{"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":"# **Generative Artificial Intelligence**\n# Evolution in the last two years\n\nEssay Submitted by C.R. Suthikshn Kumar\nProfessor of Computer Engineering and Mathematical Sciences,\nDIAT, Pune, India\n\n**Introduction**\nThe computing field has had remarkable advancements with Generative AI being prominent among them. Generative AI is a subfield of AI and is dealing with generation of new content such as text, image, music, code, video etc. In the last two years, many innovative applications of Generative AI have emerged which have captured the imagination of public and catapaulted AI as a disruptive technology. For example, Generative AI based ChatGPT a chatbot garnered a million users within 5 days of its launch[1]. Generative AI market is projected to be worth USD 110 Billion by 2030[16]. Thus interest in Generative AI is growing exponentially. This essay chronicles the latest developments in Generative AI which have happened in the last two  years also  bringing out the issues and challenges, applications, pros and cons of this technology. \n\nThere is vast amount of literature on Generative AI. There are wiki articles[2,4], magazine articles, blogs,  research papers[5],  books[8,9,10] and reports[6]. There is so much buzz around the hot topic of Generative AI nowadays. Generative AI technological developments and achievements are extensively covered in news. The  entrepreuners and researchers  working \non Generative AI are receiving massive research funding with investments reaching USD 100 Billion[16]. The technological promise of Generative AI is astounding and its impact almost touching eveny human  on the planet. \n\n**Timeline**\nGenerative AI is seeing an exponential growth in the last two years. GPT-3 which is transformer model was released to public in 2020. It has whopping 150 Billion parameters and within a short period of time attracted millions of users. ChatGPT is web based chat application based on openAI GPT model. The applications such as ChatGPT and Google Bard have capabilities for natural languages conversation, supporting several languages and have even left the AI experts in awe. There were also flurry of activities with the release of text-to-image models such as DALL-E from openAI and imagen from Google. The notable \ndevelopment was that of Latent Diffusion, a diffusion model trained in the autoencoder. For a detailed discussion on the Generative AI timeline refer to [12]. Listed below are the major milestones of Generative AI:\n* 2021: DALL-E, Stable Diffusion : Practical High quality image generation  from text  AI tools. LaMDA, a conversational large language model by Google launched. \n* 2022: Launching of chatbot ChatGPT from openAI based on GPT-3.\n* 2023: GPT-4 touted as Artificial General Intelligence made public. Pause AI movement\n\n**Transformers**\nThe core of the latest Generative AI models has been the Transformer which relies on deep learning with attention mechanism[17]. It has many advantages such as short training time compared to previous models and is generally adopted for Large Language models. It has parallel processing capability and is\nutilized in GPT-3 and BERT. The block diagram of Transformer model is shown here[17]. The trasformer model is readily available for programmers in TensorFlow and PyTorch.\n!![The-Transformer-model-architecture.png](attachment:020601fd-dbc5-4d73-9fed-560eb0fc762d.png)[https://en.wikipedia.org/wiki/Transformer_(machine_learning_model)#/media/File:The-Transformer-model-architecture.png](http://)\n\n\n**Text Generation**\nThe text generation and large language models(LLMs) have made tremendous strides in the last two years. The chatGPT[1] and Google Bard[2]  which are released to public in last two years, are two examples which have become household names. They  have touched each and every computer user with better comprehension, fluency and semantic understanding. The impact of them have been felt in almost all walks of life such as education, conversation, news, writings, commentary, programming, summarisation etc.  The LLMs have capability to generate human-like text and speech.  NotebookLM from google is an Generative AI tool can summarize facts and explain conplex concepts[21].\n\n**Image Generation**\nGenerative Adversarial Networks ( GANs) have been successfully utilized for generating creative and innovative images. GAN research and development has achieved new heights in the last two years. GAN architectures such as StyleGAN and BigGAN have been in the forefront of artificial image generation of high quality, realistic and detailed images. These images generated have found new applications in art, graohics, advertisement, computer games and inspired creativity of new heights. DALL-E-2 is one such Generative AI tool for realistic image and art generation from plaintext description[7].\n\n**Code Generation **\nThe programmers tasks are much simplified today thanks to generative AI.\nGenerative AI can automatically  generate  source code and complete applications \nbased on natural language description or user requirements.  Example is google PalM-2 which is latest LLM and excels at code generation and math. PalM-2 can \nnot only do coding, but also achieve tasks such as language  translation and reasoning. PalM-2 can program in 10 different programming languages and support 100 different natural languages for conversation, documentation and translation[13].\n\n**Music Generation**\nMusicLM from google is a generative AI tool that can generate music from text descriptions. MusicLM can be helpful in creative task of music composition and is useful for even professional musicians[22].  Other tools such as Jukebox from OpenAI, MusicGen from Meta are examples of Generative AI useful for music generation.\n\n\n**Major Applications**\nGenerative AI have many real world applications. ChatGPT and similar applciations are being routinely being used  in many domains for text generation. The chatGPT and Bard have been utilized by professionals such as\nDoctors, Lawyers, Engineers for their work and documentation. Students have\nbeen using these tools for their learning and writing. The LLM chatbots\nare routinely used for learning about new subjects, summarisation, elaboration,\narticulation etc. Then the image generation AI such as DALL-E 2 are useful for\ngenerating realistic images and paintings. There are a growing number of innovative applications of Generative AI as researchers uncover their true potentials. \n\n**Pros and cons**\nGenerative AI has brought revolution in Computer based AI as a partner and collaborator for humans in their day to day activities. Artists, musicians,\ndesigners, photographers, movie makers, writers, and even programmers are using these generative AI based tools for enhancing their creativity and productivity.\nA powerful chatbot can be developed which can converse in 130 different languages using Generative tool such as Google Bard  within a few minutes!. Thats the amazing capability of Generative AI. \n\n\nThere are growing number of applications for generative AI. The generative AI \nhas captured the imagination of public and catapaulted AI as a number one technology. The benefits of generative AI are that it can be an amplifier for\nhuman capabilities. Children can learn new skills and have information at their\nfinger tips. Concepts can be  explained by generative AI tools matching the level of user and ensuring easy comprehension. AI tools can be useful for\nelderly for conversation. Doctors and Lawyers can utilize the Generative AI\nfor consultation, report writing, information presentation etc. Researchers \ncan utilize the power of generative AI for their advantage in carrying out\nresearch work, writing reports and papers, assimilating latest developments etc.\n\nAccording to a study published by MIT researchers, inexperienced workers gain by the use of generative AI in performing their work[18]. Generative AI tools enhanced worker productivity by as much as 14%. It also helped improve customer satisfaction. Employees with skillsets to utilize the generative AI in their work were more productive and perceived as better workers. Most of the innovative companies were of the opinion that Generative AI was instrumental in their being faster, efficient and highly productive. PwC study has predicted that the global GDP will be increased by 14% by the year 2030 due to the breakthrough innovations in Generative AI[19] AI is providing competitive advantage to organizations which are proactive in using it in their work.\n\nOn the flipside, generative AI has recieved backlash with many serious repurcussions and side effects. The impact is felt to such an extent that \ngroup of scientists, entrepreneurs, academics and others have even escalated the matter to pause the Generative AI research[11].  This is unprecidented in any other field except in nuclear research. There is also errors and mistakes made by Generative AI which has raised many alarms. One example is where generative AI tool recommended \"suicide\" as a solution to an user[14].  The \ngenerative AI may also  be misused for generatng deep fakes which can fool the public, generate misinformation etc. \n\nGenerative AI has massive impact on job opportunities. It may replace workers requiring repetitive, mundane tasks. The creative tasks are also being accelerated with the help of generative AI. As the Generative AI gets better and better, it may be able to replace large number of workers[20]. However, the threat of large scale job loss due to induction of AI has resulted in many protests being organized worldwide. \n\nProf Geoffrey Hinton considered as Godfather of AI, pioneer of many algorithms in Machine learning and Deep learning, recently raised alarm for Generative AI. His opinion was that generative AI could spread misinformation and may be harmful for humans in longer run[23]. In his message, he has conveyed that AI may be \"Existential Threat\". Prof Hinton's opinions are very relevant and should be the inputs for regulating AI and ensuring that they are beneficial to humans. \n\n\n**Ethical Considerations**\nGenerative AI has been a disruptive Technology which has found both proponents and opponents. It has led to many negative applications which has raised concerns and urgent need for regulation. Group of Scientists, Engineers, and others have gone to an extent of raising petitions for stopping further research into Generative AI till precautionary measures are in place.  Top AI scientists such \nas Prof Geoffrey Hinton have raised concerns about AI. \n\n**Responsible AI**\nResponsible AI refers to ethical and responsible development, deployment and application of AI. It involves factoring for potential social, economic and environmental impacts of AI technologies. Generative AI systems are developed \nin such a way as to ensure that they are respectful for human rights and social values. Significant research effort is being dedicated to developing policies and regulations, guidelines to ensure responsible AI development. Organizations such as Google which are at the forefront of AI research and development have published their commitment for responsible AI[15[\n\n**Conclusion**\n Generative AI has lot more potential with better computing capabilities and data. There are growing number of innovative applications of Generative AI. However, responsible and ethical ways of deploying generative AI can have positive impact on improving quality of life.  The field of Generative AI has been growing exponentially with researchers pushing the technological barriers.\nThe last two years have been eventful in Generative AI research and innovations. \nThe user base for Generative AI has been growing and is currently about 100 million worldwide. \n \n\n**References**\n1. Introduring Chat GPT,https://openai.com/blog/chatgpt ( last accessed on 29th June 2023.\n2. Wikipedia on Generative AI, https://en.wikipedia.org/wiki/Generative_Artificail_Intelligence ( last accessed on 29th June 2023)\n3. Google Bard, https://bard.google.com/ ( last accessed on 29th June 2023)\n4. Large Language Model, https://en.wikipedia.org/wiki/large_language_model/ (last accessed on 29th June 2023)\n5. C.Zhang et al, \"A Complete Survey of Generative AI\", https://arxiv.org/abs/2303.11717/ ( last accessed on 29th June 2023)\n6. KPMG Survey on Generative AI: https://info.kpmg.us/news-perspective/technology-innovation/kpmg-generative-ai-2023.html ( last accessed on 29th June 2023)\n7. DALL-E 2 : AI system for creating realistic Images: https://openai.com/dall-e-2 ( last accessed on 30th June 2023)\n8. David Foster, \"Generative Deep Learning: Teaching Machines to Paint, Write,Compose,  and Play\" O'Reilly Media Publishers, May 2023.\n9. HBR, \"Generative AI: Insights from HBR\", Kindle Edition.\n10. A. Courville, I Goodfellow and Y. Bengio, \"Deep Learning\", MIT Press.\n11. Pause AI: https://pauseai.info ( Last Accessed on 30th June 2023)\n12. D. Foster, \"Generative AI Timeline\"  https://linkedin.com/davidfoster_datascience-machinelearning-ai-activity-7044233450295316480-nd31/ ( Last accessed on 1st  July 2023)\n13. PalM-2: https://ai.google/discover/palm2 ( Last accessed on 4th July 2023)\n14. AI-Time to press Pause Button, https://bcs.org/articles-opinion-and-research/ai-time-to-press-pause-button ( Last accessed on 4th July 2023)\n15. Responsible AI https://ai.google/responsibility ( Last accessed on 4th July 2023)\n16. D. Chew, \"Generative AI and ChatGPT stats to know in 2023\", https://dialpad.com/blog/generative-ai-stats/ ( Last accessed on 5th July 2023)\n17. Wikipedia on Transformers, https://en.wikipedia.org/Transformer_(machine_learning_model)\n18. B. Eastwood, \"Workers with less experience gain the most from Generative AI\", https://mitsloan.mit.edu/ideas-made-to-matter/workers-less-experience-gain-most-generative-ai ( Last accessed on 11th July 2023)\n19. PwC's Global Artificial Intelligence Study: Exploiting the AI revolution, https://pwc.com/gx/en/issues/data-and-analytics/publications/artificial-intelligence-study.html ( last accessed on 11th July 2023)\n20. The Guardian, \"Why would we employ people? Experts on five ways AI will change the work\", https://theguardian.com/global-development/2023/may/12/why-would-we-employ-people-experts-on-five-ways-ai-will-change-work ( Last accessed on 11th July 2023)\n21. Introducing NotebookLM, https://notebooklm.google.com/about ( last accesed on 16th July 2023)\n22. AI Test Kitchen, https://aitestkitchen.withgoogle.com/ ( Last accessedon 16th July 2023)\n23. S.Brown, \"Why Neural Networks pioneer Geoffrey Hinton is sounding alarm on AI\", https://mitsloan.mit.edu/ideas-made-to-matter/why-neural-net-pioneer-geoffrey-hinton-sounding-alarm-ai ( Last accessed on 16th July 2023)","metadata":{},"attachments":{"020601fd-dbc5-4d73-9fed-560eb0fc762d.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"","metadata":{}}]}