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The Rise of the AI Engineer

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Summary

The article discusses the emergence of a new role in the AI engineering world, the AI engineer. AI engineers are individuals who are able to use the emergent capabilities of foundation models to create AI products without a PhD or having to take Andrew Ng's Coursera course. The article examines the differences between ML engineers and AI engineers, the role of code in the evolution from Software 2.0 to Software 3.0, the importance of Python and JavaScript in the field, the growth of generative AI, the impact of GPU hoarding on AI engineers, and the need for a conference to bring together AI engineers, founders, and investors.

Q&As

What are the emerging capabilities of Foundation Models that are driving the rise of AI Engineers?
The emerging capabilities of Foundation Models that are driving the rise of AI Engineers are in-context learning, zero shot transfer capabilities, and the ability to generalize beyond the original intent of model trainers.

What are the core differences between ML Engineers, AI Engineers and Software Engineers?
The core differences between ML Engineers, AI Engineers and Software Engineers are that ML Engineers focus on fraud risk, recommendation systems, anomaly detection, and feature stores, while AI Engineers are building writing apps, personalized learning tools, natural language spreadsheets, and Factorio-like visual programming languages. Software Engineers are focused on hand-coded programming languages that precisely model logic.

How is the global chip shortage impacting the role of AI Engineers?
The global chip shortage is creating a shortage of capacity for AI Engineers to use models, rather than train them.

What are the advantages of a "fire, ready, aim" approach to AI product development?
The advantages of a "fire, ready, aim" approach to AI product development are that it allows product managers/software engineers to prompt an LLM and build/validate a product idea before getting specific data to finetune, and it enables them to validate AI products 1,000-10,000x cheaper than traditional ML.

What is the AI Engineer Summit and how can people participate?
The AI Engineer Summit is the first independently run, builder-oriented AI conference. It is a 500 person conference in San Francisco (and online) that aims to convene AI Engineers, founders, and investors together to learn about the state of the art, attend/teach workshops, and find everything from the great new tool they’ll use at work, to their next new hire/cofounder/round. People can participate by submitting a speaker CFP or sponsoring the event.

AI Comments

👍 This article provides a great overview of the emerging role of AI Engineers and how they are changing the way software is developed. It also provides great insights into the future of AI Engineering and how it will benefit the development of AI products.

👎 This article provides a lot of technical details that may not be relevant to the average reader, making it difficult to understand the concept of AI Engineering. Additionally, the author's use of jargon and acronyms may be confusing for those who don't have a background in the industry.

AI Discussion

Me: It's about the rise of the AI Engineer! Emergent capabilities are creating this new role for software engineers since AI tasks that used to take 5 years and a research team to accomplish now just require API docs and a spare afternoon. It's predicted to be the highest-demand engineering job of the decade.

Friend: Wow, that's fascinating! What implications does it have?

Me: It means that the demand for ML engineers will eventually be outpaced by the demand for AI engineers, and that AI engineers will be able to validate AI products much more quickly and cheaply than before. It also means that with the emergence of AI engineers, there will be a greater emphasis on human-written code to orchestrate and supplant LLM power. Software engineering is going to evolve into a new subdiscipline. Lastly, it means that AI engineers will be able to take advantage of the increasing availability of open source models and APIs to create products that are used by millions virtually overnight.

Action items

Research the various AI Engineer roles and tools mentioned in the article to gain a better understanding of the field.
Join the AI Engineer Summit to network with other AI Engineers and learn about the state of the art.
Experiment with open source models and tools to gain hands-on experience with AI engineering.

Technical terms

Emergent capabilities: Emergent capabilities are the abilities that arise from the combination of different elements or components.
Prompt Engineer: A prompt engineer is someone who uses natural language processing (NLP) to generate text or other outputs from a given prompt.
Software: Software is a set of instructions or programs that tell a computer how to perform a task.
LLM: LLM stands for Latent Language Model, which is a type of artificial intelligence (AI) model that can generate text from a given prompt.
API: An application programming interface (API) is a set of protocols, routines, and tools for building software applications.
Data Scientist/ML Engineer: Data scientists and machine learning (ML) engineers are professionals who use data to build and optimize ML models.
Data Lake/Data Warehouse: A data lake is a storage repository that holds a large amount of raw data in its native format until it is needed. A data warehouse is a repository of organized data used for reporting and analysis.
Attention is All You Need: Attention is All You Need is a paper published in 2017 that introduced a new type of neural network architecture called the Transformer.
GPT-3: GPT-3 is a large-scale language model developed by OpenAI that can generate text from a given prompt.
Stable Diffusion: Stable Diffusion is a type of artificial intelligence (AI) model developed by OpenAI that can generate text from a given prompt.
Few Shot Learners: Few shot learners are AI models that can learn from a small amount of data.
LangChain: LangChain is a tool for creating natural language processing (NLP) models.
LLaMA: LLaMA is an open source natural language processing (NLP) model developed by the Latent Space team.
Huggingface: Huggingface is an open source natural language processing (NLP) model developed by the Huggingface team.
Auto-GPT: Auto-GPT is an autonomous agent developed by OpenAI that can generate text from a given prompt.
BabyAGI: BabyAGI is an autonomous agent developed by OpenAI that can generate text from a given prompt.
Software 2.0: Software 2.0 is a term used to describe software that is powered by machine learning (ML) models.
Software 3.0: Software 3.0 is a term used