On August 22, 2019, the United States Patent and Trademark Office (USPTO) published a request for comments on patenting artificial intelligence inventions. Written comments must be received on or before October 11, 2019. The AI inventorship issue came to a head earlier this year when the inventor of an algorithm named DABUS (device for the autonomous bootstrapping of unified sentience) filed beverage container and flashing light patent applications in DABUS' name in the United Kingdom, Europe, and the United States. Additionally, many today see patent eligibility as a significant hurdle to obtaining patent protection in AI technologies. China and the United States lead in patent filings in all AI techniques and functional applications, as well as AI application fields.

The robots are coming, but not necessarily for your job. The likelihood that robots, automation and artificial intelligence (AI) will completely wipe out large swaths of the workforce is exaggerated, a new MIT report finds. The report, from MIT's "Work of the Future" task force, examines the relationship between technology and work, drawing on research from more than 20 faculty members. There's no doubt technology will impact jobs, but researchers say there is a larger concern when it comes to the future of work: Increasing inequality. And the impact of that inequality has given workers legitimate concerns about the role of technology in the future.

Today, 2020 Democratic presidential candidate and Massachusetts Rep. Seth Moulton is expected to introduce a bill that would limit how smart device manufacturers like Amazon and Google can collect your data. The Automatic Listening and Exploitation Act, or the ALEXA Act for short, would empower the Federal Trade Commission to seek immediate penalties if a smart device is found to have recorded user conversations without the device's wake word being triggered. For Google's home devices, for instance, that would mean recording a conversation without being prompted by "Hey, Google." Moulton's bill also addresses smart doorbells and their video capabilities. "Smart speakers and doorbells are great, but consumers should have a way to fight back when tech companies collect more data than Americans have agreed to give up," Moulton said.

In recent presentations, I have been theorising about how advances in technology might change the threat landscape, and one such advance I think will lead to a much more fundamental change than any other. That technology is the field of artificial intelligence (AI). My original prediction of criminal co-opting of AI dates back to a panel I was on at IPExpo in London in 2017. At the time I was challenged by a journalist to give a clearer account of myself but my thoughts were very much in embryonic form. To tell you the truth, I had only articulated those thoughts when I was asked the question of "What keeps me up at night?" up on that stage.

SYRACUSE, NY – The GENIUS NY program, the largest unmanned systems accelerator in the world, is now opening its applications to include startups in big data (smart cities, cybersecurity) and internet of things (IoT) (smart devices, AI). GENIUS NY is CenterState CEO's in-residence business accelerator program at The Tech Garden in Central New York. The program invests $3 million in five early stage companies each year, while also providing incubator space, business programming, mentors and advisers, and resources. Now in its third year, it has already invested $9 million in 17 startups. Applications are open through Oct.1, 2019.

But when it comes to AI, he doesn't mince words. AI is humanity's "biggest existential threat," he once proclaimed to some controversy. While that statement may be overblown, the fears aren't: AI will be the next technological force that transforms the face of society--for better or worse--much as the industrial revolution once did. The potential threats of AI are many, and most people agree that ethical AI that benefits humanity as a whole is critical for this technological quantum leap. But what exactly does "ethical AI" mean?

To better understand the impacts of similarities and dissimilarities in human and AV personalities we conducted an experimental study with 443 individuals. Generally, similarities in human and AV personalities led to a higher perception of AV safety only when both were high in specific personality traits. Dissimilarities in human and AV personalities also yielded a higher perception of AV safety, but only when the AV was higher than the human in a particular personality trait.

Neural networks have been proven to be vulnerable to a variety of adversarial attacks. From a safety perspective, highly sparse adversarial attacks are particularly dangerous. On the other hand the pixelwise perturbations of sparse attacks are typically large and thus can be potentially detected. We propose a new black-box technique to craft adversarial examples aiming at minimizing $l_0$-distance to the original image. Extensive experiments show that our attack is better or competitive to the state of the art. Moreover, we can integrate additional bounds on the componentwise perturbation. Allowing pixels to change only in region of high variation and avoiding changes along axis-aligned edges makes our adversarial examples almost non-perceivable. Moreover, we adapt the Projected Gradient Descent attack to the $l_0$-norm integrating componentwise constraints. This allows us to do adversarial training to enhance the robustness of classifiers against sparse and imperceivable adversarial manipulations.

Abstract: Recent automated crop mapping via supervised le arning - based methods have demonstrated unprecedented improvement over classical techniques. However, m ost crop mapping studies are limited to same - year crop mapping in which the present year's labeled data is used to predict the same year's crop map. Cross - y ear crop mapping is more useful as it allows the prediction of the following years' crop maps using previously labeled data. We propose Vector Dynamic Time Warping ( VD TW), a novel multi - year classification approach based on warping of angular distances between phenological vectors. The results prove that the proposed VDTW method is robust to temporal and spectral v ariations compensating for different farming practices, climate and atmospheric effects, and measurement errors between years. We also describe a method for determining the most discriminative time window that allows high classification accuracies with lim ited data. We carried out test s of our approach with Lan dsat 8 time - series imagery from years 2013 to 2016 for classification of corn and cotton in the Harran Plain, and corn, cotton, and soybean in the Bismil Plain of Southeastern Turkey. In addition, we tested VDTW corn and soybean in Kansas, the US for 2017 and 2018 with the Harmonized Landsat Sentinel data . The VDTW method achieved 99.85% and 99.74% overall accuracies for the same and cross years, respectively with fewer training samples compared to oth er state - of - the - art approaches, i.e. spectral angle mapp er ( SAM), dynamic time warping ( DTW), time - weighted DTW ( TWDTW), random forest (RF), support vector machine ( SVM) and deep long short - term memory ( LSTM) methods. The proposed method could be expanded for other crop types and/or geographical areas. Keywords: Time series; phenology; multi - year classification; dynamic programming; Landsat; crop mapping; land use; corn; cotton; soybean 1. Introduction T he world population is expected to exceed nine billion in 2050 [1] . Providing adequate nutrition for the increasing human population is a significant concern. Advanced agri cultural technologies, such as precision agriculture and precision irrigation are rapidly emerging to optimize water, fertilizers, and pesticides; thereby enabling higher crop yield. Accurate crop maps are the first requirements of advanced agriculture app lications such as yield forecasting . Early - season crop yield estimates are a crucial factor for food security and monitor ing agricultural subventio ns. Crop maps are also an essential tool for statistical purposes to analyze annual changes in agricultural p roduction. However, there are a variety of field crops with similar phenologies and spectral signatures.

Many question answering (QA) tasks only provide weak supervision for how the answer should be computed. For example, TriviaQA answers are entities that can be mentioned multiple times in supporting documents, while DROP answers can be computed by deriving many different equations from numbers in the reference text. In this paper, we show it is possible to convert such tasks into discrete latent variable learning problems with a precomputed, task-specific set of possible "solutions" (e.g. different mentions or equations) that contains one correct option. We then develop a hard EM learning scheme that computes gradients relative to the most likely solution at each update. Despite its simplicity, we show that this approach significantly outperforms previous methods on six QA tasks, including absolute gains of 2--10%, and achieves the state-of-the-art on five of them. Using hard updates instead of maximizing marginal likelihood is key to these results as it encourages the model to find the one correct answer, which we show through detailed qualitative analysis.