Side Projects to Get a Job in ML, a Survey of Small Language Models, How to Build ML Pipelines, and More
Society's Backend Reading List 11-11-2024
Here's a comprehensive AI reading list from this past week. Thanks to all the incredible authors for creating these helpful articles and learning resources.
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What Happened Last Week
Here are some resources to learn more about what happened in AI last week and why those happenings are important:
AI Roundup by
The Batch by Andrew Ng
Papers Podcast
ML papers are difficult to keep up with. Here’s this week’s NotebookLM-generated podcast going over important papers you should know:
Last Week's Reading List
Reading List
A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness
This survey examines small language models (SLMs) in comparison to large language models (LLMs), highlighting their advantages like lower costs and better adaptability for specialized tasks. SLMs are particularly useful in resource-limited settings and for applications requiring quick responses and privacy. The authors propose a standardized definition for SLMs and provide frameworks to enhance their development and use.
How to build a side-project to get a job in Machine Learning
By
To land your first job in Machine Learning, focus on creating 1-3 standout side projects instead of many mediocre ones. Personal projects can significantly boost your chances, even if you lack a strong GPA or network. This article will guide you on how to build exceptional projects that will make you stand out in the job market.
How To Build The Future: Sam Altman
Sam Altman discusses ideas for creating a better future. He emphasizes the importance of innovation and technology. The focus is on building solutions that can improve lives globally.
AI Safety Under Republican Leadership
By
The author argues that a potential second Trump administration may handle AI risks more effectively than the Biden administration, which tends to overcomplicate issues by linking them to broader social concerns. Republicans are more likely to prioritize focused research on AI safety, while Democrats' "everything is everything" approach makes it harder to address specific technical challenges. The author believes there's an opportunity for Republicans to engage seriously with AI risks if they can overcome their current skepticism towards the AI safety movement.
Strengthening AI Accountability Through Better Third Party Evaluations
Third-party evaluations of AI systems are important because they provide independent assessments that consider various perspectives. Researchers are calling for more legal protections and standardized processes for these evaluations, similar to those in software security. A recent workshop highlighted the need to improve these practices to address the unique challenges posed by AI technologies.
The Rise of AI and Machine Learning Jobs
The job market is rapidly changing due to the growth of artificial intelligence (AI) and machine learning (ML), creating many new job opportunities. Demand for AI and ML skills is increasing across various industries, with roles like machine learning engineer and data scientist in high demand. To succeed in these fields, individuals should focus on building technical skills, gaining practical experience, and staying updated on industry trends.
Open Source AI Can Help America Lead in AI and Strengthen Global Security
Meta is making its open source Llama AI models available to U.S. government agencies and their partners to enhance national security and improve efficiency. Collaborations with major companies will help streamline various operations, from aircraft maintenance to data analysis. By promoting responsible AI use, the U.S. aims to lead in global AI standards and support democratic values.
The AI War and How to Win It
The future of warfare will be dominated by AI, and the U.S. is currently falling behind China in military AI advancements. To compete, the U.S. must invest significantly in AI technologies and adapt its military strategies accordingly. Without rapid action, America risks losing its leadership and effectiveness in future conflicts.
How to become a more effective engineer
By
To become a more effective engineer, it's crucial to understand how your organization works, including its implicit hierarchies and constraints. Focus on building relationships, seeking small wins, and navigating the complexities of your workplace. Balancing idealism with practicality can help you succeed in a messy organizational environment.
Building a Robust Machine Learning Pipeline: Best Practices and Common Pitfalls
Building a robust machine learning pipeline is essential for delivering valuable predictions, and it involves understanding the entire machine learning lifecycle. Key pitfalls to avoid include ignoring data quality, overcomplicating models, and inadequate monitoring. Best practices include using appropriate evaluation metrics, implementing MLOps for deployment and monitoring, and maintaining thorough documentation.
Personalization of Large Language Models: A Survey
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