Rosaria Silipo
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Articles by Rosaria Silipo
How ChatGPT speaks so many languages
A quick overview of datasets, emergent behavior, and tokenization Reposted from mydataguest.substack.com Have you ever wondered how ChatGPT can communicate in so many languages? Officially, ChatGPT supports 58 languages. But according to Ilias Ilm in a January 2025 article, the real number is closer to 95+ languages, including programming languages. Multilingual training data The pre-training corpus contains text from multiple languages, mainly English but also a wide variety of others.
Announcing the new "My Data Guest"
Your go-to podcast to master AI without the hype Author: Rosaria Silipo Moving from the old "My Data Guest" podcast to a new "My Data Guest" space After four seasons of interviewing data professionals and KNIME experts - you can find all past episodes on YouTube: season 4, season 3, season 2, and season 1 - I am going to broaden the horizon of the "My Data Guest" podcast. 1. An Injection of AI Inevitably, there will be a consistent injection of AI topics.
No PhD? No Problem: How Accessible AI Is Making Data Science Everyone’s Business
Not long ago, manipulating large datasets, training machine learning models, or visualizing results required advanced programming skills and specialized statistical knowledge. Today, intuitive AI tools and natural language interfaces are allowing nearly everyone – not just data scientists, engineers, and technical experts – to analyze and act on data. In fact, nearly 8 in 10 organizations now use AI in at least one business function. For organizations, this shift represents a double-edged sword.
AI in the Classroom: Added Value or Lazy Shortcut?
AI in the Classroom: Added Value or Lazy Shortcut? Yesterday, I asked a student to write a short article about the latest project. They returned surprisingly quickly with a near-perfect piece. So polished that it immediately raised my suspicion. Was it the result of a ChatGPT prompt or truly the student’s own work? Most likely the former. In all my years supervising interns, I’ve never received something so well-written and in such a short time. Not once.
Veränderungen in der Datenanalyse seit den 1990ern und ihre Bedeutung für Unternehmen
Stand: 20.11.2020 Es ist für uns eine Selbstverständlichkeit, dass wir verantwortungsvoll mit Ihren personenbezogenen Daten umgehen. Sofern wir personenbezogene Daten von Ihnen erheben, verarbeiten wir diese unter Beachtung der geltenden Datenschutzvorschriften. Detaillierte Informationen finden Sie in unserer Datenschutzerklärung. Ich bin damit einverstanden, dass die Vogel IT-Medien GmbH, Max-Josef-Metzger-Straße 21, 86157 Augsburg, einschließlich aller mit ihr im Sinne der §§ 15 ff.
The Revenge of the Arts
The Revenge of the Arts The Changing Value of Skills in the Age of AI: A Reflection AI is getting better and better. It’s increasingly capable of doing parts of our jobs, forcing us to rethink how we work — and even how we think. Back in March 2024, I wrote an article titled “Is Data Science Dead?”, reflecting on how AI was reshaping our field. At the time, the notion seemed radical. And yet, I was right: Data science is not dead, but it is certainly changing.
đ˘ Call for Editors đ˘
EDITORâS PICK | CALL FOR EDITORS | KNIME ANALYTICS PLATFORM Got a flair for words and a love for visual workflows with KNIME? Join the Editorial Board of âLow Code for Data Scienceâ The current Editorial Board â , and Want to join our Editorial Board? Check the âHow to Applyâ section below and send your application by June 30, 2025 to: LowCodeJournal@knime.com.
Revolution in der Datenwissenschaft
Kommentar von von Rosaria Silipo, KNIME Anbieter zum Thema Die Erfahrungen, die Unternehmen im vergangenen Jahr gemacht haben, zeigen: Generative Künstliche Intelligenz – GenAI – ist nicht einfach nur ein weiteres technisches Tool. Sie verändert grundlegend, wie wir künftig an Aufgaben herangehen – insbesondere an solche, die komplexe analytische Herausforderungen mit sich bringen. Besonders in der Datenwissenschaft, dem Ursprung vieler GenAI-Ansätze, sind die Auswirkungen eindeutig sichtbar.
How to Learn Data Science & AI with KNIME Cheat Sheets
GETTING STARTED | DATA LITERACY | KNIME ANALYTICS PLATFORM Improve your KNIME proficiency with cheat sheets organized by learning level, profession, and specialization Rosaria Silipo · Follow Published in Low Code for Data Science · 5 min read · -- Some people learn best from books, some people learn best from video playlists, some people learn best from structured courses. One lesson after the next, one book chapter after the next, one video after the next.
Balancing Efficiency with Performance: The Role of GenAI in Modern Data Strategies
Generative AI (GenAI) has undoubtedly taken the spotlight as this year’s defining innovation. Models like ChatGPT and LLama can generate text and code, perform exploratory data analysis, and automate documentation, which introduces countless opportunities for data science efficiencies. However, tech work is often a compromise between implementation efficiency and performance. In some cases, GenAI can unnecessarily complicate workflows – like using a cannon to kill a fly.
How AI powers faster, more accurate fraud alerts
In 2023, US consumers reported losing more than , underscoring the need for financial institutions to respond quickly and effectively to emerging threats. By promptly alerting victims, institutions can empower account holders to t********** and prevent additional losses. However, traditional methods for generating fraud alerts are cumbersome and hard to maintain, especially if alerts need to be sent in multiple languages.
What Can AI Do for Data Science?
Figure 1. KNIME AI nodes to prompt an LLM. <AI prov> indicates the selected AI provider. Rather than pondering whether AI will substitute data science, we can start thinking of what AI can actually do for data science. Is there a way to exploit what AI can do best to enrich our data science solutions? LLMs were born to analyze and generate text. This is what they can do best. Take a question and produce a human-like answer. Take a comment and interpret its sentiment. Take a book and summarize it.
How Data Science Skills Can Advance Your IT Career — and How to Build Them - DATAVERSITY
The data science landscape has changed considerably in the last few months. The rise of AI tools and user-friendly data platforms has democratized access to data-driven insights, giving even non-experts access to an essential toolkit that enhances efficiency and decision-making. As data science becomes more integral to business operations, employers expect data literacy from more of their workforce.
Overcoming Data Preparation Challenges with Low-Code Tools
Low-code tools can automate tasks that have historically consumed a significant portion of data teams’ days liberating them to focus more on analysis and less on preparation. Data teams face a critical challenge: the imbalance between the extensive amount of time spent on data preparation and the relatively limited amount of time available for the analysis and model training. Data scientists spend the majority of their work days on data-curation activities like data preparation and cleansing.
Overcoming Data Preparation Challenges with Low-Code Tools
Low-code tools can automate tasks that have historically consumed a significant portion of data teams’ days liberating them to focus more on analysis and less on preparation. Data teams face a critical challenge: the imbalance between the extensive amount of time spent on data preparation and the relatively limited amount of time available for the analysis and model training. Data scientists spend the majority of their work days on data-curation activities like data preparation and cleansing.
Is Data Science dead? The Rebuke
EDITOR'S PICK | AI & DATA SCIENCE | DEEP DIVES My previous article " Is Data Science dead? " caused quite a stir. The title was provocative, but the question is surfacing in the head of many data scientists. It was worth addressing it. The article generated quite a number of comments and reactions. If I had to summarize the nature of the comments, I could come up with the following topics: Data science is more than coding. How will the job market change? Is AI future-proof?
On The Yellow Carpet of the KNIME Spring Summit 2024
DATA STORIES | KNIME SPRING SUMMIT | KNIME ANALYTICS PLATFORM Here is special edition of the My Data Guest interview series. On April 15-17th, I was at KNIME Spring Summit 2024 in Austin (US), together with Riddhima as my co-host. We were at the entrance of the first event of the KNIME Spring Summit: the KNIME Partner Award Ceremony 2024. In this roundup, we've captured key insights from industry leaders and innovators.
Crunching Data to Fight Financial Crime-The Expert’s Take
DATA STORIES | FINANCIAL ANALYTICS | KNIME ANALYTICS PLATFORM My Data Guest — An Interview with Emil Cimpean It was my pleasure to recently interview Emil Cimpean as part of the My Data Guest interview series. Emil works as the head of the Financial Crime Compliance at ING Romania. He has been working in this field, fighting financial crimes, detecting frauds and money laundering activities for many years now.
Is Data Science dead?
EDITOR’S PICK | AI & DATA SCIENCE | KNIME ANALYTICS PLATFORM In the last six months I have heard this question thousands of time: “Is data science dead?” Now that there is AI, is it still worth it to train your own Machine Learning models? Now that there is AI, is it still worth it to learn Python? Now that there is AI, is KNIME still in business? Now that there is AI, is data science still needed? Or shall we declare it dead? Now that there is AI, do we still need data scientists?
Driving Value and Making an Impact with Data
Rosaria Silipo, PhD, now VP of data science evangelism at KNIME, has spent 25+ years in applied AI, predictive analytics and machine learning at Siemens, Viseca, Nuance Communications, and private consulting.
Driving Value and Making an Impact with Data
Rosaria Silipo, PhD, now VP of data science evangelism at KNIME, has spent 25+ years in applied AI, predictive analytics and machine learning at Siemens, Viseca, Nuance Communications, and private consulting.
Who’s your influencer?
Examples of how to use newest techniques The giant in the left chart is mlauber71, absolutely the KNIME user with the most downloaded workflows. He is a prolific member of the KNIME Community Hub with frequent workflow uploads. Still, the number of downloads for his workflows is nothing short of impressive. mlauber71, aka Markus Lauber, has long been an outstanding member of the KNIME community. He was one of the first to receive a KNIME COTM award as Contributor of the Month, in September, 2020.
Mentorship Matters: Upskilling, Innovation and Career Tips from an Analytics Lead
Mentorship Matters: Upskilling, Innovation and Career Tips from an Analytics Lead It was my pleasure to interview Riddhima as part of the My Data Guest interview series. In our conversation with her, we delved into the significance of mentors in the realm of data science. Riddhima shared profound insights from her journey with KNIME both as a student and as a trainer, and provided her perspective as a talented professional on the future landscape of data science.
From KNIME Nodes to Musical Notes
In this episode of My Data Guest, we had the pleasure of interviewing Stephen Roughley, a distinguished professional in the field of medicinal chemistry and cheminformatics. As a Principal Scientist at Vernalis in Cambridge (UK), Stephen is not only a seasoned KNIME enthusiast but also the driving force behind the widely acclaimed Vernalis extension. In our insightful conversation, we delved into Stephen’s multifaceted role as a KNIME user, tech support expert, trumpeter, and more.
How to identify the best AI opportunities for your business
EDITOR'S PICK | LEADERSHIP | KNIME ANALYTICS PLATFORM Many companies nowadays feel the urge to develop an artificial intelligence (AI) strategy. However, they often get caught in a dilemma: How to hire the talent and identify the opportunities? From my experience, to test the AI waters, it is not necessary to hire new, fully trained talent from the start.
30 Years of Data Science: A Review From a Data Science Practitioner
Image by Editor 30 years of KDnuggets and 30 years of data science. More or less 30 years of my professional life. One of the privileges that comes with working in the same field for a long time - aka experience - is the chance to write about its evolution, as a direct eye witness.
A Dialogue with Cheminformatic Experts
Rosaria: Let's start from the question that is now in everybody's head. What is a cheminformatician? Heather: A cheminformatician is a scientist who blends chemistry with informatics, extending into other sciences like biology, pharmacology, and medicine. We're trying to replicate what happens in a lab in silico, on a computer. One of the biggest parts we focus on is the discovery process.
Why Geospatial Data Should Be Easily Accessible for Every Employee
Unlocking the power of geospatial data can give organizations a competitive edge, from optimizing supply chain logistics and enhancing customer experience to mitigating fraud and improving public health outcomes. But despite its far-reaching benefits, many organizations fail to fully harness geospatial data’s potential. Why? Because geospatial data is voluminous, complex, and often distributed across multiple sources.
The Data Professionals: What Are Recruiters Looking For?
Data scientists used to be more in demand, while now the new category of data engineers seems to be equally desirable. The corporate world of data science has changed. There used to be a data science lab, similar to a research lab, where things were experimented, one at a time, until the best model or dashboard was found.
On The Yellow Carpet of the KNIME Spring Summit 2023
DATA STORIES | SPRING SUMMIT 2023 | KNIME ANALYTICS PLATFORM My Data Guest - Meet the KNIME Data Science Community Co-host: Philipp Kowalski We are presenting you another special edition of the My Data Guestinterview series. On April 17-19th, I was at KNIME Spring Summit 2023 in Berlin (Germany), together with Philipp Kowalski as my co-host. We were at the entrance of the first event of the KNIME Spring Summit: the KNIME Partner Award Ceremony.
A Conversation with 5 Prominent Women in Tech
Rosaria: Let's start with a round of introductions. I have introduced you briefly but I am sure I did not give justice to your expertise. Would you like to spend a few words to tell us more about yourselves, your career, and what you are currently working on? Kate B.: I'm leading the data science efforts at Clickvoyant, which is a software-as-a-service marketing analytics software. We're trying to bring the power of data and decision making to businesses of all sizes.
Understanding Its Future
Generative AI has been taking the world by storm lately and its impact on society is only expected to increase. The global Generative AI market size accounted for $7.9 billion in 2021 and is projected to occupy a market size of , marking a growth of 34% from 2022 to 2030. The latest game-changing innovation has been ChatGPT, but before digging deeper into that, there is another simple but important question that must be answered: what exactly is generative AI?
How to identify the best AI opportunities for your business
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How to identify the best AI opportunities for your business
Check out the on-demand sessions from the Low-Code/No-Code Summit to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers. Watch now. Many companies nowadays feel the urge to develop an artificial intelligence (AI) strategy. However, they often get caught in a dilemma: How to hire the talent and identify the opportunities? From my experience, to test the AI waters, it is not necessary to hire new, fully trained talent from the start.
Introducing Top KNIME Experts
GETTING STARTED | COTM AWARD | KNIME ANALYTICS PLATFORM You might have seen award cards or badges for KNIME COTM (Contributor of the Month) on social media that look like this one below. If you have not already wondered what this celebrates, now's the time to start wondering and also get an answer. What is a KNIME COTM?
Low-Code AI Tools: How to Implement AI-Based Apps Easily
This is part of Solutions Review’s Premium Content Series, a collection of contributed columns written by industry experts in maturing software categories. In this submission, CelerData Vice President of Strategy Li Kang offers an overview of real-time analytics use cases so you can stay informed. It has become easier and easier to build artificial intelligence (AI) solutions. You do not need to be a full-fledged data scientist to train a machine learning (ML) model.
Where Is Data Science Education Going? Let the Expert Speak
DATA STORIES | DATA SCIENCE EDUCATION | KNIME ANALYTICS PLATFORM It was my pleasure to recently interview live on LinkedIn Keith McCormick as part of the My Data Guest interview series. He shared insights into the world of data science education, explained how he uses KNIME in his teaching, and spoke about the importance of good mentorship for young data scientists. Keith McCormick is an independent data scientist, trainer, conference speaker, and author.
7 Things You Didn't Know You Could do with a Low Code Tool - KDnuggets
Today I want to play the myth buster. I want to demystify this myth that low code tools can only solve simple problems. In particular, I would like to take you on a tour of unsuspectable applications developed with the open source low code tool for data science KNIME Analytics Platform and its commercial counterpart KNIME Server. For example, did you know that with KNIME software you can connect to a sensor board and transfer data into a repository?
Movie Recommendations with Spark Collaborative Filtering
By Rosaria Silipo, Head of Data Science Evangelism at KNIME As first published in Datanami Collaborative filtering (CF) based on the alternating least squares (ALS) technique is another algorithm used to generate recommendations. It produces automatic predictions (filtering) about the interests of a user by collecting preferences from many other users (collaborating).
Four Basic Steps in Data Preparation
By Rosaria Silipo, Head of Data Science Evangelism at KNIME What are the steps in data preparation? Are there specific steps we need to take for specific problems? The answer is not that straightforward: Practice and knowledge will design the best recipe for each case. First, there are two types of data preparation: KPI calculation to extract the information from the raw data and data preparation for the data science algorithm.
Rosaria Silipo on Codeless Deep Learning and Visual Programming
Transcript Introductions [00:16] Srini Penchikala: Hey everyone, just to let you know, our online software development conference, QCon Plus is back this November, the event will be held from November 1st through November 12th. You can expect curated learning on the topics that are relevant and matter right now in software development. QCon Plus is a practical conference, laser-focused on learning from the successes and failures of domain experts at early adopter companies.
The Importance of Community in Data Science
GETTING STARTED | COMMUNITY | KNIME ANALYTICS PLATFORM Co-author: Paolo Tamagnini As first published in Data Science Central Assembling predictive analytics workflows benefits from help and reviews; on processes and algorithms by data science colleagues; on IT infrastructure to deploy, manage, and monitor the AI-based solutions by IT professionals; on dashboards and reporting features to communicate the final results by data visualization experts; as well as on automatization features for...
Fun with Tags
DATA STORIES | DISEASE NAME TAGGING | KNIME ANALYTICS PLATFORM By Jeanette (Jeany) Prinz The rapid growth in the amount of biomedical literature becoming available makes it impossible for humans alone to extract and exhaust all of the useful information it contains. There is simply too much there. Despite our best efforts, many things would fall through the cracks, including valuable disease-related information.
Outlier Detection Techniques for Fraud
Based on an August 2020 report by Interpol, more people have been spending time online since the start of the coronavirus pandemic, which has resulted in increased cybercrime. UK Finance also claimed remote banking fraud losses soared by 21% to reach 80 million pounds during the first half of 2020. Online fraud is on the rise.
My machine learning model does not learn. What should I do?
By Rosaria Silipo, KNIME & Diego Arenas, St Andrews University, UK If you work with data in general, and machine learning algorithms in particular, you might be familiar with that feeling of frustration when a model really does not want to learn the task at hand. You have tried it all, but the accuracy metric just won’t rise. What next? Where is the problem? Is this an unsolvable task or is there a solution somewhere you’re not aware of? First of all, do not worry.
Codeless Deep Learning with KNIME
A new book by Packt Publishing A few days ago, at the KNIME Fall Summit, I announced the new book “Codeless Deep learning with KNIME”, which I co-authored with my colleague Kathrin Melcher and is soon to be published by Packt Publishing. Now, in the post Fall Summit relaxation, and talking to myself in these times of COVID induced isolation, I was wondering why we invested all this time writing this book and why people should read it. I will report here this short conversation I had with myself. Q.
From Data Collection to Text Interpretation. An interview on exploring techniques and use cases for text mining
Meet two text mining experts in today’s interview, which explores some of the common issues faced by data scientists in text analytics. Prof. Dursun Delen and Scott Fincher are the teachers of the “[L4-TP] Introduction to Text Processing” course regularly run by KNIME. This course is based on the KNIME Text Processing Extension. Prof. Dursun Delen is the Holder of William S.
Rosaria Silipo's Page
Please check your browser settings or contact your system administrator. To not miss this type of content in the future, subscribe to our newsletter. • None How to Automatically Determine the Number of Clusters in your Data • None New Machine Learning Cheat Sheet | Old one • None New Perspectives on Statistical Distributions and Deep Learning • None Fascinating New Results in the Theory of Randomness
Codeless Deep Learning with KNIME
Codeless Deep Learning with KNIME Coming Dec 9: Build, train, and execute various deep neural network architectures using KNIME. Attend the Fall Summit, watch the Community Update and find out how you can get 25% off your copy. Register for Fall Summit Enhance KNIME Analytics Platform with artificial intelligence capabilities by using KNIME's deep learning extensions. Book description Authors: Kathrin Melcher and Rosaria Silipo (KNIME).
Ten Common Issues When Using Excel for Data Operations
Click to learn more about co-author Rosaria Silipo. Using Excel to Transform/Analyze Your Data? I know you are still using Excel sheets to transform and/or analyze your data! I know, because most of us still use it to some extent. There is nothing wrong with using Excel. Excel spreadsheets are a great tool to collect and transform small amounts of data. However, when the game becomes harder and requires larger amounts of data, Excel starts showing its limitations. You do not believe me?
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