Audrey Azoulay, Director-General of UNESCO, presented last week the first-ever global standard on the ethics of artificial intelligence, adopted by UNESCO's 193 Member States at the international organization's General Conference. UNESCO had highlighted back in November 2019 the need for regulatory frameworks at the national but also international level to ensure that innovative AI technologies can benefit all humanity. This recommendation, the result of the work of 24 international experts appointed on March 11, 2020, sets a global normative framework and gives its member states the responsibility to translate this framework at their level. Over the past decade, AI has experienced a considerable boom. Experts agree that humanity is on the threshold of a new era and that artificial intelligence will transform our lives in ways we cannot imagine.

A year ago I found out, from one of my direct reports, that I had apparently resigned. I had just been fired from Google in one of the most disrespectful ways I could imagine. Thanks to organizing done by former and current Google employees and many others, Google did not succeed in smearing my work or reputation, although they tried. My firing made headlines because of the worker organizing that has been building up in the tech world, often due to the labor of people who are already marginalized, many of whose names we do not know. Since I was fired last December, there have been many developments in tech worker organizing and whistleblowing.

The UK's newly launched national strategy will help keep the UK competitive as AI transforms businesses and jobs The power of AI to drive growth and innovation is clear, evidenced by the McKinsey Global Survey on AI that suggests that organisations are using AI as a tool for generating value, increasingly, in the form of revenues. But the black box approach taken by most AI companies comes with serious risks to scale bias like never before, intentional or not. As part of any AI strategy, diverse teams must be part of the process from the ground up to recognise biases in the data on which models are trained and to scenario plan how minorities may be impacted. Ensuring AI is explainable with a greater degree of transparency in training data, data gaps, and algorithmic logic can further reduce bias at scale. These are the key ways to mitigate this very real risk.

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.

Sen. Joni Ernst, R-Iowa, and former under Secretary of Defense for policy Michèle Flournoy discuss possible actions to take if Russia invades Ukraine. This is a rush transcript of "Fox News Sunday" on December 5, 2021. This copy may not be in its final form and may be updated. President Biden and Russia's Vladimir Putin will hold a superpower phone JOE BIDEN, PRESIDENT OF THE UNITED STATES: I don't accept anybody's red We'll discuss the standoff with Senate Armed Services Committee member Joni Just how much of a threat is China? We'll talk about how to keep law and order in space with the vice chief of So, we need to be ready. U.S. faces around the world.

Rep. Elise Stefanik, R-NY, reacts to the former CNN anchor being fired over his role in former Gov. Andrew Cuomo's sexual harassment scandal. White House chief of staff Ronald Klain confused readers Sunday as he promoted a Washington Post op-ed that argued President Biden gets worse media treatment than his predecessor, former President Trump, whose verbal duels with the press were weekly staples during his four-year residency at 1600 Penn. "For your consideration," Klain tweeted with a link to the op-ed from Dana Millbank, titled, "The media treats Biden as badly as - or worse than - Trump. WHITE HOUSE'S RON KLAIN PANNED FOR RETWEETING POST ON'ULTIMATE WORK AROUND' FOR FEDERAL VACCINE MANDATE Millbank's "proof" was research from Forge.ai, a data analytics unit of the information company FiscalNote. The study used algorithms focused on adjectives and their placement in articles - more than 200,000 of them - to rate the coverage Biden received in the first 11 months of 2021 and the coverage Trump got in the first 11 months of 2020. The process was referred to as "sentiment analysis." "My colleagues in the media are serving as accessories to the murder of democracy," Millbank said. "Too many journalists are caught in a mindless neutrality between democracy and its saboteurs, between fact and fiction.

Motivation: Computational prediction of multiple-type drug-drug interaction (DDI) helps reduce unexpected side effects in poly-drug treatments. Although existing computational approaches achieve inspiring results, they ignore that the action of a drug is mainly caused by its chemical substructures. In addition, their interpretability is still weak. Results: In this paper, by supposing that the interactions between two given drugs are caused by their local chemical structures (sub-structures) and their DDI types are determined by the linkages between different substructure sets, we design a novel Substructure-ware Tensor Neural Network model for DDI prediction (STNN-DDI). The proposed model learns a 3-D tensor of (substructure, in-teraction type, substructure) triplets, which characterizes a substructure-substructure interaction (SSI) space. According to a list of predefined substructures with specific chemical meanings, the mapping of drugs into this SSI space enables STNN-DDI to perform the multiple-type DDI prediction in both transductive and inductive scenarios in a unified form with an explicable manner. The compar-ison with deep learning-based state-of-the-art baselines demonstrates the superiority of STNN-DDI with the significant improvement of AUC, AUPR, Accuracy, and Precision. More importantly, case studies illustrate its interpretability by both revealing a crucial sub-structure pair across drugs regarding a DDI type of interest and uncovering interaction type-specific substructure pairs in a given DDI. In summary, STNN-DDI provides an effective approach to predicting DDIs as well as explaining the interaction mechanisms among drugs.

Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on. In this paper, we present NL-Augmenter, a new participatory Python-based natural language augmentation framework which supports the creation of both transformations (modifications to the data) and filters (data splits according to specific features). We describe the framework and an initial set of 117 transformations and 23 filters for a variety of natural language tasks. We demonstrate the efficacy of NL-Augmenter by using several of its transformations to analyze the robustness of popular natural language models. The infrastructure, datacards and robustness analysis results are available publicly on the NL-Augmenter repository (\url{https://github.com/GEM-benchmark/NL-Augmenter}).

It's important to remember that these images aren't created from scratch. They're built from "training sets" of images that human researchers feed into the AI to help it learn and recognise patterns. If you're not familiar with how such AI apps work, this old article from 2015 does a pretty good job of explaining this. I suspect that while the process has become more sophisticated over the years, the basic principle of recursively feeding images back into neural nets until the AI "gets it" hasn't changed. Therefore, human biases do exist in the patterns chosen and images generated, which turns these images into AI interpretations of human biases.

This week, the Partnership on AI (PAI), a nonprofit committed to responsible AI use, released a paper addressing how technology -- particularly AI -- can accentuate various forms of biases. While most proposals to mitigate algorithmic discrimination require the collection of data on so-called sensitive attributes -- which usually include things like race, gender, sexuality, and nationality -- the coauthors of the PAI report argue that these efforts can actually cause harm to marginalized people and groups. Rather than trying to overcome historical patterns of discrimination and social inequity with more data and "clever algorithms," they say, the value assumptions and trade-offs associated with the use of demographic data must be acknowledged. "Harmful biases have been found in algorithmic decision-making systems in contexts such as health care, hiring, criminal justice, and education, prompting increasing social concern regarding the impact these systems are having on the wellbeing ...