There are no known nontrivial compact Calabi-Yau metrics, objects of central importance in string theory and algebraic geometry, despite decades of study. The essence of the problem is that theorems by Calabi [1] and Yau [2, 3] guarantee the existence of a Ricci-flat Kähler metric (Calabi-Yau metric) when certain criteria are satisfied, but Yau's proof is non-constructive. It is not for lack of examples satisfying the criteria, since topological constructions ensure the existence of an exponentially large number of examples [4-6]. The problem also does not prevent certain types of progress in string theory, since aspects of Calabi-Yau manifolds can be studied without knowing the metric. For instance, much is known about volumes of calibrated submanifolds [7], an artifact of supersymmetry and the existence of BPS objects, as well a metric deformations the preserve Ricci-flatness, the (in)famous moduli spaces [8].

Deep-learning based Automatic Essay Scoring (AES) systems are being actively used by states and language testing agencies alike to evaluate millions of candidates for life-changing decisions ranging from college applications to visa approvals. However, little research has been put to understand and interpret the black-box nature of deep-learning based scoring algorithms. Previous studies indicate that scoring models can be easily fooled. In this paper, we explore the reason behind their surprising adversarial brittleness. We utilize recent advances in interpretability to find the extent to which features such as coherence, content, vocabulary, and relevance are important for automated scoring mechanisms. We use this to investigate the oversensitivity i.e., large change in output score with a little change in input essay content) and overstability i.e., little change in output scores with large changes in input essay content) of AES. Our results indicate that autoscoring models, despite getting trained as "end-to-end" models with rich contextual embeddings such as BERT, behave like bag-of-words models. A few words determine the essay score without the requirement of any context making the model largely overstable. This is in stark contrast to recent probing studies on pre-trained representation learning models, which show that rich linguistic features such as parts-of-speech and morphology are encoded by them. Further, we also find that the models have learnt dataset biases, making them oversensitive. To deal with these issues, we propose detection-based protection models that can detect oversensitivity and overstability causing samples with high accuracies. We find that our proposed models are able to detect unusual attribution patterns and flag adversarial samples successfully.

The Army Research Laboratory is exploring new applications of AI designed to better enable forward operating robot "tanks" to acquire targets, discern and organize war-crucial information, surveil combat zones and even fire weapons when directed by a human. "For the first time the Army will deploy manned tanks that are capable of controlling robotic vehicles able to adapt to the environment and act semi-independently. Manned vehicles will control a number of combat vehicles, not small ones but large ones. In the future we are going to be incorporating robotic systems that are larger, more like the size of a tanks," Dr. Brandon Perelman, Scientist and Engineer, Army Research Laboratory, Combat Capabilities Development Command, Army Futures Command, told Warrior in an interview, Aberdeen Proving Ground, Md. The concept is aligned with ongoing research into new generations of AI being engineered to not only gather and organize information for human decision makers but also advance networking between humans and machines.

Every year, millions of students sit down for standardized tests that carry weighty consequences. National tests like the Graduate Record Examinations (GRE) serve as gatekeepers to higher education, while state assessments can determine everything from whether a student will graduate to federal funding for schools and teacher pay. Traditional paper-and-pencil tests have given way to computerized versions. And increasingly, the grading process--even for written essays--has also been turned over to algorithms. Natural language processing (NLP) artificial intelligence systems--often called automated essay scoring engines--are now either the primary or secondary grader on standardized tests in at least 21 states, according to a survey conducted by Motherboard.

A few months into my postdoc, an Excel spreadsheet dealt me quite a blow. As I was preparing to perform some statistical analyses, I made a horrifying discovery: some of my sample metadata had been incorrectly merged into a single Excel spreadsheet. The metadata had to be fixed, and all of the preliminary analyses I had done had to be repeated. Sadly, even after fixing my metadata, the dataset was unsalvageable. Not enough samples had been collected and categorical metadata were missing for some samples -- there were no statistical tests I could do to identify any meaningful patterns.

Students work on computers in Henderson, Nev. Several states including Utah and Ohio use automated grading on student essays written as part of standardized tests. Students work on computers in Henderson, Nev. Several states including Utah and Ohio use automated grading on student essays written as part of standardized tests. B: They can be scored quickly. C: They score without human bias.

A recent study suggests that computers can score student essays about as well as human beings. Les Perelman, a director of writing at MIT, isn't impressed: While his research is limited, because E.T.S. is the only organization that has permitted him to test its product, he says the automated reader can be easily gamed, is vulnerable to test prep, sets a very limited and rigid standard for what good writing is, and will pressure teachers to dumb down writing instruction. The e-Rater's biggest problem, he says, is that it can't identify truth. He tells students not to waste time worrying about whether their facts are accurate, since pretty much any fact will do as long as it is incorporated into a well-structured sentence. "E-Rater doesn't care if you say the War of 1812 started in 1945," he said.

If a robot was grading this article as if it were an essay on the SAT, the perfect opening line would go a little something like this: Computerized robotic technology has been shown to be a highly efficient device to grade standardized examinations, however, when put to the test the mechanized system is easy to thwart. That's according to The New York Times, who challenged recent findings that claimed there was little difference between human and robot graders. The Times had Les Perelman, a director of writing at the Massachusetts Institute of Technology and opponent of electronic grading, kick the tires on Education Testing Service's e-Rater, which the service says it uses in conjunction with human essay readers. Among other faults, Perelman found the e-Rater is not capable of telling truth from fiction, so there is little incentive for test takers to get their facts straight. He got the highest possible score.)

We introduce a dynamical system which we call the AdaBoost flow. The flow is defined by a system of ODEs with control. We show that three algorithms of the AdaBoost family (i) the AdaBoost algorithm of Schapire and Freund (ii) the arc-gv algorithm of Breiman (iii) the confidence rated prediction of Schapire and Singer can be can be embedded in the AdaBoost flow. The nontrivial part of the AdaBoost flow equations coincides with the equations of dynamics of nonperiodic Toda system written in terms of spectral variables. We provide a novel invariant geometrical description of the AdaBoost algorithm as a gradient flow on a foliation defined by level sets of the potential function. We propose a new approach for constructing boosting algorithms as a continuous time gradient flow on measures defined by various metrics and potential functions. Finally we explain similarity of the AdaBoost algorithm with the Perelman's construction for the Ricci flow.