Another year has flown by and stores everywhere are yet again full of school supplies, one more useful (or eccentric) than others. The back-to-school season is a stressful season, for students, parents and teachers alike. But stress is a part of life and back-to-school stress is supposed to be worth it: educated kids will turn into smart adults who'll ensure everyone's future. We've only taken just a few steps into the 21st Century, after all. With smartphones in our hands, virtual assistants in our homes and various ed-tech tools in our classrooms, we all rely on technology to make our lives easier during this stressful period.

In the period of AI superpowers, Finland is no counterpart for the US and China. So the Scandinavian nation is taking an alternate tack. It has set out on an eager test to show the fundamentals of AI to 1% of its populace, or 55,000 individuals. When it achieves that objective, it intends to go further, expanding the offer of the populace with AI know-how. The plan is all piece of a more noteworthy exertion to build up Finland as a pioneer in applying and utilizing the innovation.

"The financial markets are ahead of others in this," said John Micklethwait, the editor in chief of Bloomberg. In addition to covering company earnings for Bloomberg, robot reporters have been prolific producers of articles on minor league baseball for The Associated Press, high school football for The Washington Post and earthquakes for The Los Angeles Times. MANCHESTER, N.H. (AP) -- Jonathan Davis hit for the cycle, as the New Hampshire Fisher Cats topped the Portland Sea Dogs 10-3 on Tuesday. Last week, The Guardian's Australia edition published its first machine-assisted article, an account of annual political donations to the country's political parties. And Forbes recently announced that it was testing a tool called Bertie to provide reporters with rough drafts and story templates.

The increasing adoption of machine learning tools has led to calls for accountability via model interpretability. But what does it mean for a machine learning model to be interpretable by humans, and how can this be assessed? We focus on two definitions of interpretability that have been introduced in the machine learning literature: simulatability (a user's ability to run a model on a given input) and "what if" local explainability (a user's ability to correctly indicate the outcome to a model under local changes to the input). Through a user study with 1000 participants, we test whether humans perform well on tasks that mimic the definitions of simulatability and "what if" local explainability on models that are typically considered locally interpretable. We find evidence consistent with the common intuition that decision trees and logistic regression models are interpretable and are more interpretable than neural networks. We propose a metric - the runtime operation count on the simulatability task - to indicate the relative interpretability of models and show that as the number of operations increases the users' accuracy on the local interpretability tasks decreases.

Despite the fact that deep neural networks are powerful models and achieve appealing results on many tasks, they are too gigantic to be deployed on edge devices like smart-phones or embedded sensor nodes. There has been efforts to compress these networks, and a popular method is knowledge distillation, where a large (a.k.a. teacher) pre-trained network is used to train a smaller (a.k.a. student) network. However, in this paper, we show that the student network performance degrades when the gap between student and teacher is large. Given a fixed student network, one cannot employ an arbitrarily large teacher, or in other words, a teacher can effectively transfer its knowledge to students up to a certain size, not smaller. To alleviate this shortcoming, we introduce multi-step knowledge distillation which employs an intermediate-sized network (a.k.a. teacher assistant) to bridge the gap between the student and the teacher. We study the effect of teacher assistant size and extend the framework to multi-step distillation. Moreover, empirical and theoretical analysis are conducted to analyze the teacher assistant knowledge distillation framework. Extensive experiments on CIFAR-10 and CIFAR-100 datasets and plain CNN and ResNet architectures substantiate the effectiveness of our proposed approach.

Significant parts of the recent learning literature on stochastic optimization algorithms focused on the theoretical and practical behaviour of stochastic first order schemes under different convexity properties. Due to its simplicity, the traditional method of choice for most supervised machine learning problems is the stochastic gradient descent (SGD) method. Many iteration improvements and accelerations have been added to the pure SGD in order to boost its convergence in various (strong) convexity setting. However, the Lipschitz gradient continuity or bounded gradients assumptions are an essential requirement for most existing stochastic first-order schemes. In this paper novel convergence results are presented for the stochastic proximal point algorithm in different settings. In particular, without any strong convexity, smoothness or bounded gradients assumptions, we show that a slightly modified quadratic growth assumption is sufficient to guarantee for the stochastic proximal point $\mathcal{O}\left(\frac{1}{k}\right)$ convergence rate, in terms of the distance to the optimal set. Furthermore, linear convergence is obtained for interpolation setting, when the optimal set of expected cost is included in the optimal sets of each functional component.

Software for wildlife research is often developed in isolation and rarely evolves beyond a single project. Compounding this problem is a lack of software development skills in the wildlife research community. Wild Incubator is an on-site, staffed software incubator in Portland, Oregon. The incubator allows code school graduates and undergraduate computer science students to hone their skills on directed, open source wildlife software problems (e.g., develop a new feature for Wildbook to help whale shark researchers). Students work in an environment where professional engineers can guide their experience and focus their efforts on real world problems.

A lost city dating back to the 1400s hidden underneath the South African landscape has been brought back to life by experts. Researchers found ruins of the settlement known as Kweneng just south of Johannesburg using Lidar, a combination of'light' and'radar' technology. The Kweneng ruins are one of several large settlements occupied by Tswana-speakers that dotted the northern parts of South Africa for generations. In the 1820s all these Tswana city states collapsed in what became known as the Difaqane civil wars. After this time, the ruins were overgrown with vegetation until, in 2018, experts used laser technology to rediscover the lost Kweneng settlement.

Docebo CEO Claudio Erba shares insights on how elearning tools can help companies close skills gaps, and why adaptability is the most important skill above all. Companies have been struggling with a shortage of digital talent for years, and the gap is growing. Globally this deficit is predicted to reach 4.3 million unfilled jobs by 2030, costing hundreds of billions of dollars in lost revenue. Meanwhile, machine learning (ML) presents companies with an unprecedented opportunity to drive business growth if it can be implemented strategically and early enough. For organizations to benefit from the advantages that ML will bring – and compete on a level playing field with competitors – they will have to skill up their workforce quickly and comprehensively, and maintain a strong training program for data science skills.

How did I get to be all of those things? I wish I could say "I did well in school" or "I always loved computers." But the reality is, in middle/high school, I was bored stiff. At 16, I dropped out of school to begin an illustrious career in office cleaning. The odds were stacked against me entering the computing industry for many reasons. Being female, thanks to a decades-long initiative by the British government to keep women out of computing.