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We are now looking for a Deep Learning Intern for our Reality Gap Robotics team! For two decades, we have pioneered visual computing, the art and science of computer graphics. With our invention of the GPU – the engine of modern visual computing – the field has expanded to encompass video games, movie production, product design, medical diagnosis and scientific research. Today, we stand at the beginning of the next era, the AI computing era, ignited by a new computing model, GPU deep learning. This new model – where deep neural networks are trained to recognize patterns from massive amounts of data – has shown to be deeply effective at solving some of the most complex problems in everyday life.

We are now looking for a Deep Learning Intern for our Reality Gap Robotics team! For two decades, we have pioneered visual computing, the art and science of computer graphics. With our invention of the GPU – the engine of modern visual computing – the field has expanded to encompass video games, movie production, product design, medical diagnosis and scientific research. Today, we stand at the beginning of the next era, the AI computing era, ignited by a new computing model, GPU deep learning. This new model – where deep neural networks are trained to recognize patterns from massive amounts of data – has shown to be deeply effective at solving some of the most complex problems in everyday life.

At Starmind, we believe that the combined knowledge and cognitive capabilities of humans far exceed any existing technology's computing power; thus, we developed AI to empower and enhance rather than replace the human mind. Our platform helps expedite people's advancement by exploring undocumented human intelligence and enabling real-time access to skills, knowledge, and solutions within corporations, communities, and, since we like to think of the bigger picture: the world. We are backed by some of the best VC investors in Europe, becoming one of the fastest growing AI companies in Europe; and are rapidly expanding our team with the crème de la crème from Airbnb, Twitter, Salesforce, Zuora and many more. Do you have what it takes to help us shape the future of AI? Watch this video to find out how Starmind works!

At Starmind, we believe that the combined knowledge and cognitive capabilities of humans far exceed any existing technology's computing power; thus, we developed AI to empower and enhance rather than replace the human mind. Our platform helps expedite people's advancement by exploring undocumented human intelligence and enabling real-time access to skills, knowledge, and solutions within corporations, communities, and, since we like to think of the bigger picture: the world. We are backed by some of the best VC investors in Europe, becoming one of the fastest growing AI companies in Europe; and are rapidly expanding our team with the crème de la crème from Airbnb, Twitter, Salesforce, Zuora and many more. Do you have what it takes to help us shape the future of AI? Watch this video to find out how Starmind works!

Frankfurt (IAA), 12th September 2019 EDAG BFFT Electronics have developed software that uses artificial intelligence to support assisted and automated driving, even when visibility is poor. The DiFoRem (Dirt & Fog Removal) system is able to compensate for image errors caused by dirt, fogging or camera lens defects with the help of neural networks in real time. The reconstructed image can then be used by other assistance systems or for automated driving, providing a significant increase in image and information quality. In order to digitally compensate for image errors, the impaired areas of every single ingoing image are first identified algorithmically and marked accordingly. To this end, neural networks have been trained to learn the relation between image sections with and without errors.

Machine learning techniques have so far proved to be very promising for the analysis of data in several fields, with many potential applications. However, researchers have found that applying these methods to quantum physics problems is far more challenging due to the exponential complexity of many-body systems. Quantum many-body systems are essentially microscopic structures made up of several interacting particles. While quantum physics studies have focused on the collective behavior of these systems, using machine learning in these investigations has proven to be very difficult. With this in mind, a team of researchers at Harvard University recently developed a quantum circuit-based algorithm inspired by convolutional neural networks (CNNs), a popular machine learning technique that has achieved remarkable results in a variety of fields.

Integration of Dynamic Infrastructure Cloud Service with MissingLink.ai Dynamic Infrastructure announced the integration of MissingLink.ai Dynamic Infrastructure cloud-based solution utilizes proven, cutting-edge Artificial Intelligence (AI) technology that has been tested by certified inspection engineers to find critical faults in bridges and tunnels. DeepOps platform provisions Dynamic Infrastructure's solution to address the surge in the number of active projects, number of assets managed per project and number of survey images per asset. Dynamic Infrastructure has developed a unique tool that provides a rapid, detailed and all-encompassing view of infrastructure assets, for continuous and objective assessment of the asset condition.

Winjit is an AI platform engineering company that has built and deployed a large number of AI solutions that include use cases involving complex machine learning, computer vision with neural network, abstraction layer with different blockchain technology and distributed deep learning platform.

Machine learning techniques have so far proved to be very promising for the analysis of data in several fields, with many potential applications. However, researchers have found that applying these methods to quantum physics problems is far more challenging due to the exponential complexity of many-body systems. Quantum many-body systems are essentially microscopic structures made up of several interacting particles. While quantum physics studies have focused on the collective behavior of these systems, using machine learning in these investigations has proven to be very difficult. With this in mind, a team of researchers at Harvard University recently developed a quantum circuit-based algorithm inspired by convolutional neural networks (CNNs), a popular machine learning technique that has achieved remarkable results in a variety of fields.