Open-domain dialogue agents have vastly improved, but still confidently hallucinate knowledge or express doubt when asked straightforward questions. In this work, we analyze whether state-of-the-art chit-chat models can express metacognition capabilities through their responses: does a verbalized expression of doubt (or confidence) match the likelihood that the model's answer is incorrect (or correct)? We find that these models are poorly calibrated in this sense, yet we show that the representations within the models can be used to accurately predict likelihood of correctness. By incorporating these correctness predictions into the training of a controllable generation model, we obtain a dialogue agent with greatly improved linguistic calibration.

In a factory production line, different industry parts need to be quickly differentiated and sorted for further process. Parts can be of different colors and shapes. It is tedious for humans to differentiate and sort these objects in appropriate categories. Automating this process would save more time and cost. In the automation process, choosing an appropriate model to detect and classify different objects based on specific features is more challenging. In this paper, three different neural network models are compared to the object sorting system. They are namely CNN, Fast R-CNN, and Faster R-CNN. These models are tested, and their performance is analyzed. Moreover, for the object sorting system, an Arduino-controlled 5 DoF (degree of freedom) robot arm is programmed to grab and drop symmetrical objects to the targeted zone. Objects are categorized into classes based on color, defective and non-defective objects.

When compared to unimodal systems, multimodal biometric systems have several advantages, including lower error rate, higher accuracy, and larger population coverage. However, multimodal systems have an increased demand for integrity and privacy because they must store multiple biometric traits associated with each user. In this paper, we present a deep learning framework for feature-level fusion that generates a secure multimodal template from each user's face and iris biometrics. We integrate a deep hashing (binarization) technique into the fusion architecture to generate a robust binary multimodal shared latent representation. Further, we employ a hybrid secure architecture by combining cancelable biometrics with secure sketch techniques and integrate it with a deep hashing framework, which makes it computationally prohibitive to forge a combination of multiple biometrics that pass the authentication. The efficacy of the proposed approach is shown using a multimodal database of face and iris and it is observed that the matching performance is improved due to the fusion of multiple biometrics. Furthermore, the proposed approach also provides cancelability and unlinkability of the templates along with improved privacy of the biometric data. Additionally, we also test the proposed hashing function for an image retrieval application using a benchmark dataset. The main goal of this paper is to develop a method for integrating multimodal fusion, deep hashing, and biometric security, with an emphasis on structural data from modalities like face and iris. The proposed approach is in no way a general biometric security framework that can be applied to all biometric modalities, as further research is needed to extend the proposed framework to other unconstrained biometric modalities.

On Monday, the Federal Aviation Administration (FAA) shared its latest set of drone regulations. When the new rules go into effect early next year, they'll allow licensed drone operators to fly their UAVs at night, provided they complete additional training and outfit their vehicles with anti-collision lights. The new allowance is seen as a crucial step in allowing companies like Amazon and Alphabet's Wing subsidiary to operate drone delivery services -- even as some of them have seemingly scaled back their ambitions. However, if you fly drones recreationally, today's announcement includes an even more significant change. Starting in 2022, the FAA's Remote ID requirement will necessitate every drone sold in the US that weighs more than 0.55 pounds (that includes popular models like the DJI Mavic Air 2) to come with a way to broadcasts its location and identification -- as well as your location -- to local authorities.

PS4; Vanillaware/Atlus You'd think a game with 13 protagonists would be a bloated mess, but this is anything but. With a complex, superbly rewarding storyline to piece together and mind-blowing, time-bending revelations around every corner, this mix of visual novel and tower-defence game is a must-play. PC; Colestia Forget Call of Duty: Black Ops – this is the CIA conspiracy thriller you're looking for. The twist is that this isn't just a detective puzzler – the more you unearth, the scarier your surroundings become. PC, Mac, Nintendo Switch; Adamgryu This wonderful little Zelda-inspired hiking adventure came out on PC last year, but you might have missed it on Switch this year. It leads to such an evocative few hours, with gorgeous natural colours filtered through its retro art style.

Anyone who's ever been on an earnings call knows company executives already tend to look at the world through rose-colored glasses, but a new study by economics and machine learning researchers says that's getting worse, thanks to machine learning. The analysis found that companies are adapting their language in forecasts, SEC regulatory filings, and earnings calls due to the proliferation of AI used to analyze and derive signals from the words they use. In other words: Businesses are beginning to change the way they talk because they know machines are listening. Forms of natural language processing are used to parse and process text in the financial documents companies are required to submit to the SEC. Machine learning tools are then able to do things like summarize text or determine whether language used is positive, neutral, or negative.

There are at least two clear patterns that show a demand-supply mismatch in tech occupations in front line IT fields, for example, Artificial Intelligence and Machine Learning. One is by means of industry predictions that gauge growth in the AI market from $21.46 Bn to $190.61 Machine learning and AI, cloud computing, cybersecurity and data science are the most pursued fields of knowledge and skills, and as innovation experts contend in the digital space quickly being surpassed by automation, huge numbers of them are upskilling themselves. As indicated by the report from Gartner, AI-related job creation will arrive at 2,000,000 net-new openings in 2025. Notwithstanding, there aren't that numerous experts with the range of abilities to match this requirement.

Fox News Flash top entertainment and celebrity headlines are here. Check out what's clicking today in entertainment. Britain's Channel 4 last week produced a stunningly real-looking parody video of Queen Elizabeth's annual Christmas Day message that the network claims highlights the dangers of "deepfake" technology. Channel 4 has been releasing its own "alternative" Christmas message for nearly 30 years and decided to make a deepfake video this year as a warning about the technology's potential dangers. The technique of manipulating someone's face and voice in a "deepfake" video is "more easy than most people would think," the channel said in a separate video showing how it synthetically recreated the queen with the help of actress Debra Stephenson.

Humans give contrastive explanations that explain why an observed event happened rather than some other counterfactual event (the contrast case). Despite the important role that contrastivity plays in how people generate and evaluate explanations, this property is largely missing from current methods for explaining NLP models. We present Minimal Contrastive Editing (MiCE), a method for generating contrastive explanations of model predictions in the form of edits to inputs that change model outputs to the contrast case. Our experiments across three tasks -- binary sentiment classification, topic classification, and multiple-choice question answering -- show that MiCE is able to produce edits that are not only contrastive, but also minimal and fluent, consistent with human contrastive edits. We demonstrate how MiCE edits can be used for two use cases in NLP system development -- uncovering dataset artifacts and debugging incorrect model predictions -- and thereby illustrate that generating contrastive explanations is a promising research direction for model interpretability.

Although targeted at kids, this extension to Microsoft's learning paths teaching Python programming inspired by NASA scientists, is recommended for anyone who wants a novel way into coding and machine learning. Last summer Microsoft Learn and NASA partnered up to teach Python programming applied to Space exploration. Now they've added three new modules this time inspired by the Netflix's animation film "Over the Moon". The protagonist of the film is a young girl, Fei Fei, who wants to build a rocket to fly over the Moon in order to prove that the legendary Moon Goddess exists.Where the film meets science is when Fei Fei faces the same issues that NASA's engineers face when planning missions to Space. As such the learning path involves calculating the weight of Moon rocks that can be carried by an Apollo Space shuttle, predict when the Goddess is going to cause meteor showers, and finally employ machine learning to enable the Lunar rover to identify Bungee, the rabbit character of the film, while on the moon.