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) …
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.
Understanding text, images, and sounds is not a uniquely human prerogative anymore. Artificial intelligence is transforming virtually every business. AI's ability to derive data-driven insights is paving the road to better digital marketing. From the vast data analysis, marketers gain valuable consumer insights and change how they connect brands with their audiences. Why artificial intelligence cannot be separated from digital marketing anymore?
Artificial intelligence is about to change lead generation and conversion as you know it. In the process, it'll have a transformative impact on companies and careers. AI is a blanket term that covers several different technologies. You might have heard of some of them, like machine learning, computer vision, and natural language processing. Even if you don't know much about it, though, you probably use AI-powered technology dozens or hundreds of times per day.
With the ever-increasing volume, variety, and velocity of available data, scientific disciplines have provided us with advanced mathematical tools, processes, and algorithms enabling us to use this data in meaningful ways. Data science (DS), machine learning (ML), and artificial intelligence (AI) are three such disciplines. A question that frequently comes up in many data-related discussions is what the difference between DS, ML, and AI is? Can they even be compared? Depending on who you talk to, how many years of experience they have had, and what projects they have worked on, you may get widely different answers to the above question. In this blog, I will attempt to answer this based on my research, academic, and industry experience; and having facilitated numerous conversations on the topic.