Conversational agents, or chatbots, providing question-answer assistance on smart devices, have proliferated in recent years and are poised to transform online customer services of corporate sectors.1,6 Implemented through dialogue management systems, chatbots converse through voice-based and textual dialogue, and harness natural language processing and artificial intelligence to recognize requests, provide responses, and predict user behavior.5,28 Market analysts concur on current adoption trends and the magnitude of growth and impact of chatbots anticipated in the next five years. According to a report by Grand View Research, for instance, already 45% of users prefer chatbots as the primary point of communications for customer service enquiries, translating into a global'chatbot' market of $1.23 billion by 2025, at a compounded annual growth rate (CAGR) of 24.3%.9 The strategy for conducting conversations using chatbots requires an efficient resolution of two key aspects. First, user queries or automatically perceived needs through user interactions have to be interpreted and mapped into categories, or user intents. This is based on historical processing of queries and needs, and the use of intent classification techniques.12 Second, conversations must be constructed for specific intents using frame-based dialogue management2 and neural response generation techniques.15 In frame-based dialogue management, the chatbot needs to converse with the user to have a fully filled frame (for example, flight information) in which all slot values are provided by the user (for example, airline carrier, departure time, departure location, and arrival location). The dialogue flow is constructed through an ordered sequence of frames.

I don't love that I am doing it, but my current take on anything related to "are drones safe in cities, what will people be using them for, consumer drones, Slaughterbots, etc." is the trite: they'll probably be banned. The public is starting to realize the risk of consumer drones. That being said, most people probably forgot the Gatwick Drone Incident, where one drone shut down thousands of flights across a couple of days in London (about 140,000 people affected). I would also guess if you ask people about hacked drones, they understand they are scary but unexpected. This difficulty with regulating drones and dealing with vast numbers of them lies parallel to the fact that no one was charged in the Gatwick incident (2 arrests, released without charge)! It is a very messy space, and consumers have a weird infatuation with their loud flying friends.

The wildfires raging across BC are destructive and costly, but an advisor for the United Nations (UN) believes there's a solution that lies a little outside of the box. Neil Sahota is an IBM Master Inventor, UN Artificial Intelligence (AI) subject matter expert, and Professor at UC Irvine, a University in Irvine, California. Sahota sat down with MyPGNow and discussed how AI could help predict and fight fires across BC, and some of the utilizations of AI already in place across the world. "A lot of the focus is on early detection. We know that there are different variables in play that could trigger a wildfire. So we look at three things: fuel, oxygen, and energy," said Sahota.

We propose a novel framework for real-time black-box universal attacks which disrupts activations of early convolutional layers in deep learning models. Our hypothesis is that perturbations produced in the wavelet space disrupt early convolutional layers more effectively than perturbations performed in the time domain. The main challenge in adversarial attacks is to preserve low frequency image content while minimally changing the most meaningful high frequency content. To address this, we formulate an optimization problem using time-scale (wavelet) representations as a dual space in three steps. First, we project original images into orthonormal sub-spaces for low and high scales via wavelet coefficients. Second, we perturb wavelet coefficients for high scale projection using a generator network. Third, we generate new adversarial images by projecting back the original coefficients from the low scale and the perturbed coefficients from the high scale sub-space. We provide a theoretical framework that guarantees a dual mapping from time and time-scale domain representations. We compare our results with state-of-the-art black-box attacks from generative-based and gradient-based models. We also verify efficacy against multiple defense methods such as JPEG compression, Guided Denoiser and Comdefend. Our results show that wavelet-based perturbations consistently outperform time-based attacks thus providing new insights into vulnerabilities of deep learning models and could potentially lead to robust architectures or new defense and attack mechanisms by leveraging time-scale representations.

When humans solve complex problems, they rarely come up with a decision right-away. Instead, they start with an intuitive decision, reflect upon it, spot mistakes, resolve contradictions and jump between different hypotheses. Thus, they create a sequence of ideas and follow a train of thought that ultimately reaches a conclusive decision. Contrary to this, today's neural classification models are mostly trained to map an input to one single and fixed output. In this paper, we investigate how we can give models the opportunity of a second, third and $k$-th thought. We take inspiration from Hegel's dialectics and propose a method that turns an existing classifier's class prediction (such as the image class forest) into a sequence of predictions (such as forest $\rightarrow$ tree $\rightarrow$ mushroom). Concretely, we propose a correction module that is trained to estimate the model's correctness as well as an iterative prediction update based on the prediction's gradient. Our approach results in a dynamic system over class probability distributions $\unicode{x2014}$ the thought flow. We evaluate our method on diverse datasets and tasks from computer vision and natural language processing. We observe surprisingly complex but intuitive behavior and demonstrate that our method (i) can correct misclassifications, (ii) strengthens model performance, (iii) is robust to high levels of adversarial attacks, (iv) can increase accuracy up to 4% in a label-distribution-shift setting and (iv) provides a tool for model interpretability that uncovers model knowledge which otherwise remains invisible in a single distribution prediction.

The sun may be the most powerful source of energy in the Milky Way, but NASA researchers are using artificial intelligence to get a better view of the giant ball of gas. The US space agency is using machine learning on solar telescopes, including its Solar Dynamics Observatory (SDO), launched in 2010, and its Atmospheric Imagery Assembly (AIA), imaging instrument that looks constantly at the sun. This allows the agency to snap incredible pictures of the celestial giant, while limiting the effects of solar particles and'intense sunlight,' which begins to degrade lenses and sensors over time. The sun goes through an 11-year cycle where it goes from very active to less active. It is tracked by sunspots and it is currently going through a quiet phase.

Moving products from delivery trucks to storage areas, then to shelves, is hazardous work. Forklift incidents, lifting injuries and falling objects are some of the hazards workers face. Data from the Bureau of Labor Statistics shows that 10 occupations accounted for 33.2% of all private industry cases involving days away from work in 2018 and 2019. Of these, laborers and freight, stock, and material movers (hand) had the highest number of DAFW cases: 64,160. So, how can employers prevent injuries among warehouse workers or other workers who move materials?

"AI is an instrument just like anything else. You can do harm and you can do wonderful things. ESG is the embodiment of all the good things you can do with AI. Squeeze all the juice out of AI but at the same time we need to understand the consequences so we can do things responsibly!" The wise words from Aiko Yamashita, Senior Data Scientist at the Advanced Analytics Centre of Excellence in DNB Bank, during our conversation on Altair's'Future Says'.

The more general point is that computer algorithms will have a devil of a time predicting which jobs are most at risk for being replaced by computers, since they have no comprehension of the skills required to do a particular job successfully. In one study that was widely covered (including by The Washington Post, The Economist, Ars Technica, and The Verge), Oxford University researchers used the U.S. Department of Labor's O NET database, which assesses the importance of various skill competencies for hundreds of occupations. For example, using a scale of 0 to 100, O*NET gauges finger dexterity to be more important for dentists (81) than for locksmiths (72) or barbers (60). The Oxford researchers then coded each of 70 occupations as either automatable or not and correlated these yes/no assessments with O*NET's scores for nine skill categories. Using these statistical correlations, the researchers then estimated the probability of computerization for 702 occupations.

One of the main challenges in autonomous robotic exploration and navigation in unknown and unstructured environments is determining where the robot can or cannot safely move. A significant source of difficulty in this determination arises from stochasticity and uncertainty, coming from localization error, sensor sparsity and noise, difficult-to-model robot-ground interactions, and disturbances to the motion of the vehicle. Classical approaches to this problem rely on geometric analysis of the surrounding terrain, which can be prone to modeling errors and can be computationally expensive. Moreover, modeling the distribution of uncertain traversability costs is a difficult task, compounded by the various error sources mentioned above. In this work, we take a principled learning approach to this problem. We introduce a neural network architecture for robustly learning the distribution of traversability costs. Because we are motivated by preserving the life of the robot, we tackle this learning problem from the perspective of learning tail-risks, i.e. the Conditional Value-at-Risk (CVaR). We show that this approach reliably learns the expected tail risk given a desired probability risk threshold between 0 and 1, producing a traversability costmap which is more robust to outliers, more accurately captures tail risks, and is more computationally efficient, when compared against baselines. We validate our method on data collected a legged robot navigating challenging, unstructured environments including an abandoned subway, limestone caves, and lava tube caves.