Our paper, Prototypes of Women in American Advertisements: A Machine Aided Image Analysis is freely available in How Images Behave (Eds. Petra Aczél, András Benedek, Kristóf Nyíri).
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The Social Dilemma, a recent Netflix documentary on the negative effects of social media and personalization on our personal and social life, is a film that changes your mindset on the future of AI. It sets the stage for the discussion of essential issues, which is considered the first step to raise social and ethical awareness in the technology sector. Are you devoted to a more ethical and human-sensitive AI? Then, don’t hesitate to read on.
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Knowledge graphs are increasingly becoming important in the AI world as an enabling technology for data integration and analytics, semantic search and question answering, and other cognitive applications. However, developing and maintaining large knowledge graphs in a manual way is too expensive and time consuming. To accelerate things, methods and techniques from the areas of information extraction and natural language processing (NLP) can be very helpful.
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We are presenting at the 9th Visual Learning Conference. Below, you can see a short teaser video on the main issues of our research. More to come on November 26.
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Orsolya Putz’s recent paper, Image metaphors of Trianon, has been published in Cognitive Linguistic Studies.
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It’s commonsense that AI applications are changing our life. We are in the midst of an AI hype with high expectations towards technology. Governments using AI are transforming our societies. More and more money has been poured into AI startups. These phenomena made us interested in the business side of AI, so we collected the hottest titles on the topic.
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We close our series on Graph Theory and Network Science for NLP with an overview of the recent developments in the field. It means deep learning enters the scene – finally.
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Our work on automatic metaphor identification has just been published in the Current Trends in Corpus Linguistics by Peter Lang.
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Today, with the ever more long documents and multimedia data, finding the right information is more important and challenging than ever. The rise of deep learning has ushered in a new era of “neural search”. However, building a neural search system is non-trivial work for many engineers. The main challenges are: (1) long dev cycle due to the complex tech stack (2) poor scalability due to the glued-architecture (3) strong requirements on the domain knowledge to fine-tune the results. With Jina (https://github.com/jina-ai/jina), engineers can quickly build up a search engine powered by state-of-the-art AI in just minutes. In this talk, I will introduce the design philosophy and the key features of Jina; and showcase how Jina bootstraps a QA semantic search system and a short-video search system in just lines of code.
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