By Zilun Peng, Akshay Budhkar, Jumana Nassour, Ilana Tuil and Jason Levy

Thanks to advances in speech recognition, companies can now build a whole range of products with accurate transcription capabilities at their heart. Conversation intelligence platforms, personal assistants and video and audio editing tools, for example, all rely on speech to text transcription. However, you often need to train these systems for every domain you want to transcribe, using supervised data. In practice, you need a large body of transcribed audio that’s similar to what you are transcribing just to get started in a new domain.

Recently, Facebook released…

By Jason Brenier

AI at Georgian: What I’ve learned

In my work leading product and strategy for Georgian, I’m obsessively focused on process. I’ve spent the past few years experimenting to find a process that meets the specific challenges of building AI products.

AI products tend to be more complex, highly cross-functional, have ongoing governance needs, non-deterministic outcomes…the list goes on. It’s no wonder that only 13% of data science projects make it into production.

You might be asking yourself — why is a VC firm building products? Every industry is going through digital transformation and ours is no different. Historically, we’ve differentiated through helping our…

By Azin Asgarian and Rohit Saha

Computer Vision (CV) is an area of AI that focuses on enabling computers to identify and process objects in images and videos in the same way that humans do. Until recently, computer vision only worked in limited capacity. But thanks to advances in deep learning, the field has been able to take great leaps in recent years and is revolutionizing different industries rapidly now!

Credit: /U/DEADBUILTIN Consolia-comic.com

CV is moving so fast that we’ve practically had a decade of change in the last year alone, with more than 45,000 papers being published and many monster models being…

By: Parinaz Sobhani

I recently gave a lecture for the Bias in AI course launched by Vector Institute for small-to-medium-sized companies. In this lecture, I introduced seven principles of building fair Machine Learning (ML) systems as a framework for organizations to address bias in Artificial Intelligence (AI) systematically and sustainably and go beyond the desire to be ethical in deploying AI technologies.

We’ve all heard examples of unfair AI. Job ads targeting people similar to current employees drive only young men to recruiter inboxes. Cancer detection systems that don’t work as well on darker skin. …

By Zilun Peng and Akshay Budhkar

Hi there! This is the first blog post in a series on GPT-3. If you’re interested in this and other applied research work, you can follow us on twitter here and here.

GPT-3 is the latest language model from OpenAI. It garnered a lot of attention last year when people realized its generalizable few-shot learning capabilities, as seen in articles like OpenAI’s new language generator GPT-3 is shockingly good — and completely mindless and What Is GPT-3 And Why Is It Revolutionizing Artificial Intelligence?. GPT-3’s generative text-in text-out setup allows people to use this…

By Jing Zhang

Have you ever been in a situation where you’ve built a powerful reporting tool hoping to enable data self-serve only to get requests asking for data in a spreadsheet? Even a platform that gathers the latest data can struggle on the last mile delivery.

Let’s face it — spreadsheets are deeply wired into daily workflows for many types of analysis. Building something to replace them could be a losing battle. Instead, how about we make it easier for data self-serve inside spreadsheets to complement other more advanced internal systems?

Google Cloud Platform (GCP) has already made efforts…

By Zilun Peng, Akshay Budhkar, Jumana Nassour, Ilana Tuil and Jason Levy

We talked about wav2vec 2.0 in our first post and showed how to compress wav2vec 2.0 in our second post in this series, to increase inference speed. To round out this series, we’ll show you how to perform inference with wav2vec 2.0 in this post.

We’ll start by walking you through the code of a Viterbi decoder to decode wav2vec 2.0. Then, we’ll compare the Viterbi decoder with the beam search decoder. We will also describe how to run inferences efficiently using Ray, a distributed computing framework. Ray…

By Zilun Peng, Akshay Budhkar, Jumana Nassour, Ilana Tuil and Jason Levy

Huge transformer models have revolutionized the AI landscape. Big language models such as BERT [5], GPT3 [6] , and ULMFiT [7] have disrupted the NLP world effectively replacing the prior methods for all NLP tasks.

However, these improvements come with a cost. Recent papers such as works by Strubell et al. [8] and Bender et al. [9] warn of costs of training and using these giant models to the environment.

In a business setting these costs are also prohibitive due to giant cloud computation bills associated with slow…

[Colab] [Github]

By Ken Gu

Transformer-based models are a game-changer when it comes to using unstructured text data. As of September 2020, the top-performing models in the General Language Understanding Evaluation (GLUE) benchmark are all BERT transformer-based models. At Georgian, we find ourselves working with supporting tabular feature information as well as unstructured text data. We found that by using the tabular data in our models, we could further improve performance, so we set out to build a toolkit that makes it easier for others to do the same.

The 9 tasks that are part of the GLUE benchmark
The 9 tasks that are part of the GLUE benchmark
The 9 tasks that are part of the GLUE benchmark

Building on Top of Transformers

The main benefits of using transformers are that they can learn…

A starting point to help you choose the right platform for your ML project.

By Jing Zhang

Introduction

If you’re looking for an end-to-end machine learning (ML) platform, you’re spoiled for choice. There are three main choices for cloud providers: Google Cloud Platform (GCP), Amazon Web Service (AWS) and Microsoft Azure Platform (Azure). The question is: how do you choose between the three? What functionality do they provide to build ML pipelines? We set out to answer these questions in a recent hackathon.

The R&D team at Georgian, where I work as an ML Engineer, decided to organize a hackathon to…

Georgian

Investors in high-growth business software companies across North America. Applied artificial intelligence, security and privacy, and conversational AI.

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