Only, it was a deepfake. So was the video of Donald Trump taunting Belgium for remaining in the Paris climate agreement and Barack Obama's public service announcement as posted by Buzzfeed. These great examples of deepfakes are the 21st Century's answer to Photoshopped images and videos. Synthetic media, deepfakes, use artificial intelligence (AI) -- deep learning technology, to replace an existing person in an image or video with someone else. One reason for the widespread use of deepfake technology in popular celebrities is that these personalities have a large number of pictures available on the internet, allowing AI to train and learn from.

Earlier this week, footage was released showing a Tesla autopilot system crashing directly into the side of a stalled vehicle on a motorway in June of 2021. The fact that the car was dark and difficult to discern has prompted discussion on the limitations of relying on computer vision in autonomous driving scenarios. Footage released in December 2021 depicts the moment of impact. Though video compression in the widely-shared footage gives a slightly exaggerated impression of how quickly the immobilized truck'snuck up' on the driver in this case, a higher-quality video of the same event demonstrates that a fully-alert driver would also have struggled to respond with anything but a tardy swerve or semi-effective braking. The footage adds to the controversy around Tesla's decision to remove radar sensors for Autopilot, announced in May 2021, and its stance on favoring vision-based systems over other echo-location technologies such, as LiDAR.

For full eligibility requirements for a position as Full Professor see "Appointment Procedure at Jönköping University" As a professor, you will together with other professors and the department management lead the development of our research and education portfolios, and you will participate in research projects and educational programmes on first-, second, and third-cycle level. You will participate in the scientific community on high international level through, e.g., joint project applications and projects, arranging conferences, reviewing articles and appointments, etc. You are also expected to represent Jönköping University and the JAIL group in outreach activities to industry and the broader society; regionally, nationally, and internationally. As a professor at the Department of Computing you will be appointed to the Department Management Team, dealing with short- and long-term development issues and strategy; the DMT consists of the Head of Department, the Deputy Head of Department for Education, and the professors employed within the department. The School of Engineering is one of four schools within Jönköping University.

With 2021 drawing to a close, we take a look back at our most viewed long reads over the course of the past year. Why Biden's Executive Order is a green light for US open banking Paige McNamee, Finextra reporter, covered Biden's'Executive Order on Promoting Competition in the American Economy' in July 2021 and the impact it could have on US open banking. Adam Lieberman, head of artificial intelligence and machine learning at Finastra, discussed how harnessing data will be key to bolstering business strategies and enabling new areas of growth. Peter Wickes, general manager enterprise, EMEA at Worldpay, explored how consumers now becoming more comfortable with payment trends and how we can expect to see continued innovation and acceleration in the payments industry. Michele Foradori, investment director at BlackFin Tech wrote about new frontiers for payments, B2B SaaS momentum, European fintech IPOs, lending, wealth management and SME tools.

Questions that loom large for the wider application of artificial intelligence (AI) in Defense Department operations often center on trust. How does the operator know if the AI is wrong, that it made a mistake, that it didn't behave as intended? Answers to questions like that come from a technical discipline known as Responsible AI (RAI). It's the subject of a report issued by the Defense Innovation Unit (DIU) in mid-November called Responsible AI Guidelines in Practice, which addresses a requirement in the FY21 National Defense Authorization Act (NDAA) to ensure that the DoD has "the ability, requisite resourcing, and sufficient expertise to ensure that any artificial intelligence technology…is ethically and reasonably developed." DIU's RAI guidelines provide a framework for AI companies, DOD stakeholders and program managers that can help to ensure that AI programs are built with the principles of fairness, accountability, and transparency at each step in the development cycle of an AI system, according to Jared Dunnmon, technical director of the artificial intelligence/machine learning portfolio at DIU.

Urban planning refers to the efforts of designing land-use configurations given a region. However, to obtain effective urban plans, urban experts have to spend much time and effort analyzing sophisticated planning constraints based on domain knowledge and personal experiences. To alleviate the heavy burden of them and produce consistent urban plans, we want to ask that can AI accelerate the urban planning process, so that human planners only adjust generated configurations for specific needs? The recent advance of deep generative models provides a possible answer, which inspires us to automate urban planning from an adversarial learning perspective. However, three major challenges arise: 1) how to define a quantitative land-use configuration? 2) how to automate configuration planning? 3) how to evaluate the quality of a generated configuration? In this paper, we systematically address the three challenges. Specifically, 1) We define a land-use configuration as a longitude-latitude-channel tensor. 2) We formulate the automated urban planning problem into a task of deep generative learning. The objective is to generate a configuration tensor given the surrounding contexts of a target region. 3) We provide quantitative evaluation metrics and conduct extensive experiments to demonstrate the effectiveness of our framework.

Explainable Artificial Intelligence (XAI) has been identified as a viable method for determining the importance of features when making predictions using Machine Learning (ML) models. In this study, we created models that take an individual's health information (e.g. their drug history and comorbidities) as inputs, and predict the probability that the individual will have an Acute Coronary Syndrome (ACS) adverse outcome. Using XAI, we quantified the contribution that specific drugs had on these ACS predictions, thus creating an XAI-based technique for pharmacovigilance monitoring, using ACS as an example of the adverse outcome to detect. Individuals aged over 65 who were supplied Musculo-skeletal system (anatomical therapeutic chemical (ATC) class M) or Cardiovascular system (ATC class C) drugs between 1993 and 2009 were identified, and their drug histories, comorbidities, and other key features were extracted from linked Western Australian datasets. Multiple ML models were trained to predict if these individuals would have an ACS related adverse outcome (i.e., death or hospitalisation with a discharge diagnosis of ACS), and a variety of ML and XAI techniques were used to calculate which features -- specifically which drugs -- led to these predictions. The drug dispensing features for rofecoxib and celecoxib were found to have a greater than zero contribution to ACS related adverse outcome predictions (on average), and it was found that ACS related adverse outcomes can be predicted with 72% accuracy. Furthermore, the XAI libraries LIME and SHAP were found to successfully identify both important and unimportant features, with SHAP slightly outperforming LIME. ML models trained on linked administrative health datasets in tandem with XAI algorithms can successfully quantify feature importance, and with further development, could potentially be used as pharmacovigilance monitoring techniques.

Artificial intelligence is a rapidly evolving field of technology. Kenya as a third world country has not lagged behind in making a comprehensive step towards promoting and implementing AI technology although it will take time to fully embrace it. The Kenyan government has used this technology to improve health, agriculture and government services. As an AI engineer, I firmly believe that these emerging technologies have a significant impact on national security. As NIS mission is to gather intelligence, analyse and apply results to predict the various outcomes and actions that need to be taken, it follows new integration tools provided by the machine learning that will significantly reduce the time NIS analysts and operators may use to analyze Intelligence data. it draws conclusions in it, and advises the Government accordingly on appropriate intelligence reports.

The administration of President Joe Biden entered office on January 20, 2021, pledging a broad-strokes overhaul of how Washington interacts with the world, promising to be a distinct counterpoint to the disruptive, go-it-alone posture of former President Donald Trump, and tying stability and prosperity at home to US interests abroad in his so-called "foreign policy for the middle class". As 2021 ends, the administration has indeed sought to re-up relations with key allies and position itself as a central player in combating global crises, but has faced criticism for failing to live up to vows of a human rights-leading foreign policy and for what some have described as an over-emphasis on sweeping ideological differences at a time when global cooperation -- particularly between superpowers -- is sorely needed. "2021 was a year of transition. President Biden replaced Trump's impetuousness with pragmatism and realism. There is a greater understanding of what US policy actually is," PJ Crowley, the former US assistant secretary of state for public affairs under President Barack Obama, told Al Jazeera.

The United States Patent and Trademark Office (USPTO) is in the process of developing artificial intelligence (AI) capabilities to add to the tool …