Google’s language model, Bard, is receiving a significant update today that aims to improve its logic and reasoning capabilities.

Jack Krawczyk, the Product Lead for Bard, and Amarnag Subramanya, the Vice President of Engineering for Bard, announced in a blog post.

A Leap Forward In Reasoning & Math

These updates aim to improve Bard’s ability to tackle mathematical tasks, answer coding questions, and handle string manipulation prompts.

To achieve this, the developers incorporate “implicit code execution.” This new method allows Bard to detect computational prompts and run code in the background, enabling it to respond more accurately to complex tasks.

“As a result, it can respond more accurately to mathematical tasks, coding questions and string manipulation prompts,” the Google team shared in the announcement.

System 1 and System 2 Thinking: A Blend of Intuition and Logic

The approach used in the update takes inspiration from the well-studied dichotomy in human intelligence, as covered in Daniel Kahneman’s book, “Thinking, Fast and Slow.”

The concept of “System 1” and “System 2” thinking is central to Bard’s improved capabilities.

System 1 is fast, intuitive, and effortless, akin to a jazz musician improvising on the spot.

System 2, however, is slow, deliberate, and effortful, comparable to carrying out long division or learning to play an instrument.

Large Language Models (LLMs), such as Bard, have typically operated under System 1, generating text quickly but without deep thought.

Traditional computation aligns more with System 2, being formulaic and inflexible yet capable of producing impressive results when correctly executed.

“LLMs can be thought of as operating purely under System 1 — producing text quickly but without deep thought,” according to the blog post. However, “with this latest update, we’ve combined the capabilities of both LLMs (System 1) and traditional code (System 2) to help improve accuracy in Bard’s responses.”

A Step Closer To Improved AI Capabilities

The new updates represent a significant step forward in the AI language model field, enhancing Bard’s capabilities to provide more accurate responses.

However, the team acknowledges that there’s still room for improvement:

“Even with these improvements, Bard won’t always get it right… this improved ability to respond with structured, logic-driven capabilities is an important step toward making Bard even more helpful.”

While the improvements are noteworthy, they present potential limitations and challenges.

It’s plausible that Bard may not always generate the correct code or include the executed code in its response.

There could also be scenarios where Bard might not generate code at all. Further, the effectiveness of the “implicit code execution” could depend on the complexity of the task.

In Summary

As Bard integrates more advanced reasoning capabilities, users can look forward to more accurate, helpful, and intuitive AI assistance.

However, all AI technology has limitations and drawbacks.

As with any tool, consider approaching it with a balanced perspective, understanding the capabilities and challenges.

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