Games

And yet, AI's current automated task-mastering was first posited by the French philosopher René Descartes almost 400 years ago. Descartes, who famously coined, "I think, therefore I am," pondered about the ability of machines to reason. While machines may be able to "do some things as well, or better, than humans, they would inevitably fail in others," whereas human reason can universally adapt to any task. Though Descartes' idea of machines differs from today's reality, some say he threw down the gauntlet for what we now refer to as general AI--or machines that can think like humans. Though Descartes' idea of machines differs from today's reality, some say he threw down the gauntlet for what we now refer to as general AI--or machines that can think like humans.

If you want to experience the whole breadth of video game history, not just the narrow slice that publishers are willing to sell you on a given day, chances are you're using emulators. Retro consoles are cool and all, but emulators give you even more control as to what classic blasts for the past you can (legally) play on your PC. Emulators can even offer modern conveniences those old games lacked like online functionality or generous save states. And now a nifty new 1.7.8 update to RetroArch (coming soon to Steam) uses machine learning to translate Japanese console and arcade video games in real time. As reported by Kotaku, the feature is called AI Service and offers several different translation tools.

We've seen people turn neural networks to almost everything from drafting pickup lines to a new Harry Potter chapter, but it turns out classic text adventure games may be one of the best fits for AI yet. This latest glimpse into what artificial intelligence can do was created by a neuroscience student named Nathan. Nathan trained GPT-2, a neural net designed to create predictive text, on classic PC text adventure games. Inspired by the Mind Game in Ender's Game, his goal was to create a game that would react to the player. Since he uploaded the resulting game to a Google Colab notebook, people like research scientist Janelle Shane have had fun seeing what a text adventure created by an AI looks like.

Codes and demo are available. This article explores what are states, actions and rewards in reinforcement learning, and how agent can learn through simulation to determine the best actions to take in any given state. After a long day at work, you are deciding between 2 choices: to head home and write an article or hang out with friends at a bar. If you choose to hang out with friends, your friends will make you feel happy; whereas heading home to write an article, you'll end up feeling tired after a long day at work. In this example, enjoying yourself is a reward and feeling tired is viewed as a negative reward, so why write articles?

Typically, a RL setup is composed of two components, an agent and an environment. Then environment refers to the object that the agent is acting on (e.g. the game itself in the Atari game), while the agent represents the RL algorithm. The environment starts by sending a state to the agent, which then based on its knowledge to take an action in response to that state. After that, the environment send a pair of next state and reward back to the agent. The agent will update its knowledge with the reward returned by the environment to evaluate its last action.

Synthetic biology is a rapidly accelerating market, with an estimated conservative global valuation of around US$14 billion.1 To polymath hacker and explorer JJ Hastings, the ability to produce fast, efficient and customisable materials enabled by machine learning and computer-aided design will change the world with an array of entirely new, advanced materials. The extraordinary is now possible: producing spider silk without spiders, egg proteins without chickens and fragrances without flowers. These materials can now be produced by renewable feedstock, reducing the need for large scale agricultural bases or energy-intensive manufacturing. The predicted impact of biomanufacturing drew US$1.7 billion in 2017 investment alone.2 Synthetic biology companies are partnering with fashion designers, heavily backed by VC dollars, as well as forming'organism foundries'.

Today, if you stop and ask anyone working in a technology company, "What is the one thing that would help them change the world or make them grow faster than anyone else in their field?" The answer would be Data. Because data can essentially change, cure, fix, and support just about any problem. Data is the truth behind everything from finding a cure for cancer to studying the shifting weather patterns. However, despite this promise there is a global sense of mistrust.

"Part of what we want is to educate the writing community in a really open way. So we have set this up to find and incubate new writing talent and get them up to speed with what we do." To qualify for The Writers' Room, a track record of screen credits was preferred but not essential.

The AI revolution has arrived. And although the technology is still in its infancy, it promises to radically transform the global economy--impacting human lives, culture, and politics in ways that we can scarcely imagine. A recent article by Forbes Technology Council member Christian Pedersen argued that artificial intelligence will create new opportunities for data scientists, researchers, analysts, and other highly educated technical specialists even as the more easily-automated job functions of low skill workers "fall to the wayside." Pedersen's argument is correct on both counts, but the AI economy is also dependent upon one more ingredient: subject-matter expertise. Participation in the burgeoning AI economy doesn't require an advanced degree in data science or fluency in the latest programming languages.

It's been ten years since the inception of the Mario AI research community, but work in this space is still as engaging and exciting as it's ever been. Today I'm going to look at a variety of research using machine learning to Super Mario level generation since the competition ceased in 2012. I'll be looking at the kinds of levels they're generating, how these algorithms go about building a Mario level and the opportunities that still lie ahead for this research field. It's time to meet the new Super Mario Makers. Before we look at the varying projects and systems in earnest, let's cover some the history of the field and a bit of background knowledge on the changes that have happened in the field in recent years.