Cognitive Design for Artificial Minds (Routledge/Taylor & Francis, 2021) explains the crucial role that human cognition research plays in the design and realization of artificial intelligence systems, illustrating the steps necessary for the design of artificial models of cognition. It bridges the gap between the theoretical, experimental, and technological issues addressed in the context of AI of cognitive inspiration and computational cognitive science. The event is moderated by Antonio Chella (Prof. of Robotics at the University of Palermo) The event is free (but the registration is mandatory) and will be held on Gather Town (you will receive the link once registered). The book "Cognitive Design for Artificial Minds" (with related editorial reviews) can be found at: Antonio Lieto is a researcher in Artificial Intelligence at the Department of Computer Science of the University of Turin, Italy, and a research associate at the ICAR-CNR in Palermo, Italy. He is the current Vice-President of the Italian Association of Cognitive Science (2017–2022) and an ACM Distinguished Speaker on the topics of cognitively inspired AI and artificial models of cognition.

When Imran Khan became prime minister of Pakistan in August 2018, the country's economy faced twin deficits – foreign exchange reserves were …

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Just a few years ago, there were no legions of deep learning scientists developing intelligent products and services at major companies and startups. When the youngest among us (the authors) entered the field, machine learning did not command headlines in daily newspapers. Our parents had no idea what machine learning was, let alone why we might prefer it to a career in medicine or law. Machine learning was a forward-looking academic discipline with a narrow set of real-world applications. And those applications, e.g., speech recognition and computer vision, required so much domain knowledge that they were often regarded as separate areas entirely for which machine learning was one small component. Neural networks then, the antecedents of the deep learning models that we focus on in this book, were regarded as outmoded tools. In just the past five years, deep learning has taken the world by surprise, driving rapid progress in fields as diverse as computer vision, natural language processing, automatic speech recognition, reinforcement learning, and statistical modeling. With these advances in hand, we can now build cars that drive themselves with more autonomy than ever before (and less autonomy than some companies might have you believe), smart reply systems that automatically draft the most mundane emails, helping people dig out from oppressively large inboxes, and software agents that dominate the worldʼs best humans at board games like Go, a feat once thought to be decades away. Already, these tools exert ever-wider impacts on industry and society, changing the way movies are made, diseases are diagnosed, and playing a growing role in basic sciences--from astrophysics to biology.

The advent of social media platforms has been a catalyst for the development of digital photography that engendered a boom in vision applications. With this motivation, we introduce a large-scale dataset termed 'Photozilla', which includes over 990k images belonging to 10 different photographic styles. The dataset is then used to train 3 classification models to automatically classify the images into the relevant style which resulted in an accuracy of ~96%. With the rapid evolution of digital photography, we have seen new types of photography styles emerging at an exponential rate. On that account, we present a novel Siamese-based network that uses the trained classification models as the base architecture to adapt and classify unseen styles with only 25 training samples. We report an accuracy of over 68% for identifying 10 other distinct types of photography styles. This dataset can be found at https://trisha025.github.io/Photozilla/

Transfer learning is critical for efficient information transfer across multiple related learning problems. A simple, yet effective transfer learning approach utilizes deep neural networks trained on a large-scale task for feature extraction. Such representations are then used to learn related downstream tasks. In this paper, we investigate transfer learning capacity of audio representations obtained from neural networks trained on a large-scale sound event detection dataset. We build and evaluate these representations across a wide range of other audio tasks, via a simple linear classifier transfer mechanism. We show that such simple linear transfer is already powerful enough to achieve high performance on the downstream tasks. We also provide insights into the attributes of sound event representations that enable such efficient information transfer.

Earlier this month, Chinese artificial intelligence (A.I.) researchers at the Beijing Academy of Artificial Intelligence (BAAI) unveiled Wu Dao 2.0, the world's biggest natural language processing (NLP) model. NLP is a branch of A.I. research that aims to give computers the ability to understand text and spoken words and respond to them in much the same way human beings can. Last year, the San Francisco–based nonprofit A.I. research laboratory OpenAI wowed the world when it released its GPT-3 (Generative Pre-trained Transformer 3) language model. GPT-3 is a 175 billion–parameter deep learning model trained on text datasets with hundreds of billions of words. A parameter is a calculation in a neural network that shapes the model's data by assigning to each chunk a greater or lesser weighting, thus providing the neural network a learned perspective on the data.

Marketers see great potential value in using artificial intelligence (AI) to support the use case of recommending highly targeted content to users in real time. That use case scored the highest among 49 use cases presented to marketers in the 2021 State of Marketing AI report by Drift and the Marketing Artificial Intelligence Institute. That use case scored a 3.96, putting it on the cusp of "high value" (4.0), with 5.0 being "transformative." "Most websites you go to today for businesses, a human is writing the rules to say which content to recommend," Paul Roetzer, CEO and founder of the Marketing Artificial Intelligence Institute, told CMSWire in a CX Decoded Podcast. "What are the related articles? There is some basic tagging system for if they read this, then read that. Most of them are human-powered. They don't have a Netflix or a Spotify type algorithm that's actually learning preferences, knows the last 15 articles someone read, and how far along he got into them. Therein lies potential, however it's something marketers and customer experience professionals remain hopeful about: 54% of them told CMSWire researchers in the State of Digital Customer Experience 2021 report they see AI having significant impacts on digital customer experience over the next two to five years. And most of them see "gaining actionable customer insights" (27%) as the area where they see the most potential. Roetzer said it is hard to find really good solutions to do this out-of-the-box. Noz Urbina of Urbina Consulting agreed, calling the technology nascent. The bigger question for marketers beyond what kind of tools are out there is do we have the data to support the use case, according to Roetzer. And do we have a strong foundation of metadata, content tagging and content taxonomies, according to Urbina. "You need enough data, for one," Roetzer said. "Sometimes the problem is smaller data, not necessarily the cost.

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