There is something so awe-inspiring and sublime about watching unmanned drones or flying cars or the stuff of dreams. Now, you don't need to worry as the future of Artificial Intelligence is here. From visors to Virtual Reality or Augmented Reality headsets to mechanized robots which would replace humans in the immediate future. One prominent example of the lure and reach that Artificial Technology has is that we grapple with it daily; we are oblivious to that fact. A restaurant in Indira Nagar in the hinterland of Bangalore is robot-centric wherein your food is served by this.

Introduction to machine learning: An early draft of a proposed textbook.

DeepMind, the Alphabet-backed machine learning lab that's tackled chess, Go, Starcraft 2, Montezuma's Revenge, and beyond, believes the board game Diplomacy could motivate a promising new direction in reinforcement learning research. In a paper published on the preprint server Arxiv.org, the firm's researchers describe an AI system that achieves high scores in Diplomacy while yielding "consistent improvements." AI systems have achieved strong competitive play in complex, large-scale games like Hex, shogi, and poker, but the bulk of these are two-player zero-sum games where a player can win only by causing another player to lose. That doesn't reflect the real world, necessarily; tasks like route planning around congestion, contract negotiations, and interacting with customers all involve compromise and consideration of how preferences of group members coincide and conflict. Even when AI software agents are self-interested, they might gain by coordinating and cooperating, so interacting among diverse groups requires complex reasoning about others' goals and motivations.

At the Faculty of Social Sciences, Department of Business and Management, a PhD scholarship in Economic Complexity and Emerging Ecosystems of AI Technologies is open for appointment on 1 September 2020 or as soon as possible. The capability to fast and efficiently select and acquire relevant knowledge, and transfer insights and applications into new fields of economic activity is critical in times of rapid technological change, where new technological opportunities spring up frequently. In the advent of potential general-purpose-technologies (GPT), these capabilities provide opportunities for firms to disrupt established industries, and for countries to trigger changes in industrial and technological leadership. Machine learning and artificial intelligence (ML&AI) are generally perceived as a likely candidate for a GTP that will revolutionize the economy. Consequently, many developed and emerging economies have entered the race for leadership in the field of AI, leading to numerous national and supranational strategic initiatives to boost the development and application of AI technologies.

Using data from China and New York, the new mobile app, which has been developed by researchers NYU College of Dentistry, works to help clinicians identify which COVID-19 patients are most at risk of suffering a high severity of the disease. The Artificial Intelligence (AI) is used to help the clinicians assess the risk factors and identify biomarkers from blood tests. The findings have been published Royal Society of Chemistry journal Lab on a Chip. This new mobile app could be a vital tool in the fight against COVID-19 as current tests only test whether someone does or does not have the virus, not how sick they may become. Lead researcher John McDevitt, professor of biomaterials at NYU College of Dentistry, said: "Identifying and monitoring those at risk for severe cases could help hospitals prioritise care and allocate resources like ICU beds and ventilators. Likewise, knowing who is at low risk for complications could help reduce hospital admissions while these patients are safely managed at home. "We want doctors to have both the information they need, and the infrastructure required to save lives.

Underpinning the success of deep learning is effective regularizations that allow a variety of priors in data to be modeled. For example, robustness to adversarial perturbations, and correlations between multiple modalities. However, most regularizers are specified in terms of hidden layer outputs, which are not themselves optimization variables. In contrast to prevalent methods that optimize them indirectly through model weights, we propose inserting proximal mapping as a new layer to the deep network, which directly and explicitly produces well regularized hidden layer outputs. The resulting technique is shown well connected to kernel warping and dropout, and novel algorithms were developed for robust temporal learning and multiview modeling, both outperforming state-of-the-art methods.

Label noise is ubiquitous in the era of big data. Deep learning algorithms can easily fit the noise and thus cannot generalize well without properly modeling the noise. In this paper, we propose a new perspective on dealing with label noise called Class2Simi. Specifically, we transform the training examples with noisy class labels into pairs of examples with noisy similarity labels and propose a deep learning framework to learn robust classifiers directly with the noisy similarity labels. Note that a class label shows the class that an instance belongs to; while a similarity label indicates whether or not two instances belong to the same class. It is worthwhile to perform the transformation: We prove that the noise rate for the noisy similarity labels is lower than that of the noisy class labels, because similarity labels themselves are robust to noise. For example, given two instances, even if both of their class labels are incorrect, their similarity label could be correct. Due to the lower noise rate, Class2Simi achieves remarkably better classification accuracy than its baselines that directly deals with the noisy class labels.

We present TURB-Rot, a new open database of 3d and 2d snapshots of turbulent velocity fields, obtained by Direct Numerical Simulations (DNS) of the original Navier-Stokes equations in the presence of rotation. The aim is to provide the community interested in data-assimilation and/or computer vision with a new testing-ground made of roughly 300K complex images and fields. TURB-Rot data are characterized by multi-scales strongly non-Gaussian features and rough, non-differentiable, fields over almost two decades of scales. In addition, coming from fully resolved numerical simulations of the original partial differential equations, they offer the possibility to apply a wide range of approaches, from equation-free to physics-based models. TURB-Rot data are reachable at http://smart-turb.roma2.infn.it

Reinforcement learning has achieved great success in various applications. To learn an effective policy for the agent, it usually requires a huge amount of data by interacting with the environment, which could be computational costly and time consuming. To overcome this challenge, the framework called Reinforcement Learning with Expert Demonstrations (RLED) was proposed to exploit the supervision from expert demonstrations. Although the RLED methods can reduce the number of learning iterations, they usually assume the demonstrations are perfect, and thus may be seriously misled by the noisy demonstrations in real applications. In this paper, we propose a novel framework to adaptively learn the policy by jointly interacting with the environment and exploiting the expert demonstrations. Specifically, for each step of the demonstration trajectory, we form an instance, and define a joint loss function to simultaneously maximize the expected reward and minimize the difference between agent behaviors and demonstrations. Most importantly, by calculating the expected gain of the value function, we assign each instance with a weight to estimate its potential utility, and thus can emphasize the more helpful demonstrations while filter out noisy ones. Experimental results in various environments with multiple popular reinforcement learning algorithms show that the proposed approach can learn robustly with noisy demonstrations, and achieve higher performance in fewer iterations.

Mass public quarantining, colloquially known as a lock-down, is a non-pharmaceutical intervention to check spread of disease. This paper presents ESOP (Epidemiologically and Socio-economically Optimal Policies), a novel application of active machine learning techniques using Bayesian optimization, that interacts with an epidemiological model to arrive at lock-down schedules that optimally balance public health benefits and socio-economic downsides of reduced economic activity during lock-down periods. The utility of ESOP is demonstrated using case studies with VIPER (Virus-Individual-Policy-EnviRonment), a stochastic agent-based simulator that this paper also proposes. However, ESOP is flexible enough to interact with arbitrary epidemiological simulators in a black-box manner, and produce schedules that involve multiple phases of lock-downs.