In short, three different fellowships are available:

1. Research Fellowship: For individuals without a PhD who are considering pursuing research studies.
2. PhD Scholarship: For those who wish to pursue a PhD in Switzerland for a period of 3+ years.
3. Post-Doc Scholarship: For individuals with a PhD who are looking to spend one year in Switzerland conducting postdoctoral research.

Please note that all fellowships are intended for individuals who have not previously spent time in Switzerland. Consequently, if you are awarded one of these fellowships, you will be ineligible to apply for the others in the future as you technicaly already have a Phd or Postdoc in Switzerland.

Let’s see each one of them in dettail:

Research Fellowship

Research Fellowships are 12-month programs aimed at highly qualified postgraduate researchers from all academic disciplines. Applicants must have obtained a master’s degree or equivalent by July 31, 2024, except for ETH Zurich applicants, who must meet this requirement by June 30, 2024. Candidates should be born after December 31, 1988. The application requires a support letter from an academic host professor at a Swiss university, including a brief CV (maximum 2 pages), explaining their willingness to supervise and support the research. This letter differs from the confidential recommendation letter also required. Additionally, applicants must submit a research proposal with a defined time frame. Notably, this fellowship is not available to those who already hold a PhD degree

PhD Scholarship

PhD Scholarships are designed for highly qualified PhD students from all academic disciplines, offering funding for 12 to 36 months, extendable in three 12-month increments based on academic progress. Applicants must have obtained a master’s degree or equivalent by July 31, 2024, except for ETH Zurich applicants, who must meet this requirement by June 30, 2024. Eligible candidates should be born after December 31, 1988. The application requires a support letter from an academic host professor at a Swiss university, including a brief CV (maximum 2 pages), detailing their commitment to supervise and support the PhD throughout its duration. If an assistant professor supports the application, a full professor must co-sign the letter. This support letter is distinct from the confidential recommendation letter also required. For PhD studies at a University of Applied Sciences, an additional support letter from a faculty member of the host research university is mandatory. The doctoral degree must be awarded by a research university. A detailed research proposal with a defined time frame is the centerpiece of the application and requires thorough attention.

Post-Doc Scholarship

Postdoctoral Scholarships are 12-month programs aimed at highly qualified early career scientists from all scientific fields. Eligible candidates must have completed their PhD between December 31, 2020, and July 31, 2024 (June 30, 2024, for ETH Zurich), and primarily for those who have not previously studied in Switzerland. The program does not fund sabbaticals. Applicants need a support letter from an academic host professor at a Swiss university, including a brief CV (maximum 2 pages), explaining their willingness to supervise the research. Former PhD mentors and co-directors for co-tutelle PhDs cannot be supervisors. A detailed research proposal with a timeframe is the centerpiece of the application. This scholarship is exclusively for full-time postdoctoral research in Switzerland, prohibiting fieldwork outside the country, and recipients are expected to reside in Switzerland during the grant period. For double doctorates, the date of the first degree is considered. The scholarship is not valid for mixed research conducted partly in Switzerland and partly in another country.

Where to find more Information

The official link of the scholarship contains all the relevant information and is the “GO TO” resources:

• https://www.sbfi.admin.ch/sbfi/en/home/education/scholarships-and-grants/swiss-government-excellence-scholarships.html

Additionally, each country has specific information and details regarding the application procedure, which can be found at the following link.

• https://www.sbfi.admin.ch/dam/sbfi/en/dokumente/2022/07/all-countries.pdf.download.pdf/2023_ESKAS_all_country_contacts_A_to_Z_for_application_documents.pdf

I recommend relying on the official information provided at the link above. The program itself warns about potential scams, and while there are numerous YouTube videos on the topic, some are generated by ChatGPT and others are, for lack of a better word, “dubious.” Here, I share videos from two YouTubers who, in my opinion, offer a reliable overview of the program. From Jama, part 1:

And part 2:

As well as from Aatiya:

My personal suggestions:

If you are interested in applying for a Phd or Postdoc in Switzerland, here are my personal suggestions:

1. Start Early (Paperwork): The application process is deliberately complex to filter out unmotivated candidates. The responsibility for managing the paperwork falls entirely on you, so begin early to ensure you have ample time to complete everything accurately.
2. Start Early (Connections in Switzerland): A critical aspect of the application is obtaining a support letter from a professor in Switzerland who is willing to endorse your candidacy and confirm that your proposal aligns with their and their institution’s research. Building these connections early is essential.
3. Start Early (Your Research Proposal): Develop a meaningful and well-written research proposal as soon as possible. Without a solid proposal, it will be challenging to secure support from a professor. You are solely responsible for writing this proposal. Once you have a polished version formatted according to the required templates, use it as your business card when reaching out to potential professors in Switzerland. Without this document, your communication is likely to be ignored or flagged as spam.

In other words, your commitment and the importance of contacting your potential supervisor as early as possible, armed with all the relevant information, cannot be overstated. This official video from the program provides additional valuable suggestions:

In addition, please note that each professor will not/cam not endorse more then one (or two) candidates per year. Therefore many of the request may simply be not answered.

Can I help you further for your Phd or Postdoc in Switzerland?

It really depends. For this specific grant you (the applicant) are really responsible for doing the heavy lifting as well as for dealing with the dettails. As for the rest, (i) if you believe that you have what it takes for this fellowship, (ii) you fulfill all the requirements, (iii) you have a nice idea to pursue and (iv) it is related to one of the following areas:

1. Internet of Things (IoT): Refers to the network of interconnected devices and objects that can communicate and share data. IoT enables smart homes, industrial automation, and more.
2. Extended Reality (XR): Encompasses virtual reality (VR), augmented reality (AR), and mixed reality (MR). XR technologies blend digital content with the real world, enhancing user experiences.
3. Human Behavioral Informatics: The study of human behavior using data analytics and computational methods. It involves understanding patterns, preferences, and decision-making based on digital interactions.
4. Computer Vision: A field of artificial intelligence that focuses on enabling machines to interpret visual information from images or videos. Applications include facial recognition, object detection, and medical imaging.
5. Language Processing: Also known as natural language processing (NLP), it involves teaching computers to understand and generate human language. NLP powers chatbots, language translation, and sentiment analysis.
6. Security and Privacy: Addresses safeguarding data, systems, and networks from unauthorized access or breaches. It includes encryption, authentication, and privacy-preserving techniques.
7. Big Data Processing: Deals with managing and analyzing large volumes of data. Technologies like Hadoop and Spark enable efficient processing of massive datasets.
8. Distributed Systems: Involves designing and managing systems that run on multiple interconnected computers. Examples include cloud computing, peer-to-peer networks, and distributed databases.
9. Serverless Computing: A cloud computing model where developers focus on writing code without managing servers. Serverless platforms automatically scale resources based on demand.
10. Digital Decision Making: The process of using data-driven insights and algorithms to make informed choices in various domains, such as business, healthcare, and finance.

You can consider dropping me a line! Are you still finishing your thesis instead? These guides may help!

Good Luck!

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In the realm of business and AI, Supervised Learning (yet another AI technique that is becoming a bit old fashioned nowadays) and Generative AI emerge as pivotal techniques offering transformative potential. They are effective especially when approached as development tools tailored to specific domains rather than mere products or services to be integrated into existing business frameworks. This perspective advocates for leveraging AI technology not only as a tool but as a toolbox containing customizable instruments for domain-specific innovation. By understanding the intricacies of these techniques, businesses can harness their capabilities more effectively, thereby maximizing their impact on society and fostering sustainable growth. In essence, it’s about not just using the tool but understanding and utilizing the toolbox itself for the betterment of society and business alike.

The video below presents the business and AI view according to the AI Fund perspective:

Moreover, I would personally (i.e. this is my opinion) advocate that the combination of both, the “old fashion” supervised learning and the popular generative AI (with LLM leading the pack), coupled with sound understanding of information enrichment techniques will probably offer the best cocktail for a successful venture capable to create value for society.

In the rest of this post I will try to expand on this point by first looking at what Large Language Models are and how they function. Next, I will share a pointer to a comprehensive (and free!) resource for familiarizing with deep learning tools and techniques.

Large Language Models: What They Are, How to Make Your Own, and How to Engineer an Application

Let’s start by looking at Large Language Models using the following two videos. LLM are text manipulation tools that are capable of both summarizing and creative writing (witting code can be considered as a creative endeavor). Thanks to recent progresses (that we can date with the launch of ChatGPT), the structure and the consistency of such generated text is increasing in accuracy and, consequently, in usefulness. The video below can serve as a good introduction to how Large Language Models work and their capability of guessing the next word:

However, there is a notable gap in research concerning the integration of such approaches into everyday industrial practices. For example (to name the one that I hinted at before), the potential fusion of structured knowledge graphs that are typical of databases-oriented information systems with AI-based semantic embedding, remains largely untapped. Furthermore, the exploration of multiagent aspects and memory-resilient LLMs holds promise for improving business processes, yet systematic empirical validation of their efficacy is lacking. The video below is an introduction to how to engineer Large Language Models in order to perform tangible tasks of value:

Promising directions of investigation include:

• Exploring diverse applications of Large Language Models (LLMs) tailored to specific subtasks in composing a comprehensive global capability model.
• Investigating optimal development configurations and orchestrating multiple-agent LLMs to enhance solution effectiveness.
• Assessing the potential of memory-based agents in facilitating the synthesis of various capabilities.
• Establishing best practices for presenting semantically enriched data to LLMs in a meaningful manner.
• Integrating embeddings and implicit semantics with explicit knowledge from knowledge graphs to enrich the understanding and inference capabilities of LLM.

While numerous methods exist for grasping the utility of a tool, I contend that learning its construction can accelerate mastery and unlock its full potential. This video provides an insightful overview on (re)implementing a transformer architecture, as detailed in the seminal paper “Attention is all you need,” which underpins the success of ChatGPT.

This concludes the conversation on Large Language Models that are a part of the “Generative AI” family. What follows is an introduction to the old fashioned deep learning that, as I mentioned at the beginning of this post, will still probably cover an important role in the next years

A Few Notes on Deep Learning

Perhaps now that we know the details of the transformer architecture (and everything else related to LLM), we can zoom out from Generative AI (and LLM) and take a look at a larger context taking into account other aspects of AI. Deep Learning and Generative AI are intertwined fields within artificial intelligence, each serving distinct yet complementary purposes. Deep Learning, a subset of machine learning, employs neural networks with multiple layers to learn representations from data, excelling in tasks like classification, regression, and pattern recognition. Generative AI, on the other hand, focuses on creating new data samples that resemble those in the training data, utilizing techniques such as generative adversarial networks (GANs) and variational autoencoders (VAEs). The relationship between Deep Learning and Generative AI is evident in how Deep Learning techniques, like convolutional and recurrent neural networks, form the foundation for building generative models. For instance, GANs employ adversarial training between a generator and discriminator network, while VAEs use encoder-decoder architectures, both rooted in Deep Learning principles. Together, Deep Learning and Generative AI enable the development of sophisticated models capable of learning from data, generating new insights, and advancing artificial intelligence across various domains.

The video below presents the book “Understanding Deep Learning”. It has been published in December 2023 by MIT Press and is presenting itself as a comprehensive guide for learning modern machine learning.

However, as mentioned in the video, the field is currently growing at the rate of 4000 papers a month. Therefore, is almost impossible to be able to cover all the relevant aspects. However, the book is free and you download it at the following link:

https://udlbook.github.io/udlbook/

A Final Note on AI, LLM, Generative AI, Supervised Learning and Everything in Between

In conclusion, the surge of interest in Generative AI and Large Language Models (LLMs) across academic and industrial spheres underscores their potential to revolutionize various sectors. Embracing Supervised Learning and Generative AI as developmental tools tailored to specific domains, rather than mere commodities, holds promise for driving transformative innovation in business and beyond. By comprehending the intricacies of these techniques, businesses can harness their capabilities effectively, thereby maximizing societal impact and fostering sustainable growth. The exploration of diverse applications, optimal configurations, memory-based agents, semantic data presentation, and knowledge integration mark promising directions for future research. While understanding the construction of tools accelerates mastery, the broader context of Deep Learning and Generative AI highlights their intertwined roles in advancing artificial intelligence.

A No-#Nonsense Approach to #deeplearning , #LLM (#LLMs), Supervised Learning, #GenerativeAI, and Everything in Between
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Genetic algorithms consist of several key components/features that work together to evolve a population of potential solutions to a problem. These components include:

1. Initialization: The process begins by creating an initial population of potential solutions (individuals) randomly or using some heuristic method.
2. Fitness Function: A fitness function quantifies how well each individual in the population solves the problem. It assigns a numerical score or fitness value to each individual based on its performance.
3. Selection: Selection involves choosing individuals from the current population to serve as parents for the next generation. Individuals with higher fitness values are more likely to be selected, as they represent better solutions.
4. Crossover (Recombination): Crossover is a genetic operator where pairs of parents are combined to create offspring. It mimics the process of genetic recombination in biology. The goal is to mix and match the genetic material of the parents to potentially create better solutions.
5. Mutation: Mutation is another genetic operator that introduces small random changes into the genetic information of individuals. It helps in maintaining diversity in the population and allows the algorithm to explore new regions of the solution space.
6. Termination Criteria: Genetic algorithms continue to evolve the population through multiple generations. Termination criteria, such as a maximum number of generations or reaching a satisfactory solution, determine when the algorithm should stop.
7. Replacement: After creating the offspring through crossover and mutation, the new generation replaces the old generation in the population. Replacement strategies can vary, but often, the least fit individuals are replaced.
8. Elitism: Some genetic algorithms incorporate elitism, which ensures that the best-performing individuals from the current generation are preserved in the next generation without undergoing crossover or mutation.
9. Parameters: Genetic algorithms involve several parameters, such as population size, crossover rate, mutation rate, and selection strategies. Tuning these parameters is crucial to the algorithm’s success and can impact its convergence and performance.

These components collectively enable genetic algorithms to iteratively explore and refine the population of solutions, with the ultimate goal of converging towards an optimal or near-optimal solution to the given problem. The process continues until a termination condition is met or a satisfactory solution is found.

As an example of use, this video presents an implementation of a particular genetic algorithm designed to efficiently address the Traveling Salesperson Problem (TSP). It illustrates how concepts from biology such as ‘survival of the fittest,’ ‘genetic diversity,’ and ‘mutation’ can be translated into code. The video concludes with a visual representation of the algorithm in action as it successfully tackles the TSP

This is just the tip of the iceberg and this lecture of Open MIT give more insights in the topic. In particular, it provides an exploration of genetic algorithms at a conceptual level. Three approaches to how a population evolves towards desirable traits are considered, culminating with assessments of both fitness and diversity. It also conclude with a brief discussion about how this space is abundant with solutions.

Genetic algorithms can be used in conjunction with a neural network in several ways to optimize and enhance the performance of neural network-based systems. Example includes the following:

1. Architecture Search: Genetic algorithms can help search for the optimal architecture of a neural network. They can evolve different network structures, including the number of layers, types of layers, and their connectivity, to find the configuration that best suits a given task.
2. Hyperparameter Tuning: Genetic algorithms can optimize hyperparameters such as learning rates, batch sizes, dropout rates, and weight initialization schemes for neural networks. This can improve the network’s training speed and overall performance.
3. Feature Selection: Genetic algorithms can be used to select the most relevant features or inputs for a neural network. By evolving subsets of input features, the algorithm can determine which features are most informative for a given problem.
4. Neuroevolution: In neuroevolution, genetic algorithms are used to evolve neural network weights and biases directly. Instead of traditional gradient-based training methods, genetic algorithms can evolve a population of neural networks and select the best-performing individuals based on their fitness.
5. Ensemble Learning: Genetic algorithms can create an ensemble of neural networks with diverse architectures or initializations. This ensemble approach often leads to improved generalization and robustness.
6. Transfer Learning: Genetic algorithms can optimize the transfer of knowledge from pre-trained neural networks to new tasks. They can evolve strategies for fine-tuning pre-trained models or selecting relevant layers for transfer.
7. Neural Network Optimization: Genetic algorithms can optimize the weights and biases of a neural network to fine-tune its performance for specific tasks, especially when traditional optimization techniques struggle with high-dimensional or non-convex parameter spaces.
8. Neural Architecture Search (NAS): Genetic algorithms can be used in NAS to automate the process of finding the best neural network architecture for a given task. NAS methods often involve evolving and selecting neural network architectures based on their performance on a validation set.

In summary, the integration of genetic algorithms with neural networks can help several problem-solving scenarios, as it offers a comprehensive solution that combines the global search capabilities of genetic algorithms with the learning and adaptation prowess of neural networks. In particular, genetic algorithms automate the optimization process, enabling efficient exploration of vast solution spaces and the discovery of optimal neural network architectures, hyperparameters, and weight configurations. This automation reduces the need for manual tuning and promotes diversity, ensuring that the best solutions are not prematurely discarded. Additionally, genetic algorithms facilitate ensemble learning, transfer learning, and fine-tuning, enhancing the adaptability, robustness, and performance of neural network-based solutions across a broad spectrum of domains and tasks.

The post On Genetic Algorithms as an Optimization Technique for Neural Networks appeared first on Francesco Lelli.

Generative AI, also known as generative artificial intelligence, refers to a field of artificial intelligence that focuses on creating models and algorithms capable of generating new, original content. Unlike traditional AI approaches that rely on explicit programming and rules, generative AI aims to develop systems that can autonomously generate outputs that are coherent, diverse, and often indistinguishable from those created by humans.

Generative AI and Large Language Models (LLMs) are related concepts within the field of artificial intelligence, but they are not synonymous. While LLMs are a specific type of generative AI model, not all generative AI models fall under the category of LLMs. While LLMs are capable of generating text, their primary focus is on language-related tasks, making them particularly powerful in natural language processing applications. They leverage the principles of generative AI to generate human-like text, but the term “generative AI” encompasses a wider range of techniques and applications beyond just language generation. Example of techniques includes Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and autoregressive models.

These models are employed in different domains like image generation, text generation, music composition, and more. Example of Applications where Generative AI techniques are currently used includes:

1. Image Generation: Generative models can create new, realistic images based on patterns and examples learned from training data. .
2. Text Generation: Language models and other generative models are employed to generate human-like text. They can be used for tasks such as language translation, text summarization, dialogue generation, and creative writing.
3. Music Generation: Generative models can compose original pieces of music based on patterns and styles observed in training data. These models can learn to generate melodies, harmonies, and even entire musical compositions.
4. Video Generation: Generative AI techniques can be applied to generate realistic videos or video frames. By learning from large datasets of videos, models can generate new video sequences, modify existing videos, or fill in missing frames.
5. Speech and Audio Generation: Generative models can synthesize human-like speech or other audio signals. These models find applications in voice assistants, text-to-speech systems, and even music synthesis.
6. 3D Object Generation: Generative models can create new 3D objects based on learned patterns and examples. This has applications in areas like computer graphics, virtual reality, and product design.
7. Data Augmentation: Generative models can be used to augment existing datasets by generating additional synthetic samples. This can help in improving the performance of machine learning models, especially in scenarios where data is limited.
8. Programming: Generative models can be used for generate code aim at aiding developers in code generation, optimization, bug detection, generation of documentation and automated testing.
9. Art Style Transfer: Generative models can transfer the style of one image onto another, allowing for artistic transformations. By learning the style characteristics of different artworks, these models can generate images with a specific artistic style while preserving the content.
10. Drug Discovery: Generative AI techniques can assist in the discovery and design of new pharmaceutical compounds. By generating novel chemical structures and predicting their properties, generative models can aid in the development of new drugs and accelerate the drug discovery process.
11. Virtual Characters and Avatars: Generative models can create virtual characters and avatars with realistic appearances, movements, and behaviors. These models can be used in video games, virtual reality environments, and other interactive applications to generate lifelike and responsive virtual entities.
12. Simulation and Scenario Generation: Generative AI can generate synthetic data and scenarios for simulation purposes. This can be valuable in various fields, including autonomous driving, robotics, and training models for decision-making in complex environments.
13. Design and Creativity Support: Generative AI can assist designers and artists in the creative process by generating design variations, suggesting new ideas, or providing inspiration. It can serve as a tool for exploring new design possibilities and aiding in the creation of novel and innovative designs.
14. Fraud Detection: Generative models can be employed to detect anomalies and patterns indicative of fraudulent activities. By learning from normal data distributions, these models can identify suspicious patterns and flag potential fraud cases in various domains, such as finance, cybersecurity, and e-commerce.

An extensive but not complete list of applications of Generative AI: image generation, text synthesis, music composition, video creation, speech generation, and more! #GenerativeAI #AI #Creativity"
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The list provided covers a wide range of applications for generative AI, however the field is continuously evolving, and new applications are being explored regularly. The applications mentioned are some of the prominent and well-known uses of generative AI, but it is possible that additional applications exist or may emerge in the future. Consequently, if there are any specific applications or areas that were not covered in the list, I apologize for the oversight. At the time of writing, Generative AI is a vast and dynamic field, and it is challenging to capture every possible application in a comprehensive manner.

Generative AI as a research field

Overall Generative AI is not a specific domain but should be considered more as a research area that ecomaps several discipline and domains where researchers focus on investigating and advancing knowledge in a particular subject. Key research areas include:

1. Generative Adversarial Networks (GANs): GANs are a prominent research domain within generative AI. GANs consist of two components—a generator and a discriminator—that compete against each other in a training process. The generator generates new samples, while the discriminator tries to distinguish between real and generated samples. Through iterative training, GANs learn to generate increasingly realistic outputs. Researchers continue to explore various aspects of GANs, including improving training stability, enhancing the diversity and quality of generated samples, addressing mode collapse (when a generator fails to capture the entire distribution), and developing novel architectures and loss functions.
2. Variational Autoencoders (VAEs): VAEs are another active research area within generative AI. VAEs are a type of generative model that employs an encoder and a decoder. The encoder compresses input data into a lower-dimensional representation (latent space), and the decoder reconstructs the original data from the latent space. VAEs allow for the generation of new data by sampling from the latent space. Researchers are working on enhancing VAE models to improve the quality and diversity of generated samples, developing better latent space representations, exploring different decoding strategies, and incorporating additional components such as disentangled representations and hierarchical structures.
3. Reinforcement Learning for Generation: Researchers are investigating the application of reinforcement learning techniques to generative models. This involves using rewards and reinforcement signals to guide the generation process, allowing models to learn to generate samples that align with desired objectives or exhibit specific behaviors.
4. Representation Learning: Representation learning focuses on learning meaningful and useful representations of data. In the context of generative AI, researchers are exploring techniques to learn disentangled representations that separate independent factors of variation in the data. This allows for more explicit control over the generated samples and enables targeted manipulation of specific attributes.
5. Autoregressive Models: Autoregressive models, such as the Transformer architecture, generate output sequentially, conditioning each step on previously generated tokens. This approach is often used in language generation tasks.
6. Cross-Modal Generation: Cross-modal generation involves generating data in one modality (such as generating an image from text descriptions or generating textual descriptions from images). Researchers are actively investigating techniques that bridge different modalities to enable multi-modal generation, leading to applications like image captioning, text-to-image synthesis, and audio-visual generation.
7. Explainability and Interpretability: Understanding and interpreting the workings of generative models is an important research direction. Researchers are working on methods to explain and interpret generative AI models to gain insights into the internal processes, improve transparency, and ensure reliable and accountable use of generative AI systems.
8. Ethical and Fair Generative AI: As generative AI systems become more powerful, addressing ethical considerations and fairness becomes crucial. Research in this domain focuses on understanding the biases present in training data, developing methods to mitigate bias in generated samples, and ensuring that generative AI systems adhere to ethical guidelines and societal norms.

Generative AI is a research area bridging various disciplines. #GenerativeAI #AIresearch #Interdisciplinary
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In summary and take home message:

Generative AI is a field of artificial intelligence that focuses on creating models and algorithms capable of generating new and original content. It encompasses various techniques, such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and autoregressive models. Generative AI has applications in image generation, text generation, music composition, video generation, speech and audio generation, 3D object generation, data augmentation, and more. Generative AI is not synonymous with Large Language Models (LLMs). LLMs are a specific type of generative AI model that excel in language processing tasks. They are trained on vast amounts of text data and can generate coherent and contextually relevant text. While LLMs focus on language-related tasks, generative AI encompasses a wider range of techniques and applications beyond language generation.

Generative AI & Large Language Models (LLMs): related concepts in AI, but not synonymous. They focus on content creation & language processing. #GenerativeAI #LLMs #AI
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It was a really fun and formative experience where we had the possibility to interact with many colleagues across Europe. We can clearly declare the workshop successful as we had to cap the contribution to only 11 despite the very large contribution that we received (total 22, acceptance rage 50%). If that is not enough for declaring it a success, it also was the largest (most participated) event of the conference.

A big thanks also goes to our sponsors and keynote speaker that stimulated the conversation as well as provided a valuable industrial experience.

Here you can find a set of useful links in case you would like to profit of the outcomes:

• Call for paper for the first workshop on AI Beyond Efficiency
• Modafferi, S., Nuñez, M. J., Lelli, F., & Dalle Carbonare, D. (2022). Artificial Intelligence beyond Efficiency. CEUR Workshop Proceedings, 3214

Full report can be downloaded here: PDF Version of the report on the workshop

The paper that where discussed are available at the following links:

• Introducing Building Blocks for Industry 4.0, an Analytics Application for the Federated EFPF Platform
  Jose Maria Gonzalez Castro Robert Sanfeliu Prat, Francesc Bonada, Fernando Gigante Valencia
• AI Ethics for Industry 5.0 – From Principles to Practice
  Alexandru Constantin Ciobanu, Gabriela Meșniță
• A Practical Experience of AI Solution Used to Improve Varnishing Process Efficiency in Furniture Manufacturing
  Juan Del Agua, Gabriel Modia
• Industry5.0 and Sociotechnical Theory: Theoretical Underpinnings
  Nicholas Fair
• Relevance of Visualization and Interaction Technologies for Industry 5.0
  Ander Garcia, Marco Quartulli, Igor G. Olaizola, Iñigo Barandiaran
• Teaming.AI: Enabling Human-AI Teaming Intelligence in Manufacturing
  Thomas Hoch, Bernhard Heinzl, Gerald Czech, Maqbool Khan, Philipp Waibel, Stefan Bachhofner, Elmar Kiesling, Bernhard Moser
• On Exploring the Possibilities and the Limits of AI for an Interoperable and Empowering Industry 4.0
  Francesco Lelli
• Artificial Intelligence from Industry 5.0 perspective: Is the Technology Ready to Meet the Challenge?
  Igor García Olaizola Marco Quartulli, Ander Garcia, Iñigo Barandiaran
• Towards Zero-Defect Manufacturing: Machine Selection through Unsupervised Learning in the Printing Industry
  Sotirios T. Spantideas, Anastasios E. Giannopoulos, Nikolaos C. Kapsalis, Angelos Angelopoulos, Stamatis Voliotis, Panagiotis Trakadas
• Towards Industry 5.0 – A Trustworthy AI Framework for Digital Manufacturing with Humans in Control
  Usman Wajid, Alexandros Nizamis, Victor Anaya
• On Developing Human Centric Digital Twins in Industry 4.0 and Beyond
  Akpofure A. Enughwure, Francesco Lelli

As organizer I hope that the connections that the participants build will remain strong, will grow, and will materialize in a tangible advance of the field.

First Workshop on AI Beyond Efficiency: proceedings of the event. #ArtificialIntelligence #workshops #interoperability #Industry40 #BigData #MachineLearning
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Folks at OpenAI developed the ChatGPT model as a way to optimizing language models for dialogues. I found this idea quite challenging with a lot of potential. Therefore, I asked to Chat.OpenAI an open question that on a topic that I am familiar with. As ChatGPT is design for dialog I asked also a few sub-questions as well where I was searching for more details. What follows is the unedited answers:

What is Service Oriented Architecture

Service-oriented architecture (SOA) is a software design approach that focuses on building a network of independent, modular components that provide services to other components over a network in order to form a complete application. This architecture is designed to make it easier to develop, deploy, and manage complex applications by breaking them down into smaller, independent units of functionality that can be developed, tested, and deployed independently. SOA is often used in distributed systems and relies on communication protocols such as web services to enable components to interact with each other.

Some of the key features of service-oriented architecture (SOA) include:

1. Modularity: SOA is based on the idea of breaking down complex applications into smaller, independent components that can be developed, tested, and deployed independently.
2. Independence: Each component in an SOA-based system is independent and can be managed and maintained separately from other components, which makes it easier to develop, deploy, and update applications.
3. Standardized communication protocols: SOA relies on standardized communication protocols, such as web services, to enable components to interact with each other. This allows different components to be developed using different programming languages and technologies, as long as they can communicate using the same protocol.
4. Reusability: Because components in an SOA-based system are independent and modular, they can be easily reused in other applications, which can save time and effort during development.
5. Flexibility: SOA allows for flexibility by enabling components to be added, removed, or updated without disrupting the overall functionality of the application. This makes it easier to adapt to changing business requirements and technology needs.

Modularity

Modularity is one of the key features of service-oriented architecture (SOA), and refers to the idea of breaking down complex applications into smaller, independent components that can be developed, tested, and deployed independently. This allows each component to be managed and maintained separately from other components, which makes it easier to develop, deploy, and update applications.

In SOA, each component is called a “service” and provides a specific function or piece of functionality. For example, a service might be responsible for handling user authentication, or for performing calculations, or for storing and retrieving data from a database. By designing an application as a network of interconnected services, developers can create more modular and scalable applications that are easier to maintain and update over time.

Modularity is an important concept in SOA because it enables developers to create applications that are flexible and adaptable to changing business requirements and technology needs. For example, if a new service is needed to support a new feature, it can be added to the application without disrupting the existing services or the overall functionality of the application. Similarly, if a service needs to be updated or replaced, it can be done without affecting the rest of the application. This makes it easier to evolve and improve an application over time without having to redesign the entire system.

Independence

Independence is another key feature of service-oriented architecture (SOA), and refers to the idea that each component in an SOA-based system is independent and can be managed and maintained separately from other components. This means that developers can work on different parts of the application without having to worry about how their changes will affect other parts of the system.

Independence is an important concept in SOA because it enables developers to create applications that are flexible and adaptable to changing business requirements and technology needs. For example, if a service needs to be updated or replaced, it can be done without affecting the rest of the application. This makes it easier to evolve and improve an application over time without having to redesign the entire system.

Additionally, because each service in an SOA-based system is independent, it can be developed using different programming languages and technologies, as long as it can communicate with other services using a standardized protocol. This allows developers to choose the best tools and technologies for each individual service, which can make the development process more efficient and effective.

Standardized Communication Protocols

Standardized communication protocols are an important aspect of service-oriented architecture (SOA), and refer to the use of specific rules and standards for enabling components to communicate with each other. In SOA, these protocols are used to enable services to send and receive messages and data to each other in order to perform their designated functions and contribute to the overall functionality of the application.

One of the most common protocols used in SOA is the web services protocol, which is a set of rules and standards for exchanging data between applications or services over a network. This protocol allows services to be developed using different programming languages and technologies, as long as they can communicate using the same protocol. This enables developers to create more modular and scalable applications that are easier to maintain and update over time.

In addition to the web services protocol, there are many other standardized communication protocols that are commonly used in SOA, such as REST, JSON, and XML. These protocols are designed to enable services to communicate with each other in a standardized and interoperable way, which makes it easier to develop and deploy complex applications.

Overall, the use of standardized communication protocols is an important aspect of SOA because it allows different components to be developed and maintained independently, while still being able to work together to form a complete application. This makes it easier to create flexible and adaptable applications that can evolve and improve over time.

Reusability

Reusability is a key benefit of service-oriented architecture (SOA), and refers to the ability to use components or services in multiple applications or contexts. In SOA, each component is called a “service” and provides a specific function or piece of functionality. By designing an application as a network of interconnected services, developers can create more modular and scalable applications that are easier to maintain and update over time.

One of the key advantages of this modular approach is that it allows services to be easily reused in other applications. For example, if a service is developed to handle user authentication, that service can be used in multiple applications without having to be redeveloped for each application. This can save time and effort during the development process, and can also help to ensure that services are well-tested and reliable.

Reusability is an important concept in SOA because it allows developers to create more flexible and adaptable applications that can evolve and improve over time. For example, if a new service is needed to support a new feature, it can be added to the application without disrupting the existing services or the overall functionality of the application. Similarly, if a service needs to be updated or replaced, it can be done without affecting the rest of the application.

Overall, the ability to reuse services in multiple applications is a key benefit of SOA, and can help to save time and effort during development, as well as improve the reliability and flexibility of applications.

Flexibility

Flexibility is one of the key advantages of service-oriented architecture (SOA), and refers to the ability to easily add, remove, or update components without disrupting the overall functionality of the application. In SOA, each component is called a “service” and provides a specific function or piece of functionality. By designing an application as a network of independent services, developers can create more modular and scalable applications that are easier to maintain and update over time.

One of the key benefits of this modular approach is that it allows developers to make changes to an application without having to redesign the entire system. For example, if a new service is needed to support a new feature, it can be added to the application without disrupting the existing services or the overall functionality of the application. Similarly, if a service needs to be updated or replaced, it can be done without affecting the rest of the application.

Flexibility is an important concept in SOA because it enables developers to create applications that are adaptable to changing business requirements and technology needs. For example, if the requirements for an application change over time, it is relatively easy to update or replace services as needed without having to redesign the entire application. This makes it easier to evolve and improve an application over time without having to start from scratch.

Overall, the ability to easily add, remove, or update components is a key advantage of SOA, and can help to improve the flexibility and adaptability of applications. This can make it easier to respond to changing business needs and technology trends, and can help to ensure that applications remain relevant and effective over time.

Summary (Still According to Chat.OpenAI)

Service-oriented architecture (SOA) is a software design approach that focuses on building a network of independent, modular components that provide services to other components over a network in order to form a complete application. This architecture is designed to make it easier to develop, deploy, and manage complex applications by breaking them down into smaller, independent units of functionality that can be developed, tested, and deployed independently. SOA is often used in distributed systems and relies on communication protocols such as web services to enable components to interact with each other.

Some of the key features of SOA include modularity, independence, and the use of standardized communication protocols. Modularity refers to the idea of breaking down complex applications into smaller, independent components that can be developed, tested, and deployed independently. Independence refers to the idea that each component in an SOA-based system is independent and can be managed and maintained separately from other components. Standardized communication protocols refer to the use of specific rules and standards for enabling components to communicate with each other.

Some of the key benefits of SOA include reusability, flexibility, and the ability to easily adapt to changing business requirements and technology needs. Reusability refers to the ability to use components or services in multiple applications or contexts. Flexibility refers to the ability to easily add, remove, or update components without disrupting the overall functionality of the application. The ability to adapt to changing business requirements and technology needs refers to the ability of SOA to enable applications to evolve and improve over time without having to redesign the entire system.

Does #OpenAI ( #OpenAIChat ) knows what is Service Oriented Architecture? #SOA #SoftwareEngineering #API
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Final Note (from the author) on OpenAI answering to What is Service Oriented Architecture

If we exclude the picture, the opening text and this closing text, the content is entirely generated by Chat.OpenAI using ChatGPT. The content of the answer looks sound and consistent and, from time to time, a bit redundant. In addition, the information are clearly correct and touch relevant sub-topics. Finally, strictly talking of the deepness of the content, it is sufficiently informative for a non expert but a bit useless for a domain expert as it can be classified as “a good introduction”. Is short? Does OpenAI knows what is Service Oriented Architecture? Well… the answer looks correct

If you are curious and you want to know more, at this link you can find another example of AI assisted writing

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In both cases you have my respect and support for trying to understand how HPC, AI, Big Data and Industry 4.0 can be used in your business.

I am honored and humbled that you selected me as part of your portfolio of experts for understanding how to leverage AI based technologies in your company. I am also expressing my gratitude to the StarwAI for supporting candidature as expert.

Please consider the following, prior contacting me:

• StairwAI is using a matchmaking tool to propose to each SME the list of AI Experts that fit the pilot’s scope and needs. Obviously you make the final decision on the Expert or Experts that would like to cooperate with. In addition, you free to select any Experts from the list of validated Experts, not proposed by the matchmaking tool. However, you may want to give a shot to the matchmaking tool first.
• You may want to drop me a line informally before moving forward, I will be happy to answer to your questions and take a virtual (or physical) coffee and discuss your ideas. In this way I may be able to understand if we are a good fit for each other and if I can really provide value to you.
• StairwAI is planning a second support program in 2023. Therefore if we will not have the possibility to work together in this round, do not despair; we will be able to do it at the next iteration.

Where to know more:

• AI4Europe
• StarwAI Project

Finally, let me thanks AI4EU for bring together AI stakeholders and resources, overcoming fragmentation issues and encouraging the acceleration of AI-based innovations. In particular for supporting SMEs that may be left behind due to their limited capability to acquire competences in this current market.

Happy to support the StarwAI Project and AI4EU in their mission of supporting SMEs in their strategies for #DataScience #AI #industry40 and #hpc .
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The benefit of getting familiar with Data Science Concepts

There are several benefits of getting familiar Data Science concepts. Here, I will list the most obvious:

• You will have another methodology in your portfolio of tools for conducting research.
• The certifications that you will obtain will be part of your CV and will be able to differentiate your profile.
• If you plan a a programming intensive and prototype driven approach for conducting your thesis (or research in general) you will be able to focus on the most interesting aspect from day one. This will prevent the unfortunate situation where you will be spending your time in catching up with your programming skills instead of really conducting research.

Courses Related to Data Science In DataCamp

This is a Python 101 course to perform Data Science:

• https://app.datacamp.com/learn/courses/introduction-to-data-science-in-python

This course is a bit more advanced compared to the previous one and focuses on Python specifically for data science:

• https://app.datacamp.com/learn/courses/intro-to-python-for-data-science

Here you learn to write your own custom functions, complete with multiple parameters and multiple return values, along with default arguments and variable-length arguments:

• https://app.datacamp.com/learn/courses/python-data-science-toolbox-part-1

This one complements the previous one and lets you practice doing a case study:

• https://app.datacamp.com/learn/courses/python-data-science-toolbox-part-2

With this you will learn how to improve the efficiency and readability of your code:

• https://app.datacamp.com/learn/courses/writing-efficient-python-code

And this one provides you with techniques for data cleaning and data (pre)elaboration:

• https://app.datacamp.com/learn/courses/cleaning-data-in-python

Finally, the following 2 courses will let you familiarize yourself with Tensorflow

• https://www.datacamp.com/tutorial/tensorflow-tutorial
• https://app.datacamp.com/learn/courses/introduction-to-tensorflow-in-python

If you like the idea of learning Data Science, consider sharing and spreading the voice:

A collection of #courses in DataCamp for getting familiar with #DataScience concepts using #Python . They are free if you are a #student. #DataScientist #pythonprogramming
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Bonus Course (in OOP):

This course is not strictly related to Data Science skills. However, the more the complexity of your code will grow the more you will need to organize it properly. If you are not familiar with OOP at all, this post is a good introduction to Object Oriented Programming.

• https://app.datacamp.com/learn/courses/object-oriented-programming-in-python

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A friend that is part of the management of Cartolibreria Biondi sent me a nice and appreciated gift. They are a collection of paper based items that I am sure they will support my creativity and the one of my partner. As a way to thank him what follows is an AI assisted article that I wrote for supporting this shop. I challenge you in discovering what was written by me and what was written by the machine!

This blog, like any other curious driven initiatives, survives thanks to donors and patrons that support the time and the effort that the contributors dedicate.

Donating to a researcher is important because it allows scientists to continue their important work and make new discoveries. Donations can also help support the work of research centers, including the cost of staff, equipment, and supplies

This post is an example of both a real support as well as a research artifact that is a tangible outcome of this donation. This article is generated by a human in cooperation with an AI that assists the writer by providing ideas as well as text. Both the human and the machine, are changeling you in defining where the human and and the machine begin.

francescolelli.info is thankful to Cartolibreria Biondi for his support in vintage digital resources! You can also connect to the shop and its fantastic employees with the following socials:

• https://www.facebook.com/CartolibreriaBiondi/
• https://www.instagram.com/cartolibreriabiondi/
• https://twitter.com/biondicartolibr

Here you can find the address if you plan a visit

The customer support is simply unmatched and you can count on them for achieving the impossible. I can not stop recommending this shop to everybody that is searching a fashion school bag.
There are many reasons why you should buy a fashion schoolbag. Some reasons include that a fashion schoolbag is stylish and will make you look fashionable, it is a great way to show your personality, and it is also a great way to show your school spirit. Additionally, a fashion schoolbag is a great way to carry all of your school supplies, and it will help keep you organized. Lastly, a fashion schoolbag is a great way to show your support for your favorite fashion designer or brand.

Cartolibria biondi offer also a personalized way to source school books and I definitively recommend it for several reasons. One reason is that it can be helpful to have someone else keep track of what textbooks are needed for each class and when they are due. This can be helpful for students who have a lot of classes and may forget to order a book for a class they are not even thinking about. Another reason to buy a personalized source service is that often times the company will offer a discount for buying all of your textbooks from them. This can be helpful for students who are on a tight budget. Finally, many students find that having their textbooks delivered directly to them is more convenient than having to go to the bookstore every time they need a new book.

Finally I definitively recommend Cartolibreria Biondi for your paper based vintage shopping. Scholars that value the importance of paper in a digital era should rush into this idea for several reasons. Perhaps the most obvious is that vintage paper products have a certain aesthetic appeal that is missing from most digital products. Additionally, many people believe that vintage items are built to last, whereas most digital products are not. Finally, buying vintage paper products can be a way to support sustainable practices and help reduce the amount of waste produced by the digital era.

In summary Cartolibreria Biondi is the best place in town to buy books. The website also has a section where customers can leave reviews of the shop. Shopping this website would be a good idea because the shop is certified by PayPal and has been collaborating with Google Shopping, Amazon, and Ebay for many years. The website also has a section where customers can leave reviews of the shop, which makes it more reliable.

You do not believe me? Check this out:

Thanks Cartoliberia Biondi!

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A knowledge graph is also known as a semantic network and represents a network of real-world entities. In other words represent “reality” as a combination of objects, events, situations, or concepts taking into account the relationship between them. This information is usually stored in a graph database and visualized as a graph structure, prompting the term knowledge “graph.”. The figure below can give you a visual idea of how these entities interlink among each other.

Intuitively, we can note that knowledge graphs take advantages of triples: subject-verb-qualifier in order to infer relationships among the data.

The knowledge graphs is at the core of the semantic web vision and standards like RDF and query languages like SPARQL are maintained by the w3c. A set of pointers for more information is available in this paper that I wrote specifically for interoperability in industry 4.0 and internet of things. However sections 2.3. (of the paper) give an overview of the semantic stack.

Existing tools and technologies that use knowledge graph

A core set of technologies that are currently in productions and used companies like Google, Facebook, Apple includes the following:

• Wikidata: Wikidata is a collaboratively edited multilingual knowledge graph hosted by the Wikimedia Foundation. It is a free and open knowledge base that can be read and edited by both humans and machines.
• schema.org is an organization that publishes documentations and guidelines to using structured data mark-up on web-pages. Its main objective is to standardize HTML tags to be used by webmasters for creating rich results about a certain topic of interest.
• Google Knowledge Graph is a knowledge base used by Google and its services to enhance its search engine’s results with information gathered from a variety of sources.

These technologies are strictly focusing on the data instead and the video below share a practical approach that focus on the data and how to combine them. Note that it does not consider any AI approach but focus on how to combine existing available data and their relationships.

However, it contains very nice examples and give an intuitive idea on how Wikidata, Schema.org and Knowledge graph works.

Research on Knowledge Graph and Artificial Intelligence

Now that we have an idea of what are the basic techniques for representing information we need to understand how we can use machine learning techniques for enriching these information and the expressiveness of the models. What follows is a set of seminars that discuss the knowledge graph from a more theoretical point of view and consider open research questions. This is the first part:

Below you can find the second part. It also contains very nice real life case studies. Including how Siri works!

If you are curious and would like to know more about the topic I encourage you in browsing the YouTube channel for more videos.

On #Knowledge Graph and #ArtificialIntelligence : how to combine data and models for better information. #BigData #DataScience
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