Mars rovers Opportunity and Spirit departed Earth in 2003. Upon successfully touching down on the red planet, they were only expected to last about 90 days. The scientists and engineers at NASA were flabbergasted that the pair survived for many years. In his latest documentary, "Good Night Oppy," director Ryan White examines the doting relationship between the control room crew members – people from across the globe – and their robotic progeny. It's a story of gumption: When a machine gets mired in quicksand 140 million miles away, how do you rescue it?

Over the years, Human-like robots have grown in popularity worldwide; we have had humanoids from Asia, North America, and finally, Africa. Equipped to meet the world, Omeife is a 6-foot-tall female "Igbo" humanoid robot designed to provide assistance, indulge in intellectual-social engagement, and serve numerous functions. She was created by STEMFocus, a subsidiary of Uniccon Group of Companies. Uniccon Group is one of Nigeria's fastest-growing Technology startups located In Mabushi Abuja. They offer eclectic, innovative Tech solutions to government agencies and businesses across the continent.

It is not just organizations based in the EU that need to pay attention. The regulation will apply to any provider that implements or develops AI systems in the EU or whose AI systems produce outputs that are used in the EU's jurisdiction, so it will affect many organizations based elsewhere. Moreover, the regulation, which is expected to come into force in 2023, is likely to bear similarities to rules currently being drawn up by other government authorities throughout the world.2 Given the impending heightened focus on new regulations, as well as the potential financial and reputational damage resulting from noncompliance, organizations urgently need to adopt measures that enable them to comply with the requirements of the emerging EU regulation. A comprehensive RAI program, based on BCG's Responsible AI Leader Blueprint, will allow them to act in accordance with and adapt to the proposed EU AI Act and other regulations that will inevitably follow (such as the Algorithmic Accountability Act of 2022 in the US).3 Notes: 3 US Congress, 2022, "Algorithmic Accountability Act of 2022."

The pattern of pedestrian crashes varies greatly depending on lighting circumstances, emphasizing the need of examining pedestrian crashes in various lighting conditions. Using Louisiana pedestrian fatal and injury crash data (2010-2019), this study applied Association Rules Mining (ARM) to identify the hidden pattern of crash risk factors according to three different lighting conditions (daylight, dark-with-streetlight, and dark-no-streetlight). Based on the generated rules, the results show that daylight pedestrian crashes are associated with children (less than 15 years), senior pedestrians (greater than 64 years), older drivers (>64 years), and other driving behaviors such as failure to yield, inattentive/distracted, illness/fatigue/asleep. Additionally, young drivers (15-24 years) are involved in severe pedestrian crashes in daylight conditions. This study also found pedestrian alcohol/drug involvement as the most frequent item in the dark-with-streetlight condition. This crash type is particularly associated with pedestrian action (crossing intersection/midblock), driver age (55-64 years), speed limit (30-35 mph), and specific area type (business with mixed residential area). Fatal pedestrian crashes are found to be associated with roadways with high-speed limits (>50 mph) during the dark without streetlight condition. Some other risk factors linked with high-speed limit related crashes are pedestrians walking with/against the traffic, presence of pedestrian dark clothing, pedestrian alcohol/drug involvement. The research findings are expected to provide an improved understanding of the underlying relationships between pedestrian crash risk factors and specific lighting conditions. Highway safety experts can utilize these findings to conduct a decision-making process for selecting effective countermeasures to reduce pedestrian crashes strategically.

Consider a finite sample from an unknown distribution over a countable alphabet. Unobserved events are alphabet symbols which do not appear in the sample. Estimating the probabilities of unobserved events is a basic problem in statistics and related fields, which was extensively studied in the context of point estimation. In this work we introduce a novel interval estimation scheme for unobserved events. Our proposed framework applies selective inference, as we construct confidence intervals (CIs) for the desired set of parameters. Interestingly, we show that obtained CIs are dimension-free, as they do not grow with the alphabet size. Further, we show that these CIs are (almost) tight, in the sense that they cannot be further improved without violating the prescribed coverage rate. We demonstrate the performance of our proposed scheme in synthetic and real-world experiments, showing a significant improvement over the alternatives. Finally, we apply our proposed scheme to large alphabet modeling. We introduce a novel simultaneous CI scheme for large alphabet distributions which outperforms currently known methods while maintaining the prescribed coverage rate.

Timely and effective response to humanitarian crises requires quick and accurate analysis of large amounts of text data - a process that can highly benefit from expert-assisted NLP systems trained on validated and annotated data in the humanitarian response domain. To enable creation of such NLP systems, we introduce and release HumSet, a novel and rich multilingual dataset of humanitarian response documents annotated by experts in the humanitarian response community. The dataset provides documents in three languages (English, French, Spanish) and covers a variety of humanitarian crises from 2018 to 2021 across the globe. For each document, HUMSET provides selected snippets (entries) as well as assigned classes to each entry annotated using common humanitarian information analysis frameworks. HUMSET also provides novel and challenging entry extraction and multi-label entry classification tasks. In this paper, we take a first step towards approaching these tasks and conduct a set of experiments on Pre-trained Language Models (PLM) to establish strong baselines for future research in this domain. The dataset is available at https://blog.thedeep.io/humset/.

Ukraine's state electricity operator has announced blackouts in the capital, Kyiv, and seven other regions of the country in the aftermath of Russia's devastating strikes on energy infrastructure. The move comes as Russian forces continue to pound Ukrainian cities and villages with missiles and drones, inflicting damage on power plants and water supplies, in a grinding war that is nearing its nine-month mark. Ukrenergo, the sole operator of Ukraine's high-voltage transmission lines, initially said in an online statement on Saturday that scheduled blackouts will take place in the capital and the greater Kyiv region, as well as several regions around it – Chernihiv, Cherkasy, Zhytomyr, Sumy, Poltava and Kharkiv. Later in the day, however, the company released an update saying that scheduled outages for a specific number of hours are not enough and instead there will be emergency outages, which could last indefinitely. Ukraine has been grappling with power outages and disruption of water supplies since Russia started unleashing barrages of missile and drone attacks on the country's energy infrastructure last month.

The American public increasingly finds itself bitterly divided over political differences. Survey indicators, partisan media, and the public's voting patterns inform this sense of division in our politics. That said, we use applications of Machine Learning and Natural Language Processing (NLP) methods in a novel way to paint a more nuanced picture of divisions in American political opinions. It turns out that even very simple NLP methods that rely on simple word frequencies in politicians' tweets can be extremely predictive when it comes to predicting party affiliation, getting over 80% accuracy without any special tuning. These simple models are very robust: a model trained on the tweets from the House of the Representatives can be equally predictive when tested on the tweets from the US Senators.

Latest buzz is that AI ought to have a warning or safety sign to let humankind know they are dealing ... [ ] with AI. Your daily activities are undoubtedly bombarded with a thousand or more precautionary warnings of one kind or another. Most of those are handy and altogether thoughtful signs or labels that serve to keep us hopefully safe and secure. Please be aware that I snuck a few "outliers" on the list to make some noteworthy points. For example, some people believe it is nutty that baby strollers have an affixed label that warns you to not fold the stroller while the baby is still seated within the contraption. Though the sign is certainly appropriate and dutifully useful, it would seem that basic common sense would already be sufficient. What person would not of their own mindful volition realize that they first need to remove the baby? Well, others emphasize that such labels do serve an important purpose. First, someone might truly be oblivious that they need to remove the baby before folding up the stroller.

Question: I have very little exposure to data analytics. However, I am told that auto ML (automated machine learning) is the new opportunity available to those with a non-technical background. Is this true and what are the prospects for one's career? I have a mechanical engineering background. ANSWER: The need for ML experts is growing by leaps and bounds.