HAI Senior Fellow Yejin Choi discussed responsible AI model training at Davos, asking, “What if there could be an alternative form of intelligence that really learns … morals, human values from the get-go, as opposed to just training LLMs on the entirety of the internet, which actually includes the worst part of humanity, and then we then try to patch things up by doing ‘alignment’?” 

Silent Speech Interfaces (SSIs) offer a nonin- vasive alternative to brain-computer interfaces for soundless verbal communication. We in- troduce Multimodal Orofacial Neural Audio (MONA), a system that leverages cross-modal alignment through novel loss functions—cross- contrast (crossCon) and supervised temporal con- trast (supTcon)—to train a multimodal model with a shared latent representation. This archi- tecture enables the use of audio-only datasets like LibriSpeech to improve silent speech recog- nition. Additionally, our introduction of Large Language Model (LLM) Integrated Scoring Ad- justment (LISA) significantly improves recogni- tion accuracy. Together, MONA LISA reduces the state-of-the-art word error rate (WER) from 28.8% to 12.2% in the Gaddy (2020) benchmark dataset for silent speech on an open vocabulary. For vocal EMG recordings, our method improves the state-of-the-art from 23.3% to 3.7% WER. In the Brain-to-Text 2024 competition, LISA per- forms best, improving the top WER from 9.8% to 8.9%. To the best of our knowledge, this work represents the first instance where noninvasive silent speech recognition on an open vocabulary has cleared the threshold of 15% WER, demon- strating that SSIs can be a viable alternative to au- tomatic speech recognition (ASR). Our work not only narrows the performance gap between silent and vocalized speech but also opens new possi- bilities in human-computer interaction, demon- strating the potential of cross-modal approaches in noisy and data-limited regimes.

Axios chief technology correspondent Ina Fried speaks to HAI Senior Fellow Yejin Choi at Axios House in Davos during the World Economic Forum.

The ML community is rapidly exploring techniques for prompting language models (LMs) and for stacking them into pipelines that solve complex tasks. Unfortunately, existing LM pipelines are typically implemented using hard-coded “prompt templates”, i.e. lengthy strings discovered via trial and error. Toward a more systematic approach for developing and optimizing LM pipelines, we introduce DSPy, a programming model that abstracts LM pipelines as text transformation graphs, or imperative computational graphs where LMs are invoked through declarative modules. DSPy modules are parameterized, meaning they can learn how to apply compositions of prompting, finetuning, augmentation, and reasoning techniques. We design a compiler that will optimize any DSPy pipeline to maximize a given metric, by creating and collecting demonstrations. We conduct two case studies, showing that succinct DSPy programs can express and optimize pipelines that reason about math word problems, tackle multi-hop retrieval, answer complex questions, and control agent loops. Within minutes of compiling, DSPy can automatically produce pipelines that outperform out-of-the-box few-shot prompting as well as expert-created demonstrations for GPT-3.5 and Llama2-13b-chat. On top of that, DSPy programs compiled for relatively small LMs like 770M parameter T5 and Llama2-13b-chat are competitive with many approaches that rely on large and proprietary LMs like GPT-3.5 and on expert-written prompt chains. DSPy is available at https://github.com/stanfordnlp/dspy