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Ron Lancaster

Thoughts on tech and leadership

Below are articles that I’m reading, that I thought you might be interested in too.

Part 2 discusses how to compose multiple tasks with control flows (e.g. if statement, for loop) and incorporate tools (e.g. SQL executor, bash, web browsers, third-party APIs) for more complex and powerful applications.

The base model is then finetuned with an instruction dataset, and the purpose of this is to teach it to be helpful, to obey the user, answer questions, and engage in conversation.

The bottom line of the thesis presented here is that there may be a path to build immensely useful AI systems that completely avoid the issue of AI alignment, which I call AI scientists because they are modeled after ideal scientists and do not act autonomously in the real world, only focusing on theory building and question answering.

The Text-to-Mel Spectrogram Encoder is an important component of real-time voice cloning systems, as it allows the system to generate the Mel-scale spectrograms that are used as input to the Vocoder to synthesize the target speaker’s voice.

What has happened is that a number of GPT-4 systems, from both OpenAI and Microsoft, have been given the ability to use tools, with dramatic effects on their abilities, and their relevance to real-world tasks.

The increased attention on AI (and making AI good) means that many more people will find their way to our communities, and recruiting will be much easier (it already is compared to a few years ago, as far as I can tell).

Besides eliminating this depth/breadth tradeoff, I think an underrated aspect of interactive learning with GPT is that you're forced to ask questions explicitly, instead of just clicking around blindly on the web.