This week, after the meeting on Wednesday with faculty and TA, the team thought that defining the right use-case example and requirement are still the main issue.
The team decided to refine SPARK’s core use-case requirements, which include four primary action categories: Synthesis, Text Reformatting, Keyword/Phrase Searching, and Response Drafting. Each use case was rewritten to include measurable behavior and concrete examples to better define expected functionality.
The team also worked together on making the block diagram for the SPARK system and discussed and prepared the design review slides for the presentation next week.
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Part A (Sida) with respect to considerations of public health, safety or welfare:
Regarding public health, some people have developed a psychological attachment/affinity to their chatbots. Although our project’s features don’t include chatting with the LLM, we still need to be cautious about the longevity of our project. Our usage of locally hosted and open-source LLMs ensures that users won’t suddenly lose access to the model.
Part B (Leonard) with consideration of social factors:
On the topic of social factors, there are many concerns about AI replacing the human workforce. Our project’s scope is to increase the user’s work efficiency, not to replace any human effort. It remains to be determined what AI’s impact on society will be, but our goal is to empower each user.
Part C (Tatyana) with consideration of economic factors:
The cost of using AI poses an economic barrier to people with different financial statuses. Large businesses can afford to pay hundreds of thousands of dollars to self-host SOTA models or use API services. Individuals can pay monthly subscriptions to use less-capable models at slower rates. Self-hosting a decent model for everyday tasks isn’t financially reasonable yet, but our project can show that smaller models can be used to perform specific tasks and can meaningfully empower users with a one-time investment.
