Asking machines to make music by themselves is kind of a strange notion. They’re machines, after all. They don’t feel happy or hurt, and as far as we know, they don’t long for the affections of other machines. Humans like to think of music as being a strictly human thing, a passionate undertaking so nuanced and emotion-based that a machine could never begin to understand the feeling that goes into the process of making music, or even the simple enjoyment of it.
The idea of humans and machines having a jam session together is even stranger. But oddly enough, the principles of the jam session may be exactly what machines need to begin to understand musical expression. As Sara Adkins explains in her enlightening 2019 Hackaday Superconference talk, Creating with the Machine, humans and machines have a lot to learn from each other.
To a human musician, a machine’s speed and accuracy are enviable. So is its ability to make instant transitions between notes and chords. Humans are slow to learn these transitions and have to practice going back and forth repeatedly to build muscle memory. If the machine were capable, it would likely envy the human in terms of passionate performance and musical expression.
Continue reading “Sara Adkins Is Jamming Out With Machines”
A Markov chain is a mathematical concept of a sequence of events, in which each future event depends only on the state of the previous events. Like most mathematical concepts, it has wide-ranging applications from gambling to the stock market, but in this case, [Jonghong Park] has applied it to art.
The installation, known simply as ‘bit’, consists of four machines. Each machine has two microswitches, which are moved around two wooden discs by a stepper motor. The microswitches read bumps on the surface of the disc as either a 0 or 1, and the two bits from the microswitches represent the machine’s “state”.
When a machine is called, the stepper motor rotates 1/240th of a revolution, and then the microswitches read the machine’s current state. Based on this state and the Markov Chain algorithm coded into the machines, a machine with the corresponding state is then called, which in turn moves, continuing the chain.
The piece is intended to reflect the idea of a deterministic universe, one in which the current state can be used to predict all future states. As an art piece, it combines its message with a visually attractive presentation of understated black metal and neatly finished wood.
We love a good art installation here at Hackaday – like this amazing snowflake install from a couple years back. Video after the break.
Continue reading “‘Bit’ Installation Combines Art, Markov Chains”
A great many of you will remember the game of Snakes and Ladders from your youth. It’s a simple game, which one grows to realise involves absolutely no skill – it’s purely the luck of the dice. [Alex Laratro] noticed that without player decisions to effect the outcome, the game was thus a prime candidate for simulation.
[Alex] wanted to dive into the question of “Who is winning a game of Snakes and Ladders?” at any given point in the gameplay. A common approach would be to state “whoever is in front”, but the ladders might have something to say about that. [Alex] uses Markov analysis to investigate, coming to some interesting conclusions about how the game works, and how this compares to the design of more complex games like Mario Kart and Power Grid.
Overall, it’s a breakdown of a popular game that’s simple enough to really sink your teeth into, but has some incredibly interesting conclusions that are well worth considering for anyone designing their own board games. We love seeing math applied to novel and fun problems – and it can solve important problems, too.