Skynet This Week #5: Company news, weapons and bias concerns, AI history, and more!

Skynet This Week #5
By Andrey Kurenkov and Henry Mei

Our latest bi-weekly quick takes on a bunch of the most important recent media stories about AI, right in your email:

AI Advances & Business

Measuring abstract reasoning in neural networks

DeepMind blog

Credit: made with images from DeepMind's blog

A recent paper by DeepMind researchers suggested a new way to measure the ‘abstract reasoning’ of neural nets, or in other words to what extent they can generalize learned skills beyond just doing pattern matching. The researchers concluded recent groundbreaking AI models have limited abstract reasoning capabilities, and that researchers ought to be careful about how they evaluate the generalization capabilities of their models:

Recent literature has focused on the strengths and weaknesses of neural network-based approaches to machine learning problems, often based around their capacity or failure to generalise. Our results show that it might be unhelpful to draw universal conclusions about generalisation: the neural networks we tested performed well in certain regimes of generalisation and very poorly in others. Their success was determined by a range of factors, including the architecture of the model used and whether the model was trained to provide an interpretable “reason” for its answer choices. In almost all cases, the systems performed poorly when required to extrapolate to inputs beyond their experience, or to deal with entirely unfamiliar attributes; creating a clear focus for future work in this critical, and important area of research.

Google announces AutoML Vision, Natural Language, Translation, and Contact Center AI

Kyle Wiggers, Venture Beat

A key aspect of Google’s cloud AI strategy is to ‘democratize’ the technology – that is, make it easily usable and more importantly adaptable for different companies without requiring a lot of AI expertise. As they explain in their blog post:

“A significant gap exists between the extremes of what’s currently possible with machine learning. At one end, experienced practitioners such as data scientists use tools like TensorFlow and Cloud ML Engine to build custom solutions from the ground up. At the other end, pre-trained machine learning models like Cloud Vision API deliver immediate results with minimal investment and technical proficiency. But what about the countless customers that fall in between? Many have needs beyond what’s available with pre-trained models, but don’t have the skills or resources to build their own custom solutions.”

The new AutoML Vision, Natural Language, Translation offerings are meant to service this ‘in-between’ set of customers to adapt it to all their needs. And that’s just AutoML; Google is also making their specialized in-house AI hardware accessible to all and addressing specific needs for industries such as call centers, as they summarize at the end of their blog post:

From hardware like Cloud TPUs to software like AutoML and vertical solutions like Contact Center AI, we’re working to advance the state of the art while lowering the barrier to entry — serving customers with a wide spectrum of needs and expertise. And we’re doing this with the aim to enhance the human experience at the center of it all.

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