Sometimes it takes something unexpected to shift people's perspectives. That's what a group of MIT and Harvard Law School researchers were aiming for when they set out to reframe fairness in AI by studying its use on the powerful rather than the powerless. They presented the results of their research in January at the ACM Conference on Fairness, Accountability and Transparency in Barcelona. In the US, over half a million people are locked up despite not yet having been convicted or sentenced--a result of pretrial detention policies. Ninety-nine percent of the jail growth since 2002 has been in the pre-trial population, much of this because of an increased reliance on bail money, according to a report by the Prison Policy Initiative.

How important is it to have a mentor? Sources suggest that mentorship in general can have a large affect on careers, improving key skills, providing a different perspective & allowing for impartial advice among other things. Another key consideration, especially when starting your career, is that there have been many in your shoes with similar experiences whose advice is the result of experience. Whilst the necessary theoretical skills can be developed during study and education, the saturated nature of the Data Science market has resulted in the need to'stand out', which can be tough unless under the mentorship or guidance of someone who'knows what they're doing'. The growth in AI focussed roles in recent years has also resulted in great competition for mentors, most of whom will also be busy with other work or simply uninterested in mentorship.

When we talk about impressive strides in AI development, there is no way we would miss the big players like the United States and China or companies like Google, Baidu, Amazon and Microsoft. Home to significant investments, cutting edge research, top talent and thriving ecosystems, pushed forward and nourished by the greatest leaders in the industry – these regions serve as the epicentre of the buzz. Yet, while focusing on the mainstream hype, we risk missing the significant leaps being taken in other parts of the world. Zooming into the Northern corner of Europe, we in fact see notable AI initiatives and strong determination to catch up in the race. The Estonia-based team behind North Star AI – a top talent community for engineers and data scientists – has taken note of its surrounding regional potential and is now leading the way in bringing Nordic AI cooperation to the next level.

Let's talk about our current tech revolution and women's involvement in it. These are troubling times, on multiple fronts. Something we need to remind ourselves on a regular basis especially for those in the tech industry and tech leadership. I was reading a few articles about this topic and would like to discuss two points. I dug a little more to find the source of that 97% and found a more complete list of all the jobs that could go away with automation in an article in The Telegraph entitled "These are the jobs most at risk of automation according to Oxford University: Is yours one of them?" Word to the wise, if yours is on the top of the list, start looking for a safer alternative by scrolling down, way down.

Why you should read it: The book was born from a challenge on LinkedIn, (where Andriy is an influencer and has Top Voice distinction for his reach on that platform). His book doesn't need too much of an introduction; it's the Amazon best seller in its category and probably the best condensed collection of knowledge on the topic. Where you can get it: Buy on Amazon. This book is distributed on the "read first, buy later" principle, which means you can freely download the book, read it, and share it with your friends and colleagues, and if you liked the book or found it useful for your work or studies then buy it. Supplement: You can find the companion wiki and the code examples on Github.

Grade prediction for future courses not yet taken by students is important as it can help them and their advisers during the process of course selection as well as for designing personalized degree plans and modifying them based on their performance. One of the successful approaches for accurately predicting a student's grades in future courses is Cumulative Knowledge-based Regression Models (CKRM). CKRM learns shallow linear models that predict a student's grades as the similarity between his/her knowledge state and the target course. However, prior courses taken by a student can have \black{different contributions when estimating a student's knowledge state and towards each target course, which} cannot be captured by linear models. Moreover, CKRM and other grade prediction methods ignore the effect of concurrently-taken courses on a student's performance in a target course. In this paper, we propose context-aware non-linear and neural attentive models that can potentially better estimate a student's knowledge state from his/her prior course information, as well as model the interactions between a target course and concurrent courses. Compared to the competing methods, our experiments on a large real-world dataset consisting of more than $1.5$M grades show the effectiveness of the proposed models in accurately predicting students' grades. Moreover, the attention weights learned by the neural attentive model can be helpful in better designing their degree plans.

The online analysis of multi-way data stored in a tensor $\mathcal{X} \in \mathbb{R} ^{I_1 \times \dots \times I_N} $ has become an essential tool for capturing the underlying structures and extracting the sensitive features which can be used to learn a predictive model. However, data distributions often evolve with time and a current predictive model may not be sufficiently representative in the future. Therefore, incrementally updating the tensor-based features and model coefficients are required in such situations. A new efficient tensor-based feature extraction, named NeSGD, is proposed for online $CANDECOMP/PARAFAC$ (CP) decomposition. According to the new features obtained from the resultant matrices of NeSGD, a new criteria is triggered for the updated process of the online predictive model. Experimental evaluation in the field of structural health monitoring using laboratory-based and real-life structural datasets show that our methods provide more accurate results compared with existing online tensor analysis and model learning. The results showed that the proposed methods significantly improved the classification error rates, were able to assimilate the changes in the positive data distribution over time, and maintained a high predictive accuracy in all case studies.

Computing the similarity between two data points plays a vital role in many machine learning algorithms. Metric learning has the aim of learning a good metric automatically from data. Most existing studies on metric learning for tree-structured data have adopted the approach of learning the tree edit distance. However, the edit distance is not amenable for big data analysis because it incurs high computation cost. In this paper, we propose a new metric learning approach for tree-structured data with pq-grams. The pq-gram distance is a distance for ordered labeled trees, and has much lower computation cost than the tree edit distance. In order to perform metric learning based on pq-grams, we propose a new differentiable parameterized distance, weighted pq-gram distance. We also propose a way to learn the proposed distance based on Large Margin Nearest Neighbors (LMNN), which is a well-studied and practical metric learning scheme. We formulate the metric learning problem as an optimization problem and use the gradient descent technique to perform metric learning. We empirically show that the proposed approach not only achieves competitive results with the state-of-the-art edit distance-based methods in various classification problems, but also solves the classification problems much more rapidly than the edit distance-based methods.

A purely inter-model version of a machine intelligence benchmark would allow us to measure intelligence directly as information without projecting that information onto labeled datasets. We propose a framework in which other learners measure the informational significance of their peers across a network and use a digital ledger to negotiate the scores. However, the main benefits of measuring intelligence with other learners are lost if the underlying scores are dishonest. As a solution, we show how competition for connectivity in the network can be used to force honest bidding. We first prove that selecting inter-model scores using gradient descent is a regret-free strategy: one which generates the best subjective outcome regardless of the behavior of others. We then empirically show that when nodes apply this strategy, the network converges to a ranking that correlates with the one found in a fully coordinated and centralized setting. The result is a fair mechanism for training an internet-wide, decentralized and incentivized machine learning system, one which produces a continually hardening and expanding benchmark at the generalized intersection of the participants.

Although deep learning models have achieved state-of-the art performance on a number of vision tasks, generalization over high dimensional multi-modal data, and reliable predictive uncertainty estimation are still active areas of research. Bayesian approaches including Bayesian Neural Nets (BNNs) do not scale well to modern computer vision tasks, as they are difficult to train, and have poor generalization under dataset-shift [27,38]. This motivates the need for effective ensembles which can generalize and give reliable uncertainty estimates. In this paper, we target the problem of generating effective ensembles of neural networks by encouraging diversity in prediction. We explicitly optimize a diversity inducing adversarial loss for learning the stochastic latent variables and thereby obtain diversity in the output predictions necessary for modeling multi-modal data. We evaluate our method on benchmark datasets: MNIST, CIFAR100, TinyImageNet and MIT Places 2, and compared to the most competitive baselines show significant improvements in classification accuracy, under a shift in the data distribution and in out-of-distribution detection.