Department: Information Systems and Operations Management (ISOM) and Marketing Division

Location: Tilburg University and Remote

Job Description:

The University of Tilburg is seeking a motivated student assistant to join a collaborative project between the Department of Information Systems and the Marketing Division. This role offers a unique opportunity to gain hands-on experience with Google Tag Manager, JavaScript, and pixel technologies such as Facebook Pixel.

Responsibilities:

• Implement and manage Google Tag Manager for various web projects.
• Develop and maintain JavaScript code to enhance website functionality.
• Set up and optimize Facebook Pixel for tracking and analytics.
• Collaborate with team members from both the Information Systems and Marketing Division.
• Assist in analyzing data and generating reports to improve marketing strategies.

Requirements:

• Current student at the Tilburg University.
• Experience (or desire to learn) with Google Tag Manager and JavaScript.
• Familiarity with pixel technologies (or desire to learn), particularly Facebook Pixel.
• Strong analytical, problem-solving skills as well as capability to proactively solve problems
• Ability to work independently and communicate your results to a multidisciplinary team.
• Good communication skills.

Benefits:

• Earn money while gaining valuable work experience.
• Opportunity to earn academic credits.
• Work on real-world projects that enhance your resume.
• Flexible working hours to accommodate your academic schedule.

How to Apply: Interested candidates should submit their resume and a brief cover letter detailing their experience with Google Tag Manager, JavaScript, and pixel technologies via email to Francesco Lelli (see Contacts page). Interested candidates may also continue this experience as part of their master thesis (see Data Driven Student Journey).

Are you interested? This is really a unique opportunity! Don’t miss this chance to gain valuable experience and contribute to exciting projects. Apply now and take the next step in your academic and professional journey in a fun and multidisciplinary environment!

The post Student Assistant – Google Tag Manager and Pixel Technology at Tilburg University 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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The post An Introduction to Generative AI appeared first on Francesco Lelli.

• After the initial pain, and maybe a bit of a slow start, your thesis will advance ten times faster and you will be capable to achieve more than you can imagine.
• What you will be learning will not be lost. Many companies use a similar environment (if not the same).
• Many of these resources are free and you will be able to use them for other projects after the end of this academic endeavor.

In other words, you have nothing to lose. All you have to do is to endure a bit of the initial annoyance that is required for a proper warm up of your programming environment. However, the reward is much greater than the initial investment.

In this article, I list only a very few resources and only the ones that are free. In the internet you can find many more.

Programming skills: no matter how much you know, there is always room for knowing more. Most libraries, both public and university, have free subscriptions to courses. For example, the library in my university gives free access to DataCamp to all the students that request it.

If you are not familiar with the platform, it contains a set of courses at all levels and many of them have code examples. As they cover a large range of cases, some of them will be relevant for your thesis. All you have to do is to dig into the list and familiarize with the code that is most promising for the work that you plan to do with your supervisor.

Get a student account in GitHub, it is a free repository that you can use for storing your code. Calming student status will grant you a pro version for free as well as many discounts for products of other companies. In addition, you will also not loose these perks after the graduation – some of them could be useful for you in the future.

Get an account in Kaggle, again, it is a free service that has courses and datasets. They also offer a web based programming environment with the idea that if you will find yourself in need of additional computation, you will use their services. As a general rule, I would recommend to have a local working environment instead of a cloud based like the one of Kaggle. However, a cloud based environment may be useful under certain conditions.

Set up Anaconda (or Eclipse) in your laptop. An Integrated Development Environment is extremely useful when you have to do some professional programming. Anaconda is free, open source and perfect for Python. Eclipse is its counterpart for Java. In the article below you can read about some of the benefits of an IDE. Once you will start using one you will never go back:

This article is a mini-tutorial on how to set up the environment for Anaconda:

Finally, in the article below, you can familiarize with the debug functionality. It is one of the most useful features of an IDE and can really save you a tremendous amount of time:

I would strongly encourage you in enduring the pain of getting familiar with the tools and the resources that I shared with you in this article. In the long run your future self will be extremely thankful.

A working environment is a very personal thing and I can imagine that every supervisor has their own. Consequently, I would encourage you to discuss the details during your first meeting. At the same time, I can imagine that these tools will be, in a way or an other, useful for your future work. In addition, the fact that you will have your working environment ready will allow you to advance conversations that are more specific to your thesis and you will result in a better use of the (limited) time that you will share with your supervisor.

#free #Resources that will #SHARP your #programming #environnement. #Java #pythonprogramming #pythoncode #DataScience #DataAnalytics #data
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By the way, here you can fine my profiles in GitHub Kaggle and DataCamp:

• https://github.com/Nym77
• https://kaggle.com/francescolelli
• https://www.datacamp.com/profile/flelli
• https://www.freecodecamp.org/francesco-lelli

Are you really committed in improving your programming skills?

Free Code Camp is an open source free course that will guide you in a project based journey towards advanced programming skills. It contains also a session on how to pass a programming oriented job interview.

Introduction to Computer Science from Harvard This is a free edX course from Harvard. You can also get a certificate but this will cost you something. Nevertheless the entire course is free.

Something else:

This article (Free Resources That Will Warm Up Your Programming Environment) is part of the miniseries on how to do a good thesis, you can see the full list of post at the following link:

This article is also part of a miniseries on how to become a better programmer. You can see the full list of posts at the following link:

The post Free Resources That Will Warm Up Your Programming Environment appeared first on Francesco Lelli.

Student associations across various Tilburg University Schools have joined forces to compose a multi-disciplinary team of 5 to 6 students to compete in an amazing Hackathon on April 2-6, 2020. The team will be actively supported and coached by a panel of expert advisors, comprising experienced practitioners and academics, throughout the entire process. No need to feel intimidated if you do not have technical skills — All are welcomed and will be guided through the process!

Key benefits to you:

• Demonstrate innovative problem-solving on your CV
• Be introduced to an incredible international network of game-changers
• Develop experience working in multi-disciplinary teams (non-technical and technical)
• Learn more about key technologies that are shaping the future of business and society
• Be coached by an experienced team of advisors who will actively support you

Key Milestones and Dates:

• Attend SOE (if undecided) session on blockchain on December 2 at 10:45 AM in CZ-007
• Interested students submit applications before Monday, December 9, 2019
• The university committee dedicated to Odyssey Hackathon selects students and composes the team on Friday, December 13, 2019
• The team meets with expert advisors and student association chairs mid-January, 2020
• The team travels to The Hague for intensive mentoring and to choose topic Feb 4-5, 2020*
• The team meets with expert advisors and student association chairs to work on topic mid-March 2020
• The team and advisors travel to Groningen for the Odyssey Hackathon April 2-6, 2020 *
• The team will have a chance to share learnings mid-May, 2020

 *All hotel and travel expenses will be covered by the Sponsoring Committee. 

Are you interested in joining Tilburg University at Odyssey Hackathon ?

Motivated students who love to solve complex, real-world challenges, please apply before Monday, December 9, 2019. The application link:

• https://forms.gle/GjyVSfNwedamuWD9A

For more information on the Odyssey Hackathon please check out these resources:

• Odyssey Hackathon 2020 Trailer:

• Odyssey Hackathon Participant Insights 2019:

Odyssey Hackathon website:

• https://www.odyssey.org/odyssey-hackathon-2020/

A few pictures of the past event:

#Tilburg #University is searching for bachelor and master #Students that will participate to the Odyssey Hackathon. All expenses payed! #Odyssey2020 #blockchain
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The post Tilburg University at Odyssey Hackathon: Team wanted! appeared first on Francesco Lelli.