Have you ever asked yourself where you currently are on your Machine Learning journey? And what’s there that you can still learn about? This checklist helps you answer such questions. It provides an…

Although Natural Language Processing (NLP) is at the core of many tools young people use in their everyday life, high school curricula (in Italy) do not include any computational linguistics education. This lack of exposure makes the use of such tools less responsible than it could be and makes choosing computational linguistics as a university degree unlikely. To raise awareness, curiosity, and longer-term interest in young people, we have developed an interactive workshop designed to illustrate the basic principles of NLP and computational linguistics to high school Italian students aged between 13 and 18 years. The workshop takes the form of a game in which participants play the role of machines needing to solve some of the most common problems a computer faces in understanding language: from voice recognition to Markov chains to syntactic parsing. Participants are guided through the workshop with the help of instructors, who present the activities and explain core concepts from computational linguistics. The workshop was presented at numerous outlets in Italy between 2019 and 2021, both face-to-face and online.

As larger and more comprehensive datasets become standard in contemporary machine learning, it becomes increasingly more difficult to obtain reliable, trustworthy label information with which to train sophisticated models. To address this problem, crowdsourcing has emerged as a popular, inexpensive, and efficient data mining solution for performing distributed label collection. However, crowdsourced annotations are inherently untrustworthy, as the labels are provided by anonymous volunteers who may have varying, unreliable expertise. Worse yet, some participants on commonly used platforms such as Amazon Mechanical Turk may be adversarial, and provide intentionally incorrect label information without the end user's knowledge. We discuss three conventional models of the label generation process, describing their parameterizations and the model-based approaches used to solve them. We then propose OpinionRank, a model-free, interpretable, graph-based spectral algorithm for integrating crowdsourced annotations into reliable labels for performing supervised or semi-supervised learning. Our experiments show that OpinionRank performs favorably when compared against more highly parameterized algorithms. We also show that OpinionRank is scalable to very large datasets and numbers of label sources, and requires considerably fewer computational resources than previous approaches.

Amazon's Global Learning & Development (GLD) Learning Science and Engineering team is growing quickly and is looking for a skilled, driven, applied scientist to develop solutions and innovate at the intersection of human and machine learning. The Learning Science and Engineering org is reinventing workplace learning by building the programs, products, technologies and mechanisms that make learning effective and scalable for Amazon employees. We use machine learning to augment human decision-making to accelerate and personalize learning and skill development. We have a passion for raising the bar on learning and learning design at Amazon, and simultaneously contributing to the science of human and machine learning. We partner with multiple businesses across Amazon to explore ways to help Amazon employees grow their knowledge and capabilities.

Machine learning is hot right now, and for good reason. We're going to break down what you need to know about what goes into a model and give you four machine learning models your business should have in production right now. The Lead/Opportunity Conversions Model The lifeblood of every business is new leads and opportunities. Having a machine learning model in place to predict where you're more likely to convert those leads can be an effective guide to growth. The Attrition/Customer Retention Model Once you have a customer in your ecosystem, it's in your best interest to keep that customer for the long haul. The attrition/customer retention model can tell you who has a high propensity to churn, so you can market to your existing base effectively. The Lifetime Value Model Increasing the lifetime value of your customers or clients is critical. Having a model in place that offers behavior-driven insight will help you keep your customers in your pipeline longer.

If you have teenagers at home, it's likely you often feel like you're speaking to a brick wall. Now, a study has confirmed that teenagers really do ignore you, spending less time looking at your face when you're speaking to them than another adult would. A team led by the University of Kent recorded three groups of volunteers, aged 10-19, 20-40 and 60-80 in real-world social interaction situations. The situations involved them having a face-to-face conversation and navigating an environment, with eye-tracking glasses used to monitor their interactions. The findings revealed that adolescents pay less attention to social cues in real-world interactions than adults.

The world of technology offers both women and men a wide variety of opportunities. Despite their superior qualifications, women are significantly underrepresented in Artificial Intelligence and Data Science today. Instead of rising to managerial and C-suite positions, they are more likely to quit their employment. AI is one of the fields in which women can excel, particularly if the right incentives are put in place to encourage female participation. They are a necessary force that businesses must incorporate in order to advance their AI development.

Recent works show that mean-teaching is an effective framework for unsupervised domain adaptive person re-identification. However, existing methods perform contrastive learning on selected samples between teacher and student networks, which is sensitive to noises in pseudo labels and neglects the relationship among most samples. Moreover, these methods are not effective in cooperation of different teacher networks. To handle these issues, this paper proposes a Graph Consistency based Mean-Teaching (GCMT) method with constructing the Graph Consistency Constraint (GCC) between teacher and student networks. Specifically, given unlabeled training images, we apply teacher networks to extract corresponding features and further construct a teacher graph for each teacher network to describe the similarity relationships among training images. To boost the representation learning, different teacher graphs are fused to provide the supervise signal for optimizing student networks. GCMT fuses similarity relationships predicted by different teacher networks as supervision and effectively optimizes student networks with more sample relationships involved. Experiments on three datasets, i.e., Market-1501, DukeMTMCreID, and MSMT17, show that proposed GCMT outperforms state-of-the-art methods by clear margin. Specially, GCMT even outperforms the previous method that uses a deeper backbone. Experimental results also show that GCMT can effectively boost the performance with multiple teacher and student networks. Our code is available at https://github.com/liu-xb/GCMT .

Equity of educational outcome and fairness of AI with respect to race have been topics of increasing importance in education. In this work, we address both with empirical evaluations of grade prediction in higher education, an important task to improve curriculum design, plan interventions for academic support, and offer course guidance to students. With fairness as the aim, we trial several strategies for both label and instance balancing to attempt to minimize differences in algorithm performance with respect to race. We find that an adversarial learning approach, combined with grade label balancing, achieved by far the fairest results. With equity of educational outcome as the aim, we trial strategies for boosting predictive performance on historically underserved groups and find success in sampling those groups in inverse proportion to their historic outcomes. With AI-infused technology supports increasingly prevalent on campuses, our methodologies fill a need for frameworks to consider performance trade-offs with respect to sensitive student attributes and allow institutions to instrument their AI resources in ways that are attentive to equity and fairness.

Interaction and navigation defined by natural language instructions in dynamic environments pose significant challenges for neural agents. This paper focuses on addressing two challenges: handling long sequence of subtasks, and understanding complex human instructions. We propose Episodic Transformer (E.T.), a multimodal transformer that encodes language inputs and the full episode history of visual observations and actions. To improve training, we leverage synthetic instructions as an intermediate representation that decouples understanding the visual appearance of an environment from the variations of natural language instructions. We demonstrate that encoding the history with a transformer is critical to solve compositional tasks, and that pretraining and joint training with synthetic instructions further improve the performance. Our approach sets a new state of the art on the challenging ALFRED benchmark, achieving 38.4% and 8.5% task success rates on seen and unseen test splits.