Beneath a recent Los Angeles Times opinion piece about the dangers of artificial intelligence, there is now an AI-generated response about how AI will make storytelling more democratic. "Some in the film world have met the arrival of generative AI tools with open arms. We and others see it as something deeply troubling on the horizon," the co-directors of the Archival Producers Alliance, Rachel Antell, Stephanie Jenkins and Jennifer Petrucelli, wrote on 1 March. Published over the Academy Awards weekend, their comment piece focused on the specific dangers of AI-generated footage within documentary film, and the possibility that unregulated use of AI could shatter viewers' "faith in the veracity of visuals". On Monday, the Los Angeles Times's just-debuted AI tool, "Insight", labeled this argument as politically "center-left" and provided four "different views on the topic" underneath.

This week, the Times alleged that OpenAI's engineers inadvertently erased data the paper's team spent more than 150 hours extracting as potential evidence. OpenAI was able to recover much of the data, but the Times' legal team says it's still missing the original file names and folder structure. According to a declaration filed to the court Wednesday by Jennifer B. Maisel, a lawyer for the newspaper, this means the information "cannot be used to determine where the news plaintiffs' copied articles" may have been incorporated into OpenAI's artificial intelligence models. "We disagree with the characterizations made and will file our response soon," OpenAI spokesperson Jason Deutrom told WIRED in a statement. The New York Times declined to comment.

Scientists trained an AI through the eyes of a baby in an effort to teach the tech how humanity develops - amid fears it could destroy us. Researchers at New York University strapped a headcam recorder to Sam when he was just six months old through his second birthday. The footage of 250,000 words and corresponding images was fed to an AI model, which learned how to recognize different objects similar to how Sam did. The AI developed its knowledge in the same way the child did - by observing the environment, listening to nearby people and connecting dots between what was seen and heard. The experiment also determined the connection between visual and linguistic representation in the development of a child.

This project is under construction and we will have all the code ready soon. Language serves as an interface for LLMs to connect numerous AI models for solving complicated AI tasks! We introduce a collaborative system that consists of an LLM as the controller and numerous expert models as collaborative executors (from HuggingFace Hub). However, it means that Jarvis is restricted to models running stably on HuggingFace Inference Endpoints. Now you can access Jarvis' services by the Web API.

Teeth found in Oxfordshire, Gloucestershire and Dorset are believed to belong to the maniraptorans, a group of dinosaurs, including Velociraptor, which include birds as their closest relatives. These dinosaurs evolved into numerous species during the Middle Jurassic, but because fossils during this time are scarce, knowledge of their origins are scarce too. Researchers from the Natural History Museum and Birkbeck College used pioneering machine learning techniques to train computer models to identify the mystery teeth, which push back the origin of some of the group's members by almost 30 million years. Simon Wills, a Ph.D. student at the Natural History Museum who led the research, says, "Previous research had suggested that the maniraptorans were around in the Middle Jurassic, but the actual fossil evidence was patchy and disputed. Along with fossils found elsewhere, this research suggests the group had already achieved a global distribution by this time."

Frameworks: Repositories are classified by framework by inspecting the contents of every GitHub repository and checking for imports in the code. We limit to repositories that are implementations of papers. The date axis is the date the repository was created (NOTE: pytorch/tf support might have been added later - which explains why some repositories originally started in 2014/2015 are marked as pytorch/tf). Code Availability: For every open access machine learning paper, we check whether a code implementation is available on GitHub. The date axis is the publication date of the paper.

This special issue publishes research studies on the advances in the field of computing and artificial intelligence and collects state-of-the-art contributions on the latest research and development and challenges in the field of Medical Informatics and Biomedical Image Processing for the analysis and exploration of the nervous system. We hope to receive innovative contributions in both theoretical and practical aspects. Strong emphasis is placed on innovative results in theory, methodology and applications of artificial intelligence. Topics may be related to computer vision and image understanding, machine learning, search techniques, medical image or data analysis, and use of relevant specialized hardware/software architectures. Papers must be submitted online to Neuroscience Informatics on the online submission website Editorial Manager by August, 31 2022 to be considered and accepted by October 4, 2022.

Interpretable machine learning has been focusing on explaining final models that optimize performance. The current state-of-the-art is the Shapley additive explanations (SHAP) that locally explains variable impact on individual predictions, and it is recently extended for a global assessment across the dataset. Recently, Dong and Rudin proposed to extend the investigation to models from the same class as the final model that are "good enough", and identified a previous overclaim of variable importance based on a single model. However, this method does not directly integrate with existing Shapley-based interpretations. We close this gap by proposing a Shapley variable importance cloud that pools information across good models to avoid biased assessments in SHAP analyses of final models, and communicate the findings via novel visualizations.

Further Reading: Regarding this discussion, reading the original paper and the authors' subsequent reply can be interesting. Fast forward to 2022, although the authors rectified most concerns, the initial consideration should not be forgotten: transparency and reproducibility are paramount.

Artificial Intelligence is gently but surely taking over the world. You are constantly trying to keep your hand on the pulse, learn new tricks, and at least somewhat anticipate the upcoming trends in machine learning. So how do you keep up with all the news and navigate through this endless stream of AI information? First, you need to know what you want and correctly formulate your request. Secondly, you need to know where to look and follow to keep up with all the AI trends.