On November 29, 2021, World Artificial Intelligence & Data Congress organized a conference to discuss'Next generation advancement with Artificial Intelligence & Data Science'. Speakers from reputed companies and organizations made their viewpoints on the current and future implications of technology. The World Data Congress event started with an introductory note. The first speaker, Ashley Casovan, Executive Director of Responsible AI Institute and Director of Data and Digital, Government of Canada, gave a curtain-raiser keynote talk on the influence of artificial intelligence. Following this, Dr. Kevin M. Coleman, Empowerment Coach, Trainer, and Speaker, CEO KMCEmpowerment, USA conducted a workshop on "A Seat at the Table: Candid Conversation on Diversity, Equity, and Inclusion… Are you ready to talk?"

If America has stood for anything, it's surely forward-looking optimism. In New York, Chicago, Detroit and other shining cities, its soaring skyscrapers pointed to the future. But has the bubble burst in the 21st century? "We don't see ourselves striding toward a better tomorrow," columnist Frank Bruni wrote in the New York Times last month, citing research that found 71% of Americans believe that this country is on the wrong track. "We see ourselves tiptoeing around catastrophe. That was true even before Covid. That was true even before Trump."

Often described as austere and even robotic, Social Democrat Olaf Scholz nonetheless managed to inspire German voters in this year's election with a campaign that played on his reputation as a safe pair of hands. Scholz, 63, is on the brink of becoming the next German chancellor, replacing Angela Merkel who is leaving the political stage after 16 years. The Social Democrats (SPD) had begun the election campaign at rock bottom in the polls, with many completely writing off Scholz's chances of heading the next government -- so much so that he doesn't even have an official biography. But Scholz managed to stage a stunning upset, beating Merkel's conservatives by positioning himself as the best candidate to continue her legacy, even adopting her famous "rhombus" hand gesture on a magazine cover. Olaf Scholz staged an upset poll win by positioning himself as the best candidate to continue Angela Merkel's legacy as German chancellor Photo: AFP / Odd ANDERSEN Unlike his rivals, he also managed not to make embarrassing mistakes during a campaign that drew on his reputation as a quiet workhorse, using the slogan "Scholz will sort it".

Clearview AI, the company behind a controversial facial recognition system that scrapes social media sites to add pictures of people to its database, is on the cusp of receiving a patent for its technology. The company confirmed Saturday that the US Trademark and Patent Office had sent it a notice of allowance, which means Clearview's application is set to be granted once the company pays administrative fees. News of the notice was reported earlier Saturday by Politico, which said critics worry that the granting of the patent could speed the development of similar technologies before lawmakers have had time to come to grips with them. Clearview AI's system, which is used by law enforcement agencies including the FBI and the Department of Homeland Security, has been criticized for feeding its database of billions of images by trawling social media sites and harvesting pictures of people without their consent. The company says the pictures it gathers are publicly available and thus should be fair game.

Clearview AI is on track to receive a US patent for its facial recognition technology, according to a report from Politico. The company was reportedly sent a "notice of allowance" by the US Patent and Trademark Office, which means that once it pays the required administration fees, its patent will be officially approved. Clearview AI builds its facial recognition database using images of people that it scrapes across social media (and the internet in general), a practice that has the company steeped in controversy. The company's patent application details its use of a "web crawler" to acquire images, even noting that "online photos associated with a person's account may help to create additional records of facial recognition data points," which its machine learning algorithm can then use to find and identify matches. Critics argue that Clearview AI's facial recognition technology is a violation of privacy and that it may negatively impact minority communities.

"We need to know how the many subjective decisions that go into building a model lead to the observed results, and why those decisions were thought justified at the time, just to have a chance at disentangling everything when something goes wrong," the paper reads. "Algorithmic impact assessments cannot solve all algorithmic harms, but they can put the field and regulators in better positions to avoid the harms in the first place and to act on them once we know more." A revamped version of the Algorithmic Accountability Act, first introduced in 2019, is now being discussed in Congress. According to a draft version of the legislation reviewed by WIRED, the bill would require businesses that use automated decision-making systems in areas such as health care, housing, employment, or education to carry out impact assessments and regularly report results to the FTC. A spokesperson for Senator Ron Wyden (D-Ore.), a cosponsor of the bill, says it calls on the FTC to create a public repository of automated decision-making systems and aims to establish an assessment process to enable future regulation by Congress or agencies like the FTC.

A politically informed citizenry is imperative for a welldeveloped democracy. While the US government has pursued policies for open data, these efforts have been insufficient in achieving an open government because only people with technical and domain knowledge can access information in the data. In this work, we conduct user interviews to identify wants and needs among stakeholders. We further use this information to sketch out the foundational requirements for a functional political information technical system.

Effective water resource management requires information on water availability, both in terms of quality and quantity, spatially and temporally. In this paper, we study the methodology behind Transfer Learning (TL) through fine-tuning and parameter transferring for better generalization performance of streamflow prediction in data-sparse regions. We propose a standard recurrent neural network in the form of Long Short-Term Memory (LSTM) to fit on a sufficiently large source domain dataset and repurpose the learned weights to a significantly smaller, yet similar target domain datasets. We present a methodology to implement transfer learning approaches for spatiotemporal applications by separating the spatial and temporal components of the model and training the model to generalize based on categorical datasets representing spatial variability. The framework is developed on a rich benchmark dataset from the US and evaluated on a smaller dataset collected by The Nature Conservancy in Kenya. The LSTM model exhibits generalization performance through our TL technique. Results from this current experiment demonstrate the effective predictive skill of forecasting streamflow responses when knowledge transferring and static descriptors are used to improve hydrologic model generalization in data-sparse regions.

Artificial intelligence is being utilized in many domains as of late, and the legal system is no exception. However, as it stands now, the number of well-annotated datasets pertaining to legal documents from the Supreme Court of the United States (SCOTUS) is very limited for public use. Even though the Supreme Court rulings are public domain knowledge, trying to do meaningful work with them becomes a much greater task due to the need to manually gather and process that data from scratch each time. Hence, our goal is to create a high-quality dataset of SCOTUS court cases so that they may be readily used in natural language processing (NLP) research and other data-driven applications. Additionally, recent advances in NLP provide us with the tools to build predictive models that can be used to reveal patterns that influence court decisions. By using advanced NLP algorithms to analyze previous court cases, the trained models are able to predict and classify a court's judgment given the case's facts from the plaintiff and the defendant in textual format; in other words, the model is emulating a human jury by generating a final verdict.

We study the functional task of deep learning image classification models and show that image classification requires extrapolation capabilities. This suggests that new theories have to be developed for the understanding of deep learning as the current theory assumes models are solely interpolating, leaving many questions about them unanswered. We investigate the pixel space and also the feature spaces extracted from images by trained models (in their hidden layers, including the 64-dimensional feature space in the last hidden layer of pre-trained residual neural networks), and also the feature space extracted by wavelets/shearlets. In all these domains, testing samples considerably fall outside the convex hull of training sets, and image classification requires extrapolation. In contrast to the deep learning literature, in cognitive science, psychology, and neuroscience, extrapolation and learning are often studied in tandem. Moreover, many aspects of human visual cognition and behavior are reported to involve extrapolation. We propose a novel extrapolation framework for the mathematical study of deep learning models. In our framework, we use the term extrapolation in this specific way of extrapolating outside the convex hull of training set (in the pixel space or feature space) but within the specific scope defined by the training data, the same way extrapolation is defined in many studies in cognitive science. We explain that our extrapolation framework can provide novel answers to open research problems about deep learning including their over-parameterization, their training regime, out-of-distribution detection, etc. We also see that the extent of extrapolation is negligible in learning tasks where deep learning is reported to have no advantage over simple models.