--The reduction of metal artifacts in computed tomography (CT) images, specifically for strong artifacts generated from multiple metal objects, is a challenging issue in medical imaging research. Although there have been some studies on supervised metal artifact reduction through the learning of synthesized artifacts, it is difficult for simulated artifacts to cover the complexity of the real physical phenomena that may be observed in X-ray propagation. In this paper, we introduce metal artifact reduction methods based on an unsupervised volume-to-volume translation learned from clinical CT images. We construct three-dimensional adversarial nets with a regularized loss function designed for metal artifacts from multiple dental fillings. The results of experiments using 915 CT volumes from real patients demonstrate that the proposed framework has an outstanding capacity to reduce strong artifacts and to recover underlying missing voxels, while preserving the anatomical features of soft tissues and tooth structures from the original images. EDICAL procedures such as diagnosis, surgical planning, and radiotherapy can be seriously degraded by the presence of metal artifacts in computed tomography (CT) imaging. Metal objects such as dental fillings, fixation devices, and other electric instruments implanted in patients' bodies inhibit X-ray propagation [1], preventing accurate calculation of the CT values during image reconstruction and yielding dark bands or streak artifacts in the CT images [2][3]. To correct the images, missing CT values for the underlying anatomical features must be compensated at the same time as the artifacts are removed. Although doctors make clinical efforts to manually collect such artifacts, this is a labor-intensive and time-consuming task. M. Nakao and T. Matsuda are with the Graduate School of Informatics, Kyoto University, Y oshida-Honmachi, Sakyo, Kyoto 606-8501, JAP AN; email: megumi@i.kyoto-u.ac.jp.

-- Recently, the Deep Planning Network (PlaNet) approach was introduced as a model-based reinforcement learning method that learns environment dynamics directly from pixel observations. This architecture is useful for learning tasks in which either the agent does not have access to meaningful states (like position/velocity of robotic joints) or where the observed states significantly deviate from the physical state of the agent (which is commonly the case in low-cost robots in the form of backlash or noisy joint readings). In this work, we introduce Robo-PlaNet, an asynchronous version of PlaNet. This algorithm consistently reaches higher performance in the same amount of time, which we demonstrate in both a simulated and a real robotic experiment. Teaching a robot a new trick can prove challenging. Currently, many methods rely on transfer from simulation to physical platforms (i.e.

-- Object detection is an essential task for autonomous robots operating in dynamic and changing environments. A robot should be able to detect objects in the presence of sensor noise that can be induced by changing lighting conditions for cameras and false depth readings for range sensors, especially RGB-D cameras. T o tackle these challenges, we propose a novel adaptive fusion approach for object detection that learns weighting the predictions of different sensor modalities in an online manner . Our approach is based on a mixture of convolutional neural network (CNN) experts and incorporates multiple modalities including appearance, depth and motion. We test our method in extensive robot experiments, in which we detect people in a combined indoor and outdoor scenario from RGB-D data, and we demonstrate that our method can adapt to harsh lighting changes and severe camera motion blur . Furthermore, we present a new RGB-D dataset for people detection in mixed in-and outdoor environments, recorded with a mobile robot. I. INTRODUCTION Most autonomous robots operating in complex environments are equipped with different sensors to perceive their surroundings.

The artificial voices of Siri, Alexa, or Google, and their often awkward responses, leave no room for doubt that we are not talking to a real person. The latest technological breakthroughs that combine artificial intelligence with deceptively realistic human voices now make it possible for bots to pass themselves off as humans. This has led to new ethical issues: Is bots' impersonation of humans a case of deception? Previous research has shown that humans prefer not to cooperate with intelligent bots. But if people do not even notice that they are interacting with a machine and cooperation between the two is therefore more successful, would it not make sense to maintain the deception in some cases?

The artificial voices of Siri, Alexa, or Google, and their often awkward responses, leave no room for doubt that we are not talking to a real person. The latest technological breakthroughs that combine artificial intelligence with deceptively realistic human voices now make it possible for bots to pass themselves off as humans. This has led to new ethical issues: Is bots' impersonation of humans a case of deception? Previous research has shown that humans prefer not to cooperate with intelligent bots. But if people do not even notice that they are interacting with a machine and cooperation between the two is therefore more successful, would it not make sense to maintain the deception in some cases?

In India, healthcare is an issue not because it's hard for people of lower economic status to afford. Often times, it's hard to even find a doctor or hospital in a remote rural area. That's why this particular piece of new technology could be incalculably valuable. Shivanshu Mathur and Raghav Jain, two students pursuing their BTech in Computer Science Engineering at Lovely Professional University, have won the second prize at the NEC India Hackathon 2019 organized by HackerEarth. They received a cash prize of Rs. 1.5 lakh for developing a software they call Medikare.

As early as 1997, McKinsey coined the concept "war for talent" and identified it as a pressing challenge facing workplaces. To be sure, the war for talent has only intensified in recent years. The supply of top-tier artificial intelligence (AI) talent is in short supply. And, with the likes of Facebook and Google vying for top-notch talent, recruiting efforts can prove incredibly challenging. Fortunately, by embracing some key strategies, companies can effectively compete with even today's most sought-after employers.

Gartner has unveiled its biggest predictions for IT organisations and users for 2020 and beyond. These predictions analyse how technology is changing society and the expectations of users. "Technology is changing the notion of what it means to be human," said Daryl Plummer, VP and Fellow at Gartner. "CIOs in end-user organizations must understand the effects of the change and reset expectations for what technology means." "This year's predictions help us move beyond thinking about mere notions of technology adoption and draw us more deeply into issues surrounding what it means to be human in the digital world," said Plummer.

Apple (NasdaqGS:AAPL) and Salesforce (NYSE:CRM) have announced the launch of two new apps, including the AI-powered redesigned Salesforce Mobile App with features exclusive to iOS and iPadOS. "With Salesforce Mobile, Salesforce and Apple are empowering sales, service and marketing professionals on the go to deliver game-changing customer experiences, powered by AI. And with Trailhead GO, millions more can now skill up for free, anytime and anywhere, to learn in-demand skills and fill the jobs of today and tomorrow." Intel Corporation (NasdaqGS:INTC) has unveiled oneAPI, "a unified and scalable programming model to harness the power of diverse computing architectures in the era of HPC/AI convergence", and "a general-purpose GPU optimized for HPC/AI acceleration based on the Xe architecture". "HPC and AI workloads demand diverse architectures, ranging from CPUs, general-purpose GPUs and FPGAs, to more specialized deep-learning NNPs, which Intel demonstrated earlier this month. Simplifying our customers' ability to harness the power of diverse computing environments is paramount, and Intel is committed to taking a software-first approach that delivers a unified and scalable abstraction for heterogeneous architectures."

August Bradley (@augustbradley) is a futurist business consultant, founder/host of the Mind & Machine podcast and former COO of the pioneering virtual reality company Kite & Lightning. August's worked with brands including Coke, Xbox, Kia, Gap, J.Crew, Banana Republic, Fisker Automotive, and many more. He is also a Board Member & Director of the AI & Machine Learning Society, Chairman of the Technarte International Conference, technology series moderator for The Los Angeles ... See More World Affairs Council (LAWAC) and head of the Los Angeles Self Driving Car Meetup. In today's episode we discuss: - Why August and I won't let voice assistants in our homes - What happens next with VR and where it is headed - Which technologies worry August most and why - What does privacy look like in a more connected world - Why Facebook and social media are so bad for all of us - How Amazon helps transform healthcare - The reason AI and automation will be net-negative on jobs - What we have to look forward to when it comes to future technologies - The reason space exploration is so important and exciting - What do we do about big tech and regulation - Why August isn't worried about AI consciousness or superintelligence - The reason food science and clean meat is such a promising field to pursue - Why immigration is a stupid topic to focus on when it comes to jobs - Science fiction as a safety net