First photographers were creating portraits of people that don't exist, now Aurel Manea has created a series of "landscape photos" using a new artificially intelligent (AI) software program called Stable Diffusion. Manea tells PetaPixel that he has been blown away by what the London and Los Altos-based startup Stability AI has created. "I can't, as a landscape photographer myself, emphasize enough what these new technologies will mean for photography," explains Manea. "Of course, they are not real photos and they only resemble real places (for now, as the input data becomes larger and larger) but for most of the people that consume the images, it is only about the beauty of those images." Manea says that he has used DALLE-2 and it does "great images of people's faces that look like photos." But, he says, DALL-E fails to create landscape photography.

Did Meta not learn anything from Microsoft's infamous chatbot Tay? On August 5, Meta released BlenderBot 3, an AI chatbot, to users in the US. As Meta warned, BlenderBot indeed was "likely to make untrue or offensive statements": it described Mark Zuckerberg as "too creepy and manipulative" to a reporter from Insider and claimed Trump was still president and "always will be" to a Wall Street Journal reporter. Users can flag BlenderBot's inappropriate and offensive responses, and Meta claims it has reduced offensive responses by 90 percent. Our Take: Color me amused and not surprised.

What would happen if we asked Artificial Intelligence to show us its face?

In my opinion robots get this done via sensors and actuators that are determined and set by a human.

Artificial intelligence is growing rapidly in China, but there are still wide gaps in industrial application.

Machine learning could improve medicine by analyzing data to improve diagnoses and target cures, but technological, bureaucratic, and regulatory …

Dr. Lynne Parker, Deputy Chief Technology Office and Director of the National Artificial Intelligence Initiative Office within the White House, …

Every sound that we hear is the result of successive convolutional operations (e.g. room acoustics, microphone characteristics, resonant properties of the instrument itself, not to mention characteristics and limitations of the sound reproduction system). In this work we seek to determine the best room in which to perform a particular piece using AI. Additionally, we use room acoustics as a way to enhance the perceptual qualities of a given sound. Historically, rooms (particularly Churches and concert halls) were designed to host and serve specific musical functions. In some cases the architectural acoustical qualities enhanced the music performed there. We try to mimic this, as a first step, by designating room impulse responses that would correlate to producing enhanced sound quality for particular music. A convolutional architecture is first trained to take in an audio sample and mimic the ratings of experts with about 78 % accuracy for various instrument families and notes for perceptual qualities. This gives us a scoring function for any audio sample which can rate the perceptual pleasantness of a note automatically. Now, via a library of about 60,000 synthetic impulse responses mimicking all kinds of room, materials, etc, we use a simple convolution operation, to transform the sound as if it was played in a particular room. The perceptual evaluator is used to rank the musical sounds, and yield the "best room or the concert hall" to play a sound. As a byproduct it can also use room acoustics to turn a poor quality sound into a "good" sound.

Fake news existed ever since there was news, from rumors to printed media then radio and television. Recently, the information age, with its communications and Internet breakthroughs, exacerbated the spread of fake news. Additionally, aside from e-Commerce, the current Internet economy is dependent on advertisements, views and clicks, which prompted many developers to bait the end users to click links or ads. Consequently, the wild spread of fake news through social media networks has impacted real world issues from elections to 5G adoption and the handling of the Covid- 19 pandemic. Efforts to detect and thwart fake news has been there since the advent of fake news, from fact checkers to artificial intelligence-based detectors. Solutions are still evolving as more sophisticated techniques are employed by fake news propagators. In this paper, R code have been used to study and visualize a modern fake news dataset. We use clustering, classification, correlation and various plots to analyze and present the data. The experiments show high efficiency of classifiers in telling apart real from fake news.

This is the author's version of the article that has been published in IEEE Transactions on Visualization and Computer Graphics. The final version of this record is available at: xx.xxxx/TVCG.201x.xxxxxxx/ Example of PromptIDE's interface to explore variations of different prompts. Each variation is tested against up to twenty data examples and represented as a template card (a). For each variation, rich detail can be tracked by using the detail stripes (b). If performance and qualitative detail are convincing, a user can collect the prompt in the prompt cart (c). Abstract-- State-of-the-art neural language models can now be used to solve ad-hoc language tasks through zero-shot prompting without the need for supervised training. This approach has gained popularity in recent years, and researchers have demonstrated prompts that achieve strong accuracy on specific NLP tasks. However, finding a prompt for new tasks requires experimentation. Different prompt templates with different wording choices lead to significant accuracy differences. PromptIDE allows users to experiment with prompt variations, visualize prompt performance, and iteratively optimize prompts. We developed a workflow that allows users to first focus on model feedback using small data before moving on to a large data regime that allows empirical grounding of promising prompts using quantitative measures of the task.