If you get it wrong, you can really upset people. AI should be thought of as augmented intelligence. As with anything new and innovative, there are going to be things that you fail at with AI. It's crucial to treat it as a learning opportunity. We often read how marketing is such a creative function, requiring imaginative thinking, and that this will spare the marketer from replacement by robots. But algorithms now allow computers to think intuitively.

This course is a lead-in to deep learning and neural networks - it covers a popular and fundamental technique used in machine learning, data science and statistics: logistic regression. We cover the theory from the ground up: derivation of the solution, and applications to real-world problems. We show you how one might code their own logistic regression module in Python. This course does not require any external materials. Everything needed (Python, and some Python libraries) can be obtained for free.

Artificial Intelligence no longer lives in the realm of fiction movies rather it has slowly crept its way into our education system and is transforming the teaching-learning pedagogies across the globe. Multiple breakthrough AI technologies have been developed for image identification, speech recognition, text to speech, chatbot engines and has made significant contributions to a wide range of education solutions. Definitely, we are not at a point where robots are taking classes but at a point where AI has started to customize the learning experience and make it more personalized. AI is utilized by universities the world over in increasing admissions and student retention. AI-powered student performance progress tracking for identification of knowledge gaps and appropriate follow up, still in nascent stages, has the potential to transform the teaching-learning experience, ensuring satisfaction for all stakeholders.

These days, education is more important to businesses than ever. Not only do companies need to keep employees properly trained and certified, but employers also have to be mindful of how their remote employees are educating their children: Parents who are dissatisfied with how their kids are learning or who are even resorting to homeschooling will probably demonstrate the impact of those burdens in terms of productivity. One option that could make both of those scenarios easier is using artificial intelligence (AI) for teaching--and it's not as far-fetched as you might think. A recent study by Tidio, an AI chatbot developer for apps such as help desks, shows that 53% of its US respondents said they'd be fine with an AI teaching their kids. The study collected answers from 1,027 respondents using Amazon's Mechanical Turk and Reddit.

This article was published in the Spring 2021 issue of Litterae Populi. The full issue can be found here. The Center for Human Nature, Artificial Intelligence, and Neuroscience (CHAIN) was established as Japan's only research center specializing in hybrid research and education in the humanities, social sciences, neuroscience and AI. With the mission to create new knowledge on the nature of human existence, the Center conducts research and education taking advantage of the strengths and characteristics of Hokkaido University as a leading research university. In July 2019, the Center for Human Nature, Artificial Intelligence, and Neuroscience (CHAIN) was established as a university facility and it launched its activities. CHAIN's mission is to provide interdisciplinary research and education that integrate arts and sciences at the intersection of the humanities, social sciences, neuroscience and artificial intelligence (AI), i.e., to be a place where new knowledge is generated.

We describe a Question Answering (QA) dataset that contains complex questions with conditional answers, i.e. the answers are only applicable when certain conditions apply. We call this dataset ConditionalQA. In addition to conditional answers, the dataset also features: (1) long context documents with information that is related in logically complex ways; (2) multi-hop questions that require compositional logical reasoning; (3) a combination of extractive questions, yes/no questions, questions with multiple answers, and not-answerable questions; (4) questions asked without knowing the answers. We show that ConditionalQA is challenging for many of the existing QA models, especially in selecting answer conditions. We believe that this dataset will motivate further research in answering complex questions over long documents. Data and leaderboard are publicly available at \url{https://github.com/haitian-sun/ConditionalQA}.

We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite. It offers 3,025 hours of daily-life activity video spanning hundreds of scenarios (household, outdoor, workplace, leisure, etc.) captured by 855 unique camera wearers from 74 worldwide locations and 9 different countries. The approach to collection is designed to uphold rigorous privacy and ethics standards with consenting participants and robust de-identification procedures where relevant. Ego4D dramatically expands the volume of diverse egocentric video footage publicly available to the research community. Portions of the video are accompanied by audio, 3D meshes of the environment, eye gaze, stereo, and/or synchronized videos from multiple egocentric cameras at the same event. Furthermore, we present a host of new benchmark challenges centered around understanding the first-person visual experience in the past (querying an episodic memory), present (analyzing hand-object manipulation, audio-visual conversation, and social interactions), and future (forecasting activities). By publicly sharing this massive annotated dataset and benchmark suite, we aim to push the frontier of first-person perception. Project page: https://ego4d-data.org/

Supersized pre-trained language models have pushed the accuracy of various NLP tasks to a new state-of-the-art (SOTA). Rather than pursuing the reachless SOTA accuracy, most works are pursuing improvement on other dimensions such as efficiency, leading to "Pareto SOTA". Different from accuracy, the metric for efficiency varies across different studies, making them hard to be fairly compared. To that end, this work presents ELUE (Efficient Language Understanding Evaluation), a standard evaluation, and a public leaderboard for efficient NLP models. ELUE is dedicated to depicting the Pareto Front for various language understanding tasks, such that it can tell whether and how much a method achieves Pareto improvement. Along with the benchmark, we also pre-train and release a strong baseline, ElasticBERT, whose elasticity is both static and dynamic. ElasticBERT is static in that it allows reducing model layers on demand. ElasticBERT is dynamic in that it selectively executes parts of model layers conditioned on the input. We demonstrate the ElasticBERT, despite its simplicity, outperforms or performs on par with SOTA compressed and early exiting models. The ELUE benchmark is publicly available at http://eluebenchmark.fastnlp.top/.

An interactive instruction following task has been proposed as a benchmark for learning to map natural language instructions and first-person vision into sequences of actions to interact with objects in a 3D simulated environment. We find that an existing end-to-end neural model for this task is not robust to variations of objects and language instructions. We assume that this problem is due to the high sensitiveness of neural feature extraction to small changes in vision and language inputs. To mitigate this problem, we propose a neuro-symbolic approach that performs reasoning over high-level symbolic representations that are robust to small changes in raw inputs. Our experiments on the ALFRED dataset show that our approach significantly outperforms the existing model by 18, 52, and 73 points in the success rate on the ToggleObject, PickupObject, and SliceObject subtasks in unseen environments respectively.

Training data for machine translation (MT) is often sourced from a multitude of large corpora that are multi-faceted in nature, e.g. containing contents from multiple domains or different levels of quality or complexity. Naturally, these facets do not occur with equal frequency, nor are they equally important for the test scenario at hand. In this work, we propose to optimize this balance jointly with MT model parameters to relieve system developers from manual schedule design. A multi-armed bandit is trained to dynamically choose between facets in a way that is most beneficial for the MT system. We evaluate it on three different multi-facet applications: balancing translationese and natural training data, or data from multiple domains or multiple language pairs. We find that bandit learning leads to competitive MT systems across tasks, and our analysis provides insights into its learned strategies and the underlying data sets.