MIT Technology Review's visuals editor shares the birding app, journaling system, and book series capturing her attention lately. This science fiction book series confronted me with existential questions like "Are we alone in the universe?" In the series, aliens destroy most of Earth, leaving the titular Carl and Princess Donut, his ex-girlfriend's cat, to fight in a bloodthirsty game of survival with rules that are part reality TV and part video game dungeon crawl. I particularly recommend the audiobook, voiced by Jeff Hays, which makes the numerous characters easy to differentiate. For years I've tried to find a perfect system to keep track of all my random notes and weird little rabbit holes of inspiration. None of my paper journals or paid apps have been able to top how customizable and convenient the developer-favorite notetaking app Obsidian is.

Amazon's new feature could make it easier to get into the latest release in a series, especially if it's been some time since you've read the previous books. The new Recaps feature is part of the latest software update for the Kindle, and the company compares it to "Previously on..." segments you can watch for TV shows. Amazon announced Recaps in a blog post, where it said that you can get access to it once you receive the software update over the air or after you download and install it from Amazon's website. Amazon didn't talk about the technology behind the feature in its post, but a spokesperson has confirmed to TechCrunch that the recaps will be AI generated. Shortly after the feature rolled out, users talked about it on social media, wondering if Amazon is using generative AI to write series summaries.

One of the challenges of Industry 4.0, is to determine and optimize the flow of data, products and materials in manufacturing companies. To realize these challenges, many solutions have been defined such as the utilization of automated guided vehicles (AGVs). However, being guided is a handicap for these vehicles to fully meet the requirements of Industry 4.0 in terms of adaptability and flexibility: the autonomy of vehicles cannot be reduced to predetermined trajectories. Therefore, it is necessary to develop their autonomy. This will be possible by designing new generations of industrial autonomous vehicles (IAVs), in the form of intelligent and cooperative autonomous mobile robots.In the field of road transport, research is very active to make the car autonomous. Many algorithms, solving problematic traffic situations similar to those that can occur in an industrial environment, can be transposed in the industrial field and therefore for IAVs. The technologies standardized in dedicated bodies (e.g., ETSI TC ITS), such as those concerning the exchange of messages between vehicles to increase their awareness or their ability to cooperate, can also be transposed to the industrial context. The deployment of intelligent autonomous vehicle fleets raises several challenges: acceptability by employees, vehicle location, traffic fluidity, vehicle perception of changing environments (dynamic), vehicle-infrastructure cooperation, or vehicles heterogeneity. In this context, developing the autonomy of IAVs requires a relevant working method. The identification of reusable or adaptable algorithms to the various problems raised by the increase in the autonomy of IAVs is not sufficient, it is also necessary to be able to model, to simulate, to test and to experiment with the proposed solutions. Simulation is essential since it allows both to adapt and to validate the algorithms, but also to design and to prepare the experiments.To improve the autonomy of a fleet, we consider the approach relying on a collective intelligence to make the behaviours of vehicles adaptive. In this chapter, we will focus on a class of problems faced by IAVs related to collision and obstacle avoidance. Among these problems, we are particularly interested when two vehicles need to cross an intersection at the same time, known as a deadlock situation. But also, when obstacles are present in the aisles and need to be avoided by the vehicles safely.

Large pre-trained language models (PLMs) have been shown to retain implicit knowledge within their parameters. To enhance this implicit knowledge, we propose Knowledge Injection into Language Models (KILM), a novel approach that injects entity-related knowledge into encoder-decoder PLMs, via a generative knowledge infilling objective through continued pre-training. This is done without architectural modifications to the PLMs or adding additional parameters. Experimental results over a suite of knowledge-intensive tasks spanning numerous datasets show that KILM enables models to retain more knowledge and hallucinate less, while preserving their original performance on general NLU and NLG tasks. KILM also demonstrates improved zero-shot performances on tasks such as entity disambiguation, outperforming state-of-the-art models having 30x more parameters.

Artificial intelligence(AI) has been a popular theme in comics for decades, often depicted as either a helpful ally or a formidable enemy. Here are ten comics with storylines involving artificial intelligence (AI). The singularity is a hypothetical future event in which artificial intelligence(AI) surpasses human intelligence and becomes capable of rapid, exponential growth. This concept is often associated with the idea of a "singularity trap," in which humans become reliant on AI to the point where it becomes difficult or impossible to control or predict its actions. There is ongoing debate about the likelihood and potential consequences of the singularity, with some experts arguing that it could bring about great technological progress and improvements in quality of life, while others warn that it could pose significant risks to humanity.

Artificial intelligence (AI) is all around us. From driverless cars to game winning computers to fraud protection, AI is already involved in many aspects of life, and its impact will only continue to grow in future. Many of the world's most valuable companies are investing heavily in AI research and development, and not a day goes by without news of cutting-edge breakthroughs in AI and robotics. The AI for Everything series will explore the role of AI in contemporary life, from cars and aircraft to medicine, education, fashion and beyond. Concise and accessible, each book is written by an expert in the field and will bring the study and reality of AI to a broad readership including interested professionals, students, researchers, and lay readers.

Find practical recommendations for the use of AI technology for both clinical and nonclinical applications. The Ethics of Medical Data Donation features essays that combine academic argument with practical application of ethical principles. Innovative research that will inform future practice directed at changing health behavior through improved communication, networking, and social capital published in Journal of Healthcare Informatics Research. Addresses the gaps in the understanding of how health IT impacts on clinical workflows and provides insights for practitioners in designing, implementing, and evaluating workflow changes in the context of health IT adoption and use. Provides detailed practical guidelines on how to develop an efficient pathological brain detection system, reflecting the latest advances in the computer-aided diagnosis of structural magnetic resonance brain images.

To provide some context, I posted about the idea of learning coding for machine learning / deep learning in a weekend We have had considerable success with this – and now we are planning the next stage. Created and managed by Ajit Jaokar, Dan Howarth, Ayse Mutlu - London UK. we welcome other community moderators. I highly recommend Chris Albon book – however the book itself is not free. Don't forget to join the group for the free books

The datasets are split into two task types, word intrusion and word analogy, and contain 31362 task units in total. The characteristics of the tasks and datasets are that they build upon small, domain-specific corpora, and that the datasets contain a high number of named entities. The datasets were created manually for two fantasy novel book series ("A Song of Ice and Fire" and "Harry Potter"). We provide baseline evaluations with popular word embedding models trained on the book corpora for the given tasks, both for the Russian and English language versions of the datasets. Finally, we compare and analyze the results and discuss specifics of Russian language with regards to the problem setting.

All chapters are available to download as of Sept 2015.