Episode 55 Large Language Models: Myths, Facts and the Future of AI
Download MP3Large Language Models (LLMs) have emerged as one of the most powerful and versatile artificial intelligence technologies of our time. By training massive neural networks on vast datasets of human-generated text, LLMs have developed an unprecedented ability to understand and generate human-like language with robust fluency and comprehension. This breakthrough has unlocked a wide range of innovative applications across industries, from content creation and language translation to conversational AI assistants and code generation.
More recently Open AI released ChatGPT 4o that they say can reason across different modalities in real time. They trained a single new model end-to-end across text, vision, and audio, meaning that all inputs and outputs are processed by the same neural network. This is still early days but this idea of developing a multi-modal model has vast potential to create much more effective outputs that can help yield better decision making.
The nascency of this technology has yet to be fully understood–language, image, audio understanding, the generation capabilities that can drive substantial productivity gains, and enable new forms of human-machine collaboration and even question which human jobs are replaceable– are still emerging.
As well, LLM technology has limitations and risks including issues of factual inaccuracies, biases inherited from training data, lack of common-sense reasoning, and pervasive potential for misuse, and more recently the data privacy implications that we’ve seen from OpenAI’s unconsented use of Scarlett Johansson’s voice.
Techniques like Retrieval Augmented Generation (RAG) are highlighted as promising approaches to enhance LLMs' knowledge grounding, improve their accuracies over time.
We welcomed Amir Feizpour, CEO and founder of AI.Science, a platform for expert-in-the-loop business workflow automation. In this episode of Tech Uncensored, we will delve into the transformative impacts of LLMs across sectors, the applications both present and future, the current challenges and risks and what does this mean to startups developing in this space.
More recently Open AI released ChatGPT 4o that they say can reason across different modalities in real time. They trained a single new model end-to-end across text, vision, and audio, meaning that all inputs and outputs are processed by the same neural network. This is still early days but this idea of developing a multi-modal model has vast potential to create much more effective outputs that can help yield better decision making.
The nascency of this technology has yet to be fully understood–language, image, audio understanding, the generation capabilities that can drive substantial productivity gains, and enable new forms of human-machine collaboration and even question which human jobs are replaceable– are still emerging.
As well, LLM technology has limitations and risks including issues of factual inaccuracies, biases inherited from training data, lack of common-sense reasoning, and pervasive potential for misuse, and more recently the data privacy implications that we’ve seen from OpenAI’s unconsented use of Scarlett Johansson’s voice.
Techniques like Retrieval Augmented Generation (RAG) are highlighted as promising approaches to enhance LLMs' knowledge grounding, improve their accuracies over time.
We welcomed Amir Feizpour, CEO and founder of AI.Science, a platform for expert-in-the-loop business workflow automation. In this episode of Tech Uncensored, we will delve into the transformative impacts of LLMs across sectors, the applications both present and future, the current challenges and risks and what does this mean to startups developing in this space.
Creators and Guests
Host
Hessie Jones
Advocating for #DataPrivacy, Human-Centred #AI, Fair & Ethical Distribution 4 all; @forbes she/her; Developing Data Privacy Solutions https://t.co/PudK3nLMU9
