This is a special double weekend crosspost of AI podcasts, helping attendees prepare for the AI Engineer Summit next week. Swyx gave a keynote on the Software 3.0 Landscape recently (referenced in our recent Humanloop episode) and was invited to go deeper in podcast format, and to preview the AI Engineer Summit Schedule.
For those seeking to ramp up on the current state of thinking on AI Engineering, this should be the perfect place to start, alongside our upcoming Latent Space University course (which is being tested live for the first time at the Summit workshops).
While you are listening, there are two things you can do to be part of the AI Engineer experience. One, join the AI Engineer Summit Slack. Two, take the State of AI Engineering survey and help us get to 1000 respondents!
Full transcript available here!
Links
* AI Engineer Summit (Join livestream and Slack community)
* State of AI Engineering Survey (please help us fill this out to represent you!)
* Podrocket full episode by Tejas Kumar
Show notes
* Explaining Software 1.0, 2.0, and 3.0
* Software 1.0: Hand-coded software with conditional logic, loops, etc.
* Software 2.0: Machine learning models like neural nets trained on data
* Software 3.0: Using large pre-trained foundation models without needing to collect/label training data
* Foundation Models and Model Architecture
* Foundation models like GPT-3/4, Claude, Whisper - can be used off the shelf via API
* Model architecture refers to the layers and structure of a ML model
* Grabbing a pre-trained model lets you skip data collection and training
* Putting Foundation Models into Production
* Levels of difficulty: calling an API, running locally, fully serving high-volume predictions
* Key factors: GPU utilization, batching, infrastructure expertise
* The Emerging AI Developer Landscape
* AI is becoming more accessible to "traditional" software engineers
* Distinction between ML engineers and new role of AI engineers
* AI engineers consume foundation model APIs vs. developing models from scratch
* The Economics of AI Engineers
* Demand for AI exceeds supply of ML experts to build it
* AI engineers will emerge out of software engineers learning these skills
* Defining the AI Engineering Stack
* System of reasoning: Foundation model APIs
* Retrieval augmented generation (RAG) stack: Connects models to data
* AI UX: New modalities and interfaces beyond chatbots
* Building Products with Foundation Models
* Replicating existing features isn't enough - need unique value
* Focus on solving customer problems and building trust
* AI Skepticism and Hype
* Some skepticism is healthy, but "AI blame" also emerges
* High expectations from media/industry creators
* Important to stay grounded in real customer needs
* Meaningful AI Applications
* Many examples of AI positively impacting lives already
* Engineers have power to build and explore - lots of opportunity
* Closing and AI Engineer Summit Details
* October 8-10 virtual conference for AI engineers
* Speakers from OpenAI, Microsoft, Amazon, etc
* Free to attend online
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