Six stage gates to a successful AI governance

Gateway

Responsible use of AI should start with a detailed assessment of the key risks posed by AI [1], followed by a good understanding of the principles that should be followed [2], and then the governance of AI from a top-down and end-to-end perspective [3]. We have discussed these in our previous articles [1, 2, 3]. In this article, we focus on the first line of defense and dive into the nine-step data science process [4] of value scoping, value discovery, value delivery, and value stewardship and highlight the dimensions of governance.

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Is Google’s AI research about to implode?

Platform game

What does Timnit Gebru’s firing and the recent papers coming out of Google tell us about the state of research at the world’s biggest AI research department. The high point for Google’s research in to Artifical Intelligence may well turn out to be the 19th of October 2017. This was the date that David Silver and his co-workers at DeepMind published a report, in the journal Nature, showing how their deep-learning algorithm AlphaGo Zero was a better Go player than not only the best human in the world, but all other Go-playing computers.

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My machine learning model does not learn. What should I do?

If you work with data in general, and machine learning algorithms in particular, you might be familiar with that feeling of frustration when a model really does not want to learn the task at hand. You have tried it all, but the accuracy metric just won’t rise. What next? Where is the problem? Is this an unsolvable task or is there a solution somewhere you’re not aware of?

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Vision Transformers: Natural Language Processing (NLP) increases efficiency and model generality

Vision Transformers: Natural Language Processing (NLP) Increases Efficiency and Model Generality. Why do we hear so little about transformer models applied to computer vision tasks? What about attention in computer vision networks? Transformers Are for Natural Language Processing (NLP), Right?

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Why building a Machine Learning Model is like cooking

The first step in building a machine learning model is to prepare the data. This may involve pulling raw data from a variety of sources to load into a database. Likewise, the first step in cooking is to get the ingredients (the data). You may need to go to the grocery store to buy ingredients you don’t have at home (pull from a variety of sources).

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