What’s the Future for Software Developers?
Paige Bailey, who began coding at age 9 in rural Texas, now leads the GenAI developer experience at Google. In a conversation with Chris Pirillo on The New Stack Makers, Bailey reflected on the evolving role of software development in the era of generative AI. While she once urged her nieces and nephews to pursue computer science degrees, Bailey now believes that critical thinking and problem-solving may be more crucial for future tech careers.
She emphasized that generative AI is democratizing software development, making it more accessible and enabling developers to focus on creative tasks rather than the minutiae of coding. Bailey's experience at Google highlights this shift, as she now acts more as a reviewer and overseer of AI-generated code. She sees GenAI not as a replacement for developers but as a tool to accelerate their creativity and tackle longstanding backlogs. Bailey believes the key is ensuring everyone understands how to effectively apply generative AI to their work.
Learn more from The New Stack about the future of development:
7 Ways to Future Proof Your Developer Job in the Age of AI
The Future of Developer Careers
4 Forecasts for the Future of Developer Relations
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E57: Google's PaLM 2 with Paige Bailey
In this episode, Nathan sits down with Paige Bailey, Lead Product Manager of Generative Models at Google Deepmind. In this conversation, they discuss what it's like to be a PM for an LLM as opposed to an app, defining ideal LLM behaviour, and reasoning - how do you distinguish real abilities vs pattern matching? If you're looking for an ERP platform, check out our sponsor, NetSuite: http://netsuite.com/cognitive
We're hiring across the board at Turpentine and for Erik's personal team on other projects he's incubating. He's hiring a Chief of Staff, EA, Head of Special Projects, Investment Associate, and more. For a list of JDs, check out: eriktorenberg.com.
TIMESTAMPS:
(00:00) Episode Preview
(00:01:15) Introducing Paige Bailey
(00:04:21) Paige’s background at Google Brain and the Deepmind merger
(00:07:00) PM for a LLM vs being a PM for an app
(00:11:21) The development timeline and compute budget of PaLM-2
(00:14:30) Paige’s role in the PaLM 2 project
(00:15:30) Sponsors: Netsuite | Omneky
(00:17:26) Defining desired capabilities for PaLM-2
(00:19:17) The amount of work that went into elevating PaLM 2 from PaLM 1
(00:20:28) Has Google lost its ability to ship?
(00:24:240) Paige's "eureka" moment seeing GitHub Copilot capabilities
(00:27:47) Competing PaLM 2 with other models
(00:32:20) Grokking and the predictability of emergent capabilities
(00:37:30) Citizen scientists and the multilingual capabilities of PaLM 2
(00:39:29) Distinguishing real reasoning vs pattern matching
(00:45:51) Products using PaLM-2 that people should try
(00:50:35) Most exciting AI projects that you can try out
(00:52:29) Curriculum learning and successor to the transformer
LINKS:
PaLM 2
Duet AI for developers
Avenging Polayni’s Revenge
X/TWITTER:
@DynamicWebPaige (Paige)
@labenz (Nathan)
@eriktorenberg
@CogRev_Podcast
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Music Credit: GoogleLM
A conversation with the folks building Google's AI models
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Exploring TensorFlow 2.0 with Paige Bailey - TWiML Talk #242
Today we're joined by Paige Bailey, TensorFlow developer advocate at Google, to discuss the TensorFlow 2.0 alpha release. Paige and I talk through the latest TensorFlow updates, including the evolution of the TensorFlow APIs and the role of eager mode, tf.keras and tf.function, the evolution of TensorFlow for Swift and its inclusion in the new fast.ai course, new updates to TFX (or TensorFlow Extended), Google’s end-to-end ML platform, the emphasis on community collaboration with TF 2.0, and more.
TensorFlow.js and Machine Learning in JavaScript
Panelists Suz Hinton and Nick Nisi discuss TensorFlow.js and Machine Learning in JavaScript with special guest Paige Bailey, TensorFlow mom and developer Advocate for Google AI.
Join the discussion
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Featuring:
Paige Bailey – Website, GitHub, X
Suz Hinton – GitHub, Mastodon, X
Nick Nisi – Website, GitHub, Mastodon, X
Show Notes:
TensorFlow.js
Google AI
ml5.js - Friendly Machine Learning for the Web
Machine Learning Glossary
TensorFlow tutorials
Tero Parviainen on CodePen
tfjs-layers - High-level machine learning model API
tfjs-models - Pre-trained TensorFlow.js models
tfma-slicing-metrics-browser.gif 📷
TensorFlow Model Analysis (TFMA) - a library for evaluating TensorFlow models
What-If Tool - Building effective machine learning systems means asking a lot of questions. It’s not enough to train a model and walk away. Instead, good practitioners act as detectives, probing to understand their model better.
EthicalMachineLearning.ipynb
TensorBoard: Visualizing Learning
TensorBoard: Graph Visualization
People + AI Research (PAIR) - Human-centered research and design to make AI partnerships productive, enjoyable, and fair.
Distill - Clear explanations of machine learning
Book: Technically Wrong: Sexist Apps, Biased Algorithms, and Other Threats of Toxic Tech
Book: Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy
A new course to teach people about fairness in machine learning
List of cognitive biases
CleverHans - a Python library to benchmark machine learning systems’ vulnerability to adversarial examples
CleverHans paper
Breaking linear classifiers on ImageNet
CV Dazzle - explores how fashion can be used as camouflage from face-detection technology, the first step in automated face recognition
Something missing or broken? PRs welcome!