September brought us two interesting AI-related stories, both with a surprising social context.
Despite its enormous impact on our daily lives, Artificial Intelligence (AI) is often still regarded as too hermetic and obscure for ordinary people to understand. As a result, an increasing number of people use Natural Language Processing-powered personal assistants, yet only a tiny fraction try to understand how they work and how to use them effectively. This makes them somewhat of a black box.
Making the field more comprehensible and accessible is one aspect of AI researchers’ mission. That’s why research recently done by OpenAI is so interesting.
Hide-and-Seek – the reinforcement learning way
Reinforcement learning has delivered inspiring and breathtaking results. The technique is used in the training models behind autonomous cars and the controlling of sophisticated devices like automated arms and robots.
Unlike in supervised learning, a reinforcement learning model learns by interacting with the environment. The scientist can shape its behavior by applying a policy of rewards and punishments. The mechanism is close to that which humans use to learn.
Reinforcement learning has been used to create super killing agents to go toe-to-toe against human masters in Chess, Go and Starcraft. Now OpenAI, the company behind the GPT-2 model and several other breakthroughs in AI, has created agents that play a version of hide-and-seek, that most basic and ageless of children’s games.
OpenAI researchers divided the agents into two teams, hiders and seekers, and provided them a closed environment with walls and movable objects like boxes, walls and ramps. Any team could “lock” these items to make them unmovable for the opposing team. The teams developed a set of strategies and counter-strategies in a bid to successfully hide from or seek out the other team. The strategies included:
- Running – the first and least sophisticated ability, enabling one to avoid the seekers.
- Blocking passages – the hider could block passages with a box in order to build a safe shelter.
- Using a ramp – to overcome the wall or a box, the seekers team learned to use a ramp to jump over an obstacle or climb a box and see the hider.
- Blocking the ramp – to prevent the seekers from using the ramp to climb the box, the hiders could block access to the ramp. The process required a great deal of teamwork, which was not supported by the researchers in any way.
- Box surfing – a strategy developed by seekers who were basically exploiting a bug in the system. The seekers not only jumped on a box using a ramp that had been blocked by the hiders, but also devised a way to move it while standing on it.
- All-block – the ultimate hider-team teamwork strategy of blocking all the objects on the map and building a shelter.
The research delivered, among other benefits, a mesmerizing visual of little agents running around.
Why does it matter?
The research itself is neither groundbreaking nor breathtaking. From a scientific and developmental point of view, it looks like little more than elaborate fun. Yet it would be unwise to consider the project insignificant.
AI is still considered a hermetic and difficult field. Showing the results of training in the form of friendly, entertaining animations is a way to educate society on the significance of modern AI research.
Also, animation can be inspiring for journalists to write about and may lead youth to take an interest in AI-related career paths. So while the research has brought little if any new knowledge, it could well end up spreading knowledge on what we already know.
AI-generated stock photos available for free
Generative Adversarial Networks have proved to be insanely effective in delivering convincing images of not only hamburgers and dogs, but also human faces. One breakthrough is breathtaking indeed. Not even a year ago the eerie “first AI-generated portrait” was sold on auction for nearly a half-million dollars.
Now, generating faces of non-existent people is as easy as generating any other fake image – a cat, hamburger or landscape. To prove that the technology works, the team behind the 100K faces project delivered a hundred thousand AI-generated faces to use in any stock usage, from business folders, to flyers to presentations. Future use cases could include delivering on-the-go image generators that, powered by a demand forecasting tool, provides an image that best suits demand.
More information on the project can be found on the team’s Medium page.
Why does it matter
The images added to the free images bank are not perfect. With visible flaws in a model’s hair, teeth or eyes, some are indeed far from it. But that’s nothing a skilled graphic designer can’t handle. Also, there are multiple images that look nearly perfect – especially when there are no teeth visible in the smile.
Many photos are good enough to provide a stock photo as a “virtual assistant” image or to fulfill any need for a random face. This is an early sign that professional models and photographers will see the impact of AI in their daily work sooner than expected.