{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":" # Role Of Generative AI In Industry 5.0","metadata":{}},{"cell_type":"markdown","source":"> “AI can not only boost our analytic and decision-making abilities but also heighten creativity.”\n*-Harvard Business Review*","metadata":{}},{"cell_type":"markdown","source":"## Introduction","metadata":{}},{"cell_type":"markdown","source":"Machines have simplified our lives to a great extent. It is hardly possible for anyone who hasn't used a computer or smartphone.The fourth industrial revolution, or Industry 4.0, focused on development through cyber physical systems, which resulted in industry dehumanization. Industry 4.0 saw a rapid surge in the reliance on artificial computing capacities and the Internet of Things. However, with this increased usage, the role of man became unclear, which created a rift between man and machine. In response to the industry's increased reliance on machines and increased automation, the fifth industrial revolution, also known as Industry 5.0, was first introduced in 2015. The industry 5.0 added a human touch to the existing pillars of industry 4.0. [1]\n\nAs stated by the EU, Industry 5.0 aims beyond efficiency and productivity as the sole goals and reinforces the role and the contribution of industry to society. “It places the wellbeing of the worker at the centre of the production process and uses new technologies to provide prosperity beyond jobs and growth while respecting the production limits of the planet.” [2]\n\nIt differentiates itself from Industry 4.0 by “specifically putting research and innovation at the service of the transition to a sustainable, human-centric, and resilient European industry.” Instead of being entirely focused on machines and a computer-centric approach, industry 5.0 establishes the role of man as an intellectual entity rather than a physical one. [2]\n\nGenerative AI can assist humans in the decision-making process by generating insights, suggestions, and simulations based on large sets of data thus supporting the human-centric approach of Industry 5.0. [7] Generative AI is a set of algorithms which use neural networks to match and identify patterns from a large pool of data to generate text, audio, images, 3d models as well as videos.\n\nUnlike discriminative AI, which classifies elements based on the differences, for instance, classification of dog and cat, generative AI checks for the similarity of pattern in underlying samples to generate highly complex and realistic content from pre-existing data.\n\nAlthough there has been a recent buzz about this technology with the rise in popularity of platforms such as chatgpt, dall-E, and Midjourney, however, this technology dates back to the 1950s when generative AI was first introduced in chatbots.\n\n","metadata":{}},{"cell_type":"markdown","source":"## Uses of Generative AI\n","metadata":{}},{"cell_type":"markdown","source":"Generative AI uses the joint probability distribution of input and output features to understand common patterns from underlying data and generate new data.\nGenerative AI can be divided broadly into two main categories : Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). \nGANs are made up of  a generator, a discriminator and two neural networks which compete to generate realizable data. VAEs are made up of an encoder and a decoder that collaborate to compress data into latent variables and from these latent variables it generates new data. The various models that use generative AI include Neural Networks, computer vision  and various deep learning models.[3]\nGenerative AI has revolutionized the way we do things. As of 2023, it has been reported that there are over 350 Generative AI applications and the numbers are ever-increasing. [4]\n\n- Text Generation:-\n  This technique utilizes pre-existing text data to generate text based on common patterns. For example:- chatGPT, Quillbot\n\n- Image Generation:-\n  This is being used to generate images that are visually similar to real-world images by using deep learning algorithms such as stable diffusion and VAEs.\n For example Midjourney, DALL-E\n\n- Video Generation:-\n  This involves deep learning methodologies to generate new frames based on pre-existing frames by using video diffusion or GANs.\nFor example:- NVIDIA Maxine\n\n- Speech Generation:-\n  This is being widely used for social media and has been proven extremely useful for content creators who are not comfortable using their voice in videos. This is made possible by utilizing the text-to-speech capabilities of transformers.\n","metadata":{"execution":{"iopub.status.busy":"2023-07-05T21:52:05.161582Z","iopub.execute_input":"2023-07-05T21:52:05.161893Z","iopub.status.idle":"2023-07-05T21:52:05.174094Z","shell.execute_reply.started":"2023-07-05T21:52:05.161872Z","shell.execute_reply":"2023-07-05T21:52:05.173006Z"}}},{"cell_type":"markdown","source":"## Artificial Intelligence Towards Human-Centered Approaches","metadata":{}},{"cell_type":"markdown","source":"Human-centered approaches to AI mostly involve AI systems with an emphasis to develop systems that align with improving the condition and solving the basic need of the human stakeholders. [7][8]\n\nThe rapid progress in generative AI has been driven by four factors: previous innovation in model architecture, computing power, refined training techniques and use of unlabeled data.\nThis has proven instrumental in the development of GPT models as using unlabeled text data allowed the model to understand and learn human language without any external stimuli. Currently, all the LLMs are being trained using unlabeled data and following reinforcement learning principles.[5]\n\nEmerging uses of Generative AI include Explainable AI and using GANs for coding. The most recent development in this field is the release of the codex by OpenAI, a gpt-based model which has been developed based on data from GitHub.[8]","metadata":{}},{"cell_type":"markdown","source":"## Ethics And Practices\n","metadata":{}},{"cell_type":"markdown","source":"Recently there has been massive development and an increase in the usage of generative AI. However, this has led to much speculation and disputes among creative professionals. Now that visual and written content are being easily developed by AI many professionals feel that now they have to compete with AI for their jobs. This is all because the AI has access to a more diverse as well as a large pool of data and is capable of getting the job done with more accuracy and in less time. Out of the twenty -three responses, there were only three such responses where the professionals were not worried about AI taking up their jobs. The reason being AI can't function without human intervention and the high level of complexity their job involved.[6][10]\n\nOver the last few months, there have been many journals and publications which have been co-written by using GANs. It is now common knowledge that any content written by AI can't be detected by plagiarism detectors. This has led to a debate about authorship and how this might result in a huge collection of low-quality manuscripts. We must understand that although AI can find publicly available manuscripts and generate content based on those ideas, At present, they cannot cite the sources adequately and the content produced solely depends on the question asked by the user. This means right now this technology can only be used to write blogs and student essays. Although generative AI can provide syllogism and ethical arguments based on publicly available findings, it is not capable of qualitative research. Thus a research paper cannot be solely written by an AI the originality, nuance and analytical approaches would require human intervention.[9]\n\n","metadata":{}},{"cell_type":"markdown","source":"## Role Of Kaggle As A Community ","metadata":{}},{"cell_type":"markdown","source":"Over the last few years Kaggle has played a significant role in advancing and empowering the data science community. It has provided researchers, scientists and data enthusiasts from all over the world a platform to learn, compete and collaborate with each other. \n\nGenerative AI has indeed been a game changer in Industry 5.0. It has simplified various tedious tasks, however as with any technological advancement, there are both benefits and challenges that we should consider.\n\nThe potential diversion arises from the ease with which generative AI can generate false or misleading information. As technology advances, it becomes more difficult to distinguish between what is real and what is artificially generated. This raises ethical concerns, particularly in areas such as disinformation, fake news, and deepfakes, which can have negative consequences for individuals and society as a whole.\n\nTo address these challenges,we as a community, must adopt responsible AI principles such as accountability, inclusiveness, reliability and safety, fairness, transparency, and privacy and security.[11]\n\nBy following these guidelines,we can maximize the benefits of generative AI while minimizing any negative consequences. Responsible AI practices will not only assist us in leveraging AI as a blessing, but will also result in a more safe,improved and equitable life for all.\n\n\n","metadata":{}},{"cell_type":"markdown","source":"## Conclusion","metadata":{}},{"cell_type":"markdown","source":"To conclude, generative AI can prove to be an invaluable asset in the context of Industry 5.0 by enabling enhanced collaboration between machines and humans, fostering creativity and personalization, optimizing processes, and facilitating continuous learning and improvement. \nHowever, at the same time, we must not forget that we must be responsible and ethical while using this technology. By incorporating generative AI technologies thoughtfully and ethically, Industry 5.0 can further strengthen its focus on human well-being, sustainability, and the advancement of society as a whole. ","metadata":{}},{"cell_type":"markdown","source":"## References","metadata":{}},{"cell_type":"markdown","source":"- [1] Nikolić, Gojko. “NASTAVAK ? IS INDUSTRY 5 . 0 RESPONDING TO INDUSTRY 4 . 0 OR ITS RESUME ?” (2018).\n- [2] What Is Industry 5.0 And How It Will Radically Change Your Business Strategy?https://www.forbes.com/sites/jeroenkraaijenbrink/2022/05/24/what-is-industry-50-and-how-it-will-radically-change-your-business-strategy/\n- [3] Bai, Wenjun, Changqin Quan and Zhiwei Luo. “Improving Generative and Discriminative Modelling Performance by Implementing Learning Constraints in Encapsulated Variational Autoencoders.” Applied Sciences (2019): n. pag.\n- [4] A survey of Generative AI Applications Roberto Gozalo-Brizuela, Eduardo ,C. Garrido-Mercha ́n.arXiv:2306.02781 \n- [5] Brynjolfsson, Erik, Danielle Li and Lindsey Raymond. “Generative AI at Work.” SSRN Electronic Journal (2023): n. Pag.\n- [6] Inie, Nanna, Jeanette Falk and Steve Tanimoto. “Designing Participatory AI: Creative Professionals’ Worries and Expectations about Generative AI.” Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems (2023): n. Pag.\n- [7] Michael Muller, Lydia B Chilton, Anna Kantosalo, Q. Vera Liao, Mary Lou Maher, Charles Patrick Martin, and Greg Walsh. 2023. GenAICHI 2023: Generative AI and HCI at CHI 2023. In - Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems (CHI EA '23). Association for Computing Machinery, New York, NY, USA, Article 350, 1–7. https://doi.org/10.1145/3544549.3573794\n- [8] Sun, Jiao, Qingzi Vera Liao, Michael J. Muller, Mayank Agarwal, Stephanie Houde, Kartik Talamadupula and Justin D. Weisz. “Investigating Explainability of Generative AI for Code through Scenario-based Design.” 27th International Conference on Intelligent User Interfaces (2022): n. Pag.\n- [9] Zohny, Hazem, John McMillan and Mike R. King. “Ethics of generative AI.” Journal of Medical Ethics 49 (2023): 79 - 80.\n- [10] Hughes, Rowan T., Liming Zhu and Tomasz Bednarz. “Generative Adversarial Networks–Enabled Human–Artificial Intelligence Collaborative Applications for Creative and Design Industries: A Systematic Review of Current Approaches and Trends.” Frontiers in Artificial Intelligence 4 (2021): n. Pag\n- [11] Shneiderman, Ben. “Responsible AI.” Communications of the ACM 64 (2021): 32 - 35.\n","metadata":{}}]}