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From Fantasy to Reality: The Enchantment of GPT-2

“GPT-2 transformative”, developed by OpenAI, stands as a groundbreaking achievement in the realm of artificial intelligence and natural language processing. GPT-2, short for “Generative Pre- trained Transformer 2” excels at predicting the next word in a sequence of text, showcasing its remarkable language modelling capabilities. GPT-2 was introduced by OpenAI in a research paper titled “Language Models are Unsupervised Multitask Learners” which was published on February 14, 2019. The paper presented the architecture and capabilities of GPT-2, marking its official debut in the field of natural language processing.

https://medium.com/geekculture/auto-code-generation-using-gpt-2-4e81cb05430

What sets GPT-2 apart is its ability to generate coherent and contextually relevant text passages based on a given prompt. Trained on vast amounts of internet text, GPT-2 learns to predict and generate text by capturing intricate patterns and structures within language. This pre-training equips GPT-2 with an extensive understanding of grammar, vocabulary, and context, enabling it to generate human-like text, answer questions, complete sentences, and even engage in creative writing tasks. GPT-2’s capacity for generating high-quality, contextually appropriate text has found applications in various fields, including content creation, conversational agents, and language translation, making it a versatile tool in the domain of natural language processing.

Content creators leverage GPT-2 to automate writing tasks, generate marketing copy, or brainstorm ideas. In conversational AI, it serves as the backbone for chatbots and virtual assistants, enabling them to engage in more natural and context-aware conversations with users. Moreover, GPT-2 has proven invaluable in translation tasks, where it can convert text from one language to another while preserving the original context and meaning.

The impact of GPT-2 extends beyond its ability to generate text. Its underlying architecture, the transformer model, has inspired subsequent developments in natural language processing and machine learning. Researchers and developers continue to explore its potential, pushing the boundaries of what AI-powered language models can achieve, making GPT-2 a cornerstone in the evolution of artificial intelligence and human-computer interaction.

The combination of language models like GPT-2 with vision transformers represents a powerful approach in the realm of multimodal AI, where both textual and visual information are processed together. By integrating GPT-2 with vision transformers, complex tasks involving both text and images can be tackled, leading to advancements in areas such as image captioning, visual question answering, and more.

https://www.kaggle.com/code/manann/generating-quotes-using-gpt-2-language- model/notebook

Empowering AI: The Fusion of GPT-2 and Vision Transformers Unleashes Multimodal Brilliance

The combination of language models like GPT-2 with vision transformers represents a powerful approach in the realm of multimodal AI, where both textual and visual information are processed together. By integrating GPT-2 with vision transformers, complex tasks involving both text and images can be tackled, leading to advancements in areas such as image captioning, visual question answering, and more. Here’s how GPT-2 can be combined with vision transformers:

  1. Multimodal Inputs: Vision transformers process images into a format understandable by transformers. These processed visual embeddings can be integrated into GPT-2 as additional input alongside text. This creates a multimodal input where GPT-2 receives both textual and visual information.
  2. Text-Image Context Understanding: GPT-2 excels at understanding textual context. By incorporating visual information, it gains the ability to comprehend the context of images, allowing it to generate more informed and contextually relevant textual responses. For example, when describing an image, the model can generate detailed and coherent textual descriptions.
  3. Applications in Image Captioning: In image captioning tasks, where an AI system generates textual descriptions for images, GPT-2 can leverage the visual embeddings provided by vision transformers to create rich and descriptive captions. This ensures that the generated captions not only describe the visual
    content accurately but also exhibit a natural language flow.
  4. Visual Question Answering (VQA): In VQA tasks, where the AI system answers questions related to images, combining GPT-2 with vision transformers allows for a more nuanced understanding of both the question and the image. This enables the model to provide contextually appropriate answers, taking into
    account the visual elements present in the image.
  5. Enhanced Creativity and Understanding: By understanding both text and images, the combined model can exhibit a higher level of creativity and nuanced understanding. It can generate creative stories inspired by images or
    answer questions about images with more depth and insight.
  6. Training Paradigms: During training, the multimodal model can be trained on tasks that involve both textual and visual inputs. This joint training enhances the model’s ability to learn the intricate relationships between textual and visual data, improving its performance on multimodal tasks.

Previous versions and development - This is where it all begins

GPT-2, the second version of the Generative Pre-trained Transformer developed by OpenAI, introduced several key differences and improvements compared to its predecessor, GPT-1:

  1. Scale and Size: GPT-2 is much larger than GPT-1, both in terms of the number of parameters and the model's overall size. GPT-2 has 1.5 billion or 1.5k million parameters, making it significantly larger than GPT-1, which had 117 million parameters. This increase in scale allows GPT-2 to capture more complex patterns in the data it is trained on.
  2. Performance: Due to its increased size, GPT-2 demonstrated superior performance in various natural language processing tasks. It exhibited a better understanding of context, allowing it to generate more coherent and contextually relevant text. The larger model size contributed to improved fluency and the ability to handle a wider range of topics and prompts
    effectively.
  3. Few-Shot and Zero-Shot Learning: GPT-2 showcased the ability to perform few-shot and even zero-shot learning. Few-shot learning means the model can generalise and generate text given a few examples or prompts. Zero-shot learning means it can generate text for tasks it has never seen before, just based on a description of the task.
  4. Controllability: GPT-2 allowed for more fine-grained control over the generated text. OpenAI demonstrated this control by conditioning the model on specific instructions, styles, or topics, resulting in text that adheres to those constraints.
  5. Ethical and Safety Concerns: The release of GPT-2 raised significant ethical concerns regarding the potential misuse of the technology for generating deceptive or malicious content. Due to these concerns, OpenAI initially refrained from releasing the full model but later decided to make it publicly available.
  6. Research Focus: GPT’s release sparked discussions in the research community about responsible AI development, the potential societal impact of highly advanced language models, and the ethical considerations in AI research. This led to increased awareness and ongoing research into the ethical use of such technologies
https://www.revechat.com/blog/chatbot-quotes/

Epilogue: Embracing the Language Revolution

As we conclude this exploration of GPT-2's transformative impact on our world, it becomes evident that we stand on the precipice of a linguistic revolution. The emergence of GPT-2 not only expanded the horizons of artificial intelligence but also ushered in a new era of human- machine interaction. Its remarkable ability to generate coherent, contextually rich text has opened doors to unprecedented possibilities, from revolutionising content creation and translation services to empowering educators and journalists. 

With great power, however, comes great responsibility. As we continue to integrate advanced language models like GPT-2 into our daily lives, it is crucial to navigate the ethical waters with vigilance. Striking a balance between innovation and ethical application will be the cornerstone of our journey forward. Let us embrace this linguistic revolution with wisdom and empathy, ensuring that the transformative potential of GPT-2 and its successors is harnessed for the betterment of humanity, heralding an era where the boundaries between human creativity and artificial intelligence blur, fostering a future where the art of communication knows no bounds.

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Giorgos Papadopoulos

Associate Researcher at Centre for Research & Technology Hellas (CERTH)

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A Recap of the VOXReality General Assembly and Plenary Meeting  

The DIS (Distributed & Interactive Systems) Group had the immense pleasure in hosting the three-day long General Assembly and Plenary Meeting for VOXReality Project from 9-11th October 2023 at Centrum Wiskunde & Informatica (CWI), Science Park, Amsterdam. As teamwork and collaboration are the driving forces behind groundbreaking projects, we were thrilled to collaborate with our use-case and technology partners to come together and share insights to plan the upcoming R&D, pilots, foreseeable challenges and internal demos for our XR technologies.

The first day kicked off with a hands-on workshop tailored for Immersive Tech Week, 2023. It was an exciting opportunity to plan our round table discussion session for ITW, diving into the intricate world of XR, providing valuable insights and expertise.  

The second day began with a warm welcome and a comprehensive overview of the day’s agenda mainly focusing on each work package, related risks and their mitigation strategies. All consortium partners provided updates for each use case, highlighting the dynamic range of applications within VOXReality. The team also engaged in a discussion on Ethics and Rights considerations within VOXPress Analysis, navigating the ethical landscape of XR is vital to ensure responsible and sustainable use.  As the sun set over Amsterdam for day 2, participants enjoyed a Boat dinner and canal city tour, allowing for more informal discussions and networking in a picturesque setting. 

The final day of the event was dedicated to the project’s management and planning for the road ahead. Throughout the three days, one-on-one discussions were held, enabling participants to delve deeper into specific topics and challenges. It was a fruitful way to address individual concerns and establish further collaboration. As the event drew to a close, there was a sense of accomplishment and optimism in the air. DIS group is indeed pleased to host this productive plenary meeting. The VOXReality GA event demonstrated the power of collaboration, innovation, and dedication within the consortium.  

Stay tuned for more updates on VOXReality!  

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Moonisa Ahsan

Moonisa Ahsan is a post-doc researcher in the DIS (Distributed & Interactive Systems) Group of CWI (NWO-I). In VOXReality, she is contributing in understanding next-generation applications within Extended Reality (XR), and to better understand user needs and leveraging that knowledge to develop innovative solutions that enhance the user experience in all three use-cases. She is a Marie-Curie Alumna and her scientific and research interests are Computer Graphics (CG), Interface Design, Cultural Heritage (CH), Human-Computer-Interaction (HCI), and User Experience (UX).

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Revolutionising Industrial Training with Augmented Reality: A Glimpse into the VOXReality Project

Augmented Reality (AR), with its powerful immersive capabilities, is painting a new future for industrial environments. By merging the physical and digital worlds, AR provides a groundbreaking platform for prototype design, industrial site planning, operational training, and safety promotion. It allows content to be visualized in a way that is not possible in traditional environments, and provides the ability to interact virtually with complex machinery in a risk-free environment, making it a significant asset in industrial training.

The transformative VOXReality project seamlessly integrates AR with innovative technologies such as XR Streaming and advanced Artificial Intelligence (AI) language models. This integration is designed to enhance user training experiences by providing interactive, high-performance interactions with virtual personal assistants that provide guidance and support during AR-based training.

Industrial AR Training with XR Streaming and 3D CAD Visualization

Augmented Reality training can take many forms. Hololight’s XR Engineering application, Hololight Space, addresses the common need for practical knowledge and experience in machine assembly in industrial environments. The solution goes beyond traditional visualisation, allowing users to engage with specific sub-components of machines that are critical to assembly instructions. 

Powered by Hololight Stream, the company’s proprietary XR Streaming technology, Hololight Space provides remote rendering and application streaming capabilities, ensuring powerful and high-quality AR experiences while overcoming the processing limitations of XR mobile devices. This feature integrates VOXReality’s AI models, enriching AR training experiences using Microsoft’s HoloLens 2 AR headset as the medium for visualising virtual content and communication.

Human-Centric Training: AI-Driven Virtual Personal Assistants

Users engaged in AR assembly training often benefit from the presence of an instructor to oversee the steps of the training. However, it is not always possible or operationally practical to have an instructor present. Nevertheless, users should be able to have a source of guidance during their training experience when needed. VOXReality aims to create a truly novel addition to AR assembly training by integrating AI language models developed by other VOXReality consortium partners into the Hololight Space application.

In conjunction with these AI models, users participating in the training will be provided with a unique support system in the form of a virtual personal assistant. This assistant monitors the trainee’s progress and has the ability to step in and offer support and guidance when the trainee needs it. The virtual personal assistant to be developed in this project will also be able to interact with the student throughout the training process. Interacting with this virtual personal assistant creates a personalised support system during training that enhances learning during the AR experience. By using this virtual personal assistant, users will be able to receive live feedback and immediate support.

Strengthening Industrial Performance and Safety

The combination of AI and AR developed by the VOXReality project forms a unique industrial training solution that provides immediate virtual support and allows trainees to learn at their own pace. The use of Augmented Reality for training minimises the need for physical machines and additional personnel, fostering a learning environment that is more efficient, effective and safe.

These AI-enabled dialog systems and AR technologies form a synergy that not only reduces the time required for complete training, but also helps provide the necessary hands-on skills that trainees need to gain a concrete understanding of the tasks to be performed, and enhances the quality of learning by ensuring immediate feedback and support. 

The use of the dialog system and AI models is critical to this step, as the virtual personal assistant provides immediate, personalised support and engagement that can reduce the time required for complete training. Having an assistant to interact with also provides peace of mind, as immediate feedback supports the learning process. This revolutionary approach promises a well-trained workforce that contributes to improved performance and safety in industrial environments.

VOXReality – Advanced AR Training Experiences

The VOXReality project is a pioneering effort to expand the potential of AR in industrial training by incorporating innovative XR Streaming and advanced AI models. The project promises a future where AR is not just a visualisation tool, but a rich, interactive and immersive learning experience. The integration of AR and AI not only changes the way we learn and interact in industrial environments, but also lays the foundation for a safer and more efficient industrial future.

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Carina Pamminger

Carina Pamminger is the Head of Research at Holo-Light, an innovative company and global leader in Extended Reality (XR) technologies. Carina brings over ten years of research experience across several disciplines, ranging from the games and transportation industry to augmented and virtual reality sectors. As Head of Research at Holo-Light, Carina actively engages in research projects with various academic, industrial, and non-profit partners such as BMW, Engineering Ingegneria Informatica, and more. Her main interest areas are in investigating novel ways of leveraging innovative XR technologies to further enable and enhance the Industrial Metaverse.

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Embracing Humanity in Virtual Realms: A Journey Towards Inclusivity and Accessibility

In the bustling realm of technology, where advancements seem to leapfrog one another, the emergence of extended reality (XR) technologies has marked a significant milestone. As we delve into the vast possibilities that XR brings, it’s crucial to remember the heart of this revolution: the people. In a world rapidly embracing immersive technologies, the human-centered approach stands as the guiding light, ensuring that progress aligns with inclusivity and accessibility, fostering a society where no one is left behind.

Immersive Technologies: A Human-Centric Odyssey

In the corridors of innovation, the team at VOXReality recognises the paramount importance of putting humanity at the forefront of our extended reality projects. With a profound understanding that technology should enhance lives rather than alienate, we have embarked on a human-centric odyssey, ensuring that our immersive experiences cater to the diverse needs of people.

Our team’s commitment goes beyond the realms of technology; it’s a commitment to building bridges, connecting hearts, and making the extraordinary accessible to everyone. In this human-centric odyssey, VOXReality doesn’t just create virtual worlds; we aim to craft inclusive spaces where differences are celebrated, where barriers are shattered, and where the shared human experience becomes the cornerstone of innovation. 

It’s a conscious effort to empower individuals, irrespective of their abilities or backgrounds, to not just participate in the digital revolution but to lead it, ensuring that the promise of a better, more connected future is within reach for all.

Inclusivity and Accessibility

One of the fundamental pillars of VOXReality’s human-centred approach is inclusivity. XR technologies are breaking barriers, enabling individuals regardless of physical abilities, to explore new worlds and partake in experiences previously deemed impossible. From virtual assistants for events to immersive access to theatre plays, inclusivity is not just a concept but a tangible reality within VOXReality’s vision.

In the tapestry of human experiences, accessibility weaves the threads that connect us all. VOXReality takes pride in our meticulous design process, ensuring that XR applications are not only user-friendly but also accessible. Through several iterations from the consortium partners, we aim to empowering potential users to navigate and engage effortlessly.

The Future Beckons: A Harmonious Coexistence

As we stand on the adge of a future where the lines between reality and virtuality blur, VOXReality exemplifies the harmonious coexistence of humanity and technology. Our commitment to a human-centred approach ensures that the digital realms we create are not just immersive but also inherently humane. In this symbiotic relationship, technology amplifies human potential, fostering empathy, understanding, and shared moments of joy.

In conclusion, as we navigate the uncharted territories of extended reality, let us remember that the true essence of progress lies in the way it uplifts the human spirit. VOXReality’s unwavering dedication to a human-centred approach serves as an example illuminating the path towards an inclusive, accessible, and harmonious digital future. 

Together, as we embrace the boundless possibilities of XR technologies, let us continue this journey, ensuring that no one is left behind, and every soul finds solace and belonging in the immersive worlds we create.

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Natalia Cardona

Hi! My name is Natalia Cardona and i'm a Corporate communications specialist, Master in Journalism and Digital Content Innovation by the Autonomous University of Barcelona. Currently working in dissemination, communication, and marketing of technology, innovation, and science for projects funded by the European Commission at F6S.

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Enhancing Open-Domain Dialogue Answer Selection through Intent-Calibrated Self-Training

Can predicted intents calibrate correct answers in open-domain dialogues?

The capability of predicted intents to refine answer selection in open-domain dialogues is a topic of significant interest.

The mission of VOXReality is to explore the development of advanced context-aware task-oriented dialogue systems. In this context, Centrum Wiskunde & Informatica (CWI) has extensively explored and provided insights into whether predicted intent labels have the potential to calibrate answer labels in open-domain dialogues.

Spearheaded by the Distributions and Interactive Systems (DIS) group, this initiative has culminated in the publication of a paper titled “Intent-Calibrated Self-Training for Answer Selection in Open-domain Dialogues” on Transactions of the Association for Computational Linguistics (TACL).

This publication serves as an evidence of the significant progress made in understanding the intricate interplay between predicted intent labels and calibrated answer selection. Full paper is available here. 

Challenge

Answer selection models have achieved notable success through training on extensive labelled datasets. However, the process of amassing large-scale labelled data is not only labour-intensive but also time-consuming. This challenge is further exacerbated for Open-Domain Systems (ODSs) as they grapple with deciphering users’ information needs due to the unstructured nature of open-ended goals (Huang et al., 2020).

Motivation

The concept of user intents, encompassing a taxonomy of utterances, has been introduced to provide guidance to the information-seeking process (Qu et al., 2018, 2019a; Yang et al., 2020). When a potential answer (PA) does not satisfy the intent of the original question (OQ), the subsequent intent of the user is likely to be an information request (IR). For instance, if a user queries, “Can you direct me to a website for more information?” their intent is classified as IR. Overlooking the intent label IR may result in providing an answer that fails to fulfil the user’s request.

Method

We introduce a novel approach known as Intent-Calibrated Self-Training (ICAST) to enhance answer label calibration within a self-training framework. Specifically, our proposal involves leveraging predicted intent labels to calibrate answer labels. The ICAST method encompasses the following steps:

    1. Teacher Model Training: A teacher model is trained on labelled data (D^l) to predict pseudo intent labels for unlabeled data (D^u).
    2. Intent-Calibrated Pseudo Labelling: High-quality intent labels are identified using intent confidence gain, subsequently influencing the selection of samples. The answer labels are calibrated by integrating selected intent labels as supplementary inputs for answer selection.
    3. Student Model Training: The student model is trained using both labelled and pseudo-labeled data.

Figure (b) visually represents the Intent-Calibrated Self-Training (ICAST) process. The flow involves training the teacher model, intent-calibrated pseudo labelling, and student model training. In contrast to the basic teacher-student training depicted in Figure (a), ICAST enhances the quality of pseudo-labeled data, resulting in significant performance gains.

Conclusion

In this article, we introduce Intent-Calibrated Self-Training (ICAST), a framework rooted in teacher-student self-training and intent-calibrated answer selection. The approach entails training a teacher model on labelled data to predict intent labels for unlabeled data, selecting high-quality intents via intent confidence gain to enhance pseudo answer label prediction, and retraining a student model using labelled and pseudo-labeled data.

Extensive experimentation on two benchmark datasets demonstrates the superiority of ICAST over baselines even with minimal labelled data (1%, 5%, and 10%). Our future work aims to explore additional predictable dialogue contexts, such as user profiles, beyond intents.

Thanks to this research, VOXReality will stand poised to harness these insights to advance the frontiers of context-aware task-oriented dialogue systems. These insights will serve as the driving force propelling us to push the boundaries, ushering in a new era of inquiry, innovation, and seamless application.

Resources

References

  • Deng, W., Pei, J., Ren, Z., Chen, Z., & Ren, P. (2023). Intent-calibrated Self-training for Answer Selection in Open-domain Dialogues. arXiv preprint arXiv:2307.06703.
  • Minlie Huang, Xiaoyan Zhu, and Jianfeng Gao. 2020. Challenges in building intelligent opendomain dialog systems. ACM Transactions on Information Systems.
  • Chen Qu, Liu Yang, W. Bruce Croft, Johanne R Trippas, Yongfeng Zhang, and Minghui Qiu.2018. Analyzing and characterizing user intent in information-seeking conversations. In Proceedings of International ACM SIGIR conference on research and development in information retrieval.
  • Chen Qu, Liu Yang, W. Bruce Croft, Yongfeng Zhang, Johanne R. Trippas, and Minghui Qiu. 2019a. User intent prediction in informationseeking conversations. In Human Information Interaction and Retrieval.
  • Liu Yang, Minghui Qiu, Chen Qu, Cen Chen, Jiafeng Guo, Yongfeng Zhang, W. Bruce Croft, and Haiqing Chen. 2020. IART: Intent-aware response ranking with transformers in informationseeking conversation systems. In The Web Conference.
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Jiahuan Pei

As a researcher at the CWI (NWO-I), I focus on generative dialogue systems in extended reality (XR) specifically for the VOXReality project. This project combines the fields of artificial intelligence, natural language processing, and immersive technologies to create interactive and engaging conversational experiences in virtual and augmented reality environments. We explore innovative ways to enhance human-computer interactions by enabling natural and realistic conversations with virtual entities. By leveraging the power of generative dialogue systems, we aim to develop intelligent agents capable of understanding and responding to user input in a dynamic and contextually appropriate manner.

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Writing a Blog Post with ChatGPT: Unlocking the Power of Generative AI

Unless you have been living under a rock for the last couple of months you should have heard about ChatGPT. It is an example of a generative AI model – a neural network that is trained on existing data to generate new content.

In this blog post, we will look at using OpenAI’s ChatGPT to write a blog post about writing a blog post with ChatGPT. Let us get the basics sorted out first. You can access the algorithm to go along with this blog post or to use it in your projects by following this link and creating a free account. And that is it! You have the power of generative AI at your fingertips.

Introduction

The goal of the VOXReality project is to enhance the speech-based interface in extended reality. Our team at Maastricht University is working on AI models that can transcribe and translate speech into a chosen language while also taking into account the surrounding context – previous sentences, visual information, etc.

This task is part of a broader field of Natural Language Processing, which also includes Large Language Models such as ChatGPT. Recently, researchers have found that ChatGPT is performing well on context-aware translation.

While ChatGPT cannot be used directly in VOXReality due to its memory and computational requirements, we are investigating what makes it and other models work well on this task and how we can transfer this knowledge into models that are more suitable to our needs. But before we dive deeper, let us take a look at ChatGPT from the everyday user’s perspective.

We can instruct ChatGPT to write the whole blog post in one go and then ask it to refine it or we can take a more step-by-step approach and first work on the structure of the text and later expand each section. Here we will use the second approach. We can start by asking ChatGPT to write us the outline of our blog:

> I want you to write me the structure of a short blog post for a general audience about writing a blog post with ChatGPT. Keep only the section titles

Title: Writing a Blog Post with ChatGPT: Unlocking the Power of AI

  • Introduction: Embracing the Potential of AI in Blogging
  • Understanding ChatGPT:
  • What is ChatGPT?
  • How does ChatGPT work
  • Benefits of using ChatGPT in blogging
  • Choosing the Right Topic:
  • Selecting an engaging topic
  • Considering the target audience
  • Researching and gathering information
    […]

We asked to only include the titles of the sections. The algorithm does not always follow instructions precisely. This can be mitigated by asking to refine the answer – ChatGPT remembers our conversation. It has a limit of 16 or 32 thousand tokens of memory, depending on the version. For simplicity, you can think of tokens as words (although less common words can be split into sub-words). So, let us give it another chance and ask again:

> Keep only the section titles

Title: Writing a Blog Post with ChatGPT: Unlocking the Power of AI

  1. Understanding ChatGPT
  2. Choosing the Right Topic
  3. Planning Your Blog Post
  4. Engaging Your Readers
  5. Enhancing Readability and Coherence
  6. Leveraging the Power of ChatGPT
  7. Human Touch and Editing
  8. Optimizing for SEO
  9. Conclusion: Unleashing Your Blogging Potential with ChatGPT

Now, that is better. The structure is not what I had in mind, though. It would be nice to know a bit more about ChatGPT and how it works, but we already know the topic. Also, planning, engaging readers, and SEO could be left for another post. We can touch on “Enhancing Readability and Coherence” in the “Human Touch and Editing” section. Let us change that:

> Remove sections: 2, 3, 4, 5, 8. Add the section “Limitations of ChatGPT” before Conclusions.

Title: Writing a Blog Post with ChatGPT: Unlocking the Power of AI

  1. Understanding ChatGPT
  2. Leveraging the Power of ChatGPT
  3. Human Touch and Editing Limitations of ChatGPT
  4. Conclusion: Unleashing Your Blogging Potential with ChatGPT

Understanding ChatGPT

We are ready to expand the first section. Let us see what ChatGPT can tell us about itself. I split the response to discuss each part in detail:

> Write the “Understanding ChatGPT” section in less than 100 words

ChatGPT is an AI-powered language model that generates text responses based on input prompts. It uses deep learning and extensive training on vast amounts of text data to mimic human-like conversations.

The first two sentences answer the question of what ChatGPT is. It is a language model, meaning it is trained to predict the next word, based on the previous words. To illustrate this assume that we have this not-fully-complete sentence:
“How are you …”


The task of the language model would be to predict the next word (probably “doing”) in this case. We can use this mechanism to generate text word-by-word (we call this type of algorithm autoregressive). The algorithm is trained on the vast amount of text coming from the Internet, books, newspapers, and others. As this text is written by humans, the algorithm learns to mimic them. 

This is only the first step, though. The problem with learning just by trying to complete the input of the user is that it might not be the actual expected answer. That is why ChatGPT is next trained on the questions and answers from the internet and produced by humans hired specifically to generate data to train the model. 

In the last step, ChatGPT is trained using Reinforcement Learning from Human Feedback, in which humans rate the performance and the model is trained to obtain the highest rating.

Architecturally, ChatGPT is based on the Transformer – a type of neural network used extensively in many other tasks of Natural Language Processing. Two characteristics made Transformers a de-facto industry standard for NLP. 

First, they are highly parallelizable, meaning that we can train them substantially faster than previous architectures on modern graphics cards. Graphics cards can make a vast amount of computations at the same time, and a model that can make use of this fact can benefit greatly. 

Second, it makes use of the self-attention, which allows it to learn long-term dependencies. In the self-attention module, the words (or tokens more specifically) can gather information from even very distant parts of the sentence. This makes the Transformer very powerful and allows it to accept and produce long inputs and outputs.

While it can be a valuable tool for generating ideas and refining content, it’s important to remember that ChatGPT lacks true understanding and may produce inaccurate or nonsensical responses. By combining the power of ChatGPT with human judgment and critical thinking, bloggers can harness its capabilities to enhance their writing process.

ChatGPT has made a good point there. There is no guarantee that whatever it writes is true or makes sense. We will touch on this topic more in the “Limitations of ChatGPT” section.

Leveraging the Power of ChatGPT

> Write the “Leveraging the Power of ChatGPT” section in less than 150 words

ChatGPT offers a range of benefits that bloggers can leverage to enhance their writing process. Firstly, it serves as a valuable brainstorming tool by generating ideas and expanding on concepts. You can use it to overcome writer’s block or explore different angles for your blog post.
Additionally, ChatGPT can help you clarify and elaborate on specific topics. If you need more information or want to dive deeper into a subject, ChatGPT can provide insights and explanations to enrich your content.

In this blog post we use ChatGPT to accomplish a specific goal – to write a blog post on a concrete topic. But it can be used almost as a search engine but without links. You can ask questions on a wide range of topics and use it as a stepping stone if you are interested in pursuing the topic in more depth using other tools.

Another advantage of using ChatGPT is its ability to assist in proofreading and refining your blog post. It can offer alternative phrasing, suggest improvements, or help identify errors or inconsistencies in your writing.
However, it’s important to exercise caution and critically evaluate the responses generated by ChatGPT. While it can be a valuable tool, it may occasionally provide inaccurate or irrelevant information. Therefore, it’s crucial to review and validate the content before incorporating it into your blog post.

We see this warning reiterated again and again throughout this blog. ChatGPT is very cautious and avoids presenting itself as a trusted source of knowledge. It reminds us that the responsibility for the text is on us – human (co)authors. This is recognized by the scientific community and increasing correctness and alignment is an active area of research.

By harnessing the power of ChatGPT effectively, bloggers can enhance their writing process, overcome challenges, and create engaging and informative content for their readers.

Human Touch and Editing

> Write the “Human Touch and Editing” section in less than 100 words

While ChatGPT can assist in generating content, it’s essential to add your unique human touch to the writing process. Human editing and refinement are crucial to ensure clarity, coherence, and a personal voice in your blog post. Take the time to review and revise the content generated by ChatGPT, making sure it aligns with your style and resonates with your audience. Injecting your expertise, creativity, and personality into the post adds authenticity and makes it stand out. Embrace the role of an editor, fine-tuning the content to perfection and crafting a compelling narrative that captures the attention of your readers.

One point that I hoped ChatGPT would touch on is plagiarism. Remember, language models are trained on existing texts – books, blogs, etc. They learn to mimic the authors by predicting the next word autoregressive. While the amount of data used to train the models like ChatGPT gives hope that it can learn to generalize and not memorize the training data, we cannot be sure that it does not rehearse what it has already seen before. Thus, it is important to check the responses for plagiarism if we intend to incorporate them into our work.

Limitations of ChatGPT

> Write the “Limitations of ChatGPT” section in less than 100 words

While ChatGPT is a powerful tool, it does have limitations. One key limitation is that it lacks true understanding and consciousness, relying solely on patterns and statistical correlations in the training data. As a result, it may generate responses that are incorrect, nonsensical, or biased. ChatGPT also tends to be sensitive to the input phrasing, meaning slight changes in wording can yield significantly different responses. It’s important to critically evaluate and verify the information provided by ChatGPT. Balancing the use of ChatGPT with human judgment is crucial to ensure the accuracy, quality, and ethical considerations of the content produced.

While ChatGPT is correct here, the list of limitations is longer than we are led to believe by reading that paragraph. It can give incorrect information and it does not have an option to access the Internet to retrieve necessary information. It was trained on the data that can be dated, so it is not “aware” of any information after the data cut-off.

Another thing is that it has problems with mathematical calculations and specialised topics. We can also see that it repeats the need for human review for its outputs. It is important to keep those limitations in mind when using this powerful tool.

Conclusions

> Write the “Conclusions” section in less than 100 words

ChatGPT presents an exciting opportunity to augment your blogging journey with the power of AI. By understanding its capabilities and limitations, you can leverage ChatGPT effectively to generate ideas, clarify concepts, and refine your content. Remember to incorporate your unique human touch and editorial skills to add authenticity and engage your readers. While ChatGPT is a valuable tool, it should be used in conjunction with critical thinking and careful review. By harnessing the potential of ChatGPT responsibly, you can unlock new levels of creativity, efficiency, and impact in your blog posts.

Let me close this post by stating that in VOXReality we are using the same type of neural network that ChatGPT is based on for the task of translation and speech recognition. Apart from that, we are also investigating how to incorporate language models, such as ChatGPT, to enhance our algorithms.
Finally, let us ask ChatGPT to write the introduction and put everything together. Here is the whole blog post written by ChatGPT:

Welcome to the future of blogging with ChatGPT! As AI technology continues to evolve, writers now have access to powerful language models like ChatGPT to enhance their creative process. In this blog post, we’ll explore how you can effectively utilize ChatGPT to generate engaging content, overcome writing challenges, and amplify your blogging potential. From understanding the fundamentals of ChatGPT to adding your personal touch, we’ll delve into the tips and strategies that will empower you to create captivating blog posts that leave a lasting impact on your readers. Let’s unlock the power of AI in blogging together!

Understanding ChatGPT

ChatGPT is an AI-powered language model that generates text responses based on input prompts. It uses deep learning and extensive training on vast amounts of text data to mimic human-like conversations.

While it can be a valuable tool for generating ideas and refining content, it’s important to remember that ChatGPT lacks true understanding and may produce inaccurate or nonsensical responses. By combining the power of ChatGPT with human judgment and critical thinking, bloggers can harness its capabilities to enhance their writing process.

Leveraging the Power of ChatGPT

ChatGPT offers a range of benefits that bloggers can leverage to enhance their writing process. Firstly, it serves as a valuable brainstorming tool by generating ideas and expanding on concepts. You can use it to overcome writer’s block or explore different angles for your blog post.

Additionally, ChatGPT can help you clarify and elaborate on specific topics. If you need more information or want to dive deeper into a subject, ChatGPT can provide insights and explanations to enrich your content.

Another advantage of using ChatGPT is its ability to assist in proofreading and refining your blog post. It can offer alternative phrasing, suggest improvements, or help identify errors or inconsistencies in your writing.

However, it’s important to exercise caution and critically evaluate the responses generated by ChatGPT. While it can be a valuable tool, it may occasionally provide inaccurate or irrelevant information. Therefore, it’s crucial to review and validate the content before incorporating it into your blog post.

By harnessing the power of ChatGPT effectively, bloggers can enhance their writing process, overcome challenges, and create engaging and informative content for their readers.

Human Touch and Editing

While ChatGPT can assist in generating content, it’s essential to add your unique human touch to the writing process. Human editing and refinement are crucial to ensure clarity, coherence, and a personal voice in your blog post. Take the time to review and revise the content generated by ChatGPT, making sure it aligns with your style and resonates with your audience. Injecting your expertise, creativity, and personality into the post adds authenticity and makes it stand out. Embrace the role of an editor, fine-tuning the content to perfection and crafting a compelling narrative that captures the attention of your readers.

Limitations of ChatGPT

While ChatGPT is a powerful tool, it does have limitations. One key limitation is that it lacks true understanding and consciousness, relying solely on patterns and statistical correlations in the training data. As a result, it may generate responses that are incorrect, nonsensical, or biased. ChatGPT also tends to be sensitive to the input phrasing, meaning slight changes in wording can yield significantly different responses. It’s important to critically evaluate and verify the information provided by ChatGPT. Balancing the use of ChatGPT with human judgment is crucial to ensure the accuracy, quality, and ethical considerations of the content produced.

Conclusions: Unleashing Your Blogging Potential with ChatGPT

ChatGPT presents an exciting opportunity to augment your blogging journey with the power of AI. By understanding its capabilities and limitations, you can leverage ChatGPT effectively to generate ideas, clarify concepts, and refine your content. Remember to incorporate your unique human touch and editorial skills to add authenticity and engage your readers. While ChatGPT is a valuable tool, it should be used in conjunction with critical thinking and careful review. By harnessing the potential of ChatGPT responsibly, you can unlock new levels of creativity, efficiency, and impact in your blog posts.

 

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Paweł Mąka

Hello! My name is Paweł Mąka and I am a PhD candidate at Maastricht University. I am working on Natural Language Processing, particularly focusing on Machine Translation. My research centers around empowering artificial neural networks to leverage translation context more effectively. My passion lies in the fascinating world of neural networks and their deep understanding of language.

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Understanding AR Modes and Choosing AR Devices

Extended reality – also called XR for short- is a term that props up ever more frequently in blog posts, articles and social media, and is gaining irreversible traction. In such a field marked by rapid evolution, it is worth wondering, what is the shape of XR today and how can it shape our collective tomorrow?

XR describes a spectrum of interactive environments which combine physical with virtual space to varying degrees with Virtual Reality sitting at one end and physical reality at the other.

The Virtual Reality end of this spectrum is a combination which can be clearly described and
communicated. It describes experiences where human vision to the physical world is entirely blocked and the user is encompassed instead with virtual content. Such experiences require a specialised device to be delivered, called a virtual reality headset. 

The headset is typically also equipped with speakers for spatial audio output, so that the resulting experience occupies two of the user’s predominant senses for perceiving the environment. Virtual reality applications use human physical movement as an input modality for driving interactions, frequently assisted using controllers which can also provide haptic input to the sense of touch, thus situating the user in an interactive virtual reality environment in an immersive way.

Augmented reality, on the other hand, is not as straightforward to describe. The definition for AR is the overlay of virtual content on physical space, but the execution of this idea entails variations that end up offering a range of experiences with significant differences.

First, unlike with the specialised devices required to run VR applications, the public so far has associated the technology of Augmented Reality with commonplace smartphone devices that have the necessary hardware and software components to support AR applications. In hardware terms, that involves adequate computation power, one or more embedded cameras and one or more inertia measurement units (IMU). 

In software terms, it involves at the minimum algorithms for tracking translations in a relative coordinate system by using the input from the IMUs, image analysis with feature extraction and matching for correcting positional drift, and depth estimation and mesh generation for understanding the geometry of the physical space. 

The application can then overlay digital content on the feed from the smartphone’s camera in a way that respects geometry and device orientation, and display the composite on the smartphone device screen. Users are able to interact with the digital content by interacting with the device, e.g. with touches on the touchscreen, as in other non-XR mobile applications. 

It follows that viewing and interacting with the AR application as a user experience is not innovative per se. AR technology in this context has so far been used for very popular games, like Pokémon Go, and in the art space, e.g. for AR art galleries like in Artivive.

On another line of thought, the public is slowly but steadily getting introduced to devices called smart-glasses or AR glasses or AR displays – the terminology is still not standardised, since this is a rather new field for mass production. For the scope of this post, we will adopt the term AR displays. 

AR displays are glass frames with an embedded display, either only on one lens (monocular, for less obtrusion) or on both (binocular, for more immersion). AR displays can cast a projection on the surface of the lens so that the virtual content is interjected between the user’s eye and physical space. In other words, AR displays can be perceived as portable monitor displays. Just like with traditional monitors, the size of the projection area and its resolution define its visualisation capabilities. 

AR displays may have no or limited computation power themselves and usually depend on their connection to an external device. AR displays in the monocular category can usually be connected wirelessly to a smartphone device for increased portability, while AR displays in the binocular category (which typically support higher resolution) can relate to a cable to a variety of devices, notably laptops and handheld consoles.

Common use cases for monocular AR displays include short interactions on the go, like reading messages, previewing notifications, getting directions, viewing photos or watching videos. Common use cases for binocular AR displays include a great range of everyday tasks, like office work, multimedia editing or gaming. AR displays may also have a built-in camera, so that the user can take pictures of their surroundings in a less intrusive manner (as you would e.g., with a GoPro camera).

In conclusion, it is important to differentiate that although these devices indeed succeed in overlaying virtual content on physical space, the composite result is restricted to overlaying a flat 2D screen on 3D space, unlike in the previously mentioned smartphone AR mobile applications, where the virtual content is placing in a way that respects depth and perspective to simulate 3D objects.

The next question that sensibly comes to mind is: Is there an AR display with compositing capabilities on par with AR mobile applications? Here is where the waters become even more murky. It can be stated that there are currently no consumer-oriented devices that support such an experience. The lay of the land is shifting fast though, and change is coming from two opposing directions towards this desired middle ground. 

On the one hand, the VR space is working towards lifting the impenetrable visual barrier between the user and the physical space. Producers are testing out VR headsets with front-facing colour cameras of adequate resolution to record and stream the physical environment back to the VR display, so that the user can re-establish visual contact with their surroundings while wearing the headset. 

This feature, called pass-through, can allow developers to produce applications which combine physical world data with virtual world data in novel ways. One could not only view 3D virtual objects positioned in their physical space in a more immersive way compared to a flat smartphone screen, but one could also manipulate said objects using controllers and/or their hands, as well as define how these objects interact with the rest of the physical world (such as collisions or context-aware behaviours). This combination is frequently called Mixed Reality.

On the other hand, the AR space is also evolving rapidly by experimenting with AR devices that have a glass-like form factor, but adequate sensors and computational power to independently support AR like a smartphone device would. Such devices can vary greatly in their concept and the related keywords used for communication can become even more confusing. 

Terms like head mounted AR device, holographic device and/or Mixed Reality device can be sometimes used interchangeably. Regardless of the choice of words, the decisive factor remains the same: these headsets allow for a more seamless and powerful blend between the virtual and the physical to offer higher valuable experiences to their users.

It is worth noting that such devices are currently restricted by short battery times (approximately 2 hours depending on intensity of use), may exhibit (over)heating issues due to their compressed form factor that also relate to intensity and duration of use, and may be reported as uncomfortable by their users due to weight or fitting issues. 

They also may compete or require additional lens fittings for individuals with vision- correction glasses. Due to their high price ranges, they are currently out of reach for the greater public and are mainly used by enterprises in research and industry. Nonetheless, popularisation is growing fast, as the VR industry has proven in the past 5 years. 

Therefore, it is reasonable to expect decisive changes in the few coming years that will determine if one tendency or the other will take hold and which concepts will pass the experimental phase and establish themselves as the XR standards for the future. The future is promising and unraveling fast!

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Tareq Chihabi

Project Manager.

Projects mainly focus on Medical and Health, IT, and Cybersecurity, XR/AR domains. both on the management and technical level.
Moreover, a deep background in Business modelling and Business planning, constructing an exploitation plan for a specific product.

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Exploring Mozilla Hubs: Creating Immersive Social Experiences

One of the objectives of VOXReality is to transform the future of virtual events. Those events can be completely hosted and run online, allowing attendees to participate from anywhere in the world. To achieve this, a virtual conference platform is utilised to develop and share the virtual environment. Specifically, VOXReality explores the possibilities of Mozilla Hubs for the creation of immersive experiences for virtual conferences. 

In the ever-evolving landscape of virtual reality (VR) and augmented reality (AR), Mozilla Hubs has emerged as a powerful platform for creating and sharing immersive social experiences. Developed by the Mozilla Foundation, the same organisation behind the Firefox web browser, Mozilla Hubs offers a unique blend of collaboration, communication, and creativity. 

In this article, we will delve into the capabilities of Mozilla Hubs and explore how it enables users to connect and interact in virtual spaces, fostering a new era of social engagement.

 

Virtual Avatars by Mozilla Hubs

Virtual Spaces and Customisation

At the core of Mozilla Hubs lies the ability to create and customize virtual spaces, allowing users to design their own digital environments. Whether it’s a conference room, art gallery, classroom, or a dreamy landscape, the possibilities are endless.

Users can access a diverse range of 3D models, images, and audio files to customise their environments and create a special ambiance. With an easy-to-use interface, users can modify layouts, rearrange objects, and create interactive elements, providing a truly immersive experience for participants.

Seamless Communication and Collaboration

Mozilla Hubs facilitates real-time communication and collaboration, enabling users to interact with one another within the virtual space. 

Integrated voice chat allows participants to engage in natural conversations, making it ideal for meetings, workshops, and social gatherings. Additionally, text chat features enable users to share messages, links, and emojis, enhancing communication in noisy or crowded environments. The platform also supports screen sharing, enabling easy collaboration on documents, presentations, and media content. 

These collaborative features empower users to work together, learn, and share ideas, breaking down geographical barriers and fostering meaningful connections.

Cross-Platform Accessibility

Mozilla Hubs’ cross-platform compatibility is one of its key advantages. The platform can be accessed from various devices, including desktop computers, laptops, smartphones, and VR headsets. 

This inclusivity ensures that users can connect and participate regardless of their preferred device or technology. With the rise of WebXR, Mozilla Hubs leverages web technologies, allowing users to access virtual spaces through web browsers, eliminating the need for additional software installations. 

This accessibility makes Mozilla Hubs an attractive choice for a broad range of users, extending its reach and impact in the virtual world.

Integration with Web-Based Content

Mozilla Hubs smoothly integrates with web-based content, unlocking a world of possibilities for immersive experiences. Users can embed websites, videos, images, and interactive applications within virtual spaces, augmenting the environment with dynamic content. 

This integration enhances the versatility of Mozilla Hubs, making it suitable for educational purposes, virtual events, art installations, and more. By incorporating web-based content, users can leverage existing resources and create interactive experiences that blend the real and virtual worlds.

Office scenario in Hubs, by Mozilla Hubs

Privacy and Security

Mozilla Hubs prioritizes user privacy and security, adopting measures to protect participants’ personal information and provide a safe virtual environment. The platform adheres to Mozilla’s data practices, which emphasize user control and data minimization. Additionally, Mozilla Hubs supports room moderation, enabling hosts to manage and control access to spaces, ensuring a secure and respectful environment for all participants.

Mozilla Hubs is a powerful platform that unlocks the potential of robust social interactions. In the VOXReality project, we intend to build a truly immersive experience by fusing Hubs’ features with VOXReality AI algorithms and leveraging all the advantages that a virtual event has to offer. Attending conferences has never been so enjoyable and simple.

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Dimitris Kontopoulos

Hello, I am Dimitris Kontopoulos, a virtual reality engineer with a passion for creating immersive experiences in virtual spaces. Drawing on my background of utilizing machine learning algorithms in virtual reality development, along with my passion in game development, I constantly strive to push the boundaries of interactive and captivating virtual reality adventures. I find it fascinating how virtual reality technologies have the power to revolutionize the way people engage in various activities across all aspects of life.

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Understanding the User-Center Design Process of VOXReality: A Comprehensive Overview 

Enhancing VR Conferences, Augmented Theatre, and Training Assistants.

The development of VOXReality is centred around prioritising the current needs of the users. This interactive systems development approach places significant emphasis on comprehending and assessing user requirements and preferences. With this approach at the core, VOXReality endeavours to unlock the potential XR applications that will revolutionise immersive experiences and drive progress in various industries. The efficacy of these technologies will be demonstrated through three distinct use cases: VR Conferences, Augmented Theatres, and Training Assistants.

This blog post provides the first year timeline of requirement gathering phases, and then an overview of each of the three focus-group workshops (VR Conferences, Augmented Theatres, and Training Assistants), followed by a brief conclusion regarding our data collection and analysis of the user-centric methods employed in our focus group study. 

Timeline of our initial requirement gathering leading towards planning for developmental stages.

First Focus Group Workshop: VR conference

The CWI Team conducted a focus-group workshop on January 27th, 2023, at Spaces Herengracht in Amsterdam, (Netherlands) for getting the user requirements for the VOXReality project. The goal was to gather deeper insights and understand the needs of users and organisers at VR conferences.

The workshop had 6 participants, including VRDays conference organisers and experienced end-users. With the help of a moderator and an assistant, we brainstormed ideas for the role and design of virtual agents and language translation at VR conferences. We collected and documented all the activity sheets and transcribed the voice recordings of the participants.

The outcomes were then analyzed by our user-centered research team and turned into a final list of high-quality user requirements, which are scientifically documented in the D2.1 Project Deliverable.

Our workshop was a success in gathering valuable insights and meeting the needs of our stakeholders. We extend our sincere appreciation to the hosts and participants for their invaluable contributions to furthering scientific research. 

Second Focus Group Workshop: Augmented Theatre

On January 30th, 2023 CWI Team organised their second focus group workshop in Athens to gather user-requirements for the Augmented Theatre Use Case in partnership with Athens Epidaurus Festival.

The session provided detailed user-information aimed to understand the preferences and requirements of both users and organisers regarding subtitles and visual effects in theatre plays, thereby enhancing the overall theatre experience.

Through participant insights in user-centric activities, specific needs and preferences were identified to inform improvements in these areas. Furthermore, the workshop facilitated a brainstorming session to explore ideas for the user interface and interaction methods between the audience and augmented reality (AR) application.

This collaborative exercise fostered the exploration of innovative approaches, enabling seamless and intuitive interactions with AR technology to enhance audience engagement and immersion during theatre performances.

After the workshop, our user-centred research team diligently analysed the outcomes and transformed them into a finalised and scientifically documented list of high-quality user requirements, which can be found in the deliverable report of the project. The workshop’s collaborative atmosphere and active participation contributed to significant advancements in our understanding and meaningful progress in the Augmented Theater Use Case.

Thanks to hosts and participants for their invaluable contributions in advancing scientific research. 

Third Focus Group Workshop: Training Assistant

On February 28th, 2023 a focus group workshop was conducted with HOLO-LIGHT to acquire user-centric prerequisites for AR-Training Assistant use-case.

We sought to gain a deep understanding of the requirements involved in delivering machine assembly training through virtual agents utilising AR glasses. The activities were user-centric to identify the specific needs and considerations for effective training using this technology.

This brainstorming session with the chief project manager and a scientific researcher from HOLO-LIGHT generated innovative ideas regarding the role and design of AR Training Agents in machine assembly setup. Since the discussions included inputs from sophisticated/expert users, therefore outputs resulted in finest user-requirements.

This workshop marked the conclusion of our focus-group workshops trilogy, and we are pleased to share that the outcomes have been successfully processed and documented in the project deliverable.

By the time this post is public, this documentation is already in the pipeline for further developmental stages. We thank the hosts and participants for their valuable contribution towards scientific research. 

Collected Data and Analysis

During the sessions, a wide range of data was collected, including text, images/drawings, and audio. Textual data consisted of worksheets with questions and answers about users’ introductions, expertise in VR/AR, and related domains, as well as their past experiences and preferences.

Images and drawings included timeline visuals depicting their entire experience as participants and visual markers representing user choices. Audio recordings of interactive discussions were also transcribed for easier analysis. We used Thematic Analysis [1] to analyze the collected qualitative data from the focus groups, about which we will explore further in detail in our future blog posts.  

Selected user-activities scans from the data worksheets filled by participants

Towards the evaluations

Through thorough analysis and synthesis of the collected data, we were able to uncover significant themes, patterns, and insights into the attitudes and behaviours of the participants in augmented reality (AR) and virtual reality (VR) applications across all the use-cases of the project. 

This analysis allowed us to develop a comprehensive understanding of how users engage with and perceive the AR/VR experiences, providing valuable insights that can inform the design and improvement of our VOXReality applications in these use cases. 

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Moonisa Ahsan

Moonisa Ahsan is a post-doc researcher in the DIS (Distributed & Interactive Systems) Group of CWI (NWO-I). In VOXReality, she is contributing in understanding next-generation applications within Extended Reality (XR), and to better understand user needs and leveraging that knowledge to develop innovative solutions that enhance the user experience in all three use-cases. She is a Marie-Curie Alumna and her scientific and research interests are Computer Graphics (CG), Interface Design, Cultural Heritage (CH), Human-Computer-Interaction (HCI), and User Experience (UX).

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References

[1] Sueyoon Lee, Abdallah El Ali, Maarten Wijntjes, and Pablo Cesar. 2022. Understanding and Designing Avatar Biosignal Visualizations for Social Virtual Reality Entertainment. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22). Association for Computing Machinery, New York, NY, USA, Article 425, 1–15. https://doi.org/10.1145/3491102.3517451

[2] Sueyoon Lee, Alina Striner, and Pablo Cesar. 2022. Designing a VR Lobby for Remote Opera Social Experiences. In ACM International Conference on Interactive Media Experiences (IMX ’22). Association for Computing Machinery, New York, NY, USA, 293–298. https://doi.org/10.1145/3505284.3532980

[3] Greg Guest, Kathleen M. MacQueen, and Emily E. Namey. 2012. Applied Thematic Analysis, Sage Publications, ISBN: 9781412971676.

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VOXReality review of Once for All: Training One Network for Efficient Deployment in AI and Machine Learning

The article “VOXReality Review of Once for All: Training One Network for Efficient Deployment in AI and Machine Learning” serves as a comprehensive summary of the original research conducted by Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, and Song Han from the esteemed Massachusetts Institute of Technology (MIT) and MIT-IBM Watson AI Lab. 

In recent years, advancements in technology, machine learning, and artificial intelligence have revolutionised various fields. One notable development in this area is the Once for All (OFA) approach, introduced in the paper “Once for All: Train One Network and Specialize it for Efficient Deployment.” This approach aims to train a single neural network and then specialise it for efficient deployment across different platforms and tasks.

In this article, we will explore the concept of OFA, its applications, and its potential impact on the field of machine learning and artificial intelligence.

What is Once for All (OFA)?

The Once for All (OFA) approach is a novel technique that involves training a single neural network and then specialising it for efficient deployment. Traditional approaches require training multiple networks for different tasks and platforms, which can be time-consuming and resource-intensive.

OFA addresses this challenge by training a large “super-network” that contains multiple sub networks, each tailored to a specific task or platform. By using a combination of network pruning and architecture search techniques, OFA allows for the efficient adaptation of a single network to various deployment scenarios.

Figure 1: Left: a single once-for-all network is trained to support versatile architectural configurations including depth, width, kernel size, and resolution. Given a deployment scenario, a specialized subnetwork is directly selected from the once-for-all network without training. Middle: this approach reduces the cost of specialized deep learning deployment from O(N) to O(1). Right: once-for-all network followed by model selection can derive many accuracy-latency trade-offs by training only once, compared to conventional methods that require repeated training.

Technical background

The Once for All approach optimises several key aspects of the network, including kernel size, depth, and number of channels to strike a balance between performance and efficiency. To optimise the network architecture, the authors propose a differentiable architecture search space that allows for efficient exploration of various network configurations. 

This search space encompasses a wide range of kernel sizes, depths, and channel configurations, enabling the network to adapt to different deployment scenarios. By training a large “super-network” that contains multiple sub-networks, OFA leverages network pruning techniques to determine the optimal combination of kernel sizes, depths, and channels for each specialised sub-network.

In detail, a resource-aware training algorithm that takes into account the computational cost and latency constraints of different deployment platforms is introduced. By incorporating resource constraints during the training process, the resulting specialised sub-networks are not only efficient but also tailored to the specific requirements of each platform. In terms of kernel size, the OFA approach explores a range of kernel sizes for convolutional layers, including 1×1, 3×3, and 5×5.

This allows the network to adapt to different receptive field sizes and capture both local and global features effectively. OFA explores a wide range of depths, from shallow to deep architectures. This flexibility enables the network to strike a balance between model complexity and computational efficiency, depending on the deployment scenario and task requirements. 

In terms of channels, the OFA approach optimises the number of channels in each layer to achieve an optimal trade-off between model capacity and computational efficiency. By dynamically adjusting the number of channels, the network can adapt to different levels of feature representation and information flow.

Figure 2: Comparison with SOTA hardware-aware NAS methods on Pixel1 phone. OFA decouples model training from neural architecture search. The search cost and training cost both stay constant as the number of deployment scenarios grows. “#25” denotes the specialized sub-networks are fine-tuned for 25 epochs after grabbing weights from the once- for-all network. “CO2e” denotes CO2 emission which is calculated based on Strubell et al. (2019). AWS cost is calculated based on the price of on-demand P3.16xlarge instances.

Key benefits of OFA

The Once for All (OFA) approach offers several benefits that make it an attractive option for efficient deployment in machine learning and artificial intelligence:

  • Simplicity and Efficiency: OFA simplifies the training process by eliminating the need to train multiple networks for different tasks and platforms. This leads to significant time and resource savings.
  • Flexibility: OFA enables the adaptation of a single network to different deployment scenarios, such as mobile devices, data centres, or edge devices. This flexibility allows for more efficient resource utilisation and improved performance.
  • State-of-the-art Performance: Despite its efficiency, OFA achieves state-of-the-art performance across various tasks and platforms. This makes it a promising approach for real-world applications.
  • Adaptability: The OFA approach can be applied to a wide range of machine learning tasks, including image classification, object detection, and natural language processing. This adaptability makes it a versatile tool for researchers and practitioners.

Applications of OFA

The Once for All (OFA) approach has gained significant attention and adoption in the machine learning and artificial intelligence community. Some notable applications and achievements include:

  • SONY Neural Architecture Search Library: SONY has adopted the OFA Network in its Neural Architecture Search Library, highlighting its potential for efficient deployment in real-world applications.
  • ADI MAX78000/MAX78002 Model Training and Synthesis Tool: ADI has also adopted the OFA
    Network in its Model Training and Synthesis Tool, further demonstrating its effectiveness in
    specialised domains.
  • Alibaba’s MLPerf Inference Benchmark: OFA ranked first in the open division of the MLPerf Inference Benchmark, conducted by Alibaba. This achievement showcases its performance and efficiency in data center and edge computing scenarios.
  • CVPR Low-Power Computer Vision Challenge: OFA secured first place in the CVPR Low-Power Computer Vision Challenge, both in the CPU detection and FPGA track. This success highlights its potential for low-power and resource-constrained environments.

Conclusion

The Once for All (OFA) approach presents a groundbreaking solution for training one network and specialising it for efficient deployment in machine learning and artificial intelligence. By eliminating the need for training multiple networks, OFA simplifies the process, improves resource utilisation, and achieves state-of-the-art performance. Its wide range of applications and notable achievements in various domains further validate its potential impact. As the field of machine learning and artificial intelligence continues to advance, the Once for All approach holds great promise for driving innovation and efficiency in the deployment of neural networks.

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Stefanos Biliousis

Hello! My name is Stefanos Biliousis and I'm a computer vision researcher with a passion for exploring the latest advances in artificial intelligence. With a background in machine learning and computer science, I spend my days developing innovative algorithms and techniques for image and video analysis. I'm fascinated by the many ways that computer vision and AI are revolutionising the world around us.

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