Brute-Forced Copyrighting: Liberating All The Melodies

Bluntly stated, music is in the end just applied physics. Harmony follows — depending on the genre — a more or less fixed set of rules, and there  are a limited amount of variation possible within the space of music itself. So there are technically only so many melodies possible, making it essentially a question of time until a songwriter or composer would come up with a certain sequence of notes without knowing that they’re not the first one to do so until the cease and desist letters start rolling in.

You might well argue that there is more to a song than just the melody — and you are absolutely right. However, current copyright laws and past court rulings may not care much about that. Aiming to point out these flaws in the laws, musician tech guy with a law degree [Damien Riehl] and musician software developer [Noah Rubin] got together to simply create every possible melody as MIDI files, releasing them under the Creative Commons Zero license. While their current list is limited to a few scales of fixed length, with the code available on GitHub, it’s really just a matter of brute-forcing literally every single possible melody.

Admittedly, such a list of melodies might not have too much practical use, but for [Damien] and [Noah] it’s anyway more about the legal and philosophical aspects: musicians shouldn’t worry about getting sued over a few overlapping notes. So while the list serves as a “safe set of melodies” they put in the public domain, their bigger goal is to mathematically point out the finite space of music that shouldn’t be copyrightable in the first place. And they definitely have a point — just imagine where music would be today if you could copyright and sue over chord progressions.
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Pack Your Bags – Systemd Is Taking You To A New Home

Home directories have been a fundamental part on any Unixy system since day one. They’re such a basic element, we usually don’t give them much thought. And why would we? From a low level point of view, whatever location $HOME is pointing to, is a directory just like any other of the countless ones you will find on the system — apart from maybe being located on its own disk partition. Home directories are so unspectacular in their nature, it wouldn’t usually cross anyone’s mind to even consider to change anything about them. And then there’s Lennart Poettering.

In case you’re not familiar with the name, he is the main developer behind the systemd init system, which has nowadays been adopted by the majority of Linux distributions as replacement for its oldschool, Unix-style init-system predecessors, essentially changing everything we knew about the system boot process. Not only did this change personally insult every single Perl-loving, Ken-Thompson-action-figure-owning grey beard, it engendered contempt towards systemd and Lennart himself that approaches Nickelback level. At this point, it probably doesn’t matter anymore what he does next, haters gonna hate. So who better than him to disrupt everything we know about home directories? Where you _live_?

Although, home directories are just one part of the equation that his latest creation — the systemd-homed project — is going to make people hate him even more tackle. The big picture is really more about the whole concept of user management as we know it, which sounds bold and scary, but which in its current state is also a lot more flawed than we might realize. So let’s have a look at what it’s all about, the motivation behind homed, the problems it’s going to both solve and raise, and how it’s maybe time to leave some outdated philosophies behind us.

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AI And Art Appreciation

In 2019, using AI to evaluate artwork is finally more productive than foolish. We all hope that someday soon our Roomba will judge our living habits and give unsolicited advice on how we could spruce things up with a few pictures and some natural light. There is already an extensive amount of Deep Learning dedicated to photo recognition but a team in Croatia is adapting them for use on fine art. It makes sense that everything is geared toward cameras since most of us have a vast photographic portfolio but fine art takes longer to render. Even so, the collection on Wikiart.org is vast and already a hotbed for computer classification work, so they set to work there.

As they modify existing convolutional neural networks, they check themselves by comparing results with human ratings to keep what works and discard what flops. Fortunately, fine art has a lot of existing studies and commentary, whereas the majority of photographs in the public domain have nothing more than a file name and maybe some EXIF data. The difference here is that photograph-parsing AI can say, “That is a STOP sign,” while the fine art AI can say, “That is a memorable painting of a sign.” Continue reading “AI And Art Appreciation”

Doing One Thing, Well: The UNIX Philosophy

The Unix operating system has been around for decades, and it and its lookalikes (mainly Linux) are a critical part of the computing world. Apple’s operating system, macOS, is Unix-based, as are Solaris and BSD. Even if you’ve never directly used one of these operating systems, at least two-thirds of all websites are served by Unix or Unix-like software. And, if you’ve ever picked up a smart phone, chances are it was running either a Unix variant or the Linux-driven Android. The core reason that Unix has been so ubiquitous isn’t its accessibility, or cost, or user interface design, although these things helped. The root cause of its success is its design philosophy.

Good design is crucial for success. Whether that’s good design of a piece of software, infrastructure like a railroad or power grid, or even something relatively simple like a flag, without good design your project is essentially doomed. Although you might be able to build a workable one-off electronics project that’s a rat’s nest of wires, or a prototype of something that gets the job done but isn’t user-friendly or scalable, for a large-scale project a set of good design principles from the start is key.

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Émilie Du Châtelet: An Energetic Life

Émilie du Châtelet lived a wild, wild life. She was a brilliant polymath who made important contributions to the Enlightenment, including adding a mathematical statement of conservation of energy into her French translation of Newton’s Principia, debunking the phlogiston theory of fire, and suggesting that what we would call infrared light carried heat.

She had good company; she was Voltaire’s lover and companion for fifteen years, and she built a private research institution out of a château with him before falling in love with a younger poet. She was tutored in math by Maupertuis and corresponded with Bernoulli and Euler. She was an avid gambler and handy with a sword. She died early, at 41 years, but those years that she did live were pretty amazing. Continue reading “Émilie Du Châtelet: An Energetic Life”

The Future Of Artificial Intelligence

Last week we covered the past and current state of artificial intelligence — what modern AI looks like, the differences between weak and strong AI, AGI, and some of the philosophical ideas about what constitutes consciousness. Weak AI is already all around us, in the form of software dedicated to performing specific tasks intelligently. Strong AI is the ultimate goal, and a true strong AI would resemble what most of us have grown familiar with through popular fiction.

Artificial General Intelligence (AGI) is a modern goal many AI researchers are currently devoting their careers to in an effort to bridge that gap. While AGI wouldn’t necessarily possess any kind of consciousness, it would be able to handle any data-related task put before it. Of course, as humans, it’s in our nature to try to forecast the future, and that’s what we’ll be talking about in this article. What are some of our best guesses about what we can expect from AI in the future (near and far)? What possible ethical and practical concerns are there if a conscious AI were to be created? In this speculative future, should an AI have rights, or should it be feared?

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AI And The Ghost In The Machine

The concept of artificial intelligence dates back far before the advent of modern computers — even as far back as Greek mythology. Hephaestus, the Greek god of craftsmen and blacksmiths, was believed to have created automatons to work for him. Another mythological figure, Pygmalion, carved a statue of a beautiful woman from ivory, who he proceeded to fall in love with. Aphrodite then imbued the statue with life as a gift to Pygmalion, who then married the now living woman.

chateau_de_versailles_salon_des_nobles_pygmalion_priant_venus_danimer_sa_statue_jean-baptiste_regnault
Pygmalion by Jean-Baptiste Regnault, 1786, Musée National du Château et des Trianons

Throughout history, myths and legends of artificial beings that were given intelligence were common. These varied from having simple supernatural origins (such as the Greek myths), to more scientifically-reasoned methods as the idea of alchemy increased in popularity. In fiction, particularly science fiction, artificial intelligence became more and more common beginning in the 19th century.

But, it wasn’t until mathematics, philosophy, and the scientific method advanced enough in the 19th and 20th centuries that artificial intelligence was taken seriously as an actual possibility. It was during this time that mathematicians such as George Boole, Bertrand Russel, and Alfred North Whitehead began presenting theories formalizing logical reasoning. With the development of digital computers in the second half of the 20th century, these concepts were put into practice, and AI research began in earnest.

Over the last 50 years, interest in AI development has waxed and waned with public interest and the successes and failures of the industry. Predictions made by researchers in the field, and by science fiction visionaries, have often fallen short of reality. Generally, this can be chalked up to computing limitations. But, a deeper problem of the understanding of what intelligence actually is has been a source a tremendous debate.

Despite these setbacks, AI research and development has continued. Currently, this research is being conducted by technology corporations who see the economic potential in such advancements, and by academics working at universities around the world. Where does that research currently stand, and what might we expect to see in the future? To answer that, we’ll first need to attempt to define what exactly constitutes artificial intelligence.

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