Finding examples of "problematic" AI is relatively easy these days. Microsoft has inadvertently given rise to an unhinged, neo-nazi Twitter Bot while an AI beauty contest judge seems to strongly favor white women. Despite the sensational nature of these examples, they reflect a pervasive problem plaguing many modern AI systems.
Machine learning is designed to discover and exploit patterns in data so as to optimize some notion of performance. Most measures of good performance involve maximizing accuracy, yet this performance metric is often sufficient only for situations in which perfect accuracy can be achievedThe notion of "perfect accuracy" is also simplistic in general. If an AI system is being used to screen candidates to hire, deciding how to define accuracy is already a value judgment. . When a task is difficult enough that the system is prone to errors, AI agents may fail in ways that we, as humans, may consider unfair or that take advantage of undesirable patterns in the data. Here, I discuss the issue of bias in AI and argue that great care must be taken to train a machine learning system to avoid systematic bias.
The notion of "perfect accuracy" is also simplistic in general. If an AI system is being used to screen candidates to hire, deciding how to define accuracy is already a value judgment.
In short, if you are a business professional looking to use some form of machine learning, you need to be aware of how bias can manifest itself in practice.
Just over a two weeks ago, NVIDIA showcased vid2vid, their new technique for video-to-video translation. Their paper shows off a number of different applications including one particularly striking example in which the researchers automatically convert sketchy outlines of vlog-style videos from YouTube into compellingly realistic videos of people talking to the camera. The results are incredible and really need to be seen to be believed:
When most people hear the term "translation" they think of translating natural language: e.g. translating text or speech from Mandarin to EnglishMachine learning is, of course, an incredibly powerful tool for language translation. Recently, researchers from Microsoft achieved human-level translation performance on translating news articles from Mandarin to English. . Today I want to reinforce the idea that translation can be applied to different types of data beyond language. The vid2vid paper I mentioned above is just the latest and most visually striking example of the transformative power of AI, and modern machine learning is making incredibly rapid progress in this space.
Machine learning is, of course, an incredibly powerful tool for language translation. Recently, researchers from Microsoft achieved human-level translation performance on translating news articles from Mandarin to English.
In the remainder of this article, I will cover:
- A brief definition of "translation" in the context of AI;
- An overview of how modern machine learning systems tackle translation;
- A list of application domains and some influential research for each.
As the use of machine learning systems grows beyond the academic sphere, one of the more worrying features I have witnessed is a lack of understanding of how machine learning systems should be trained and applied. The lessons the AI community has learned over the last few decades of research are hard-earned, and it should go without saying that those who do not understand the inner workings of a machine learning toolThis advice is not limited to AI. Using any stochastic system without an understanding of when or how it is likely to fail comes with inherent risk. risk having that system fail in often surprising ways.
This advice is not limited to AI. Using any stochastic system without an understanding of when or how it is likely to fail comes with inherent risk.
However, the potential advantages of AI are many, and using machine learning to accelerate your business, whether empowering employees or improving your product, may outweigh potential pitfalls. If you are looking to use machine learning tools, here are a few guidelines you should keep in mind:
- Establish clear metrics for success.
- Start with the simplest approach.
- Ask yourself if machine learning is even necessary.
- Use both a test and a validation dataset.
- Understand and mitigate data overfitting.
- Be wary of bias in your data.