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No Priors Ep. 7 | With Stanford Professor Dr. Percy Liang

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💫 Short Summary

The video discusses the evolution of Foundation models and their impact on academia and industry, emphasizing the importance of transparency, accountability, and ethical considerations in AI development. It explores the challenges and advancements in language models, question answering systems, and data efficiency, highlighting the potential for creative applications and scientific discovery. The discussion also addresses the use of compute resources, training models like Transformers, and leveraging distributed computing for academic research. The video concludes by emphasizing the need for community involvement, open-mindedness, and ethical guidelines in shaping the future of AI technologies.

✨ Highlights
📊 Transcript
Speaker's background in machine learning and natural language processing.
Speaker's interest in systems that can understand natural language.
Shift in focus to Foundation models and large language models after GPT-3 release during the pandemic.
GPT-3's training method of predicting the next word billions of times leading to fluent text generation and in-context learning.
Capabilities of the model for tasks like summarization and examples.
Lack of transparency and accessibility in Foundation models.
Models only accessible through APIs, limiting understanding of inner workings.
Shift towards limited access due to high cost of training, competitive advantages, and safety concerns.
Center for Research on Foundation Models aims to increase transparency and accessibility.
Contrasts with open culture of deep learning in the past decade.
Evolving roles of academia and industry in ML, NLP, and AI.
Academia previously focused on making models work, leading to advancements that influenced industry.
Academia now focuses on understanding the principles behind models due to a lack of understanding on how they work and their impact.
Industry benefits from resources to scale and overcome barriers, with a focus on practical applications.
The relationship between academia and industry has become more specialized and complementary.
Overview of the Center for Research on Foundation Models (CRFM) at Stanford.
The center comprises over 30 faculty members from 10 different departments, engaging in interdisciplinary research on foundation models.
Research areas include technical aspects, economic impacts, challenges surrounding copyright and legality, social biases, and risks of disinformation.
Focus is also on leveraging foundation models in medicine, particularly for clinical practice.
Emphasis on addressing concerns such as privacy, robustness, and integration of foundation models into real clinical care.
Importance of defining objective measure for AI performance beyond human standards.
Emphasis on reliability and statistical evidence in AI development.
Advocacy for technology that is principled and rational, rather than just mimicking human capabilities.
Exploration of computational semantics and its significance in AI development.
Highlight on deriving meaning from language text and application of semantic parsing in mapping natural language to formal spaces for machine execution.
Advancements in question answering systems and the development of powerful language models like Bert and Roberta are discussed.
The shift from symbolic AI to neural AI is emphasized, with a focus on planning and reasoning in AI.
Symbolic AI's relevance in tackling more complex tasks beyond simple classification and entity extraction is highlighted.
The ongoing debate on integrating neural and symbolic AI approaches for improved AI capabilities and research programs is addressed.
Importance of data efficiency and speed in achieving benchmarks.
Emphasizes the need for models to handle greater context and complex reasoning chains.
Discusses the limitations of fixed models like Transformers.
Explores the concept of emergent context learning demonstrated by GPT-3.
Notes the potential for improved performance with better models and data.
Language models can creatively mix and match concepts, showcasing their ability to learn and fuse different ideas.
Models are not simply memorizing text, as demonstrated by explaining quicksort in Shakespearean style.
The creative potential of these models opens up possibilities for scientific discovery and pushing beyond human limitations.
Instructing models using natural language for various tasks is becoming more prevalent, highlighting the evolving nature of AI technology.
Improvements in language models for text generation.
Language models predict the next word based on context, syntax, and previous words but may produce unintended capabilities.
Current models have difficulty generating accurate and coherent text, sometimes producing nonsensical content.
Hope is that with continued improvements, issues with text generation will decrease.
Increasing context and data input can enhance the model's accuracy in predicting the next word and understanding text.
Pre-training increases accuracy by predicting the next word and developing a world model.
The success of the Transformer model is due to its scalability and emergent properties.
Concerns exist about the Transformer's limitations and the need for exploring alternative architectures.
Academia is encouraged to challenge the status quo and incorporate principles learned from Transformers in a more principled way.
Efficient use of compute resources for training large-scale models like Transformers.
The cost and importance of compute pricing for scaling models is discussed.
Testing ideas at different scales is emphasized.
Addressing the central bottleneck of compute in Foundation models and harnessing decentralized resources is highlighted.
Researchers have developed techniques for optimizing compute usage in training models.
Leveraging weekly connected compute for academic research and startups.
Projects like Folding@home and SETI@home paved the way for distributed computing.
Challenges of handling big data and task decomposition in training AI models.
Exploring incentivizing contributions to a research computer for academic purposes and the potential of open models in the commercial sector for fine-tuning and adapting AI models.
Future may see a variety of Foundation models tailored to different use cases.
Use of Foundation models for training models like PubMed articles and collaboration with Mosaic to create a model called biomed LM.
Emphasis on efficiency reasons for using smaller models despite larger models developed by Google.
Exploration of using models to detect fraud, plagiarism, and inconsistencies in biomedical information.
Caution advised in making consequential decisions based on model outputs.
Challenges faced by researchers due to the large volume of papers generated, making it hard to separate important information from irrelevant data.
Importance of tools such as literature review and summarization software for effective research.
Mention of Illicit, a company utilizing language models to assist in research processes.
Potential of advanced tools to read literature, generate hypotheses, propose experiments, and speed up scientific advancements.
Obstacles to fully automated research, but models have the ability to uncover new strategies and insights.
Research on word embeddings from over 10 years ago uncovers new thermodynamic properties of materials and suggests potential advancements with more powerful models.
Daphne Kohler's work on data generation and optimization in language models is discussed as part of the conversation.
The Helm project evaluates language models rigorously across scenarios and metrics, emphasizing accuracy, robustness, calibration, fairness, bias, toxic content, and efficiency.
The project analyzed 30 models, 42 scenarios, and seven metrics, with detailed results available on the Helm website for further insights.
Language models aim to provide transparency by showcasing their capabilities and deficiencies in a scientific manner.
The project involves dynamically updating every two weeks with new models and scenarios.
Evolving capabilities of language models now include writing emails, giving life advice, and more.
Concerns about security risks, jailbreaking, and potential cascade of errors if models interact with the world.
The project also aims to explore multimodal models and their implications in policy intersections.
Importance of Transparency in AI Model Construction.
Differing opinions exist on model construction, with some calling for more transparency.
Accountability and legitimacy in determining human values within AI models are crucial.
Developing norms, starting with transparency, is essential for policy discussions.
Transparency becomes increasingly important as AI models are deployed at scale and impact society.
Highlights of the AI model Neo X and the evolution of AGI perception.
The importance of community involvement in building Foundation models for AI development.
AGI has shifted from a laughable concept to a serious consideration due to existential risks.
Emphasis on open-mindedness in understanding evolving AI technologies and their social consequences.
The changing landscape of AI and human interaction necessitates a flexible worldview for the future.