1. Passive acoustic monitoring (PAM) coupled with artificial intelligence (AI) is becoming an essential tool for biodiversity monitoring. Traditional PAM systems require manual data offloading and impose substantial demands on storage and computing infrastructure. The combination of on-device AI-based processing and network connectivity enables local data analysis and transmission of only relevant information, greatly reducing storage needs. However, programming these devices for robust operation is challenging, requiring expertise in embedded systems and software engineering. Despite the increase in AI-based models for bioacoustics, their full potential remains unrealized without accessible tools to deploy them on custom hardware and tailor device behaviour to specific monitoring goals. 2. To address this challenge, we develop acoupi, an open-source Python framework that simplifies the creation and deployment of smart bioacoustic devices. acoupi integrates audio recording, AI-based data processing, data management, and real-time wireless messaging into a unified and configurable framework. By modularising key elements of the bioacoustic monitoring workflow, acoupi allows users to easily customise, extend, or select specific components to fit their unique monitoring needs. 3. We demonstrate the flexibility of acoupi by integrating two bioacoustic classifiers: BirdNET, for the classification of bird species, and BatDetect2, for the classification of UK bat species. We test the reliability of acoupi over a month-long deployment of two acoupi-powered devices in a UK urban park. 4. acoupi can be deployed on low-cost hardware such as the Raspberry Pi and can be customised for various applications. acoupi standardised framework and simplified tools facilitate the adoption of AI-powered PAM systems for researchers and conservationists. acoupi is on GitHub at https://github.com/acoupi/acoupi.

In this paper, we sketch a programme for AI driven social theory. We begin by defining what we mean by artificial intelligence (AI) in this context. We then lay out our model for how AI based models can draw on the growing availability of digital data to help test the validity of different social theories based on their predictive power. In doing so, we use the work of Randall Collins and his state breakdown model to exemplify that, already today, AI based models can help synthesize knowledge from a variety of sources, reason about the world, and apply what is known across a wide range of problems in a systematic way. However, we also find that AI driven social theory remains subject to a range of practical, technical, and epistemological limitations. Most critically, existing AI systems lack three essential capabilities needed to advance social theory in ways that are cumulative, holistic, open-ended, and purposeful. These are (1) semanticization, i.e., the ability to develop and operationalize verbal concepts to represent machine-manipulable knowledge, (2) transferability, i.e., the ability to transfer what has been learned in one context to another, and (3) generativity, i.e., the ability to independently create and improve on concepts and models. We argue that if the gaps identified here are addressed by further research, there is no reason why, in the future, the most advanced programme in social theory should not be led by AI-driven cumulative advances.

Have you ever wondered how machines can understand the meaning behind a photograph? CLIP, the Contrastive Language-Image Pre-training model, is changing the game of image-language understanding. In this post, we will explore why CLIP is so stunning with its ability. We have seen AI's potential to solve many problems in our world. The famous AI models such as ChatGPT, LLaMA, or DALLE, etc., changing our lives (In a good way, I suppose) are direct evidence.

A new AI-based model showed the ability to identify eyes with normal topographies at risk for developing post-LASIK ectasia. "This method increases the number of cases correctly identified as at risk and reduces the number of eyes that had been inadequately considered at risk," the authors wrote. Six features, including percent tissue altered (PTA), residual stromal bed, corneal thickness, flap thickness, central ablation depth and age, were used to engineer through machine learning 14 additional features. The different interactions between these 20 variables were tested, sampling thousands of models with diverse predictive performance. Following fivefold cross-validation, the best performing model was selected.

We begin by defining what we mean by artificial intelligence (AI) in this context. We then lay out our specification for how AI-based models can draw on the growing availability of digital data to help test the validity of different social theories based on their predictive power. In doing so, we use the work of Randall Collins and his state breakdown model to exemplify that, already today, AI-based models can help synthesise knowledge from a variety of sources, reason about the world, and apply what is known across a wide range of problems in a systematic way. However, we also find that AI-driven social theory remains subject to a range of practical, technical, and epistemological limitations. Most critically, existing AI-systems lack three essential capabilities needed to advance social theory in ways that are cumulative, holistic, open-ended, and purposeful.

An artificial intelligence (AI) model programmed using sequential health data extracted from electronic health records detected a subset of persons with a 25-fold danger of developing pancreatic cancer between 3 and 36 months, according to findings showcased at the 2022 Annual Meeting of the American Association for Cancer Research (AACR) that was held from April 8th to 13th. At the moment, there are no reliable biomarkers or screening tools that can detect pancreatic cancer early. The purpose of this study was to develop an artificial intelligence tool that can help clinicians identify people at high risk for pancreatic cancer so they can be enrolled in prevention or surveillance programs and hopefully benefit from early treatment. Bo Yuan presented the study. Pancreatic cancer is an aggressive form of cancer that is frequently diagnosed at later stages because of the absence of early symptoms and thus has a comparatively poor prognosis, said Davide Placido, a Ph.D. candidate at the University of Copenhagen and the study's co-first author.

Artificial intelligence (AI) is not new to us. It has made its integrations into human life - in our phones, smart televisions, cars, healthcare, security, and almost everything. However, it is still early to say that artificial intelligence has taken over human life. We still have a long way to go for AI-based models to analyze and process things better than a human does. To make this possible, the majority of AI companies need data annotation services to speed up the deployment of these systems.

Before moving forward, with the advancements of AI, it has become imperative to acknowledge the biases ingrained in the AI models. The death of George Floyd, Breonna Taylor, and Ahmaud Arbery in the USA has raised many brows towards existing biases in society. The unfortunate events have not only made authorities and government to reckon about their systemic strategy towards this loophole of the society but has also made many organizations to take cognizance that AI might have similar biases. With the recent announcement of Amazon's halt of providing Facial Recognition kits to the US police department for a year, it is apparent that despite its advantageous use, biases in AI are perilous. IBM trailed the same like Amazon, by abandoning the study about Facial recognition.

As the novel coronavirus pandemic COVID-19 continues to spread across the globe, researchers are racing against time to find possible preventive measures, tests and cures to arrest the spread. While the pandemic enters the stage of community spread in many parts of the world, countries are running short of essential medical kits to test sufficient numbers of people. Testing is the need of the hour, and to catalyse the pace of testing, scientists have now developed an artificial intelligence-based diagnostic tool. The incredible new tool can help predict if an individual is likely to have COVID-19 disease, based on the symptoms they display. The discovery was recently published in the journal Nature Medicine.

Artificial Intelligence technology can today automatically mine through news reports and online content from around the world, helping experts recognize anomalies that could lead to a potential epidemic or, worse, a pandemic. In other words, our new AI overlords might actually help us survive the next plague. These new AI capabilities are on full display with the recent coronavirus outbreak, which was identified early by a Canadian firm called BlueDot, which is one of a number of companies that use data to evaluate public health risks. The company, which says it conducts "automated infectious disease surveillance," notified its customers about the new form of coronavirus at the end of December, days before both the US Centers for Disease Control and Prevention (CDC) and the World Health Organization (WHO) sent out official notices, as reported by Wired. Now nearing the end of January, the respiratory virus that's been linked to the city of Wuhan in China has already claimed the lives of more than 100 people.