It is certainly an understatement to say that Rashida Hodge is an inspiration. A tenacious, 18-year tech exec, Hodge has forged an impressive career centered on exploration, expanding representation, and philanthropy. In her current role at IBM, Hodge leads product integration of artificial intelligence and other emerging technologies for key IBM clients in North America. Hodge's story will certainly motivate anyone who has the pleasure of meeting her but may be especially useful to women and people of color looking to begin a career in STEM. After our powerful discussion, it became clear that the natural choice was to let Hodge's story be told in her own, kind and confident voice. We began our conversation by discussing Hodge's childhood and early career, during which she explained how family support propelled her towards a love for and career in engineering.

Walk into any grocery store in America and you'll find a variety of fresh apples for consumption. It's remarkable to think how developed markets have managed to secure the availability of apples year-round. That miracle is made possible through lots of behind-the-scenes work that takes place at packing houses to ensure only the best fruit makes its way to grocery store produce sections. Take Washington State for example, where 58% of the apples grown in the United States are produced at a value of $2.5 billion yearly. Somewhere around 100 packing warehouses across the state work almost year-round to provide apples for domestic consumption with 30% of the product getting exported across the globe.

Hermes Logistics Technologies (HLT) is working with researchers at the IT University of Copenhagen (ITU), Denmark, and dnata Australia to explore new machine learning models aimed at delivering predictive business analytics. The Artificial Intelligence (AI) algorithms will run data from dnata Australia's new Hermes Digital Ecosystem, which has a full Datalake infrastructure that captures and stores all of dnata's Hermes New Generation (NG) Business Intelligence events.The machine learning models will enable dnata to make predictive business process decisions providing key insights on efficiencies, costs, and new services. "Machine learning is part of HLT's digital agenda and our datalakes are a fantastic source of events and data, which are always up to date and ready to inform and train AI models in the Hermes Cloud," said Alex Labonne, chief technology officer at HLT. He added, "Successfully trained models will form new predictive functionalities for dnata and help them refine an already competitive cargo handling offering." The ITU team, headed by Professor Philippe Bonnet and working with HLT, will create, test, and develop the predictive models over the coming months to explore the design of cloud-native enterprise machine learning solutions.

ANSR, a company that establishes and operates global capability centres (GCCs) for large enterprises, has announced the acquisition of FastNext in an all-stock deal. An AI-driven tech company, FastNext was incubated by CoffeeBeans, a boutique product and technology services firm, and has over 20 employees. ANSR said the acquisition will enable it to help its clients build their GCC teams using deep learning and AI-powered tools across talent acquisition and management, smart workspace management and business workflows. "Organisations aren't going digital, they are digital. Building global capability and engineering centres for technology innovation is a priority for multiple Fortune 1000 companies. We are excited to integrate FastNext's portfolio of AI-based tools and solutions into our portfolio so that data is at the core of everything that our customers do to win in the digital age," said Lalit Ahuja, founder and CEO, ANSR.

Recent work proposed the computation of so-called PIexplanations of Naive Bayes Classifiers (NBCs) [29]. PIexplanations are subset-minimal sets of feature-value pairs that are sufficient for the prediction, and have been computed with state-of-the-art exact algorithms that are worst-case exponential in time and space. In contrast, we show that the computation of one PIexplanation for an NBC can be achieved in log-linear time, and that the same result also applies to the more general class of linear classifiers. Furthermore, we show that the enumeration of PIexplanations can be obtained with polynomial delay. Experimental results demonstrate the performance gains of the new algorithms when compared with earlier work. The experimental results also investigate ways to measure the quality of heuristic explanations.

End-to-end models in NLP rarely encode external world knowledge about length of time. We introduce two effective models for duration prediction, which incorporate external knowledge by reading temporal-related news sentences (time-aware pre-training). Specifically, one model predicts the range/unit where the duration value falls in (R-pred); and the other predicts the exact duration value E-pred. Our best model -- E-pred, substantially outperforms previous work, and captures duration information more accurately than R-pred. We also demonstrate our models are capable of duration prediction in the unsupervised setting, outperforming the baselines.

This paper presents the computational details of our emotion model, EEGS, and also provides an overview of a three-stage validation methodology used for the evaluation of our model, which can also be applicable for other computational models of emotion. A major gap in existing emotion modelling literature has been the lack of computational/technical details of the implemented models, which not only makes it difficult for early-stage researchers to understand the area but also prevents benchmarking of the developed models for expert researchers. We partly addressed these issues by presenting technical details for the computation of appraisal variables in our previous work. In this paper, we present mathematical formulas for the calculation of emotion intensities based on the theoretical premises of appraisal theory. Moreover, we will discuss how we enable our emotion model to reach to a regulated emotional state for social acceptability of autonomous agents. We hope this paper will allow a better transparency of knowledge, accurate benchmarking and further evolution of the field of emotion modelling.

Deep reinforcement learning enables an agent to capture user's interest through interactions with the environment dynamically. It has attracted great interest in the recommendation research. Deep reinforcement learning uses a reward function to learn user's interest and to control the learning process. However, most reward functions are manually designed; they are either unrealistic or imprecise to reflect the high variety, dimensionality, and non-linearity properties of the recommendation problem. That makes it difficult for the agent to learn an optimal policy to generate the most satisfactory recommendations. To address the above issue, we propose a novel generative inverse reinforcement learning approach, namely InvRec, which extracts the reward function from user's behaviors automatically, for online recommendation. We conduct experiments on an online platform, VirtualTB, and compare with several state-of-the-art methods to demonstrate the feasibility and effectiveness of our proposed approach.

Artificial intelligence researchers have not been successful in giving intelligent agents the common-sense knowledge they need to reason about the world. Without this knowledge, it is impossible for intelligent agents to truly interact with the world. Traditionally, there have been two unsuccessful approaches to getting computers to reason about the world--symbolic logic and deep learning. A new project, called COMET, tries to bring these two approaches together. Although it has not yet succeeded, it offers the possibility of progress.

Data collected over a certain period of time is called Time-series data. These data points are usually collected at adjacent intervals and have some correlation with the target. There are certain datasets that contain columns with date, month or days that are important for making predictions like sales datasets, stock price prediction etc. But the problem here is how to use the time-series data and convert them into a format the machine can understand? Python made this process a lot simpler by introducing a package called Darts.