Copyright by: https://www.zdnet.com

Humans in the Loop is a social enterprise founded in 2017. It helps refugees from conflict zones like Syria, Iraq, and Afghanistan settle in the country by acquiring digital skills that they can use for working on artificial-intelligence and machine-learning-related projects.

So far, the enterprise has helped around 2,000 refugees find work and integrate into Bulgarian society.

According to its CEO, Iva Gumnishka, Humans in the Loop can provide workers for any stages of the machine-learning model training and development cycle.

“The work ranges from collecting and labeling the ground truth dataset, to verifying the model’s predictions once it’s trained, and handling edge cases, which it’s not sure about,” Gumnishka tells ZDNet.

“We do quite a lot of work in the field of labeling and annotating images and video for computer-vision purposes, which is where the biggest demand in the market is coming from.”

The organization works mostly with refugees, internally displaced people, and people living in conflict zones. It looks to offer employment for those who are left out of the labor market. Aside from the 100-people team based in Bulgaria, the company also works with 150 people across Turkey, Syria, and Iraq.

“In most countries, we partner with local NGOs, which provide digital skills and IT training. We ourselves organize upskilling training for our workforce, which includes computer skills and English,” she explains.

According to Gumnishka, these opportunities are especially suitable for women who prefer to work from home and are looking to combine their projects with housework and childcare.

“We always distribute projects according to a priority score that each worker has, determined by their legal status, number of dependents, employment status, and other vulnerability factors.” she tells ZDNet.

Over the past few years, the company has worked with many startups across Europe and the US, developing products in the fields of AI and computer vision.

Its workers have also been involved in precision agriculture projects involving the segmentation of crops from drone images, insurance tech projects related to car-damage detection and assessment, facial detection, and spoofing detection for CCTV cameras.

The company is currently looking for additional partners in the Middle East, especially in Palestine, Lebanon, Jordan, and Yemen. By 2020, Humans in the Loop aims to employ up to 500 people from conflict-affected countries, and to forge many new partnerships with AI companies.

Read more: https://www.zdnet.com

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copyright by qz.com

In the video game, two teams of five players are placed at opposite ends of a square arena, and fight past each other using melee and spells to destroy their opponent’s base. It’s one of the most lucrative e-sports right now, with this year’s biggest tournament garnering a prize pool of more than $23 million. For the researcher’s software to win against the pros, it would be like a robot learning to dunk on Michael Jordan.

Games are an easy way for those of us without PhDs to understand how far AI research has come: When put in complex situations, can an AI beat humans? We understand what it meant for IBM’s DeepBlue to beat Garry Kasparov in chess, and DeepMind’s AlphaGo beating Lee Sedol in Go—decades of human practice and skill were defeated by mechanical computation. Outside of those publicized matches, AI researchers have worked for decades to build AI agents that are superhuman at playing Atari games, checkers, and even Super Smash Bros .

Not all of the research that’s done on video-game playing AI is applicable outside of the lab, but outside of the competition, OpenAI is showing that its brand of research can be broadly applicable. An example: The same algorithm that is set to play Dota 2 tomorrow can also be taught to move a mechanical hand.

Positive reinforcement

One of the most popular methods for teaching bots to play games, the technique used by OpenAI, the AI research lab predominantly founded by Elon Musk and Sam Altman, is called reinforcement learning. It’s when you give a bot an objective, like collecting coins, and rewarding the bot when it completes the objective. At first, the bot’s movements are completely random, until it accidentally figures out how to complete the task. The moves the bot used to complete the task are weighted as better, and the bot is more likely to follow those actions when it tries the next time. After hundreds, thousands, or millions of attempts, strategies emerge.

OpenAI’s Dota 2-playing bot, for instance, plays millions of games against itself over the course of two weeks. Throughout each game, the bots’ reward is shifted from getting points for themselves to increasing the overall team’s score. The research team calls this “team spirit,” as Quartz previously reported.

Games are such a good place for AI to learn because they’re an analogue of the real world, but with an objective, New York University AI professor Julian Togelius told Quartz. “The real world doesn’t have interesting tasks,” Togelius said with a laugh. “Games are perfect, they have rewards right there—whether you win or not, and what score you get.” And games can be played an infinite number of times—they’re just software, and can be played at the same time by thousands of bots to multiply the speed at which they find the solution or strategy. […]

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