Generative AI
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Presented at All Things Open RTP Meetup Presented by Karthik Uppuluri, Fidelity Title: Generative AI Abstract: In this session, let us embark on a journey into the fascinating world of generative artificial intelligence. As an emergent and captivating branch of machine learning, generative AI has become instrumental in myriad of sectors, ranging from visual arts to creating software for technological solutions. This session requires no prior expertise in machine learning or AI. It aims to inculcate a robust understanding of fundamental concepts and principles of generative AI and its diverse applications. Join us as we delve into the mechanics of this transformative technology and unpack its potential.
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Generative AI
- 1. Generative AI Karthik Uppuluri Principal Data Scientist AI Center of Excellence, Fidelity Investments 0 8 / 0 1 / 2 0 2 3 For Wifi use Greenline
- 2. Introduction Artificial Intelligence, Machine Learning and Deep Learning q The term AI in the broadest sense refers to simulation of human intelligence processes by computer systems q Machine Learning is a subset of AI focusses on designing specific systems which can learn and make decisions/predictions based on data. q Deep Learning is a subset of Machine Learning that uses a specific set of algorithms known as Neural-Networks often with many layers. For Wifi use Greenline
- 3. Introduction Types of Machine Learning Models q Supervised Learning Supervised Learning is a type of Machine Learning model trained on labeled data Email Spam Classification Model Data: Examples of emails either tagged as Spam or not Spam Training: Discriminative – Learns the boundary that separates “spam” vs “not spam” Generative – Learns the distribution of “spam” and “not spam” emails to understand how each class generates content Inference Discriminative – Determine on which side of the boundary a new email falls Generative – Based on learned distributions compute the likelihood of the new email being “spam” vs “not spam” For Wifi use Greenline
- 4. Introduction Types of Machine Learning Models q Unsupervised Learning Unsupervised Learning is a type of Machine Learning model that identifies patterns and structures within un-labelled data Email Topic Modeling Data: A large collection of emails you may want to organize by subject matter Training: Learn the distribution that generates the structure within the data Inference - Assign new email to the cluster where they have the highest probability of belonging For Wifi use Greenline
- 5. Introduction Types of Machine Learning Models q Reinforcement Learning Interaction: Agent interacts with the environment by choosing actions from its current policy A self-driving car decides to take a left or a right based on its current strategy and current state of the road Reward/Penalty: After each action, agent receives a reward/penalty which reflects the success of the action If the car safely navigates traffic or obeys rules, it’s a success Policy Update: Agent updates the policy based on feedback received aiming to maximize the total reward over time. Based on the reward/penalty received car adjusts its driving policy, actions with positive rewards will be repeated and negative rewards will be avoided q Shallow and Deep Models Models with limited layers and capable of capturing only linear and simple nonlinear relationships are called shallow models Models with many layers and capable of capturing complex hierarchical patterns are called deep models For Wifi use Greenline
- 6. Introduction Summary Images Generated for this presentation For Wifi use Greenline
- 7. Generative AI GPT, GAN and Diffusion Models Applications of Generative AI Emerging Trends, Limitations, Potential Ahead For Wifi use Greenline
- 8. Generative AI Definition Generative AI refers to a set of artificial intelligence methodologies that can produce novel content that resembles the training data they were exposed to. For Wifi use Greenline
- 9. Generative AI Generative Pre-Trained Transformer (GPT) - Motivation Issues with CNNs, RNNs, LSTMs - Convolutional Neural Networks (CNNs) are good at local feature extraction and struggle to understand long-range dependencies in data - CNNs do not have a mechanism to understand the order of elements making it harder for problems involving text and time-series - RNNs especially LSTMs can handle long range dependencies due to their ability to process data sequentially. But as the sequences get longer, they struggle from vanishing gradient problems - CNNs, RNNs, LSTMs are suitable for specific data types and are not efficient at handling multi-modal inputs What if you can completely avoid recurrent connections, thereby avoiding vanishing gradient issues? For Wifi use Greenline
- 10. Generative AI Generative Pre-Trained Transformer (GPT) - Motivation • A new architecture called Transformers is proposed by scientists from Google which avoids the recurrent connections altogether by relying on an operation known as attention • This architecture also takes care of sequential nature of inputs by using positional embeddings https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf For Wifi use Greenline
- 11. Generative AI Generative Pre-Trained Transformer (GPT) - Attention Let's take an example sentence. Alice, who has a black cat, loves going to park - When the model is processing the word “loves”, attention mechanism allows it to associate it with “Alice” - At each word, attention mechanism allows to look at words at other positions in the input sequence to better encode the word at current position 1. At each input position, calculate query, key and value vectors (a linear transformation of embeddings using learnt weight matrices) 2. Compute dot product between each query and all the keys in the input sequence (attention) 3. Compute a weighted sum of all value vectors using attention weights as coefficients https://arxiv.org/pdf/1706.03762.pdf http://jalammar.github.io/illustrated-transformer/ For Wifi use Greenline
- 12. Generative AI Generative Pre-Trained Transformer (GPT) –Transformers Architecture https://arxiv.org/pdf/1706.03762.pdf http://jalammar.github.io/illustrated-transformer/ https://cs182sp21.github.io/static/slides/lec-12.pdf Architecture • Six Encoder layers stacked • Six Decoder layers stacked • Positional Embeddings • Masked Attention (Encoder- Decoder Attention) Advantages • Better long-range connections • Easier to parallelize • Can make the networks much deeper (more layers) than RNNs For Wifi use Greenline
- 13. Generative AI Generative Pre-Trained Transformer (GPT) A Generative-Pre-Trained Transformer is a kind of transformers model developed by OpenAI for natural language processing tasks - Generative refers to the model’s ability to generate text - Pre-Trained refers to models training process consisting of two stages - Pre-Training: Model is trained on a large corpus of text data, where the objective is to predict next word in a sentence - Fine-tuning: Once the model is pre-trained the model can be fine-tuned on a specific task with a task-specific dataset with supervised learning https://arxiv.org/pdf/2005.14165.pdf https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf For Wifi use Greenline
- 14. Generative AI Generative Pre-Trained Transformer (GPT) – GPT 3 Training Data and Parameters https://arxiv.org/pdf/2005.14165.pdf Dataset Parameters For Wifi use Greenline
- 15. Generative AI Generative Pre-Trained Transformer (GPT) – GPT 3 Unreasonable Effectiveness https://arxiv.org/pdf/2005.14165.pdf For Wifi use Greenline TriviaQA is a reading comprehension dataset containing over 650K question- answer-evidence triples. https://nlp.cs.washington.edu/triviaqa/
- 16. Generative AI Generative Pre-Trained Transformer (GPT) – LLM Landscape https://arxiv.org/pdf/2304.13712.pdf https://amatriain.net/blog/transformer-models-an-introduction-and-catalog-2d1e9039f376/ For Wifi use Greenline Encoder Models: These models map input sequences to a vector representation. Useful for extracting features (BERT) Decoder Models: These models generate an output sequence from a fixed length input vector. Useful for generation text, images etc. (GPT-3) Encoder-Decoder Models: These models are a combination of both encoder and decoder. Encoder is responsible for mapping input into vector and decoder generates output sequence from that vector. (BART/ T5/ FLAN UL2)
- 17. Generative AI Generative Pre-Trained Transformer (GPT) – Chain-of-thought Prompting https://arxiv.org/pdf/2201.11903.pdf Chain-of-Thought Prompting is a technique that enables LLMs to complex reasoning by generating a chain-of-thought, a series of intermediate reasoning steps. Prompting Datasets and Example Problems Performance For Wifi use Greenline
- 18. Generative AI Generative Pre-Trained Transformer (GPT) – Alignment - RLHF https://arxiv.org/pdf/2212.08073.pdf • Reinforcement Learning through Human Feedback is technique that allows models to learn directly from human feedback (like prompting) without the need for labeled data • Due to the nature of training data being scrapped from internet (contains a lot of mis-information, conspiracy theories etc..) the models must be further polished/aligned using RLHF to make it user appropriate https://huyenchip.com/2023/05/02/rlhf.html For Wifi use Greenline
- 19. Generative AI Generative Adversarial Networks (GANs) https://arxiv.org/pdf/1406.2661.pdf Imagine you have a bunch of cat images, and you want a machine learning model to create similar images. This is exactly what a GAN does. Generator: Takes in random numbers as input and generates the images of interest (the forger) Discriminator: Takes both the images from the generator and the real images from the data and spots the difference between them (the detective) Both the generator and the discriminator are trained together. And, over the duration of training, the generator gets better at creating images which look real, and the discriminator gets better at spotting fakes. Adversarial Objective: These two networks are pitted against each other where the generator creates more realistic synthetic images to fool the discriminator while the discriminator networks tries to get better at detecting fake images. This back-and-forth strategy forces both the networks to improve until the generator can create highly realistic synthetic images, that indistinguishable from real images For Wifi use Greenline
- 20. Generative AI Diffusion Models Diffusion models are another class of Generative models which work by adding noise to the images in the training data by a process called forward diffusion process and then reversing the process to recover the original image using reverse diffusion. These models can be trained on large unlabeled datasets in an unsupervised manner. Stable Diffusion: Stable Diffusion is a text-to-image model. A stable diffusion model has four important elements - Diffusion Probabilistic Model - U-Net Architecture - Latent Text Encoding - Classifier-Free Guidance https://stablediffusionweb.com/ For Wifi use Greenline
- 21. Applications Dall-E Example 1 Images Generated for this presentation For Wifi use Greenline
- 22. Applications Dall-E Example 2 Images Generated for this presentation For Wifi use Greenline
- 23. Applications Midjourney Image Generated for this presentation For Wifi use Greenline
- 24. Applications Midjourney Prompt: Imagine a small seed planted in the ground. It sprouts, grows into a sapling, then a small tree, and finally a large robust tree. Each year, it sprouts new branches, leaves and sometimes fruits – all from that small seed. This is how your investment grows with compounding – It branches out producing more and more just like a tree Images Generated for this presentation For Wifi use Greenline
- 25. Applications ChatGPT Images Generated for this presentation For Wifi use Greenline
- 26. Generative AI at Fidelity Ner4Opt: Named Entity Recognition for Optimization Modelling from Natural Language q Envision automated modeling assistant to turn natural language into optimization formulations q Necessary building block: finding key pieces of information relevant to optimization q Ner4Opt: extracting optimization-related information such as the objective, constraints, and variables from free-form natural language text https://link.springer.com/chapter/10.1007/978-3-031-33271-5_20 https://huggingface.co/spaces/skadio/Ner4Opt For Wifi use Greenline
- 27. Generative AI at Fidelity Ner4Opt: Named Entity Recognition for Optimization Modelling from Natural Language https://link.springer.com/chapter/10.1007/978-3-031-33271-5_20 For Wifi use Greenline
- 28. Generative AI at Fidelity Ner4Opt: Named Entity Recognition for Optimization Modelling from Natural Language https://link.springer.com/chapter/10.1007/978-3-031-33271-5_20 https://nl4opt.github.io/results/ For Wifi use Greenline
- 29. Generative AI at Fidelity Understanding BLOOM: An empirical study on diverse NLP tasks Compare the Open-Source BLOOM with other models like BERT/GPT • Does performance of BLOOM scale with parameters? Authors noticed that performance of BLOOM doesn’t scale with parameter size unlike models like BERT • Does finetuning improve the performance? Authors added multiple zero-shot cross-lingual and multi-lingual fine- tuning experiments suggesting BLOOM is at par or worse than monolingual GPT-2 models • What about toxicity in the generated data? Toxicity analysis of prompt-based text generation using the RealToxicity Prompts dataset shows that the text generated by BLOOM is at least 17% less toxic than GPT-2 and GPT-3 models. https://arxiv.org/pdf/2211.14865.pdf For Wifi use Greenline
- 30. Generative AI at Fidelity Correcting Semantic Parses with Natural Language through Dynamic Schema Encoding https://arxiv.org/pdf/2305.19974.pdf • There are several semantic and syntactic challenges in converting Natural Language Text to SQL queries • In this paper, authors approach Semantic Parse Correction using Natural Language Feedback • With just one-turn of correction, authors saw an improvement of accuracy up to 26% • They also show that a base T-5 model can correct the errors of a T-5 large model in a zero-shot cross parser setting. For Wifi use Greenline
- 31. Generative AI • Major breakthroughs in deep learning architectures like Transformers and Generative Adversarial Networks • Availability of massive datasets and GPU/TPU compute • New advances in techniques like RLHF/Prompting made it much easy to align these models • Low barrier of entry due to intuitive and user-friendly interfaces and strong open-source ecosystem • GenAI holds potential to create photo-realistic images, human-like speech and text and generate working code from natural language descriptions which was not possible until recently Emerging Trends For Wifi use Greenline
- 32. Things to Keep in Mind 1. Lack of Consistency (Hallucination): LLMs tend to produce wildly different answers, when the same question is asked multiple times 2. Bias: As the models are trained on data scrapped from internet, they might have inherited the biases present in the training data 3. Interpretability: It is difficult to understand why a particular response or content is generated, making it very challenging for use cases where explainability is inherently required. 4. Real-time Knowledge: As the models are trained on a fixed dataset at a particular point in time, they lack information/changes that occurred after that point. 5. Memory: Even though these models are getting good with context lengths that can be supported, having an efficient memory remembering the important details of conversations over a long period of time is still a challenging task. 6. Engineering Challenges: Operating these semi-non-deterministic models especially in a multi- model setting (including voice, text, images etc..) at scale remains a significant challenge For Wifi use Greenline
- 33. Potential of Generative AI https://www.forbes.com/sites/bernardmarr/2023/05/31/the-future-of-generative-ai-beyond-chatgpt/?sh=161c85da3da9 1. Low Resource Languages – Ability to understand, generate any language, especially low resource ones, could help study languages and historical documents in general 2. Inclusion and Accessibility – Avatars proficient in sign languages, high precision caption generation etc., could increase accessibility for all people 3. Personalized Content Generation – Video games, music, movies can be created that cater to users and individual interests at scale 4. AI Tutors – Imagine a world where you can conjure up a tutor to teach you any skill you would like to learn at your own pace 5. Intelligent Assistants – Laborious and repetitive tasks can be delegated to Intelligent Assistants allowing humans to focus on critical thinking and decision making 6. Accelerating Scientific Discovery- General advances in AI can help accelerate scientific discovery by generating deep insights from massive datasets and design new algorithms. This can help solve most challenging problems we face today. For Wifi use Greenline
- 34. AI Center of Excellence @ Fidelity q [arXiv’23] Explainable AI with Booleans BoolXAI https://github.com/fidelity/boolxai q [NeurIPS’22, CPAIOR’23] NER for Optimization Ner4Opt https://github.com/skadio/ner4opt q [IJAIT’21] Recommender Systems Mab2Rec https://github.com/fidelity/mab2rec q [AAAI’21] NLP/Text Featurization TextWiser https://github.com/fidelity/textwiser q [ICTAI’20] Multi-Armed Bandits MABWiser https://github.com/fidelity/mabwiser q [AI Magazine’23, AAAI’22] Sequential Mining Seq2Pat https://github.com/fidelity/seq2pat q [CPAIOR’22] Feature Selection Selective https://github.com/fidelity/selective q [ICMLA’21] Fairness & Bias Mitigation Jurity https://github.com/fidelity/jurity Research & Open-Source Software github/fidelity https://jobs.fidelity.com/ For Wifi use Greenline