Understanding AI Alignment

Artificial Intelligence will improve our lives in many ways, from safe, automated mobility to time-saving tasks. With constant innovation, AI systems will become far more capable, possibly equaling or exceeding human-level performance at most intellectual tasks.

AI is one of the most important technologies of our time and its impact is comparable to the industrial and scientific revolutions. For business leaders to utilise AI’s full capabilities it’s crucial to ensure AI is aligned with human intent and the promise of the AI product itself.

This is achieved through AI alignment.

What is AI alignment and why is it important?

AI alignment is a field of AI safety research that aims to ensure that AI systems achieve their desired outcomes and work properly for humans. It aims to create an end-to-end process where an AI-based system can align its output with desired human preferences.

Imagine playing darts, or any game for that matter, but not agreeing on what the board looks like or what you get points for. If the designer of an AI system cannot express consistent and clear expectations through feedback, the system won’t know what to learn.

At its core, alignment is about agreeing on expectations. Anyone who has managed a team knows how much communication is required to align a group of people. With the emergence of more powerful AI, this alignment exercise will be extended to include algorithms. Human feedback is central to this process, and there is still much work to be done.

As business leaders, it is imperative that we act now to ensure AI is aligned with human values and intent.

Teaching machines… It’s no easy task

Humans interpret things differently and develop preferences based on their personal perceptions. This makes it incredibly difficult to teach machines how humans think and how to tell your “machine” what high performance really is. In order to function properly, AI products need to learn the language of human preferences.

We live in a fast-changing world, and expectations evolve quickly. If you assemble a large dataset, you must expect it to evolve. The challenge now is to shape your data with this evolution in mind which in turn informs your AI products. Alignment is the way forward, and the key is to approach it with an iterative mindset. The challenge now is to explore and shape your data with this evolution in mind which in turn informs your AI products.
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