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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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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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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Objectives of the Workshop:

Industry 4.0 is the integration of IoT, artificial intelligence (AI), cyber-physical systems, and Cloud that brought us smart factories, putting technology at the forefront of manufacturing. It heavily focusses on maximizing given set of mainly technology and economic KPIs: in other words, the systems efficiency. Industry 5.0 provides a vison of industry that aims beyond efficiency and productivity as the sole goals and reinforces the
role and the contribution of industry to society.
The workshop will discuss what are the drivers, the barriers and the implications of leveraging the technological solutions developed in Industry 4.0 (I4.0) to address relevant societal impacts, so aligning with the EC agenda.

With the final goal of further interconnecting Civil Society, Government & Policy Makers, Academia and Industry, the workshop will discuss how to shape the Industry 5.0 (I5.0) paradigm to enable a human-centric, resilient society, the green transition, and the related awareness, what is the role of interoperability, and how the SMEs can contribute to this process.
It will discuss methodological approaches and models to describe from both an ICT and Social Science perspective the ever-increasing complexity of socio-technical systems that are core of I4.0 and I5.0 and foresee an extensive use of AI-based solutions. This will include, but not limit to, how to identify, measure, monitor and feedback key features and attributes to keep all the stakeholders informed and satisfied.
The workshop will also touch upon the key concepts of Data Spaces and Testing and Experimentation Facilities as a way to answer current challenges in I4.0 and support the transition to I5.0 exploring interoperability challenges reaching solutions to industry.

Topics of interest:

• Interoperability in the transition to the human centric approach.
• Interoperable socio-technical systems.
• SME supporting interoperability in I5.0.
• AI as a support to Civil Society, Government & Policy Makers.
• Digital Education and impact of ICT and data sharing on the green agenda.
• Multi-Stakeholders monitoring models and tools.
• The role of AI in governing the transitioning I4.0 to I5.0.
• The workforce in I5.0.
• Methodologies, prototypes, and tools addressing the human-centric approach in socio-technical systems.
• SME vs large industry approach to provide tools in interoperable socio-technical systems.
• Addressing privacy and trust in interoperable human-centered systems.
• Best practices and guidelines to foster the collaboration among Social and ICT Scientists.
• Practical experiences of AI solution used beyond efficiency improvement.
• Experiences, insights, and vision on setting up and use of Data Spaces to support I5.0.
• Experiences on setting up and use of Testing and Experimentation Facilities to support I5.0

AI Beyond Efficiency: Paper Submission

Papers must be in English, describes original work and must follow the CEUR guidelines for authors. Paper length should be 6 pages for a full paper and 4 pages for a short paper.
Authors should submit their contributions by email to the Workshop Chairs: sdm2d11@soton.ac.uk, mjnunez@aidimme.es, F.Lelli@tilburguniversity.edu, davide.dallecarbonare@eng.it

Important Dates:

• January 31st, February the 7th, 2022 First draft paper submission
• February 15th, 2022 Notification of acceptance
• March 23rd, 24th, 2022 I-ESA 2022 Workshops
• April 18th, 2022 Camera-ready
• June 20th, 2022 Publication of Proceedings

Further Information about the co-location with I-ESA, venues and proceeding

Since several years, I-ESA is the major event regarding Enterprise Interoperability and the official conference of Virtual Laboratory for Enterprise Interoperability (INTEROP-VLab) with the sponsorship of the International Federation for Information Processing (IFIP).
Accepted and presented papers will be published by CEUR Workshops Proceedings

Please, consider that at least one author per paper must register to the Workshops. If none of the authors is registered, the paper will not be included in the workshop program and in the proceedings

Workshop Chairs:

• Stefano Modafferi (University of Southampton, UK),
• Maria José Nunez (AIDIMME, SP),
• Francesco Lelli (University of Tilburg, NL),
• Davide Dalle Carbonare (Engineering, IT)

Program Committee (alphabetical order):

• Emiel Caron (University of Tilburg, NL);
• Luca Caviglione (CNR-IMATI, IT);
• Francesco D’Andria (Atos ARI, SP);
• Angelo Di Iorio (Universita’ di Bologna, IT);
• Akpofure Enughwure (Nigeria Maritime University, NG);
• Nicholas Fair (University of Southampton, UK);
• Alessia Focareta (FIDIA SPA, IT);
• Simone Fratini (Solenix Enineering GmbH, DE);
• Javed Khan (Sappi Europe, BE);
• Florian Kunneman (Vrije Universiteit Amsterdam, NL);
• Manuel Mazzara (Innopolis University, RU);
• Rashid Mehmood (King Abdulaziz University, SA)
• Raffaele Montella (Universita’ di Napoli Partenope, IT);
• Alexandros Nizamis (CERTH/ITI, GR);
• Antonio Nocera (Universita’ di Pavia);
• Joao Sarraipa (Universidade NOVA de Lisboa, PT);  
• Usman Wajid (Information Catalyst, UK).

Help us spread the voice:

First #workshop on #AI Beyond Efficiency: Interoperability towards Industry 5.0.#Industry40 #BigData #MachineLearning #artificialintelligence #iot #DataScience #coding #pythoncode #Software #service
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Mentor: Francesco Lelli

External Supervisor: Assigned by ESA case by case

The European Space Agency (ESA) offer a set of internship for students that want to do a thesis and contribute to the success of the European space program.  Some require you to be (just) graduated and some can be done in conjunction of your master thesis. They span in all the areas of Engineering, Computer Science and Information Management.  The department of Management has a collaboration in the areas of Software Engineering and Information Management with particular interest in AI and Big Data. In this respect, you are welcome to be an intern at ESA while pursuing your thesis for graduating in Information Management.   

4 different types of internship are available:

Student Internship

This internship cover a period of 3-6 months and is specifically focused on building a thesis at ESA. There are windows of time when they can apply and they are renewed every 6 months.

Link for more information:

• https://www.esa.int/About_Us/Careers_at_ESA/Student_Internships2

YGT: Young graduate trainee

This program is for students that are willing to consider a longer staying at ESA, the duration is 1+1 year and require you to be a graduate students or that the thesis is the only thing that you are missing. You are going to receive a proper salary and you will be employed in one of the 4 centers (Frankfurt, Leiden, Madrid or Rome) of the agency for the duration of your full time contract.

Link for more information:

• https://www.esa.int/About_Us/Careers_at_ESA/Apply_now_for_new_Young_Graduate_Trainee_opportunities

National trainee

These internships follow a similar trajectory of the YGT program, however a specific for a particular country and will require you to have a specific nationality.

Link for more information:

• https://www.esa.int/About_Us/Careers_at_ESA/Graduates_National_Trainees

These positions also require an interaction with you and the national initiatives.

Specific Trainee positions:

Link for more information (look for “trainee”) : 

• https://career2.successfactors.eu/career?company=esa

In this DB you can find all the positions that are currently open at ESA, if a particular research group requires a specific intern is advertise it here. You should look using the keyword “trainee”.

A Final Note on Internships and Fellowships at the European Space Agency

Our specific collaboration focus on the ESOC and ESTEC programs nevertheless the internships program is a single program for all the initiatives that are currently pursued at ESA.

Within this respect, the first selection process at ESA is centralized and equal for all the candidates. Consequently, if you are interested in pursuing a “thesis plus internship” you need to pass the first stage of the selection before been able to propose/define a topic of investigation.  The selection process and definition of the topic will follow this trajectory:

Phase 1: Interested candidates should apply for the proper internship program.

Phase 2: the candidate will be assessed by the ESA-HR

Phase 3: If successful, the candidate will be part of the trainee and inserted in an internal DB at ESA where scientists will look at your profile in search for a match with their interest. 

Phase 4: you will be contacted by ESA with a few possibilities for pursuing the internship

Phase 5: You will get in touch with me and we will decide how to finalize your thesis proposal in order to be able to do your thesis in Information Management as part of the ESA program.

#thesis plus #internships or Fellowships at the European Space Agency ( #ESA ) in the areas of #BigData and #AI
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For a complete list of available thesis please visit the following link: 
https://francescolelli.info/thesis/

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At a first look these questions look really obvious, to the point that they do not really need to be answered. However, “the obvious” is what most often shapes our behavior without us being aware of it. In particular, our relation with the objects around us, from an anthropological perspective, is what literally allowed us to become humans and helped us in interacting and modifying the surrounding environment.

This study intends to provide an introspective analysis of the way we approach the relationship we have with “things”. As a result, this study may foster a deeper understanding of ourselves and help us in prioritizing what we need, what we want and what we desire in life.

Dr. Heidi Toivonen and me decided to develop a multidisciplinary research on human-smart devices interaction that will try to answer some of these questions in a scientifically sound manner.  

In this respect we need your help! In particular, we are inviting you to take part in a little survey regarding your relationship with smart devices. In taking this survey, you will be able to reflect on how you relate to technology. At the same time you will be contributing to a study that aims to foster a deeper understanding of human interaction with personal smart devices that are becoming a more and more pervasive element of our everyday life. 

We sincerely appreciate your support and collaboration. 

Completing the survey will take you no more than 5 minutes.

Follow this link to the Survey:
Human-Smart Device Interaction

You are also welcome to extend this invitation to anybody you think may be interested in participating in this research. Please share this link and help us spread the voice!

Participate to a scientific #research that will #help #understanding the #interactive #relationships between #humans and smart devices. It takes only 5 minutes of your time! #IoT #HCI #psychology #AI #BigData
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You can contact us if you have any questions or feedback to share. In addition, if you have done work that you believe is relevant for this research, we look forward to learning from you! 

Thanks for your contribution to science!

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