Advancements in generative AI
1. Advancements in generative AI
Awesome work getting this far!2. Coming up...
In this video, we'll discuss the exciting future of generative AI models, like ChatGPT, as well as some of the key hurdles to overcome.3. Performance improvements
As with all technological advancements, we expect better performance with time, but let's define what we mean by better performance. Generative AI models produce content, so it's natural to compare what they can produce with what people can. We expect that generative AI models will produce content that much more closely resembles human-generated content. We also expect generative AI models to tackle more complex instructions and questions with greater reliability.4. What's driving the improvements?
So what will drive these improvements? Recall that ChatGPT, and other text-generating AI models, have a large language model at their heart. The LLM learns patterns and structure in language from being given a huge amount of text data to learn from, which we call training data. Algorithms then detect language patterns by looking at the frequency and order of words in the training data, and finally, the model is fine-tuned through iterative processes that involves rating the quality of responses.5. What's driving the improvements?
The amount of available training data will continue to increase, so models should develop a deeper understanding of language, including complex language expressions, such as sarcasm and idioms.6. What's driving the improvements?
Additionally, many generative AI models collect usage data, including user ratings of the generated content. This data allows developers to continue to fine-tune the model while it is live. Let's discuss a few hurdles related to these improvements.7. Building balanced datasets
Although there will be more training data that models can learn from, a big challenge will be ensuring that the data is balanced and high quality. Due to the sheer quantity and unstructured nature of the training data, reducing bias before training the model is difficult. As generative AI begins to become an integral part of day-to-day life, more robust procedures to mitigate bias will need to be developed.8. Opportunities for misuse
As AI-generated content becomes more human-like, there will be more opportunities for misuse, such as representing AI-generated content as human-generated or using AI to create malicious content, such as spam emails. Because of the potential for misuse, we can expect governments and lawmakers to take a more active role in AI usage through regulatory measures, which could help or hinder advancements.9. From generalized to specialized
As we've seen, ChatGPT is able to perform a very broad range of tasks, from generating marketing materials to writing and explaining code. In the future, we'll likely see more specialized models developed to generate content within a narrower scope. An example of this would be a model specifically designed to generate code for specific purposes, like software application development or database querying. More specialized models generally perform better than generalized models, as they're trained on the data most relevant to their application.10. Other types of generative AI
As well as text generation, generative AI models already exist to generate images, audio, and even video! These models operate under the same principles as ChatGPT, where training data is gathered, and a set of algorithms detects the patterns to develop its understanding, which it uses to generate new content.11. AI for everyone!
ChatGPT has opened the door to the wide-scale adoption of generative AI, and a key factor in its success is accessibility. One crucial requirement to continue the adoption of generative AI is ensuring the democratization of AI tools so everyone has access to the benefits of this amazing technology.12. Let's practice!
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