by jsendak | Dec 18, 2024 | DS Articles
[This article was first published on
R – Xi'an's Og, and kindly contributed to
R-bloggers]. (You can report issue about the content on this page
here)
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The ICSDS 2024 meeting in Nice is quite impressive and not primarily because it is in Nice under a beautiful December sun. As other (numerous) IMS meetings I attended (since the initial one in Uppsala in 1990!), the program is of high quality and along topics that are currently moving fast or emerging. From the sessions I attended, e-values are strongly represented, although it remains unclear to me why they should constitute a major departure from p-values, as they stick to hypothesis testing, Type I error, power, and the whole paraphernalia of Neyman-Pearson formalism. If I manage to attend a BIRS workshop on the subject next Summer, I may manage to get a better e-derstanding!
The MCMC (only!) session included a presentation by Guanyang Wang that generalised different approximate MCMC schemes into a unified one. And one by Filippo Ascolani on Gibbs beating the competition! I also attended the Bayesian prediction session, where my friends Sonia Petrone and Chris Holmes have presentations on their respective Series B papers. I discussed both on the ‘Og, on 15 March 2023 and 07 November 2022, respectively. This time, I found that both talks had a Bayesian bootstrap flavour, which is not surprising when considering the non-parametric nature of the approach. And they left me wondering at it being protected from overfitting.
My only plenary session was Cynthia Dwork’s on outcome indistinguishability, which, while related to the privacy topics I was topic, remained somewhat obscure as to its purpose. Meaning I have to get through the paper to get a more holistic perspective.
Of course, Nice in Winter is a very nice place, with the waterfront available for running an uninterrupted 15km as we found out with Jérémie Houssineau (at a brisk 4’09” pace I had not planned before starting!) and the sea all for myself (for a dozen minutes before losing digits!). Unfortunately I had to skip the final day due to examinations of the Paris Dauphine MASH master. And miss Stan receiving a student award. But I am looking forward the next iterations of ICSDS. (Not including Copenhagen, Madrid and many many other places in 2025, since ICSDS seemed a most common name for conferences, some presumably predatory!)
Continue reading: Nice meeting!
Highlights and Key Inferences from the ICSDS 2024 Meeting in Nice
The International Conference on Statistical Data Science (ICSDS) 2024 held in Nice, France was commendable not only for its enviable location under the warm December sun but majorly because of its rich and diverse program that focused on current and emerging topics in statistical data science. Here are some key takeaways and potential future implications of this year’s conference.
E-values vs P-values
One noticeable trend at the ICSDS 2024 meeting was the emphasis on e-values. Although why they pose a significant shift from p-values remains somewhat unclear. E-values, like p-values, adhere to hypothesis testing and Type I error. A future exploration and understanding of e-values in the context of hypothesis testing is therefore required.
The Future of MCMC Schemes
From the MCMC session that was conducted, Guanyang Wang’s work on the generalization of varying approximate MCMC schemes into one unified structure was notable. His work might shape the future development of MCMC methods, promoting productive discussion and probing into more efficient techniques.
Bayesian Prediction and the Bootstrap Approach
Sonia Petrone and Chris Holmes’s presentations on Bayesian prediction were also significant. They provided an interesting angle by linking Bayesian prediction to the Bayesian bootstrap approach. Deep dives into these sessions might help researchers in assessing the possibility of overuse or overfitting associated with such an approach.
Actionable Advice
- Researchers and statisticians interested in hypothesis testing should keep an eye on the growing importance of e-values and seek to expand their understanding of this concept.
- Those working on Markov Chain Monte Carlo methods should study Guanyang Wang’s work on generalized MCMC schemes, as this may lead to advancements in developing efficient algorithms.
- Bayesian statisticians should follow the work of Sonia Petrone and Chris Holmes for insights and challenges related to non-parametric models and the Bayesian bootstrap approach.
Despite the successful operation of the ICSDS 2024, it’s interesting to note that the conference title is becoming common, catching the attention of many affiliations, with some potentially predatory. Continued awareness and due diligence are advised to differentiate legitimate conferences from exploitative duplicate iterations. Looking forward to the upcoming versions of ICSDS, there is a collective yearning for more knowledge, progressive discussions, and valuable insights.
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by jsendak | Dec 18, 2024 | DS Articles
Learn how to enhance RAG models by combining text and visual inputs using Hugging Face Transformers.
Unveiling the Power of Enhancing RAG Models by Combining Text and Visual Inputs Using Hugging Face Transformers
In the revolutionary world of technology, where artificial intelligence (AI) and machine learning (ML) are progressively changing how we perceive and interact with the digital sphere, one can’t overlook the importance and potential of Retriever-Augmented Generation (RAG) models. Combining text and visual inputs using Hugging Face Transformers can tremendously enhance these RAG models.
The Potential Long-Term Implications
The amalgamation of text and visual inputs in RAG models signifies a considerable leap in text-to-text tasks, speech recognition, or any application requiring the understanding and manipulation of human language. This enhancement has several long-term implications.
- Improved User Experience: As the models become more sophisticated and can handle more complex language understanding tasks, the overall user experience improves. Interaction with AI-powered bots can become a lot more human and personalized.
- Advanced Research: Improvements in dealing with multi-modal inputs may open up new frontiers in AI and ML research, moving beyond the limitations of the current models.
- Service Innovation: By making AI more human-like, businesses can innovate their services, like customer support, personalized marketing, and recommendations.
Possible Future Developments
The initiative to improve RAG models by effectively using text and visual inputs sources Iargely from Hugging Face Transformers. This is just the beginning, however, and there are several directions these improvements could lead us.
- Higher Accuracy Models: As the transformers keep evolving, they’ll learn to handle even more types of inputs, consequently improving the accuracy of the models significantly.
- Democratization of AI: The advancements may usher the era of ‘democratization of AI’, making it accessible and understandable for non-experts as well.
- Robustness: Future models may be highly robust to changing data distributions and capable of handling unseen or novel situations.
Actionable Advice
The unfolding advancements in the enhancement of RAG models through the utilization of text and visual inputs suggest the following actionable advice for technology and business stakeholders.
- Invest in AI: Companies should deeply consider investing in AI technology. It’s an inevitability that AI will continue to shape business processes, and having AI integration at the core of your business strategy can yield concrete benefits.
- Focus on Research and Development: It’s important to invest in in-house R&D to stay ahead of the curve and stand out from the competition. Having a dedicated team to understand and implement these advancements can be beneficial.
- Risk Management: Although technology continues to advance at a rapid pace, it should not overshadow the importance of a robust risk management strategy. Issues of cybersecurity, privacy, and ethical considerations should always remain at the forefront.
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by jsendak | Dec 18, 2024 | DS Articles
AI’s sudden surge in popularity and capabilities empower research teams to enhance its versatility for broader use. The University of Texas at Austin (UT) recently pioneered one such research project, which used audio to produce visuals. Researchers turn auditory prompts into geospatial success Geospatial analysis recently saw an advanced breakthrough when researchers generated streetscape visuals… Read More »How AI can turn audio recordings into accurate images
Unfolding the Future of AI in Geospatial Analysis
Artificial intelligence (AI) has witnessed a significant surge in both popularity and capabilities. Its increasing versatility has empowered researchers worldwide to widen its scope of applications. One of the most recent, cutting-edge projects in this field has been conducted by the University of Texas (UT) at Austin, in which the team used audio data to transform into striking visual images.
Transforming Auditory Prompts into Visuals
This project has proved to be a leap forward in geospatial analysis. By converting auditory indications into accurate, detailed streetscape visuals, the researchers have presented a new way of interpreting and interacting with the environment around us.
Potential Long-term Implications
The astonishing developments in AI research projects, such as the one carried out at the University of Texas, could have several long-term implications.
- 1. Strengthened Geospatial Analysis: The innovative approach to convert audio signals into visual aids can augment the way geospatial analysis is conducted. It may lead to more comprehensive and accurate analyses.
- 2. Enhanced User Experience: This technological breakthrough could be implemented in various applications to improve user experiences. For example, it could be incorporated in navigation systems to generate real-time, on-the-spot visuals based on auditory cues.
- 3. Increased Accessibility: The audio-to-image conversion method may also cater to the visually impaired by converting sound prompts into meaningful representations, fostering an interactive environment for those who rely heavily on audio cues.
Future Developments
Given the advancements in this sphere, we can anticipate the future to hold more sophisticated applications of AI in numerous fields.
- The process of transforming audio cues into accurate images can be enhanced, yielding more precise and high-resolution outputs.
- This technology could be incorporated into daily use gadgets, thereby making AI-based browsing more mainstream and accessible to users.
- Further exploration in this realm could lead to the conceptualization and development of similar models across different industries, such as music, film, or marketing.
Actionable Advice
Based on these insights, entities involved in AI and data science research should invest in similar projects that explore this technology’s potential. They make use of sound cues to bridge the gap between human senses and AI, optimizing user experiences uniquely. Businesses can consider partnering with academic institutions like the University of Texas for collaborative research and development initiatives, thereby pushing the boundaries of what is possible within AI technology.
“AI’s sudden surge in popularity and capabilities empower research teams to enhance its versatility for broader use. The University of Texas at Austin (UT) recently pioneered one such research project, which used audio to produce visuals.” – Unknown
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by jsendak | Dec 17, 2024 | DS Articles
[This article was first published on
R-posts.com, and kindly contributed to
R-bloggers]. (You can report issue about the content on this page
here)
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Join our workshop on Latent Growth Curve Models using the Lavaan Package in R, which is a part of our workshops for Ukraine series!
Here’s some more info:
Title: Latent Growth Curve Models using the Lavaan Package in R
Date: Thursday, January 16th, 18:00 – 20:00 CET (Rome, Berlin, Paris timezone)
Speaker: Rogier Kievit is Professor of Developmental Neuroscience at the Donders Institute in Nijmegen, where he leads the Lifespan Cognitive Dynamics Lab (https://lifespancognitivedynamics.com/). He studies changes in cognitive abilities across the lifespan using multivariate techniques including factor analysis, growth curve models, mixture models and timeseries analysis. He using R almost every day, especially Lavaan and ggplot, and has contributed to multiple packages (e.g. ggrain, regsem, iced). If you send him exciting longitudinal data there is a real risk he may abandon other more urgent tasks.
Description: Rogier Kievit is Professor of Developmental Neuroscience at the Donders Institute in Nijmegen, where he leads the Lifespan Cognitive Dynamics Lab (https://lifespancognitivedynamics.com/). He studies changes in cognitive abilities across the lifespan using multivariate techniques including factor analysis, growth curve models, mixture models and timeseries analysis. He using R almost every day, especially Lavaan and ggplot, and has contributed to multiple packages (e.g. ggrain, regsem, iced). If you send him exciting longitudinal data there is a real risk he may abandon other more urgent tasks.
Minimal registration fee: 20 euro (or 20 USD or 800 UAH)
Please note that the registration confirmation email will be sent 1 day before the workshop.
How can I register?
- Save your donation receipt (after the donation is processed, there is an option to enter your email address on the website to which the donation receipt is sent)
- Fill in the registration form, attaching a screenshot of a donation receipt (please attach the screenshot of the donation receipt that was emailed to you rather than the page you see after donation).
If you are not personally interested in attending, you can also contribute by sponsoring a participation of a student, who will then be able to participate for free. If you choose to sponsor a student, all proceeds will also go directly to organisations working in Ukraine. You can either sponsor a particular student or you can leave it up to us so that we can allocate the sponsored place to students who have signed up for the waiting list.
How can I sponsor a student?
- Save your donation receipt (after the donation is processed, there is an option to enter your email address on the website to which the donation receipt is sent)
- Fill in the sponsorship form, attaching the screenshot of the donation receipt (please attach the screenshot of the donation receipt that was emailed to you rather than the page you see after the donation). You can indicate whether you want to sponsor a particular student or we can allocate this spot ourselves to the students from the waiting list. You can also indicate whether you prefer us to prioritize students from developing countries when assigning place(s) that you sponsored.
If you are a university student and cannot afford the registration fee, you can also sign up for the waiting list here. (Note that you are not guaranteed to participate by signing up for the waiting list).
You can also find more information about this workshop series, a schedule of our future workshops as well as a list of our past workshops which you can get the recordings & materials here.
Looking forward to seeing you during the workshop!
Latent Growth Curve Models using the Lavaan Package in R workshop was first posted on December 16, 2024 at 3:41 pm.
Continue reading: Latent Growth Curve Models using the Lavaan Package in R workshop
Implications and Future Developments of Latent Growth Curve Models Using the Lavaan Package in R Workshop
The Latent Growth Curve Models using the Lavaan Package in R workshop promises to be an enriching experience for participants, but it’s long-term implications and future developments extend far beyond its immediate content. This workshop presented by Rogier Kievit, a professor of Developmental Neuroscience and an expert in multivariate techniques like growth curve models and timeseries analysis, has potential far-reaching implications for the way cognitive abilities and other variables are studied over time.
Implications
First, this workshop makes sophisticated multivariate analysis techniques more accessible to a broader audience. Since these techniques often constitute the backbone of many empirical research studies in a variety of disciplines, the knowledge gained from this workshop allows for a deeper understanding and critical analysis of such research findings.
Secondly, the workshop presents an opportunity for Ukraine and potentially other countries facing significant challenges to receive vital funding. With all proceeds going directly to supporting Ukraine, participating in the workshop or sponsoring a participant offers an action-oriented way to make a meaningful contribution.
Future Developments
Few trends suggest the potential for future developments related to this workshop. One such trend is the continued expansion of R’s capabilities and the array of packages available. As more scholars like Rogier Kievit develop and share packages, the landscape of data analytic options using R is likely to become even more diverse. Attendees of this workshop and similar events might need to continuously update their skills to stay competent.
Advice
For those considering attending future workshops, here’s a couple of considerations to keep in mind:
- Ensure adequate preparation: Given the complexity of topics like multivariate techniques, prospective participants would do well to familiarize with the basics before attending.
- Apply the skills learned: Applying the skills soon after learning ensures better retention and more effective usage. Consider how the techniques taught might be useful in your current or upcoming projects.
- Stay updated: Regularly check the R-bloggers site or similar portals for information on future workshops and new developments in the R programming landscape.
- Support the cause: If feasible, go beyond the minimum registration fee or consider sponsoring a student’s participation. This is an excellent opportunity to contribute to a worthy cause.
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by jsendak | Dec 17, 2024 | DS Articles
Get the Full 2024 Gartner Magic Quadrant Data Integration Report.
Key Points of Gartner Magic Quadrant Data Integration Report 2024
Considering that the prompt doesn’t provide details about the Gartner Magic Quadrant Data Integration Report of 2024, we can’t summarize its key points or draw conclusions from it. Therefore, please provide relevant details or file attachment to proceed with the task.
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by jsendak | Dec 17, 2024 | DS Articles
Contents [from] which base AI models were trained, by contemporary creators of varying categories, with an expectation that their contents would be monetized, may have one possible option, amid others, for [how] AI companies may compensate for what they used: promotion. There are several base AI models, where the contents of several creators were used.… Read More »How should AI companies compensate content creators for used training data
Analysis of Compensation Methods for AI Companies Using Content Creator’s Training Data
In today’s technologically advanced world, artificial intelligence (AI) is rapidly evolving. A notable aspect of AI is the base models, an integral part which is trained on a wide variety of data inputs, often sourced from content creators operating in different domains. These creators may expect their content to be monetized, creating a potential dilemma for AI companies on how to compensate these creators for their utilized training data. One option to address this could be through promotion, a solution with possible long-term implications and future developments to consider.
Long-Term Implications and Future Developments
Promoting content creators as a form of compensation has far-reaching implications that can significantly impact the AI industry. This method could shake up traditional compensation models and set a new precedent on how AI companies interact with their data sources.
Increased Visibility for Content Creators
By promoting creators, AI companies can help them gain visibility, potentially expanding their reach and impact. The increased exposure can result in greater opportunities for the creators, and may also lead to collaboration with other organizations. Despite its potential benefits, this compensation model hinges on whether creators perceive this type of exposure equivalent to monetary compensation.
Shift In Compensation Models
This approach also signifies a shift towards non-monetary forms of compensation. With the evolving AI industry, different compensation models may arise, catering to the varying needs and expectations of content creators. The crucial factor remains: the value added for the creators must be substantial enough to warrant the usage of their data.
Actionable Advice for AI Companies
- Understand Creator Expectations: AI companies should gain insights into content creators’ compensation expectations. This knowledge can guide the formation of the most suitable compensation models and foster greater collaboration.
- Prioritize Transparency: It’s important to communicate clearly how the creators’ content contributes to enhancing AI model’s performance. This can build trust and maintain a healthy relationship.
- Consider Varied Compensation Models: Promotion may not be suitable or desirable for all creators. Diversifying compensation models to include both monetary and non-monetary benefits could be more appealing and fairer.
Success in the AI industry is not just about creating sophisticated models, but also about recognizing and compensating those who contribute to their development.
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The ICSDS 2024 meeting in Nice is quite impressive and not primarily because it is in Nice under a beautiful December sun. As other (numerous) IMS meetings I attended (since the initial one in Uppsala in 1990!), the program is of high quality and along topics that are currently moving fast or emerging. From the sessions I attended, e-values are strongly represented, although it remains unclear to me why they should constitute a major departure from p-values, as they stick to hypothesis testing, Type I error, power, and the whole paraphernalia of Neyman-Pearson formalism. If I manage to attend a BIRS workshop on the subject next Summer, I may manage to get a better e-derstanding!
My only plenary session was Cynthia Dwork’s on outcome indistinguishability, which, while related to the privacy topics I was topic, remained somewhat obscure as to its purpose. Meaning I have to get through the paper to get a more holistic perspective.
Of course, Nice in Winter is a very nice place, with the waterfront available for running an uninterrupted 15km as we found out with Jérémie Houssineau (at a brisk 4’09” pace I had not planned before starting!) and the sea all for myself (for a dozen minutes before losing digits!). Unfortunately I had to skip the final day due to examinations of the Paris Dauphine MASH master. And miss Stan receiving a student award. But I am looking forward the next iterations of ICSDS. (Not including Copenhagen, Madrid and many many other places in 2025, since ICSDS seemed a most common name for conferences, some presumably predatory!)
