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Generative AI: Challenges and Implications Generative AI LLM ChatGPT Bard Generative AI Ecosystem Challenges and Implications
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Generative AI
- 1. 2023 (1) Carlos J. Costa (ISEG) GENERATIVE AI: CHALLENGES AND IMPLICATIONS Moderator: Prof. Carlos J. Costa, ISEG
- 2. 2023 (2) Carlos J. Costa (ISEG) Index • Presentation of the moderator • Generative AI • LLM • ChatGPT • Bard • Generative AI Ecosystem • Challenges and Implications
- 3. 2023 (3) Carlos J. Costa (ISEG) Carlos J. Costa Associate Professor with Habilitation • Chat-GPT Task Force @ ISEG • Costa, C. (2023 Jan. 6) “GPT-3 e a utilização de Inteligencia Artificial no Ensino”. isegtech.blogs.sapo. https://isegtech.blogs.sapo.pt/gpt-3-e-a-utilizacao-de-ia-no- ensino-7355 • Costa, C. (2023 Jul. 16) “Bard: Finalmente em Portugal”. isegtech.blogs.sapo. https://isegtech.blogs.sapo.pt/bard-finalmente-em-portugal-8321
- 4. 2023 (4) Carlos J. Costa (ISEG) Generative AI • Generative artificial intelligenceincludes models that can generate new content, such as images, music, and text. • An example of these models is ChatGPT.
- 5. 2023 (5) Carlos J. Costa (ISEG) LLM • A large language model (LLM) is a type of language model characterized by its ability to achieve general-purpose language processing and generation.
- 6. 2023 (6) Carlos J. Costa (ISEG) ChatGPT • Conversational generative artificial intelligence chatbot • LLM-based chatbot • developed by OpenAI • Launched on November 30, 2022, • Generative, Pre-Trained, Transformer Name Release date Developer Number of parameters Corpus size Training cost (petaFLOP-day) License GPT-2 2019 OpenAI 1.5 billion 40GB[103] (~10 billion tokens) MIT GPT-3 2020 OpenAI 175 billion 300 billion tokens 3640proprietary GPT-4 March 2023 OpenAI Exact number unknow Unknown proprietary
- 7. 2023 (7) Carlos J. Costa (ISEG) Google Bard • Conversational generative artificial intelligence chatbot • Developed by Google, • Based initially on the LaMDA family of large language models (LLMs) and later the PaLM LLM. Name Release date Developer Number of parameters Corpus size Training cost (petaFLOP-day) License BERT 2018 Google 340 million 3.3 billion words 9Apache 2.0 XLNet 2019 Google ~340 million 33 billion words Apache 2.0 GLaM (Generalist Language Model) December 2021 Google 1.2 trillion 1.6 trillion tokens 5600proprietary Minerva June 2022 Google 540 billion 38.5B tokens from webpages proprietary LaMDA (Language Models for Dialog Applications) January 2022 Google 137 billion 1.56T words, 168 billion tokens 4110proprietary PaLM (Pathways Language Model) April 2022 Google 540 billion 768 billion tokens 29250proprietary PaLM 2 (Pathways Language Model 2) May 2023 Google 340 billion 3.6 trillion tokens 85000proprietary
- 8. 2023 (8) Carlos J. Costa (ISEG) Generative AI Ecosystem
- 9. 2023 (9) Carlos J. Costa (ISEG) Generative AI Ecosystem Other services: • Chat PDF https://www.chatpdf.com/ • ChatGPT https://chat.openai.com/ • Chatsonic https://app.writesonic.com/ • Consensus https://consensus.app/ • Elicit https://elicit.org/ • Google Bard https://bard.google.com/ • Microsoft Bing Chat (no Edge) https://www.bing.com/ • Research Rabit https://www.researchrabbit.ai/ • SciCite https://scite.ai/ • SciSpace https://typeset.io/ • SciStyle https://www.scistyle.com/ • You.com https://you.com/
- 10. 2023 (10) Carlos J. Costa (ISEG) Generative AI Ecosystem • GPT4ALL (https://gpt4all.io) allows to run locally many pretrained models
- 11. 2023 (11) Carlos J. Costa (ISEG) Generative AI Ecosystem • FreedomGPT (https://www.freedomgpt.com) • 100% uncensored and private AI chatbot. • ALPACA and LLAMA models
- 12. 2023 (12) Carlos J. Costa (ISEG) Challenges and Implications • Education • Research • Software development • Creative industries • Manufacturing • Cybersecurity • Client Support • Health care
Editor's Notes
- deep learning architecture that relies on the parallel multi-head attention mechanism
- deep learning architecture that relies on the parallel multi-head attention mechanism