Today’s video is the first in a series exploring how we can leverage artificial intelligence (AI) to enhance productivity. While AI has the potential to increase efficiency, it also poses challenges for certain types of employment.
In this session, we establish a basic framework for understanding AI, particularly useful for those without a technical background. However, even if you’re technically savvy, this overview will still help to clarify the advantages AI has to offer as well as some of its limitations.
First, it’s important to understand that current AI, particularly the type accessible to the public, doesn’t create new thoughts in the way humans do. Instead, AI operates by analyzing and synthesizing existing information. It generates responses based on patterns learned from vast swathes of data.
Consider the example of playing chess. If you ask a general-purpose AI to play a game of chess, it might demonstrate a foundational understanding of opening moves because it draws on the vast amounts of opening move data that is openly available. However, as the game progresses into the middlegame, It’s strength will reduce. Therefore, unless you are using a specialized chess AI, the AI’s performance will likely become less reliable as the game advances. Piece positions in the later middle game are less likely to be in the available training data. So the predefined knowledge will become sparse. Unless the AI is one with more dedicated chess logic, it’s chess playing strength will degrade rapidly.
The internet holds a vast amount of information, much of which captures human thoughts and interactions. An AI is “trained on” this data. i.e. the data is processed into a form that can be utilised for future answers. When we pose questions to an AI, it analyzes the question and constructs responses that align with the patterns it has recognized thanks to this training data.
However, importantly, it can make mistakes. These errors are not due to ‘thought processes’ but often arise from the limitations of the data the AI has been trained on and the inherent complexities in understanding human language.
René Descartes, the French philosopher, illustrated how our perceptions are influenced by past experiences. He noted that a distant tower can appear round despite our knowledge that it is likely square. This analogy parallels how AI can generate responses based on the dominant patterns in its training data, which may not always align with reality.
While AI attempts to match data patterns to queries, this process can lead to inaccuracies or – in AI parlance – “hallucinations.” The thing with a hallucination is the AI will arrange a response that is grammatically sensible and sound like it is meaningful, but some or much the response is nonsense or non factual. This is a little bit like how when we read the sentence “green ideas sleep furiously.” It sounds grammatically correct. But a with only a little further thought we realise it makes no sense.
Only AI hallucinations can be even less obviously nonsense. They can sound grammatically entirely feasible, and can be stated with apparent confidence, yet be simply wrong.
It’s then worth understanding, we can reduce the incidence of such errors by refining our inquiries and providing clear, context-rich prompts. This helps the AI better understand the intended direction of the response, leading to more accurate and relevant outputs.
The clarity and specificity of your questions plays a crucial role in the quality of the answers. By directing the AI towards a specific topic or area of expertise, you help it focus responses on relevant information, enhancing the accuracy and relevance of the output.
Understanding the importance of clear, directed prompts can be likened to guiding someone through a complex environment. The better the instructions, the less likely they are to get lost along the way.
This can be seen to be all the more important when you understand, AI models have a limited number of tokens allocated for each enquiry they handle. They progressively consume these tokens whilst processing the question, when referencing their training data and when rendering a response. The quality of the response will decrease if they run short of tokens. So if they can get in the “analytics” sweet spot efficiently – and a well constructed prompt will help them do that – then they will have more resource for providing a rich answer.
In closing, today’s objective is to think about three ways that YOU can incorporate AI into your daily work. I suggest trying to execute at least one work-related AI query today. By doing so, you’ll start to understand how to interact more effectively with AI, leveraging its capabilities to augment your productivity and decision-making processes.
For users of the Simple Focus App, there is an objective associated with this video as follows:
In the associated objective you are urged to think about three ways that YOU can incorporate AI into your daily work. I suggest trying to execute at least one work-related AI query today. By doing so, you’ll start to understand how to interact more effectively with AI, leveraging its capabilities to augment your productivity and decision-making processes.
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