At $100, Google's impressive Nest Audio was already good value for money, but a 25 percent discount has now brought it down to an even more affordable $75. Perhaps, it's a case of Google countering Amazon's early Prime Day deals on Echo speakers with a new all-time low price. Whatever the reasons behind the latest promo, it's always nice to have another option when shopping for tech. The Nest Audio is a solid bet for music fans on a budget. As we noticed in our tests, it's slightly louder than Apple's HomePod Mini and packs stronger bass, too. Inside the speaker is a 75mm woofer and 19mm tweeter, while the Google Home and Nest Mini make do with single drivers.

Global Integration of Software with Machine Learning & Artificial Intelligence Aiding Market Analysis Report 2021 and Forecast to 2029 with different …

When I mention AI (Artificial Intelligence) machines can smell now, my friends exclaim with the statement of "So What"! The best way is to explain to them the importance of smell in our lives. This article introduces considerable research to olfactory development in computer science and engineering at a high level and points out recent developments in the industry. I also touch on potential use cases and business value propositions. Let me give you a high-level background to digital scent technology as part of the technical literature review that I conducted reflecting olfactory progress in AI.

Semantic representation and inference is essential for Natural Language Processing (NLP). The state of the art for semantic representation and inference is deep learning, and particularly Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), and transformer Self-Attention models. This thesis investigates the use of deep learning for novel semantic representation and inference, and makes contributions in the following three areas: creating training data, improving semantic representations and extending inference learning. In terms of creating training data, we contribute the largest publicly available dataset of real-life factual claims for the purpose of automatic claim verification (MultiFC), and we present a novel inference model composed of multi-scale CNNs with different kernel sizes that learn from external sources to infer fact checking labels. In terms of improving semantic representations, we contribute a novel model that captures non-compositional semantic indicators. By definition, the meaning of a non-compositional phrase cannot be inferred from the individual meanings of its composing words (e.g., hot dog). Motivated by this, we operationalize the compositionality of a phrase contextually by enriching the phrase representation with external word embeddings and knowledge graphs. Finally, in terms of inference learning, we propose a series of novel deep learning architectures that improve inference by using syntactic dependencies, by ensembling role guided attention heads, incorporating gating layers, and concatenating multiple heads in novel and effective ways. This thesis consists of seven publications (five published and two under review).

State-of-the-art (SOTA) Generative Models (GMs) can synthesize photo-realistic images that are hard for humans to distinguish from genuine photos. We propose to perform reverse engineering of GMs to infer the model hyperparameters from the images generated by these models. We define a novel problem, "model parsing", as estimating GM network architectures and training loss functions by examining their generated images -- a task seemingly impossible for human beings. To tackle this problem, we propose a framework with two components: a Fingerprint Estimation Network (FEN), which estimates a GM fingerprint from a generated image by training with four constraints to encourage the fingerprint to have desired properties, and a Parsing Network (PN), which predicts network architecture and loss functions from the estimated fingerprints. To evaluate our approach, we collect a fake image dataset with $100$K images generated by $100$ GMs. Extensive experiments show encouraging results in parsing the hyperparameters of the unseen models. Finally, our fingerprint estimation can be leveraged for deepfake detection and image attribution, as we show by reporting SOTA results on both the recent Celeb-DF and image attribution benchmarks.

Physical processes, camera movement, and unpredictable environmental conditions like the presence of dust can induce noise and artifacts in video feeds. We observe that popular unsupervised MOT methods are dependent on noise-free inputs. We show that the addition of a small amount of artificial random noise causes a sharp degradation in model performance on benchmark metrics. We resolve this problem by introducing a robust unsupervised multi-object tracking (MOT) model: AttU-Net. The proposed single-head attention model helps limit the negative impact of noise by learning visual representations at different segment scales. AttU-Net shows better unsupervised MOT tracking performance over variational inference-based state-of-the-art baselines. We evaluate our method in the MNIST-MOT and the Atari game video benchmark. We also provide two extended video datasets: ``Kuzushiji-MNIST MOT'' which consists of moving Japanese characters and ``Fashion-MNIST MOT'' to validate the effectiveness of the MOT models.

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 facultymembers doing research in cybersecurity, we studied a new avenue of misinformation in the scientific community.

We know full well that AI is one of those acronyms that gets chucked around so often that it begins to lose some of its wonders. But that belies its power and significance because when it comes to the value added to video marketing by both artificial intelligence and its subset machine learning (ML), it really is worth your undivided attention. "Marketers, through AI technologies, have been both challenged and empowered," sums up Rob Freedman, VP of marketing at Fourlane. "We have greater access to data and can custom tailor our video content to fit the exact demographic of our ideal customer persona. This is an extremely powerful way to get our message in front of the right people, at the ideal time."

Society is entering the age of artificial intelligence.

I was born and raised in a calm and mostly residential district of the northern suburbs of Athens called "Vrilissia". However, this calmness is often interrupted by car crashes in the streets. And when I say often, I mean that as a kid, I remember periods of time that car accidents were happening on a daily basis. So I tried to collect data about car accidents in Vrilissia, analyze them, try to interpret them and if possible try to predict the severity of a car accident that may happen in future time. I collected data about car crashes in Vrilissia through the local news site, vrilissianews.gr.