The Artificial Inventor Project.
A federal court in Australia has ruled in our favor and ordered our patent application for an AI-generated invention reinstated by IP Australia.
Abbot, R. (2021). A Federal Court in Australia has Held AI-Generated Inventions are Patentable. The Artificial Inventor Project.
And also…
Today, the Artificial Inventor Project successfully obtained the world’s first patent, in South Africa, for an AI-generated invention without a traditional human inventor. The patent is owned by the AI’s owner, and the patent names the AI which devised the invention as the inventor. This is an important milestone for ensuring that we appropriately encourage people to make, develop, and use AI to generate socially valuable innovation.
Abbot. R. (2021). First Patent Granted to the Artificial Inventor Project. The Artificial Inventor Project.
Neither of the articles I link to above has much more information than what I’ve quoted here. There is what looks like a more detailed article here at The Times but it’s behind a paywall.
I’m not sure what to think of this development. Patent law was developed to provide legal cover for inventors to get a return on the time and cost of creating new inventions. And that seems like a good thing. But patents – and the surrounding disaster that is patent-trolling – often makes me feel like the system has failed.
I think we’re going to see an explosion of submissions to patent offices, which they’re not going to be able to keep up with, unless they build an AI-based patent officer to track the AI-generated patents. On the other hand, we may also see an explosion of creative ideas and inventions that are genuinely useful. This will be interesting to watch.
Hern. A. (2021). What’s artificial intelligence best at? Stealing human ideas. Techscape.
A new AI pair programmer that helps you write better code. It helps you quickly discover alternative ways to solve problems, write tests, and explore new APIs without having to tediously tailor a search for answers on the internet. As you type, it adapts to the way you write code – to help you complete your work faster.
Hern. A. (2021). What’s artificial intelligence best at? Stealing human ideas. Techscape.
The idea is that Github (the largest source-code repository in the world, owned by Microsoft) has a massive amount of training data that AI-based systems, like Copilot, can be trained on. Copilot should, in principle, enable software developers to make use of the expertise of others by suggesting (or autocompleting) code snippets. You can imagine a scenario where, instead of me having to create new libraries or software objects, Copilot would see what it is that I’m trying to do and make suggestions for how others have solved similar problems.
It’s a good article that describes how we’re increasingly seeing humans hand off certain tasks to AI in order to increase productivity (if you’ve ever used autocorrect on your phone then you’ve done this). It also gets into the dangers of having AI automatically create software.
Here’s the announcement.
Centaur. ARTificial Mind.
The ARTificial intelligence is both co-creator and participant in the performance, as it influences choreographic composition based on various data sets such as planetary movements, swarm technology and the dancers’ previous movements as they have been tracked and collected throughout the creative process. As a performer, the artificial intelligence can simulate consciousness, emotion and intention as it interacts with the dancers on stage. This means that each performance is a unique and unpredictable event — a neat allegory to our human relationship with technology…. There is no divisible point where the human creation ends and the machine takes over – both are entangled.
Fascinating concept. I’d definitely pay to watch this.
Kurenkov, A. (2020). A Brief History of Neural Nets and Deep Learning – The story of how neural nets evolved from the earliest days of AI to now. Skynet today – Putting AI news in perspective.
I could not help but wonder if this new ‘Deep Learning’ was anything fancy or just a scaled up version of the ‘artificial neural nets’ that were already developed by the late 80s. And let me tell you, the answer is quite a story – the story of not just neural nets, not just of a sequence of research breakthroughs that make Deep Learning somewhat more interesting than ‘big neural nets’ (that I will attempt to explain in a way that just about anyone can understand), but most of all of how several unyielding researchers made it through dark decades of banishment to finally redeem neural nets and achieve the dream of Deep Learning.
This is a bit of a long read but it does a really good job of providing a not-too-technical overview of neural nets and deep learning. Definitely something worth having in the library for future reference.