If you think of AI as something futuristic and abstract, start thinking different. We're now witnessing a turning point for artificial intelligence, as more of it comes down from the clouds and into our smartphones and automobiles. While it's fair to say that AI that lives on the "edge" -- where you and I are -- is still far less powerful than its datacenter-based counterpart, it's potentially far more meaningful to our everyday lives. One key example: This fall, Apple's Siri assistant will start processing voice on iPhones. Right now, even your request to set a timer is sent as an audio recording to the cloud, where it is processed, triggering a response that's sent back to the phone.

As infamous invaders to the North American ecosystem, the Asian giant hornet (Vespa mandarinia) is devastating not only to native bee colonies, but also to local apiculture. One of the most effective way to combat the harmful species is to locate and destroy their nests. By mobilizing the public to actively report possible sightings of the Asian giant hornet, the governmentcould timely send inspectors to confirm and possibly destroy the nests. However, such confirmation requires lab expertise, where manually checking the reports one by one is extremely consuming of human resources. Further given the limited knowledge of the public about the Asian giant hornet and the randomness of report submission, only few of the numerous reports proved positive, i.e. existing nests. How to classify or prioritize the reports efficiently and automatically, so as to determine the dispatch of personnel, is of great significance to the control of the Asian giant hornet. In this paper, we propose a method to predict the priority of sighting reports based on machine learning. We model the problem of optimal prioritization of sighting reports as a problem of classification and prediction. We extracted a variety of rich features in the report: location, time, image(s), and textual description. Based on these characteristics, we propose a classification model based on logistic regression to predict the credibility of a certain report. Furthermore, our model quantifies the impact between reports to get the priority ranking of the reports. Extensive experiments on the public dataset from the WSDA (the Washington State Department of Agriculture) have proved the effectiveness of our method.

If you use such social media websites as Facebook and Twitter, you may have come across posts flagged with warnings about misinformation. So far, most misinformation – flagged and unflagged – has been aimed at the general public. Imagine the possibility of misinformation – information that is false or misleading – in scientific and technical fields like cybersecurity, public safety and medicine. There is growing concern about misinformation spreading in these critical fields as a result of common biases and practices in publishing scientific literature, even in peer-reviewed research papers. As a graduate student and as faculty members doing research in cybersecurity, we studied a new avenue of misinformation in the scientific community.

The Air Force wants more extremely advanced unmanned drone fighter jets capable of high-speed aerial maneuvers, winning dogfights, and performing most if not all of the major missions performed by human pilots. At the same time, despite the rapid growth and promise of AI and computerized dog fighting prowess, human pilots are not likely to go anywhere anytime soon. "We are so multifaceted as human beings, yet machines will be specialists in certain areas," Air Force Chief Scientist Victoria Coleman told The Mitchell Institute for Aerospace Studies. "How do we have humans and machines operate together to get better outcomes? Experimentation is already taking place. The way to deploy more of these teams is through more and more experimentation. If we test a little, we can feel comfortable going to war," Coleman said.

Interruptions in the supply chain during the coronavirus pandemic and problems in logistics that caused constant deviations from forecasts created significant problems for production-based economies. Production and logistics problems experienced at factories in China prompted Europe to turn to Turkey. When China and the U.S. moved containers to their own countries, Turkey started transporting them to Europe with trucks. At this stage, uninterrupted production necessitated technology-oriented transformation. This week, a Ventures60 event under the title "The Age of Uninterrupted Production" addressed a series of topics – from corporate intelligence solutions in the production of unmanned aerial vehicles used in Turkey's largest refinery, Tüpraş, to corporate investors investing in production-oriented artificial intelligence (AI) and cyberattack threats.

There is an escalating need for methods to identify latent patterns in text data from many domains. We introduce a new method to identify topics in a corpus and represent documents as topic sequences. Discourse Atom Topic Modeling draws on advances in theoretical machine learning to integrate topic modeling and word embedding, capitalizing on the distinct capabilities of each. We first identify a set of vectors ("discourse atoms") that provide a sparse representation of an embedding space. Atom vectors can be interpreted as latent topics: Through a generative model, atoms map onto distributions over words; one can also infer the topic that generated a sequence of words. We illustrate our method with a prominent example of underutilized text: the U.S. National Violent Death Reporting System (NVDRS). The NVDRS summarizes violent death incidents with structured variables and unstructured narratives. We identify 225 latent topics in the narratives (e.g., preparation for death and physical aggression); many of these topics are not captured by existing structured variables. Motivated by known patterns in suicide and homicide by gender, and recent research on gender biases in semantic space, we identify the gender bias of our topics (e.g., a topic about pain medication is feminine). We then compare the gender bias of topics to their prevalence in narratives of female versus male victims. Results provide a detailed quantitative picture of reporting about lethal violence and its gendered nature. Our method offers a flexible and broadly applicable approach to model topics in text data.

We consider the problem of interpretable network representation learning for samples of network-valued data. We propose the Principal Component Analysis for Networks (PCAN) algorithm to identify statistically meaningful low-dimensional representations of a network sample via subgraph count statistics. The PCAN procedure provides an interpretable framework for which one can readily visualize, explore, and formulate predictive models for network samples. We furthermore introduce a fast sampling-based algorithm, sPCAN, which is significantly more computationally efficient than its counterpart, but still enjoys advantages of interpretability. We investigate the relationship between these two methods and analyze their large-sample properties under the common regime where the sample of networks is a collection of kernel-based random graphs. We show that under this regime, the embeddings of the sPCAN method enjoy a central limit theorem and moreover that the population level embeddings of PCAN and sPCAN are equivalent. We assess PCAN's ability to visualize, cluster, and classify observations in network samples arising in nature, including functional connectivity network samples and dynamic networks describing the political co-voting habits of the U.S. Senate. Our analyses reveal that our proposed algorithm provides informative and discriminatory features describing the networks in each sample. The PCAN and sPCAN methods build on the current literature of network representation learning and set the stage for a new line of research in interpretable learning on network-valued data. Publicly available software for the PCAN and sPCAN methods are available at https://www.github.com/jihuilee/.

One key example: This fall, Apple's Siri assistant will start processing voice on iPhones. Right now, even your request to set a timer is sent as an audio recording to the cloud, where it is processed, triggering a response that's sent back to the phone. By processing voice on the phone, says Apple, Siri will respond more quickly. This will only work on the iPhone XS and newer models, which have a compatible built-for-AI processor Apple calls a "neural engine." People might also feel more secure knowing that their voice recordings aren't being sent to unseen computers in faraway places.

Earlier this year in April, the European Commission led the way in this area suggesting a legal framework for the regulation of facial recognition and certain types of AI systems. The draft legislation (also explained in a press release here) looks to create "trustworthy AI" which protects the fundamental rights of citizens while strengthening AI investment and innovation across the EU. The proposal also outlines a risk-based approach to AI, with AI use cases ranked from unacceptable risk to high risk, through to minimal risk uses. Unacceptable risk AI (such as social scoring practices) would be banned, while high risk AI (e.g. As with GDPR, it is clear that the legislation (if adopted) would create much to be considered by those companies creating and marketing AI systems.

It is not surprising that Europe, despite having a strong industrial base and leading AI research and talent, is dragging behind the US and China. European countries are lagging behind in artificial intelligence due to the fragmentation of the EU's research space and digital market, difficulties in attracting human capital and external investment, lack of commercial competitiveness and geopolitical inequalities. Reading the ESPAS Ideas Paper Series, the Future of AI and Big Data, one could enjoy its deep insights, see the Supplement, as well as the honesty of the report as to the EU AI state of affairs. It specifically reads: "The EU will lag behind in AI for some more time, because it has a more complicated task than others. On the other hand, with a resilient and free economy, a balanced regulatory system, an interested public, intact societies and world class research it will be well-placed in the medium term... Some experts believe that the advances in machine learning are plateauing and that AI will only develop slowly and incrementally from now on. Others see much more change coming, even revolutionary jumps like super intelligent AIs that are able to be employed in many fields at the same time... While many policy makers see the question of AGI as science fiction, huge investments are made into researching it. For example, DeepMind – developers of the Go-champion AI AlphaGo and bought by Google for 500 million USD – spends up to 200 million USD each year to come closer to that goal.OpenAI, funded with an Endowment of 1 billion USD, has the same goal. Since this research is not required to be transparent, it is likely that states such as the US, Chinese and probably others are also already working on such programmes. The biggest project by the European Union is the Human Brain Project, an effort to construct a virtual human brain, although this is not exactly the same as building an AGI... Imagine, in 20 years, there will be a super intelligent, friendly, conscious AI which is a source of pride to the world and fulfils all our wishes. Would this be a paternalistic world? The difficult question goes to the core of the human condition: What are we to do, if we are not needed anymore? What then is the purpose of humanity?"