Automated decision making algorithms are now used throughout industry and government, underpinning many processes from dynamic pricing to employment practices to criminal sentencing. Given that such algorithmically informed decisions have the potential for significant societal impact, the goal of this document is to help developers and product managers design and implement algorithmic systems in publicly accountable ways. Accountability in this context includes an obligation to report, explain, or justify algorithmic decision-making as well as mitigate any negative social impacts or potential harms. Algorithms and the data that drive them are designed and created by people -- There is always a human ultimately responsible for decisions made or informed by an algorithm. "The algorithm did it" is not an acceptable excuse if algorithmic systems make mistakes or have undesired consequences, including from machine-learning processes.

Incredible drone footage of a pod of Orcas has been captured off the coast of British Colombia, Canada, by scientists monitoring the endangered population. Heart warming scenes show the moment a baby is nuzzled by its protective mother before playfully slapping her on the head with its tail. Touchingly the footage revealed to the scientists how the loving animals made physical contact with each other far more than expected. Researchers at Hakai Institute took the videos to study the feeding behaviours of endangered resident killer whales, fearing that the Killer whales' days on the coast might be numbered. They captured the serene beasts in their undisturbed habitat, and observed the day-to-day life of an orca as they had never seen it before, including hunting habits and communication between the animals - captured with an underwater microphone.

A new study by Ducker Frontier revealed that Puerto Rico could create between 26 and 34 percent additional jobs with the successful implementation of artificial intelligence (AI) in the public and private sectors. On Nov. 5, during the second annual Microsoft AI Tour held at the Sheraton Hotel and Casino in San Juan's Convention District, Pablo González, director of Ducker Frontier Latin America, discussed with THE WEEKLY JOURNAL the entity's most recent analysis of Puerto Rico's advancement in adopting AI and other emerging technologies. AI, as defined by Microsoft Caribbean General Manager Herbert Lewy, is an amplification of human ingenuity, "a tool that allows humans to achieve more and improve the things we normally do." The continuous progress of this booming technology has prompted a myriad of concerns and dystopian scenarios regarding automation, such as computers rendering humans obsolete at a plethora of jobs and services, thus amplifying economic disparity. "People think that if an algorithm can do 30 percent of our tasks they will get fired from their jobs. The study intends to demystify this perception and shed light on some issues… We also wanted to measure something that is almost never measured, which is the creation of new industries and, therefore, jobs that do not exist today to reach a net effect of how it will affect job availability in Puerto Rico," González explained.

GRAVE NEW WORLD: The U.S. needs to tackle the challenges of adapting artificial intelligence systems for modern warfare, the same way the "titans of industry" transformed Detroit into an "arsenal of democracy" during World War II, Defense Secretary Mark Esper said at a conference hosted by the National Security Commission on Artificial Intelligence. "Mastering artificial intelligence will require similar vision, ambition and commitment," Esper said. "We need the full force of American intellect and ingenuity working in harmony across the public and private sectors." Artificial Intelligence, sometimes called "machine learning," refers to advanced computer algorithms that can use data to "learn" and therefore make choices without human input. Last week a Pentagon advisory board released proposed guidelines for the ethical deployment of AI-enabled weapons on the battlefield.

Prescient are the entrepreneurs who predicted data would become the new oil, like Ali Ghodsi, Andy Konwinski, Ion Stoica, Matei Zaharia, Patrick Wendell, Reynold Xin, and Scott Shenker. They're the cofounders of Databricks, a San Francisco-based company that provides a suite of enterprise-focused scalable data science and data engineering tools. Since 2013, the year Databricks opened for business, it's had no trouble attracting customers. But this week kicked into high gear the company's uninterrupted march toward market domination. Databricks this morning announced that it's closed a $400 million series F fundraising round led by Andreessen Horowitz with participation from Microsoft, Alkeon Capital Management, BlackRock, Coatue Management, Dragoneer Investment Group, Geodesic, Green Bay Ventures, New Enterprise Associates, T. Rowe Price, and Tiger Global Management.

This article presents a novel method for predicting suicidal ideation from Electronic Health Records (EHR) and Ecological Momentary Assessment (EMA) data using deep sequential models. Both EHR longitudinal data and EMA question forms are defined by asynchronous, variable length, randomly-sampled data sequences. In our method, we model each of them with a Recurrent Neural Network (RNN), and both sequences are aligned by concatenating the hidden state of each of them using temporal marks. Furthermore, we incorporate attention schemes to improve performance in long sequences and time-independent pre-trained schemes to cope with very short sequences. Using a database of 1023 patients, our experimental results show that the addition of EMA records boosts the system recall to predict the suicidal ideation diagnosis from 48.13% obtained exclusively from EHR-based state-of-the-art methods to 67.78%. Additionally, our method provides interpretability through the t-SNE representation of the latent space. Further, the most relevant input features are identified and interpreted medically.

Transfer learning aims at improving the performance of target learners on target domains by transferring the knowledge contained in different but related source domains. In this way, the dependence on a large number of target domain data can be reduced for constructing target learners. Due to the wide application prospects, transfer learning has become a popular and promising area in machine learning. Although there are already some valuable and impressive surveys on transfer learning, these surveys introduce approaches in a relatively isolated way and lack the recent advances in transfer learning. As the rapid expansion of the transfer learning area, it is both necessary and challenging to comprehensively review the relevant studies. This survey attempts to connect and systematize the existing transfer learning researches, as well as to summarize and interpret the mechanisms and the strategies in a comprehensive way, which may help readers have a better understanding of the current research status and ideas. Different from previous surveys, this survey paper reviews over forty representative transfer learning approaches from the perspectives of data and model. The applications of transfer learning are also briefly introduced. In order to show the performance of different transfer learning models, twenty representative transfer learning models are used for experiments. The models are performed on three different datasets, i.e., Amazon Reviews, Reuters-21578, and Office-31. And the experimental results demonstrate the importance of selecting appropriate transfer learning models for different applications in practice.

As machine learning black boxes are increasingly being deployed in domains such as healthcare and criminal justice, there is growing emphasis on building tools and techniques for explaining these black boxes in an interpretable manner. Such explanations are being leveraged by domain experts to diagnose systematic errors and underlying biases of black boxes. In this paper, we demonstrate that post hoc explanations techniques that rely on input perturbations, such as LIME and SHAP, are not reliable. Specifically, we propose a novel scaffolding technique that effectively hides the biases of any given classifier by allowing an adversarial entity to craft an arbitrary desired explanation. Our approach can be used to scaffold any biased classifier in such a way that its predictions on the input data distribution still remain biased, but the post hoc explanations of the scaffolded classifier look innocuous. Using extensive evaluation with multiple real-world datasets (including COMPAS), we demonstrate how extremely biased (racist) classifiers crafted by our framework can easily fool popular explanation techniques such as LIME and SHAP into generating innocuous explanations which do not reflect the underlying biases.

We've seen no strong evidence of misuse so far They are going against their own word, but nevertheless, it's nice to see that they are releasing everything. EDIT: The unicorn example added below from https://talktotransformer.com/, which has already been updated with the newest 1.5B parameters model. Input: In a shocking finding, scientist discovered a herd of unicorns living in a remote, previously unexplored valley, in the Andes Mountains. Even more surprising to the researchers was the fact that the unicorns spoke perfect English. Output: While there are only a few documented instances of unicorns in the wild, the researchers said the finding proves that there are still large numbers of wild unicorns that remain to be studied.

Team-based multiplayer shooter Overwatch is getting a sequel: and interestingly for fans, it'll bring story missions into the game for the first time. According to Blizzard, it will also "redefine what a sequel means". Which is quite a claim for an online shooter. Unveiled with a crowd-pleasing cinematic trailer at annual fan convention BlizzCon last week, Overwatch 2 will introduce PvE missions in an all-new story mode, as well as a new core competitive mode, Push, a six-versus-six PvP team battle, which sees teams compete to have a robot push the map's objective to their opponent. Before now, the original 2016 first person shooter focused on PvP gameplay, with spin-off comic books and animated shorts filling in backstories for the popular crew of ragtag leads.