Magnetic soft robots are a promising option for contactless control in confined environments via external magnetic stimuli. Magneto-induced motions, i.e., magnetomotility, are driven by local deformation of a robot whereby particle alignments and alternating polar distributions are programmed into the body. Attempts to program magnetic anisotropy into the soft robots have been performed through direct laser printing (DLP), stereolithography (SLA) and fused filament fabrication (FDM) combined with multi-axial manipulation of electromagnets. Now, researchers have demonstrated facile preparation and actuation methods of magnetic soft robots without electromagnetic regulation. They constructed a three-dimensional helical soft robot through twisting of a two-dimensional polymer composite film.

Countries like China, Iran and Russia are known for strictly censoring what their citizens can see on the internet. These authoritarian governments do this to control their people and protect those in power. It can be very difficult, and often dangerous, to try to get around this, but a new tool looks like it could be the best way to beat censorship in these kinds of oppressive countries. Researchers at the University of Maryland have developed a kind of AI that they've named Geneva, which stands for "Genetic Evasion." This AI uses a kind of machine learning to automatically detect bugs and gaps in a country's censorship system so the user can view uncensored content.

Zeroth-order optimization is the process of minimizing an objective $f(x)$, given oracle access to evaluations at adaptively chosen inputs $x$. In this paper, we present two simple yet powerful GradientLess Descent (GLD) algorithms that do not rely on an underlying gradient estimate and are numerically stable. We analyze our algorithm from a novel geometric perspective and present a novel analysis that shows convergence within an $\epsilon$-ball of the optimum in $O(kQ\log(n)\log(R/\epsilon))$ evaluations, for any monotone transform of a smooth and strongly convex objective with latent dimension $k < n$, where the input dimension is $n$, $R$ is the diameter of the input space and $Q$ is the condition number. Our rates are the first of its kind to be both 1) poly-logarithmically dependent on dimensionality and 2) invariant under monotone transformations. We further leverage our geometric perspective to show that our analysis is optimal. Both monotone invariance and its ability to utilize a low latent dimensionality are key to the empirical success of our algorithms, as demonstrated on BBOB and MuJoCo benchmarks.

A well-constructed classification model highly depends on input feature subsets from a dataset, which may contain redundant, irrelevant, or noisy features. This challenge can be worse while dealing with medical datasets. The main aim of feature selection as a pre-processing task is to eliminate these features and select the most effective ones. In the literature, metaheuristic algorithms show a successful performance to find optimal feature subsets. In this paper, two binary metaheuristic algorithms named S-shaped binary Sine Cosine Algorithm (SBSCA) and V-shaped binary Sine Cosine Algorithm (VBSCA) are proposed for feature selection from the medical data. In these algorithms, the search space remains continuous, while a binary position vector is generated by two transfer functions S-shaped and V-shaped for each solution. The proposed algorithms are compared with four latest binary optimization algorithms over five medical datasets from the UCI repository. The experimental results confirm that using both bSCA variants enhance the accuracy of classification on these medical datasets compared to four other algorithms.

Humans and animals are capable of quickly learning new behaviours to solve new tasks. Yet, we often forget that they also rely on a highly specialized morphology that co-adapted with motor control throughout thousands of years. Although compelling, the idea of co-adapting morphology and behaviours in robots is often unfeasible because of the long manufacturing times, and the need to re-design an appropriate controller for each morphology. In this paper, we propose a novel approach to automatically and efficiently co-adapt a robot morphology and its controller. Our approach is based on recent advances in deep reinforcement learning, and specifically the soft actor critic algorithm. Key to our approach is the possibility of leveraging previously tested morphologies and behaviors to estimate the performance of new candidate morphologies. As such, we can make full use of the information available for making more informed decisions, with the ultimate goal of achieving a more data-efficient co-adaptation (i.e., reducing the number of morphologies and behaviors tested). Simulated experiments show that our approach requires drastically less design prototypes to find good morphology-behaviour combinations, making this method particularly suitable for future co-adaptation of robot designs in the real world.

Recent research has found that many families of machine learning models are vulnerable to adversarial examples: inputs that are specifically designed to cause the target model to produce erroneous outputs. In this survey, we focus on machine learning models in the visual domain, where methods for generating and detecting such examples have been most extensively studied. We explore a variety of adversarial attack methods that apply to image-space content, real world adversarial attacks, adversarial defenses, and the transferability property of adversarial examples. We also discuss strengths and weaknesses of various methods of adversarial attack and defense. Our aim is to provide an extensive coverage of the field, furnishing the reader with an intuitive understanding of the mechanics of adversarial attack and defense mechanisms and enlarging the community of researchers studying this fundamental set of problems.

Genetic programming (GP) is a general purpose bio-inspired meta-heuristic for the evolution of computer programs. In contrast to the several successful applications, there is little understanding of the working principles behind GP. In this paper we present a performance analysis that sheds light on the behaviour of simple GP systems for evolving conjunctions of n variables (ANDn). The analysis of a random local search GP system with minimal terminal and function sets reveals the relationship between the number of iterations and the progress the GP makes toward finding the target function. Afterwards we consider a more realistic GP system equipped with a global mutation operator and prove that it can efficiently solve ANDn by producing programs of linear size that fit a training set to optimality and with high probability generalise well. Additionally, we consider more general problems which extend the terminal set with undesired variables or negated variables. In the presence of undesired variables, we prove that, if non-strict selection is used, then the algorithm fits the complete training set efficiently while the strict selection algorithm may fail with high probability unless the substitution operator is switched off. If negations are allowed, we show that while the algorithms fail to fit the complete training set, the constructed solutions generalise well. Finally, from a problem hardness perspective, we reveal the existence of small training sets that allow the evolution of the exact conjunctions even with access to negations or undesired variables.

In this paper, we propose a new wrapper approach for semi-supervised feature selection. A common strategy in semi-supervised learning is to augment the training set by pseudo-labeled unlabeled examples. However, the pseudo-labeling procedure is prone to error and has a high risk of disrupting the learning algorithm with additional noisy labeled training data. To overcome this, we propose to model explicitly the mislabeling error during the learning phase with the overall aim of selecting the most relevant feature characteristics. We derive a $\mathcal{C}$-bound for Bayes classifiers trained over partially labeled training sets by taking into account the mislabeling errors. The risk bound is then considered as an objective function that is minimized over the space of possible feature subsets using a genetic algorithm. In order to produce both sparse and accurate solution, we propose a modification of a genetic algorithm with the crossover based on feature weights and recursive elimination of irrelevant features. Empirical results on different data sets show the effectiveness of our framework compared to several state-of-the-art semi-supervised feature selection approaches.

"A Breakthrough in machine learning would be worth ten Microsofts" – Bill Gates Yes, due to many obvious reasons, Bill Gates is right and we will prove it in this blog. Though the term, machine learning was tossed by Arthur Samuel in 1959 while working at the IBM, the actual serviceability of it started popping up after 2010. So, Dave Waters compares the advancement of machine learning with the baby – "A baby learns to crawl, walk and then run. We are in the crawling stage when it comes to applying machine learning." Recently, machine learning market has witnessed exceptional growth and it is estimated to reach $21 billion by 2024.

In 2007, Kenneth Stanley, a computer scientist at the University of Central Florida, was playing with Picbreeder, a website he and his students had created, when an alien became a race car and changed his life. On Picbreeder, users would see an array of 15 similar images, composed of geometric shapes or swirly patterns, all variations on a theme. On occasion, some might resemble a real object, like a butterfly or a face. Users were asked to select one, and they typically clicked on whatever they found most interesting. Once they did, a new set of images, all variations on their choice, would populate the screen.