Ok, ok. Lots of exciting events happened this past week that we want to share with you. While peeps are in-flight to Vancouver for NeurIPS 2019 (starting today), Twitter has been a’buzz with data galore. And last not but least, we gave a talk about hosting a low-resource BERT in the cloud for Question Answering.…
Read MoreHosting Bert In The Cloud
These slides are based on a talk given by CEO Ricky Costa on December 2nd 2019
NLP News Cypher | 12.01.19
NeurIPS 2019 starts in one week on Dec. 8th in Vancouver!!! Should be a good one. As of September, 1428 papers were accepted (record). Per usual, I will gather top tweets and insights from the conference. Follow our Twitter @Quantum_Stat for closer coverage. FYI, we went over a more detailed version of what the conference…
Read MoreNLP News Cypher | 11.24.19
Turkey, Set, Go… This Week 🤗 and the French Connection A Montage of Cheat Sheets Walmart Woos Siri (Into Buying Groceries) Yann LeCun Welcomes SONY to the Party! You Stay Classy San Diego… New Cross-Lingual Question Answering Benchmark BERT’s Big Squeeze NLU is Hard! 🤗 and the French Connection The French RoBERTa, aka CamemBERT, is…
Read MoreNLP News Cypher | 11.17.19
Gearing Up for NeurIPS 2019… While we wind down from the recent EMNLP conference, NeurIPS 2019 is just around the corner starting on Dec. 8 thru the 14th! For a quick rundown of the NeurIPS’ metadata (authors, topics etc), check out this post: NeurIPS 2019 Stats The Thirty-third Annual Conference on Neural Information Processing Systems…
Read MoreNLP News Cypher | 11.10.19
Echoes from EMNLP and GPT-2 Strikes Back Wow, what a week it was. The EMNLP conference gave us many treats to chew on such as the growing popularity of cross-lingual learning and the continued adoption of knowledge graphs in language models. Because of all this action, this week’s Cypher will be a bit longer than…
Read MoreNLP News Cypher | 11.03.19
This Week’s Content -Google Creates BERT, Google Adopts BERT -Summarization Meets Fact Checking -Batch Inference vs. Online Inference -Summary of Machine Learning Evaluation Metrics -Goal-Oriented Dialogue + Knowledge Base -Chitchat Dialogue is Hard! Quick note: Today the EMNLP conference gets underway thru Nov. 7th. Quantum Stat will be dishing out research news and other highlights…
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This Week’s Content -T5, Google’s New Transformer – Facebook’s RoBERTa Distilled by Hugging Face -Multiprocessing vs. Threading -Fine-Tuning BERT, a Tutorial -Microsoft’s UniLM AI Improves Summarization T5 | The New SOTA Transformer from Google A new entrant in the transformer school of hard-knocks was unveiled yesterday by Google called T5. This new transformer achieved new…
Read MoreWord Representation in Natural Language Processing (Seconda Parte)
Word Vectors and Beyond…🧐 In the prima parte of our introduction to word representations, we discussed the most basic type of representation: frequency probability. While this approach continues to be used in NLP projects requiring low semantic inference, more often than not, mission-critical projects may be dependent on word meaning. Natural language, as a complex…
Read MoreWord Representation in Natural Language Processing (Prima Parte)
From Probabilistic Models to Word Embeddings 🧐 In the world of Natural Language Processing (NLP), word representation models are key for understanding how models interpret language. So what is a representation model (RM)? Before answering this, we have to be aware of an important disclaimer: AI models run on math. The consequence of this prerequisite highlights…
Read MoreBringing Machine Learning to Video Ads | Instagram Marches On
Google Brings Machine Learning to Mobile Video Ads This week, Google introduced Bumper Machine, a machine learning application that allows brands to create 6 second ads from longer video assets on YouTube. The video editing tool is trained to recognize valuable moments in longer formats (e.g. product/brand information, human faces, motion, contrast) and translating them…
Read MoreMachine Learning With Dynamic Data
Machine Learning (abbreviated ML): 1.[mass noun] Technologies which enable the use of statistical techniques / models to give computer systems the ability to learn from data without being explicitly programmed. Machine Learning is Hard For machine learning developers, modeling an algorithm is a challenging initiative. Achieving a state-of-the-art ML model for enterprise-ready applications is predicated…
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