Technical

Amateurs talk about AI models. Pros talk about AI solutions.

Your goal as an AI leader is to get your teams to think like pros. You want them to strategically look for ways in which AI can lift the entire business instead of just solving a narrowly defined problem. Your team should constantly seek ways to advance the bigger vision of becoming an AI-driven company. In this issue of FeedForward, I’ll describe the difference between how pros and amateurs think about AI. 

Natural Language Generation - What Can You Develop in the Next 3 Years

In this video Justin Pounders, Director of Machine Learning and AI Research at Prolego, breaks down natural language generation (NLG) into its most basic components and describes how you can begin building out these components in your business. (And, no, it doesn’t depend on GPT-3!) He describes how NLG depends critically on two questions (WHAT you want to say and HOW you say it), the types of data you can feed into NLG systems, and a development path for being able to summarize multiple sources of data in plain English.

Novelty with Transformers and Clustering

Document analysis and understanding is an active area of research in the applied NLP community. In this talk, we demonstrate an unsupervised method to organize a body of text into a set of topics and outliers. This approach uses a transformer model that has been fine-tuned for semantic similarity (SentenceTransformers hyperlink: sbert.net). It can be used to quickly review a large set of documents to identify areas of interest or concern without requiring a human to exhaustively read through each document one-by-one. We demonstrate this approach applied to the lyrics of an early-2000s hit musical piece.

Blindly following the agile methodology will slowly and painfully kill your AI project

Blindly following any agile software methodology won’t work for data science. At the same time, your product teams rely on agile for accountability, and they will correctly assert that perpetual data science experimentation is an unacceptable risk. In this issue of FeedForward I’ll explore this topic and help you get your data science and product teams working together in harmony.

Weak Supervision Learning Explained

Getting labeled training data is almost always the hardest part of an NLP program. In this video Justin Pounders describes how to use label functions and weak supervision to programmatically generate labels for text classifiers. The idea is to automatically bootstrap labels based on key words or phrases used by experts familiar with the domain. This simple idea can be formalized into a powerful labeling framework (sometimes called data programming) that probabilistically assigns labels based on these pieces of “virtual evidence.” We’ve found this approach is especially powerful when paired with recent transformer-based NLP models. In this video, Justin will introduce these basic ideas, talk about the open-source library Snorkel that you can start using today, and also discuss some recent research in self-supervision that we're really excited about.

The 5 aha moments you need to reach with your AI strategy

In this issue of Feedforward, I’ll share some key concepts that pushed my AI strategies to the next level. My five aha moments can help you make your AI strategy more persuasive to your leadership and more successful in the long run. 

A smarter approach to model governance

Entrusting your business decisions to AI does indeed create new risks, and you need new policies and procedures to mitigate them. However, sensational news headlines lead companies to take ineffective approaches that result in unnecessary delays and frustration. After helping some of the world’s largest companies develop model governance policies, I’ve learned a better way to manage the complexities of AI. In this issue of FeedForward, you’ll explore some of my top recommendations.

The remarkable power of storytelling to spread AI in your company

We recently released volume 1 of Adventures in AI, the world’s first AI comic book. It was a smashing success, and many AI Leaders asked us how and why we created it. In this post we share our process and thoughts on how you can use an engaging and compelling story to communicate the value of AI to your organization.

Fortunately, all coronavirus models are wrong

Modeling anything about the pandemic is impossible—we just don't have the data. Fortunately the most unknowable factor is the one cause for optimism: the impact of human ingenuity

How your data science career can survive the Covid 19 downturn

Data science has been one of the fastest growing fields over the past five years. As we enter this economic downcycle managers will begin identifying their most valuable people—the ones they will fight to retain in the event of layoffs. What follows is advice for making yourself invaluable to any employer.

Video: Natural-language Processing (NLP) for Insurance Companies

NLP technology is accelerating while costs are plummeting. As a result, most insurance companies are leveraging new language models for revenue-driving and cost-cutting initiatives. In this technical webinar we will cover modern NLP basics and help you get started.

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