RHex (pronounced "rex") is a unique hexapedal robot that uses hybrid wheel-legs (whegs) to get around. It's surprisingly adaptable, able to adjust its gait to conquer a variety of obstacles and terrains, and it can even do some impressive parkour. RHex has been around for nearly two decades, which is practically forever in robot years, but because of how versatile it is you still see it doing cool new stuff from time to time. Carnegie Mellon University's Robomechanics Lab uses a fancy US $20,000 version of RHex called X-RHex Lite "to explore the connection between dynamic locomotion and perception," but they've only got one robot since it's wicked expensive, which limits the amount of research and outreach they can do. To fix this, they've designed a much smaller version of RHex called MiniRHex that you can build yourself for about $200.

Exploring the future of artificial intelligence (AI) in our day to day lives, computer whiz kid Tanmay Bakshi said at an event in Greater Noida that instances of fake news, hate speech and harassment on social media can be dealt with the use of AI. Fifteen-year-old Bakshi, the world's youngest IBM Watson programmer, was at Sharda University in Greater Noida on Friday for a TED Talk with students on computer programming and the future of artificial intelligence. He said AI can be monumental in curbing fake news and hate speech. "Fake news is huge and I myself have been a victim of it where one of my TED Talk videos was uploaded on Facebook with the caption that I work for Google and I make billions of dollars a year. I believe social media giants have started using AI to clamp down on fake news and hate speech. For example, Facebook is using machine learning (alternatively known as AI) to understand the content being put up, match it with trusted sources, understand the different point of views which people can have, and when they are absolutely sure that it is fake news then it will be automatically flagged for deletion," Bakshi said.

We've all heard the dire predictions about robots coming to steal our jobs. As technologies such as machine learning, AI and automation advance by the day, workplaces everywhere are being transformed; naturally, some people fear that they will become redundant -- depending on their job, some may be right. In this age of AI and ML, ambivalence is ripe; but something ironic has emerged: when it comes to advancing these cutting-edge technologies, the lack of human skills and knowledge is slowing innovation down. In a new survey by Cloudera, the software firm, exploring the benefits and roadblocks of ML adoption across Europe, 51% of business leaders said that the skills shortage was holding them back from implementation. According to Cloudera, companies are eager to use ML -- it's second only to analytics as the key investment priority for businesses; ahead of other disciplines like IoT, artificial intelligence and data science.

Recently, an opinion dynamics model has been proposed to describe a network of individuals discussing a set of logically interdependent topics. For each individual, the set of topics and the logical interdependencies between the topics (captured by a logic matrix) form a belief system. We investigate the role the logic matrix and its structure plays in determining the final opinions, including existence of the limiting opinions, of a strongly connected network of individuals. We provide a set of results that, given a set of individuals' belief systems, allow a systematic determination of which topics will reach a consensus, and which topics will disagreement in arise. For irreducible logic matrices, each topic reaches a consensus. For reducible logic matrices, which indicates a cascade interdependence relationship, conditions are given on whether a topic will reach a consensus or not. It turns out that heterogeneity among the individuals' logic matrices, including especially differences in the signs of the off-diagonal entries, can be a key determining factor. This paper thus attributes, for the first time, a strong diversity of limiting opinions to heterogeneity of belief systems in influence networks, in addition to the more typical explanation that strong diversity arises from individual stubbornness.

We present Vision-based Navigation with Language-based Assistance (VNLA), a grounded vision-language task where an agent with visual perception is guided via language to find objects in photorealistic indoor environments. The task emulates a real-world scenario in that (a) the requester may not know how to navigate to the target objects and thus makes requests by only specifying high-level endgoals, and (b) the agent is capable of sensing when it is lost and querying an advisor, who is more qualified at the task, to obtain language subgoals to make progress. To model language-based assistance, we develop a general framework termed Imitation Learning with Indirect Intervention (I3L), and propose a solution that is effective on the VNLA task. Empirical results show that this approach significantly improves the success rate of the learning agent over other baselines on both seen and unseen environments.

There is no shortage of articles attempting to lay out a step-by-step process of how to become a data scientist. Are you a recent graduate? Do this… Are you changing careers? Do that… And make sure you're focusing on the top skills: coding, statistics, machine learning, storytelling, databases, big data… Need resources? Check out Andrew Ng's Coursera ML course, …". Although these are important things to consider once you have made up your mind to pursue a career in data science, I hope to answer the question that should come before all of this. It's the question that should be on every aspiring data scientist's mind: "should I become a data scientist?" This question addresses the why before you try to answer the how. What is it about the field that draws you in and will keep you in it and excited for years to come? In order to answer this question, it's important to understand how we got here and where we are headed. Because by having a full picture of the data science landscape, you can determine whether data science makes sense for you. Before the convergence of computer science, data technology, visualization, mathematics, and statistics into what we call data science today, these fields existed in siloes -- independently laying the groundwork for the tools and products we are now able to develop, things like: Oculus, Google Home, Amazon Alexa, self-driving cars, recommendation engines, etc. The foundational ideas have been around for decades... early scientists dating back to the pre-1800s, coming from wide range of backgrounds, worked on developing our first computers, calculus, probability theory, and algorithms like: CNNs, reinforcement learning, least squares regression. With the explosion in data and computational power, we are able to resurrect these decade old ideas and apply them to real-world problems. In 2009 and 2012, articles were published by McKinsey and the Harvard Business Review, hyping up the role of the data scientist, showing how they were revolutionizing the way businesses are operating and how they would be critical to future business success. They not only saw the advantage of a data-driven approach, but also the importance of utilizing predictive analytics into the future in order to remain competitive and relevant. Around the same time in 2011, Andrew Ng came out with a free online course on machine learning, and the curse of AI FOMO (fear of missing out) kicked in. Companies began the search for highly skilled individuals to help them collect, store, visualize and make sense of all their data. "You want the title and the high pay?

To help news industry use Artificial Intelligence (AI) in more innovative ways, Google has announced a partnership with Polis, the international journalism think-tank at London School of Economics and Political Science, to create "Journalism AI". Part of the Google News Initiative (GNI), the "Journalism AI" project will focus on research and training for newsrooms on the intersection of AI and journalism. "As part of'Journalism AI', next year, we'll publish a global survey about how the media is currently using -- and could further benefit from -- this technology," Google said in a statement on Friday as it organised GNI Innovation Forum here. "We'll also collaborate with newsrooms and academic institutions to create a best practices handbook and produce free online training on how to use AI in the newsroom for journalists worldwide," informed Matt Cooke, Head of Partnerships and Training, Google News Lab. After testing with partners over the last two years, Google also introduced a new tool called Google Earth Studio which is an animation tool for Google Earth's satellite and 3D imagery.

Using the outputs obtained from the online testing, it is not so difficult to collect a large-scale of learning data. We may be able to actively tackle the collected data to find the optimal strategies for better learning methods. It is also important to analyze the data theoretically (see [23]). This paper is aimed at obtaining effective learning strategies for students at risk for failing courses and/or dropping out, using a large-scale of learning data collected from the online testings. In this paper, unlike the conventional methods using the correct answer rate (CAR) to identify the ability of a student (e.g., see [13]), we use the ability obtained from the item response theory (IRT, e.g., see [1], [4], [17]), and we show a new method to identify students at risk as early as possible using the IRT results.

They have shown to be effective in handling large-scale and high-velocity streaming data and emerged to become popular in the big data era Hoi et al. [2018, 2014]. In recent years, a number of effective online learning algorithms have been investigated and applied in a variety of high impact domains,ranging from game theory, information theory to machine learning and data mining Ding et al. [2017], Shalev-Shwartz [2011], Wang et al. [2003]. Most previously proposed online learning algorithms fall into the wellestablished frameworkof online convex optimization Gordon [1999], Zinkevich [2003]. In terms of the optimization algorithms, online learning algorithms can be grouped into the following categories: (i) first-order algorithms which aim to optimize the objective function using the first-order (sub) gradient such as the well-known OGD algorithm Zinkevich [2003]; and (ii) second-order algorithms which aim to exploit second-order information to speed up the convergence of the optimization, such as the ONS algorithm Hazan et al. [2007]. In online convex optimization, previous approaches are mainly based on the first-order optimization, i.e., optimization using the first-order derivative of the cost function. Theregret bound achieved by these algorithms is proportional to the polynomial of the number of rounds T . For example, Zinkevich [2003] showed that with the simple OGD, we can achieve the regret bound of O( T). Later on, Hazan et al. [2007] introduced a new algorithm with ONS by exploiting the second-order derivative of the cost function, which can be viewed as an online 2 analogy of the Newton-Raphson method Ypma and Tjalling [1995] in the offline learning.Although the time complexity O(d

We provide high-probability sample complexity guarantees for exact structure recovery and accurate Predictive Learning using noise-corrupted samples from an acyclic (tree-shaped) graphical model. The hidden variables follow a tree-structured Ising model distribution whereas the observable variables are generated by a binary symmetric channel, taking the hidden variables as its input. This model arises naturally in a variety of applications, such as in physics, biology, computer science, and finance. The noiseless structure learning problem has been studied earlier by Bresler and Karzand (2018); this paper quantifies how noise in the hidden model impacts the sample complexity of structure learning and predictive distributional inference by proving upper and lower bounds on the sample complexity. Quite remarkably, for any tree with $p$ vertices and probability of incorrect recovery $\delta>0$, the order of necessary number of samples remains logarithmic as in the noiseless case, i.e., $\mathcal{O}(\log(p/\delta))$, for both aforementioned tasks. We also present a new equivalent of Isserlis' Theorem for sign-valued tree-structured distributions, yielding a new low-complexity algorithm for higher order moment estimation.