We are searching for a Backend Software Engineer for Machine Learning to help us develop AI-based autonomous industrial robotic solutions and integrate them with our customers' complex software management environments. As a Backend Software Engineer for Machine Learning, you will design, develop, manage, and deploy the critical infrastructure needed to support a range of industrial automation applications. In this role, you will primarily pair with the Machine Learning team to handle creating an efficient software platform and data pipeline that supports our core backend systems. At OSARO we develop solutions to endow industrial robots with the level of autonomy needed to perform an unprecedented variety of complex pick and place tasks leveraging sophisticated robot control and neural network-based perception algorithms. We value candidates who are passionate about what they build, feel a strong sense of ownership over their work, and love being continually challenged.

Osaro, a San Francisco startup developing AI-based solutions for industrial robots, today announced that it's closed a $16 million series B funding round led by King River Capital (KRC), with participation from Alpha Intelligence Capital, Founders Fund, Fenox Venture Capital, GiTV Fund, and existing and strategic investors. It brings the startup's total raised to $29.3 million coming after a $10 million series A in April 2017, which cofounder and CEO Derik Pridmore said will bolster Osaro's hiring, international deployment, and R&D efforts. Alongside the funding round, Osaro revealed that Applied Digital Access, Mahi Networks, and Calix vereran Kevin Pope has joined as VP of engineering. "A key element of our competitive advantage is Osaro's … deep learning algorithms," said Pridmore, an MIT computer science and electrical engineering graduate who cofounded Osaro in 2015 with a team hailing from UC Berkeley, Stanford, and the University of Massachusetts. "These algorithms generalize picking tasks with minimal training data and no SKU registration for quick, scalable solutions. In addition, as a software company, we support a wide array of commodity hardware and robotic arms which lets our customers select options that best fit their needs."

Our Innovation Analysts recently looked into emerging technologies and up-and-coming startups in Industry 4.0. As there is a large number of startups working on a wide variety of solutions, we decided to share our insights with you. This time, we are taking a look at 5 promising artificial intelligence (AI) solutions. For our 5 picks of artificial intelligence startups, we used a data-driven startup scouting approach to identify the most relevant solutions globally. The Global Startup Heat Map below highlights 5 interesting examples out of 214 relevant solutions. Depending on your specific needs, your top picks might look entirely different.

Today, consumers can buy cameras powered by artificial intelligence (AI) that recognize a person at their front door. But they don't have anything close to a robot that can tie their shoelaces. Why? Simply put, because most machine learning algorithms available today in AI applications don't learn very well. Thanks to a branch of AI called unsupervised learning, however, that's about to change. Most machine learning uses a technique called supervised learning.

The robot arm is performing a peculiar kind of Sisyphean task. It hovers over a glistening pile of cooked chicken parts, dips down, and retrieves a single piece. A moment later, it swings around and places the chunk of chicken, ever so gently, into a bento box moving along a conveyor belt. This robot, created by a San Francisco–based company called Osaro, is smarter than any you've seen before. The software that controls it has taught it to pick and place chicken in about five seconds--faster than your average food-processing worker.

Google is teaching machines to play Atari games like Space Invaders, Video Pinball, and Breakout. At DeepMind, a Google subsidiary based in Cambridge, England, researchers have built artificial intelligence software that's so adept at these classic games, it can sometimes beat a human player--and a professional, at that. This may seem like a frivolous, if intriguing, pursuit. If a machine can learn to navigate the digital world of a video game, Google says, it eventually could learn to navigate the real world, too. Today, this AI can play Space Invaders.

A new artificial intelligence startup called Osaro aims to give industrial robots the same turbocharge that DeepMind Technologies gave Atari-playing computer programs. In December 2013, DeepMind showcased a type of artificial intelligence that had mastered seven Atari 2600 games from scratch in a matter of hours, and could outperform some of the best human players. Google swiftly snapped up the London-based company, and the deep-reinforcement learning technology behind it, for a reported $400 million. Now Osaro, with $3.3 million in investments from the likes of Peter Thiel and Jerry Yang, claims to have taken deep-reinforcement learning to the next level, delivering the same superhuman AI performance but over 100 times as fast. Deep-reinforcement learning arose from deep learning, a method of using multiple layers of neural networks to efficiently process and organize mountains of raw data (see "10 Breakthrough Technologies 2013: Deep Learning").

Google is teaching machines to play Atari games like Space Invaders, Video Pinball, and Breakout. At DeepMind, a Google subsidiary based in Cambridge, England, researchers have built artificial intelligence software that's so adept at these classic games, it can sometimes beat a human player--and a professional, at that. This may seem like a frivolous, if intriguing, pursuit. If a machine can learn to navigate the digital world of a video game, Google says, it eventually could learn to navigate the real world, too. Today, this AI can play Space Invaders.