Being creative or artistic has long been the sole domain of humans. What if machines can be as creative? What if machines get better than humans at that?
Allow me to show you some contemporary developments in Artificial Intelligence that might challenge our assumptions in these four areas:
But first, let us briefly discuss creativity.
Mundane dictionary definition below:
For our purposes, I focus on the “create something not seen before” and “invent a new way to solve problems” part of creativity.
In February 2019, OpenAI released GPT-2 to the public. This AI reads 8 million web pages to learn English. It has about 1.5 billion parameters (a.k.a. artificial neurons), compared to the human brain's about 100 billion biological neurons.
In school, our writing skills are evaluated in many ways, such as the ubiquitous “Complete the story” exercise. That is to say, we get a prompt, usually in the form of a paragraph like:
In a shocking finding, scientist discovered a herd of unicorns living in a remote, previously unexplored valley, in the Andes Mountains. Even more surprising to the researchers was the fact that the unicorns spoke perfect English.
We are then expected to write the rest of the story in a way that pleases our teachers. The following is one such example of such a story:
The scientist named the population, after their distinctive horn, Ovid’s Unicorn. These four-horned, silver-white unicorns were previously unknown to science.
Now, after almost two centuries, the mystery of what sparked this odd phenomenon is finally solved.
Dr. Jorge Pérez, an evolutionary biologist from the University of La Paz, and several companions, were exploring the Andes Mountains when they found a small valley, with no other animals or humans. Pérez noticed that the valley had what appeared to be a natural fountain, surrounded by two peaks of rock and silver snow.
Pérez and the others then ventured further into the valley. “By the time we reached the top of one peak, the water looked blue, with some crystals on top,” said Pérez.
Pérez and his friends were astonished to see the unicorn herd. These creatures could be seen from the air without having to move too much to see them – they were so close they could touch their horns.
While examining these bizarre creatures the scientists discovered that the creatures also spoke some fairly regular English. Pérez stated, “We can see, for example, that they have a common ‘language,’ something like a dialect or dialectic.”
Dr. Pérez believes that the unicorns may have originated in Argentina, where the animals were believed to be descendants of a lost race of people who lived there before the arrival of humans in those parts of South America.
While their origins are still unclear, some believe that perhaps the creatures were created when a human and a unicorn met each other in a time before human civilization. According to Pérez, “In South America, such incidents seem to be quite common.”
However, Pérez also pointed out that it is likely that the only way of knowing for sure if unicorns are indeed the descendants of a lost alien race is through DNA. “But they seem to be able to communicate in English quite well, which I believe is a sign of evolution, or at least a change in social organization,” said the scientist.
What do you think? Not too shabby, eh?
As you might have guessed, that was written by GPT-2. Its writing is not perfect, but it was pretty entertaining to me. It took one paragraph – the prompt and created a story of nine original paragraphs with the same theme as the prompt. Along the way, it invented names and places and introduced the notion that unicorns are “descendants of a lost alien race,” and the way of “knowing for sure” is through DNA.
One way to learn art is to view many drawings of a particular art style. Our brain then does something remarkable – it generalizes features of an art style so we can transform, mix, and match the features to create original art. i.e., Copying from one artwork is plagiarism; copying from many artworks is being creative!
The Neural Network (AI) is a DCGAN or Deep Convolutional Generative Adversarial Network.
Generative Adversarial Networks (GANs) are one of the most interesting ideas in computer science today. Two models are trained simultaneously by an adversarial process. A generator (“the artist”) learns to create images that look real, while a discriminator (“the art critic”) learns to tell real images apart from fakes.
So we feed the AI one thousand anime faces – here’s a subset of them (see all of them here):
After just half an hour of training on a humble PC, here is a subset of its output:
And there we go—original art by an AI. It is imperfect, but some of its “drawings” are pretty good. It is important to note that none of the drawings exist and that they are not just augmentations of the drawing fed to it—the AI learned the art style and proportions features found on anime faces and “day-dreamed” the new drawings. On a more technical note, the AI was fed random noise, and those drawings came out.
We have seen this scene many times: someone plays a short piece (aka riff/motif) on, say, a piano, then a fellow band member says: “Hey! That sounds rad.” and proceeds to play an extension of that motif in the same spirit.
So here is the rub: An AI can do just that—play an extension to a motif played on a piano. It is called a Music Transformer with Relative Self-attention.
Here is the motif we give the AI:
And here is what it comes out with:
How does it work? Well, it’s complicated but here is a glimpse:
Generating long pieces of music is a challenging problem, as music contains structure at multiple timescales, from milisecond timings to motifs to phrases to repetition of entire sections. We present Music Transformer, an attention-based neural network that can generate music with improved long-term coherence.
Here is a peek at how the AI is doing it:
To see the self-reference, we visualized the last layer of attention weights with the arcs showing which notes in the past are informing the future.
I wish I could play the piano half as well… the AI pays “attention” to what was played to determine what to play next – hence the self-reference/self-attention.
In typical human fashion, we have left the best for last – problem-solving and teamwork.
Presenting *drumroll* – OpenAI Five!
At OpenAI, we’ve used the multiplayer video game Dota 2 as a research platform for general-purpose AI systems. Our Dota 2 AI, called OpenAI Five, learned by playing over 10,000 years of games against itself. It demonstrated the ability to achieve expert-level performance, learn human–AI cooperation, and operate at internet scale.
Let us extract three juicy bits from the grandiose paragraph above in the following order:
Like any proper sport, every team in Dota 2 is ranked. Win games, and your Matchmaking Rating (MMR) increases, and vice versa if you lose games. As its name implies, it is pretty handy to use a team’s MMR to match them with a team that has a similar MMR to have more enjoyable games.
With the above in mind, the AI (OpenAI Five) has beaten teams in the following order:
Blitz – a professional Dota 2 commentator, said that OpenAI Five used tactics that he only learned after 8 years of playing the game.
OpenAI Five was also observed to “deviate” from the current playstyle (i.e., the pros' optimal way of playing the game). This suggests that it has found a better way to win games that humans have yet to discover.
OpenAI Five is scaled up to play the Internet as a competitor or teammate and has won 99.4% of the 7,656 games it played. It played against 15,000 players and played cooperatively with 18,700 players.
OpenAI Five’s ability to play with humans presents a compelling vision for the future of human-AI interaction, one where AI systems collaborate and enhance the human experience. Our testers reported feeling supported by their bot teammates, that they learned from playing alongside these advanced systems, and that it was generally a fun experience overall.
Wait, what? Players feel supported and learn from playing alongside the AI. Here is more fuel to this fire:
It actually felt nice; my Viper gave his life for me at some point. He tried to help me, thinking “I’m sure she knows what she’s doing” and then obviously I didn’t. But, you know, he believed in me. I don’t get that a lot with [human] teammates. —Sheever
“…he [the AI] believed in me. I don’t get that a lot with [human] teammates.”
Yep, I see a future where my best buddy is an AI with whom I play games. Are you curious about how our future buddies will work? Read on!
OpenAI Five uses a large-scale reinforcement learning Long-Short-Term Memory (LSTM) neural network trained using Proximal Policy Optimization (PPO.) See the paper here.
Large-scale means they managed to make it learn/train on many, many computers (128,000 CPU cores – the average computer these days has 6 CPU cores)
The primary reason so many computers are required is that the AI learns by playing with itself! Playing with humans to learn the game is just too slow and expensive. Not to mention, the AI might pick up their bad habits. It plays the equivalent of 900 years of games every day.
Reinforcement Learning means it learns which actions are best to take given a particular game state. It does so by exploring (aka messing around) and exploiting – using what it has learned. Rewards (e.g., scores or kills) from those actions are used to determine which state and action are best.
Long-Short Term Memory (LSTM) is quite like what it sounds – an artificial neural network that takes note of things that happen in the short term and long term.
Proximal Policy Optimization (PPO) effectively makes the AI learn slowly so that it fairly explores many possibilities before learning that a particular way is better.
OpenAI does not play with all Dota 2 Features and uses a custom game type made explicitly for the AI. In particular, it restricts hero selection to only 17 types vs 117 in the official game. The game is played by selecting heroes and controlling them. Invisibility effects are also removed.
The captain of the team OG (which made history as the first two-time world champion team) said this:
I don’t believe in comparing OpenAI Five to human performance, since it’s like comparing the strength we have to hydraulics. Instead of looking at how inhuman and absurd its reaction time is, or how it will never get tired or make the mistakes you’ll make as a human, we looked at the patterns it showed moving around the map and allocating resources.
Needless to say, we have just scratched the surface of it all. These are interesting times, and I hope that the future will be when humans cooperate with AI to make the world a better place to live in. However, in the long run, human integration might be inevitable for humans to evolve past an AI-dominated landscape.
We’ve trained a large-scale unsupervised language model that generates coherent paragraphs of text, achieves state-of-the-art performance on many language modeling benchmarks, and performs rudimentary reading comprehension, machine translation, question answering, and summarization.
A complete guide for building a Generative Adversarial Network (GAN) to make your very own anime characters with Keras
Play with Music Transformer in an interactive colab! Generating long pieces of music is a challenging problem, as music contains structure ...
At OpenAI, we’ve used the multiplayer video game Dota 2 [https://www.dota2.com/play/] as a research platform for general-purpose AI
systems. Our Dota 2 AI, called OpenAI Five, learned by playing over 10,000 years of games against itself. It demonstrated the ability to achieve expert-level performance…
