What we're about

We are a professional organization for AI practitioners in the Silicon Valley. We aim to bring together data scientists, engineers, and business people working in AI and big data area. We host seminars, interactive group meetings, and mentoring sessions. We provide an exchange platform for big data professionals to share their experiences, learn about the newest technologies and explore potential startup opportunities. Join us today. Find like-minded people in AI and grow your career and AI and big data business with us.

Upcoming events (2)

[Reading] PaLI: Scaling Language-Image Learning in 100+ Languages

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** Starting from this week, our meetup begins at 6:30 pm.

This is a new paper from Google Research on September 14, 2022
Abstract:
Effective scaling and a flexible task interface enable large language models to excel at many tasks. PaLI (Pathways Language and Image model) extends this approach to the joint modeling of language and vision. PaLI generates text based on visual and textual inputs, and with this interface performs many vision, language, and multimodal tasks, in many languages. To train PaLI, we make use of large pretrained encoder-decoder language models and Vision Transformers. We find that joint scaling of the vision and language components is important. We train the largest ViT to date to quantify the benefits from even larger-capacity vision models, by creating a large multilingual mix of pretraining tasks, containing 10B images and texts in over 100 languages. PaLI achieves state-of-the-art in multiple vision and language tasks (such as captioning, visual question-answering, scene-text understanding), while retaining a simple and scalable design.

Paper to read:
Chen, Xi, Xiao Wang, Soravit Changpinyo, A. J. Piergiovanni, Piotr Padlewski, et al. “PaLI: A Jointly-Scaled Multilingual Language-Image Model.” arXiv preprint arXiv: [masked] (Sept 14, 2022). https://arxiv.org/abs/2209.06794

Google AI blog, Sept 15, 2022, "PaLI: Scaling Language-Image Learning in 100+ Languages"

Discussion leader: Junling Hu

This is part of the bi-weekly reading series. We come together to discuss cutting-edge AI topics and papers. The discussion is led by one presenter, with group participation.

Join us online at: https://us02web.zoom.us/meeting/register/tZIudeqvqDItGNSHspgNiD-cr2nBKhnWWnHA

7-7:15pm Meet and greet
7:15-8:15pm Paper presentation and group discussion
8:15-8:30 Additional discussions

[Reading] Decoding speech from non-invasive brain recordings

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** Please note that our meetup begins at 6:30pm now.

This is a new paper from Facebook on August 25, 2022
Abstract:
Decoding language from brain activity is a long-awaited goal of neuroscience. Scaling this approach to non-invasive brain recordings remains a major challenge. Here, we propose a single end-to-end architecture trained with contrastive learning across individuals to predict representations of natural speech. We evaluate our model on four public datasets. The results show that our model can identify the corresponding speech segment with up to 72.5% top-10 accuracy out of 1,594 distinct segments (and 44% top-1 accuracy) -- hence allowing the decoding of phrases absent from the training set. Model comparison and ablation analyses show that these performances directly benefit from our original design choices, namely the use of (i) a contrastive objective, (ii) pretrained representations of speech and (iii) a common convolutional architecture simultaneously trained across several participants. Together, these results delineate a promising path to decode natural language processing in real time from non-invasive recordings of brain activity.

Défossez, Alexandre, Charlotte Caucheteux, Jérémy Rapin, Ori Kabeli, and Jean-Rémi King. "Decoding speech from non-invasive brain recordings." arXiv preprint arXiv:[masked] (Aug 25, 2022). https://arxiv.org/abs/2208.12266

Faebook AI blog, August 31, 2022, "Using AI to decode speech from brain activity", https://ai.facebook.com/blog/ai-speech-brain-activity/

Discussion leader: Junling Hu

This is part of the bi-weekly reading series. We come together to discuss cutting-edge AI topics and papers. The discussion is led by one presenter, with group participation.

Join us online at: https://us02web.zoom.us/meeting/register/tZIudeqvqDItGNSHspgNiD-cr2nBKhnWWnHA

7-7:15pm Meet and greet
7:15-8:15pm Paper presentation and group discussion
8:15-8:30 Additional discussions

Past events (244)

[Reading] Atlas: A Retrieval Augmented Model That Outperforms PaLM

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