Palantir Foundry for AI Governance Ethical AI in Action Editor’s Note: Written by Palantir’s Privacy and Civil Liberties (PCL) team, this blog post builds on our belief that, in order to preserve the truly valuable contributions of AI/ML, AI ethics and efficacy must move beyond the performative towards operational realities. In future posts, we’ll be addressing …
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Ruchir Jha, Brian Harrington, Yingwu Zhao TL;DR Streaming alert evaluation scales much better than the traditional approach of polling time-series databases. It allows us to overcome high dimensionality/cardinality limitations of the time-series database. It opens doors to support more exciting use-cases. Engineers want their alerting system to be realtime, reliable, and actionable. While actionability is subjective …
While many organizations have established environmental, social and governance (ESG) goals and made ESG commitments, driven by purpose and emerging regulatory requirements, they face several challenges when making the transition from ambition to action. A recent IBM study found that global executives cite inadequate data (41%) as the biggest obstacle to their ESG progress, followed …
Read more “How ERP is breaking down silos and driving sustainable change”
Posted by Babak Behsaz, Software Engineer, and Andrew Carroll, Product Lead, Genomics The combination of the environment an individual experiences and their genetic predispositions determines the majority of their risk for various diseases. Large national efforts, such as the UK Biobank, have created large, public resources to better understand the links between environment, genetics, and …
Read more “An ML-based approach to better characterize lung diseases”
Recent large language models (LLMs) have enabled tremendous progress in natural language understanding. However, they are prone to generating confident but nonsensical explanations, which poses a significant obstacle to establishing trust with users. In this post, we show how to incorporate human feedback on the incorrect reasoning chains for multi-hop reasoning to improve performance on …
Read more “Improve multi-hop reasoning in LLMs by learning from rich human feedback”
We are thrilled to introduce Accuracy Evaluation, the newest feature in our Cloud Speech UI, to allow for easy and seamless benchmarking of our Speech-to-Text (STT) API models and configurations. The STT API covers a wide variety of use cases, from dictation and short commands, to captioning and subtitles. Getting the most of STT, however, …
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“Please hold” may be the two words that customers hate most — and that contact center agents take pains to avoid saying. Providing fast, accurate, helpful responses based on contextually relevant information is key to effective customer service. It’s even better if answers are personalized and take into account how a customer might be feeling. …
Diffusion models (DMs) have recently emerged as SoTA tools for generative modeling in various domains. Standard DMs can be viewed as an instantiation of hierarchical variational autoencoders (VAEs) where the latent variables are inferred from input-centered Gaussian distributions with fixed scales and variances. Unlike VAEs, this formulation constrains DMs from changing the latent spaces and …
Read more “f-DM: A Multi-stage Diffusion Model via Progressive Signal Transformation”
ChatGPT users can now turn off chat history, allowing you to choose which conversations can be used to train our models.
IT Managers run into scalability challenges on a regular basis. It is difficult to predict growth rates of applications, storage capacity usage and bandwidth. When a workload reaches capacity limits, how is performance maintained while preserving efficiency to scale? The ability to use the cloud to scale quickly and handle unexpected rapid growth or seasonal shifts …