If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
The Helmholtz International BigBrain Analytics and Learning Laboratory (HIBALL) is a collaboration between McGill University and Forschungszentrum Jülich to develop next-generation high-resolution human brain models using cutting-edge Machine- and Deep Learning methods and high-performance computing. HIBALL is based on the high-resolution BigBrain model first published by the Jülich and McGill teams in 2013. Over the next five years, the lab will be funded with a total of up to 6 million Euro by the German Helmholtz Association, Forschungszentrum Jülich, and Healthy Brains, Healthy Lives at McGill University. In 2003, when Jülich neuroscientist Katrin Amunts and her Canadian colleague Alan Evans began scanning 7,404 histological sections of a human brain, it was completely unclear whether it would ever be possible to reconstruct this brain on the computer in three dimensions. At that time, there were no technical possibilities to cope with the huge amount of data.
Apple Inc. is one of the biggest technology companies in the world that designs, develops, and sells consumer electronics, computer software, and online services. Apple is constantly in need of creative, passionate, and dedicated data scientists that can sit on any number of their teams. From its researched-based artificial intelligence development team at Siri to cloud-base architecture development team at iCloud, Apple has slowly but steadily been building data science teams to handle the avalanche of data accumulated on a daily basis. As with other big tech companies, the role of a data scientist at Apple varies a lot and is dependent on the teams you are assigned to. This means the job will require everything from analytics to machine learning software design to plain engineering.
Amid a growing backlash over AI's racial and gender biases, numerous tech giants are launching their own ethics initiatives -- of dubious intent. The schemes are billed as altruistic efforts to make tech serve humanity. But critics argue their main concern is evading regulation and scrutiny through "ethics washing." At least we can rely on universities to teach the next generation of computer scientists to make. Only 15% of instructors and professors said they're teaching AI ethics, and just 18% of students indicated they're learning about the subject.
AN ARTIST has used artificial intelligence to create human-like portraits from statues and paintings of famous faces. If you've ever wondered what the Statue of Liberty or Michelangelo's David statue would look like as real people then take a look below. Dutch artist Bas Uterwijk used AI to create the photo-style portraits. He focused on well-known figures including Vincent Van Gogh and Napoleon Bonaparte. The deep learning technology enabled him to take a photo of a statue or a painting and turn it into a more human-like face.
We introduce AdaCoSeg, a deep neural network architecture for adaptive co-segmentation of a set of 3D shapes represented as point clouds. Differently from the familiar single-instance segmentation problem, co-segmentation is intrinsically contextual: how a shape is segmented can vary depending on the set it is in. Hence, our network features an adaptive learning module to produce a consistent shape segmentation which adapts to a set. Specifically, given an input set of unsegmented shapes, we first employ an offline pre-trained part prior network to propose per-shape parts. Then, the co-segmentation network iteratively and} jointly optimizes the part labelings across the set subjected to a novel group consistency loss defined by matrix ranks. While the part prior network can be trained with noisy and inconsistently segmented shapes, the final output of AdaCoSeg is a consistent part labeling for the input set, with each shape segmented into up to (a user-specified) K parts.
"Coworking and flexible Office is one of the most dynamic asset classes in commercial real estate right now, and we think Cherre is the right partner to help institutional owners, investors, and other stakeholders see the past, present and future of this asset class," said Ben Wright, Founder and CEO of Upsuite. Cherre seamlessly connects disparate real estate data into a single-source of truth, empowering companies to instantly explore all their connected data. Cherre has the largest real estate knowledge graph in the world and enables customers to uncover granular insights, automate workflows, and build models and visualizations. "Property owners need comprehensive data to make more informed investment and business decisions," said L.D. Salmanson, CEO and Co-Founder of Cherre. "Analyzing coworking and flex space data alongside other connected data sources will enable better trend and market analysis for decision making."
Designing robots capable of generating interpretable behavior is a prerequisite for achieving effective human-robot collaboration. This means that the robots need to be capable of generating behavior that aligns with human expectations and, when required, provide explanations to the humans in the loop. However, exhibiting such behavior in arbitrary environments could be quite expensive for robots, and in some cases, the robot may not even be able to exhibit the expected behavior. Given structured environments (like warehouses and restaurants), it may be possible to design the environment so as to boost the interpretability of the robot's behavior or to shape the human's expectations of the robot's behavior. In this paper, we investigate the opportunities and limitations of environment design as a tool to promote a type of interpretable behavior – known in the literature as explicable behavior. We formulate a novel environment design framework that considers design over multiple tasks and over a time horizon.
Bottom Line: Knowledge-sharing networks have been improving supply chain collaboration for decades; it's time to enhance them with AI and extend them to resellers to revolutionize channel selling with more insights. Add to that the complexity of selling CPQ and product configurations through channels, and the value of using AI to improve knowledge sharing networks becomes a compelling business case. Automotive, consumer electronics, high tech, and industrial products manufacturers are combining IoT sensors, microcontrollers, and modular designs to sell channel-configurable smart vehicles and products. AI-based knowledge-sharing networks are crucial to the success of their next-generation products. Likewise, to sell to any of these manufacturers, suppliers need to be pursuing the same strategy.
Drug discovery is a hugely expensive and often frustrating process. Medicinal chemists must guess which compounds might make good medicines, using their knowledge of how a molecule's structure affects its properties. They synthesize and test countless variants, and most are failures. "Coming up with new molecules is still an art, because you have such a huge space of possibilities," says Barzilay. "It takes a long time to find good drug candidates." By speeding up this critical step, deep learning could offer far more opportunities for chemists to pursue, making drug discovery much quicker.