The Importance of Data Quality
Here at BrandGuard, we’re huge fans of AI technology, so it should be no surprise that we’ve eagerly watched the emergence of AI note-takers like Fireflies, Otter, and Zoom's integrated solutions and even adopted some of those ourselves.
You may be wondering why we are focusing on note-takers for this article. Note-takers are a great example to demonstrate the importance of quality data in AI. First, what is an AI note-taker, and why are they important? These tools, powered by advancements in Automatic Speech Recognition (ASR) and machine learning (ML) models, offer the promise of transforming the tedious task of note-taking into an effortless, automated process.
At their core, these AI note-takers are a confluence of three critical components: ASR technology (with a tilt towards models like Whisper over traditional speech recognition models), summarization algorithms, and the undeniable importance of data quality.
How to build a note-taker?
The journey of an AI note-taker begins with ASR, where the model takes an audio stream and transcribes it into text. Following this, a sophisticated language model (usually a ChatGPT turbo model) reads the transcribed stream, parsing through hours of spoken audio to create meeting summaries and actionable notes. This seamless integration of ASR and ML models for summarization transforms how we document and retrieve information from meetings internally.
The Crucial Role of Data Quality
However, just like we say, there is no free lunch (both in life and ML), and this is not without pitfalls. The quality of data—essentially the clarity and accuracy of the audio stream—plays a pivotal role in the effectiveness of AI note-takers. The old adage "garbage in, garbage out" is particularly relevant here.
We’ve identified 3 main areas where we have seen trouble with existing AI note-taker solutions.
1. Transcription Failures in Noisy Environments: AI note-takers rely heavily on the clarity of the audio input. In less-than-ideal environments where background noise interferes, the accuracy of transcriptions can plummet, leading to incomplete or erroneous notes. If the input data (in this case, the audio stream) is of poor quality, the output (transcribed text and summaries) inevitably suffers. Factors such as background noise, poor microphone quality, or even the intricacies of human speech can significantly affect the ASR's ability to accurately transcribe conversations.
2. Hallucinations - The AI's Overactive Imagination: AI models, sophisticated as they are, can sometimes 'hallucinate'—that is, generate content that was not present in the input. In AI notetakers, as the conversation moves rapidly between multiple speakers and points of view, invariably, the transcripts reflect these half-completed ideas. When this information is pushed into the language models, due to the next token prediction, we invariably get hallucinations. This can manifest in many different ways, such as imaginary participants receiving existing action items or action items that were discussed being assigned to the team instead of a person, complicating the accuracy of meeting summaries. Having to re-read the transcript of a meeting and verifying it against your memory of the meeting wipes out the time savings promised by the technology.
3. Technical Terminology - A Stumbling Block: The specialized language of any industry vertical can also pose another significant challenge. AI models, even with advanced training, may struggle to accurately transcribe or interpret niche terminologies and concepts. Here at BrandGuard we use extensive internal technical shorthand and abbreviations, which get hilariously transcribed. In a single meeting discussing the new Groq Inference chips, we received spellings of “grok”, “groc”, “goc”, and “croc.”
The Future is Bright, Yet Cautious
As we look towards a future where AI note-takers become ubiquitous in professional settings, the emphasis on data quality cannot be overstated. Innovations in microphone technology, noise cancellation, and more sophisticated ASR models are essential in overcoming the current limitations. Additionally, the development of AI models tailored to specific industries, capable of understanding and accurately processing technical jargon, will be a critical step forward. We look forward to these notetakers incorporating continuous learning mechanisms, where the AI adapts and improves based on feedback and corrections so that we spend less time reviewing transcripts and more time building models.
In many ways, we see the development of AI note-takers as similar to the development of the BrandGuard product. Just as AI note-takers need clear, concise, and good data inputs, so do our BrandGuard models in order to realize their full effectiveness. Once the models have learned from this high-quality data, our customers are freed to spend less time reviewing and more time creating with full confidence in the output of our models.
For more information on BrandGuard or to sign up for a demo, please email contact@brandguard.ai or visit our website, www.brandguard.ai.
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