This presentation was recorded during GOTO Amsterdam 2024. #GOTOcon #GOTOams
https://gotoams.nl

Jodie Burchell – Data Scientist and Developer at JetBrains @jodieburchell

SOURCES
https://twitter.com/t_redactyl
https://www.linkedin.com/in/jodieburchell
https://github.com/t-redactyl
https://t-redactyl.io

ABSTRACT
If you're remotely tuned in to the latest developments in large language models (LLMs), you've probably been inundated with news ranging from claims that these models will replace countless white-collar jobs to statements about sentiment and an impending AI apocalypse. At this stage, the hype surrounding these models has far exceeded the actual information available.

In this talk, we will cut through the noise and take a deep dive into the current applications, risks, and limitations of LLMs. We start with early research efforts aimed at creating an "artificial brain" and follow the path that led us to today's advanced text models. Along the way we will discuss how these models came to be mistaken for intelligent systems.

We will shed light on the real requirements for developing true Artificial General Intelligence, and see how far LLMs are from this goal. We'll end with a practical demonstration of how to use LLMs in a way that leverages their strengths, by showing you how to build a system that relies on the powerful natural language capabilities of these models. […]

TIME CODES
00:00 Introduction
03:01 Where are we and how did we get here?
13:35 Are LLMs /"intelligent/"?
27:29 Using LLMs
42:29 Outro

Download slides and read the full abstract here:
https://gotoams.nl/2024/sessions/3115

RECOMMENDED BOOKS
Alex Castrounis • AI for People and Business • https://amzn.to/3NYKKTo
Phil Winder • Reinforcement Learning • https://amzn.to/3t1S1VZ
Holden Karau, Trevor Grant, Boris Lublinsky, Richard Liu & Ilan Filonenko • Kubeflow for Machine Learning • https://amzn.to/3JVngcx
Kelleher & Tierney • Data Science (the MIT Press Essential Knowledge Series) • https://amzn.to/3AQmIRg
Lakshmanan, Robinson & Munn • Machine Learning Design Patterns • https://amzn.to/2ZD7t0x
Lakshmanan, Görner & Gillard • Practical Machine Learning for Computer Vision • https://amzn.to/3m9HNjP

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