI chatbots?


The Two Systems That Drive Thinking

Picture: Aditya Anil

Kaheman’s book explores two systems of thinking –

  • intuition-based thinking (which is referred to as System 1 thinking), and
  • slow-thinking (which is referred to as System 2 thinking).

System 1 according to Kaheman is fast, instinct based and emotional; while System 2 is slow, deliberative and logical. While both systems play a crucial role in decision-making, one system tends to be more active than the other depending on the situation.

System 1 operates quickly and effortlessly. Action under this system takes little or no effort with no sense of voluntary control.

This includes actions like reading words on a poster, detecting if an object is far or near with respect to another object, identifying a sound that you hear, and so on.

System 2 on the other hand is more conscious and logical. Actions under this system take a long time, with voluntary controls. This system is activated when you perform abstract and logical thinking.


Hence, it was important for Bard to make their AI bot more accurate, consisting of less bias or misinformation. This is a challenging goal to reduce misinformation and increase efficiency in almost all AI tools out there.

Thus, owing to this, Google released a blog on June 7th under the title – “Bard is getting better at logic and reasoning”.

The blog highlighted two new features in Bard.

One of them was the export to Google Sheets feature, which allows user to export their output containing tables into Google Sheets.

The other feature allowed Bard to – in their own words – “get better at mathematical tasks, coding questions and string manipulation”

Bard earlier struggled with maths problems, and it still does every now and then. But using the approach of combining the two Systems that I mentioned above, Bard aims to get better now, correcting its silly maths errors.

This new technique that Bard uses is called the ‘implicit code execution’.

While the LLMs (System 1, consisting of fast and pattern-based responses) receive the prompt, implicit code execution allows Bard to detect computational prompts (System 2, consisting of logic and systematic execution) and run code in the background.

This helps Bard to give responses to mathematical and string-based prompts a lot easier.

In the example mentioned the blog, Google said Bard will get better at answering prompts like:

  • What are the prime factors of 15683615?
  • Calculate the growth rate of my savings
  • Reverse the word “Lollipop” for me

The following extracts from the blog capture the essence and motivation of using this approach (of using the two systems of thinking approach) –

“As a result, they’ve been extremely capable on language and creative tasks, but weaker in areas like reasoning and math.

In order to help solve more complex problems with advanced reasoning and logic capabilities, relying solely on LLM output isn’t enough.

LLMs can be thought of as operating purely under System 1 — producing text quickly but without deep thought …Traditional computation closely aligns with System 2 thinking: It’s formulaic and inflexible, but the right sequence of steps can produce impressive results, such as solutions to long division.”

—Google in its blog

This approach of keeping LLMs and Traditional computing at System 1 and System 2 respectively ensures that the response is much more accurate and efficient.

Using this approach, Bard – according to the blog – showed a nearly 30% accuracy boost in dealing with word and math problems.

How Reliable is this New Approach

While this does improve Bard’s accuracy while dealing with mathematical and word problems, it may not be the best approach to make the chatbot efficient.

While it does shows significant accuracy while dealing with maths and word problems, it still struggles while dealing with code-related problems.

“Even with these improvements, Bard won’t always get it right — for example, Bard might not generate code to help the prompt response, the code it generates might be wrong or Bard may not include the executed code in its response,” Google says at the end of the blog.

Thus, while this is a significant change, Bard still has to cover longer miles to be fully reliable.

Reducing misinformation and increasing efficiency are the challenges for nearly all the chatbots out there.

While progress is being made, there is still a long way to go.

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