In June 2021, four of us from the Salesforce AI Ethics and Conversational Design teams collaborated with the Montreal AI Ethics Institute (MAIEI) to facilitate a workshop on the responsible creation and implementation of chatbots and conversational assistants. Connor Wright has summarized 10 insights from the workshop and we’d

AI can learn the language of biology to create artificial proteins across multiple protein types that are functional and unseen in nature.

An AI model can generate sequences across natural language and proteins with attributes that go beyond the training distribution

Transformer-inspired tips for enhancing the design of neural networks that process graph-structured data

AI makes breast cancer therapy decisions more accurate, affordable and accessible using visual clues in pathology images invisible to the eye

We introduce Photon, a live demo of natural language interface to databases based on our latest research in neural semantic parsing. 🔗 https://naturalsql.com/

For all the good that AI can bring, responsible tech companies understand they must recognize, prepare for, and mitigate the potential unintended, harmful effects. That’s why Salesforce sees ethics as foundational to AI — and why we’re sharing a closer look at how we infuse an ethical process into

Morpheus exposes the potential allocative harms of popular pretrained NLP models by simulating inflectional variation. We propose adversarial fine-tuning for mitigating the effects of training only on error-free Standard English data.

Word embeddings inherit strong gender bias in data which can be further amplified by downstream models. We propose to purify word embeddings against corpus regularities such as word frequency prior to inferring and removing the gender subspace, which significantly improves the debiasing performance.

Prototypical Contrastive Learning unifies clustering and contrastive self-supervised learning to push the frontiers of unsupervised learning.

We show that deep neural models can describe common sense physics in a valid and sufficient way that is also generalizable. Our ESPRIT framework is trained on a new dataset with physics simulations and descriptions that we collected and have open-sourced.

SummaryWe investigate NVIDIA's Triton (TensorRT) Inference Server as a way of hosting Transformer Language Models. The blog is roughly divided into two parts: (i) instructions for setting up your own inference server, and (ii) benchmarking experiments. The instructions are intended to be detailed and standalone, but readers interested solely in

Learn about the ethical implications of voice for business and how to make them an operational and strategic priority now—before you’re too far down the path.