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As we have discussed, there have been some signs of open-source AI (and AI startups) struggling to compete with the largest LLMs at closed-source AI companies. This is driven by the need to eventually monetize to fund the increasingly huge LLM training costs. This would be its 5th generation AI training cluster.
By leveraging LLMs, institutions can automate the analysis of complex datasets, generate insights for decision-making, and enhance the accuracy and speed of compliance-related tasks. These use cases demonstrate the potential of AI to transform financial services, driving efficiency and innovation across the sector.
Dataquality control: Robust dataset labeling and annotation tools incorporate quality control mechanisms such as inter-annotator agreement analysis, review workflows, and data validation checks to ensure the accuracy and reliability of annotations. Data monitoring tools help monitor the quality of the data.
This includes handling unexpected inputs, adversarial manipulations, and varying dataquality without significant degradation in performance. When you ask the model to explain the process it used to generate the output, the model has to identify different the steps taken and information used, thereby reducing hallucination itself.
Automated Query Optimization: By understanding the underlying data schemas and query patterns, ChatGPT could automatically optimize queries for better performance, indexing recommendations, or distributed execution across multiple data sources. The BPE algorithm is "byte-level" because it runs on UTF-8 encoded strings.
In this talk, Stefanie will discuss Data Morph, an open-source package that builds on previous research from Autodesk (the Datasaurus Dozen) using simulated annealing to perturb an arbitrary input dataset into a variety of shapes, while preserving the mean, standard deviation, and correlation to multiple decimal points.
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