Innovation S-curve for artificial intelligence in the automotive industry. AI-assisted fault monitoring is a key innovation area in artificial …

With the growth and widespread application of artificial intelligence (AI), there is a distinct need for regulatory compliance to ensure efficient …

DeepMind's AlphaCode program takes a data-driven approach to coding.

Artificial-intelligence tools are becoming increasingly adept at autogenerating text, raising concerns around authorship and research integrity.

As someone who writes opinion columns but enjoys taking days off now and then, I was excited to learn about ChatGPT. This artificial intelligence program generates tailor-made text in seconds and has already gained mountains of coverage in the global news media. Give the "chatbot" nearly any prompt and it conjures coherent responses: essays, poems, computer code and blender instructions. Could it fill in for me? I decided to find out.

In a previous article I described the problems and progress of AI reading comprehension systems. In the last few years, AI writing systems have also improved significantly because of the emergence of an AI neural network called GPT-3. It's barely two years since GPT-3 was created but the number use cases. It has paved a path for numerous business start-ups including, story writing, blog writing, chatbots, news report writing and even quiz generation. The list is continuing to grow as developers become aware of its potential.

Officially, the Lensa AI app creates "magic avatars" that turn a user's selfies into lushly-stylized works of art. It's been touted by celebrities such as Chance the Rapper, Tommy Dorfman, Jennifer Love Hewitt and Britney Spears' husband, Sam Asghari. But for many women the app does more than just spit out a pretty picture: the final results are highly sexualized, padding women's breasts and turning their bodies into hourglass physiques. "Is it just me or are these AI selfie generator apps perpetuating misogyny?" "Here are a few I got just based off of photos of my face."

As the deep learning rapidly promote, the artificial texts created by generative models are commonly used in news and social media. However, such models can be abused to generate product reviews, fake news, and even fake political content. The paper proposes a solution for the Russian Artificial Text Detection in the Dialogue shared task 2022 (RuATD 2022) to distinguish which model within the list is used to generate this text. We introduce the DeBERTa pre-trained language model with multiple training strategies for this shared task. Extensive experiments conducted on the RuATD dataset validate the effectiveness of our proposed method. Moreover, our submission ranked second place in the evaluation phase for RuATD 2022 (Multi-Class).

Wireless Sensor Network (WSN) applications reshape the trend of warehouse monitoring systems allowing them to track and locate massive numbers of logistic entities in real-time. To support the tasks, classic Radio Frequency (RF)-based localization approaches (e.g. triangulation and trilateration) confront challenges due to multi-path fading and signal loss in noisy warehouse environment. In this paper, we investigate machine learning methods using a new grid-based WSN platform called Sensor Floor that can overcome the issues. Sensor Floor consists of 345 nodes installed across the floor of our logistic research hall with dual-band RF and Inertial Measurement Unit (IMU) sensors. Our goal is to localize all logistic entities, for this study we use a mobile robot. We record distributed sensing measurements of Received Signal Strength Indicator (RSSI) and IMU values as the dataset and position tracking from Vicon system as the ground truth. The asynchronous collected data is pre-processed and trained using Random Forest and Convolutional Neural Network (CNN). The CNN model with regularization outperforms the Random Forest in terms of localization accuracy with aproximate 15 cm. Moreover, the CNN architecture can be configured flexibly depending on the scenario in the warehouse. The hardware, software and the CNN architecture of the Sensor Floor are open-source under https://github.com/FLW-TUDO/sensorfloor.

Audio sound recognition and classification is used for many tasks and applications including human voice recognition, music recognition and audio tagging. In this paper we apply Mel Frequency Cepstral Coefficients (MFCC) in combination with a range of machine learning models to identify (Australian) birds from publicly available audio files of their birdsong. We present approaches used for data processing and augmentation and compare the results of various state of the art machine learning models. We achieve an overall accuracy of 91% for the top-5 birds from the 30 selected as the case study. Applying the models to more challenging and diverse audio files comprising 152 bird species, we achieve an accuracy of 58%.