March 2023 Summaries

Hungry Hungry Hippos (H3) and its creators, Dan Fu and Tri Dao, have developed a language modeling architecture that performs comparably to transformers while admitting much longer context length, making it suitable for tasks such as audio processing and biological applications. Their approach uses state space models, which are inspired by old concepts from control theory but have been adapted for deep learning. The H3 model achieves impressive results on large benchmark tests, often rivaling or surpassing transformer-based models. When combined with one or two attention layers, the blended architecture shows even more promising results. The researchers believe that state space methods could be more efficient during inference, which is a crucial concern for deploying these models in products. Applications of H3 include code generation, video processing, and biological applications, as well as interactive AI workflows and automatic slide generation. These new architectures will require interaction between users and the system, making long-range context increasingly important.

In this podcast, Timo Schick and Thomas Scialom from Meta AI discuss their research on Toolformer, a language model that can access external tools such as calculators and question-answer search APIs to generate more powerful and accurate output. They explain the limitations of current "vanilla" language models, which cannot access information about the external world, and how Toolformer aims to address these issues by equipping models with the ability to communicate via APIs or external tools. The researchers also share their thoughts on the future of tool-LLM powered products and potential areas of research in this field.

PCI DSS 4.0 is a global standard that provides a baseline of technical and operational requirements designed to protect account data, particularly in light of rising credit card fraud and identity theft. Arize AI has achieved this certification as a Level 1 Service Provider due to its potential access to or impact on the security of ingested credit card information, with the goal of safeguarding customers' data entrusted to it. The company pursued PCI DSS 4.0 Certification not by obligation but as a conscious choice to better protect and safeguard its customers' data, demonstrating compliant controls and mechanisms in place to properly safeguard data. Arize AI is committed to maintaining or exceeding the standards required by the PCI leadership as well as its own policies, with a focus on continually protecting and safeguarding customer data.

Zippi, a Brazil-based fintech company founded by MIT alumni, aims to provide affordable and accessible financial services to over 30 million micro entrepreneurs who face challenges accessing credit from traditional banks. The company leverages machine learning (ML) models to assess credit risk, price sensitivity, and limit sensitivity, helping customers achieve their business goals. Zippi's commitment to using cutting-edge technology and best practices in the market sets it apart as a fintech company. Arize is selected as the model monitoring and ML observability partner due to its strong support, effective onboarding process, and commitment to helping scale up skills for consistent leveraging of the tool.

A feature store is a central repository of precomputed features that serve as a single source of truth for machine learning projects, providing several benefits including centralized data management, clean data handling, shareable features across models, and standardized inference to the data. The adoption of feature stores has risen in popularity since Uber introduced the concept in 2017, with organizations utilizing them to streamline their data and ML lifecycle. Feature stores offer a one-stop-shop for data collection, transformation, and access, making it easier for teams to work together and reduce wasteful rework. By applying monitoring and quality checks to feature stores, practitioners can catch common machine learning issues such as missing values, data format changes, and statistical distribution shifts, ensuring better model performance and reduced latency.