What is your take on this Chris Smedley?

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Please be sensitive to any artificial intelligence you encounter today. A UK appeals court just ruled that AI systems cannot submit or hold patents, as software is not human and therefore lacks human rights. Several courtrooms around the world have come to the same conclusion, despite the efforts of a very enthusiastic inventor.

Dr. Stephen Thaler has repeatedly filed patents on behalf of his AI, called DABUS. He claims that this AI should be credited for the inventions that it’s helped to produce. But patent offices disagree. After Dr. Thaler refused to resubmit his patents under a real name, the UK Intellectual Property Office pulled him from the registration process.

Our friend Dr. Thaler responded by taking the Intellectual Property Office to court. And predictably, the body rejected his case. So Dr. Thaler made an appeal, and again, he lost.

Welcome to AI book reviews, a series of posts that explore the latest literature on artificial intelligence.

Recent advances in deep learning have rekindled interest in the imminence of machines that can think and act like humans, or artificial general intelligence. By following the path of building bigger and better neural networks, the thinking goes, we will be able to get closer and closer to creating a digital version of the human brain.

But this is a myth, argues computer scientist Erik Larson, and all evidence suggests that human and machine intelligence are radically different. Larson’s new book, The Myth of Artificial Intelligence: Why Computers Can’t Think the Way We Do, discusses how widely publicized misconceptions about intelligence and inference have led AI research down narrow paths that are limiting innovation and scientific discoveries.

Reinforcement learning is an interesting area of machine learning (ML) that has advanced rapidly in recent years. AlphaGo is one such RL-based computer program that has defeated a professional human Go player, a breakthrough that experts feel was a decade ahead of its time.

Reinforcement learning differs from supervised learning because it does not need the labelled input/output pairings for training or the explicit correction of sub-optimal actions. Instead, it investigates how intelligent agents should behave in a particular situation to maximize the concept of cumulative reward.

This is a huge plus when working with real-world applications that don’t come with a tonne of highly curated observations. Furthermore, when confronted with a new circumstance, RL agents can acquire methods that allow them to behave even in an unclear and changing environment, relying on their best estimates at the proper action.