Speech Recognition and Computer Vision the forgotten siblings in the AI conversation today.
There are two other pieces in the AI landscape that I do not believe are getting the attention they deserve: speech recognition and computer vision systems.
Are you thinking just about how an LLM (large language model) can code your new app? Or how you can have it rephrase that sentence you were going to send to your investors? Maybe you’re asking it to be the Tony Robbins of business development, seeing if it can create a new path for that division in your company that’s not doing great.
Generative AI can do that, but if you’re asking your team to focus only on text-based LLM tools to improve workflows, you’re leaving out powerful AI use cases in 2025 that involve speech recognition and computer vision.
By the way, all this is commercially available through the cloud. Azure, AWS, and GCP all provide high-quality versions of these technologies ready through an API.
🎙️ Speech Recognition (Voice and Ears)
Automatic Speech Recognition (ASR) and Voice-to-Text technologies technologies have been around for a very long time. (I even wrote about how McDonald’s can use it). Combining this with LLMs, the accuracy to understand intent and context is much higher.
This is a hugely underused AI technology, especially in Latin America, Africa, and the Middle East. Voice notes or messages are common for both personal and business communication. Even in North America, when you’re driving or working in a noisy warehouse, wouldn’t it be better to use voice commands powered by speech recognition systems and get accurate outcomes?
👁️ Computer Vision Systems (Eyes)
So you want to identify if something is a hotdog or not? What about if that is a car or a motorcycle? Or how to pull text from an image (reCAPTCHAs are a version of reinforced learning used to train models in classification).
Variations of this have been around for quite a while, and I’m sure there are few old texts, financial records, archival data that is just sitting around in a cupboard. Why not analyze them and combine the learnings into an LLM that can become your companies archivist?
Don’t leave these two out of the AI conversation. You might be missing out on practical, high-impact applications of AI that aren’t being fully leveraged.
💬What are you using AI for?
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