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MuseoTorino è il primo progetto in Italia ad utilizzare le tecnologie del Web 3.0. NOSQL-GraphDB (Neo4J), RDFa, Linked Open Data. MuseoTorino è un progetto di 21Style (www.21-style.com) per il Comune di Torino - Divisione Musei
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Ask any project manager and they will tell you the importance of reviewing lessons learned prior to starting a new project. The lesson learned databases are filled with nuggets of valuable information to help project teams increase the likelihood of project success. Why then do most lesson learned databases go unused by project teams? In my experience, they are difficult to search through and require hours of time to review the result set. Recently I had a project engineer ask me if we could search our lessons learned using a list of 22 key terms the team was interested in. Our current keyword search engine would require him to enter each term individually, select the link, and save the document for review. Also, there was no way to search only the database, the query would search our entire corpus, close to 20 million URLs. This would not do. I asked our search team if they would run a special query against the lesson database only, using the terms provided. They returned a spreadsheet with a link to each document containing the terms. The engineer had his work cut out for him: over 1100 documents were on the list;. I started thinking there had to be a better way. I had been experimenting with topic modeling, in particular to assist our users in connecting seemingly disparate documents through an easier visualization mechanism. Something better than a list of links on multiple pages. I gathered my toolbox: R/RStudio, for the topic modeling and exploring the data; Neo4j, for modeling and visualizing the topics; and Linkurious, a web front end for our users to search and visualize the graph database.
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MuseoTorino è il primo progetto in Italia ad utilizzare le tecnologie del Web 3.0. NOSQL-GraphDB (Neo4J), RDFa, Linked Open Data. MuseoTorino è un progetto di 21Style (www.21-style.com) per il Comune di Torino - Divisione Musei
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Ask any project manager and they will tell you the importance of reviewing lessons learned prior to starting a new project. The lesson learned databases are filled with nuggets of valuable information to help project teams increase the likelihood of project success. Why then do most lesson learned databases go unused by project teams? In my experience, they are difficult to search through and require hours of time to review the result set. Recently I had a project engineer ask me if we could search our lessons learned using a list of 22 key terms the team was interested in. Our current keyword search engine would require him to enter each term individually, select the link, and save the document for review. Also, there was no way to search only the database, the query would search our entire corpus, close to 20 million URLs. This would not do. I asked our search team if they would run a special query against the lesson database only, using the terms provided. They returned a spreadsheet with a link to each document containing the terms. The engineer had his work cut out for him: over 1100 documents were on the list;. I started thinking there had to be a better way. I had been experimenting with topic modeling, in particular to assist our users in connecting seemingly disparate documents through an easier visualization mechanism. Something better than a list of links on multiple pages. I gathered my toolbox: R/RStudio, for the topic modeling and exploring the data; Neo4j, for modeling and visualizing the topics; and Linkurious, a web front end for our users to search and visualize the graph database.
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The presentation explores the development and application of artificial intelligence (AI) from its inception to its current status in the modern world. The term "artificial intelligence" was first coined by John McCarthy in 1956 to describe efforts to develop computer programs capable of performing tasks that typically require human intelligence. This concept was first introduced at a conference held at Dartmouth College, where programs demonstrated capabilities such as playing chess, proving theorems, and interpreting texts. In the early stages, Alan Turing contributed to the field by defining intelligence as the ability of a being to respond to certain questions intelligently, proposing what is now known as the Turing Test to evaluate the presence of intelligent behavior in machines. As the decades progressed, AI evolved significantly. The 1980s focused on machine learning, teaching computers to learn from data, leading to the development of models that could improve their performance based on their experiences. The 1990s and 2000s saw further advances in algorithms and computational power, which allowed for more sophisticated data analysis techniques, including data mining. By the 2010s, the proliferation of big data and the refinement of deep learning techniques enabled AI to become mainstream. Notable milestones included the success of Google's AlphaGo and advancements in autonomous vehicles by companies like Tesla and Waymo. A major theme of the presentation is the application of generative AI, which has been used for tasks such as natural language text generation, translation, and question answering. Generative AI uses large datasets to train models that can then produce new, coherent pieces of text or other media. The presentation also discusses the ethical implications and the need for regulation in AI, highlighting issues such as privacy, bias, and the potential for misuse. These concerns have prompted calls for comprehensive regulations to ensure the safe and equitable use of AI technologies. Artificial intelligence has also played a significant role in healthcare, particularly highlighted during the COVID-19 pandemic, where it was used in drug discovery, vaccine development, and analyzing the spread of the virus. The capabilities of AI in healthcare are vast, ranging from medical diagnostics to personalized medicine, demonstrating the technology's potential to revolutionize fields beyond just technical or consumer applications. In conclusion, AI continues to be a rapidly evolving field with significant implications for various aspects of society. The development from theoretical concepts to real-world applications illustrates both the potential benefits and the challenges that come with integrating advanced technologies into everyday life. The ongoing discussion about AI ethics and regulation underscores the importance of managing these technologies responsibly to maximize their their benefits while minimizing potential harms.
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Imagine a world where information flows as swiftly as thought itself, making decision-making as fluid as the data driving it. Every moment is critical, and the right tools can significantly boost your organization’s performance. The power of real-time data automation through FME can turn this vision into reality. Aimed at professionals eager to leverage real-time data for enhanced decision-making and efficiency, this webinar will cover the essentials of real-time data and its significance. We’ll explore: FME’s role in real-time event processing, from data intake and analysis to transformation and reporting An overview of leveraging streams vs. automations FME’s impact across various industries highlighted by real-life case studies Live demonstrations on setting up FME workflows for real-time data Practical advice on getting started, best practices, and tips for effective implementation Join us to enhance your skills in real-time data automation with FME, and take your operational capabilities to the next level.
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Strategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a Fresher
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