A New Era of AI GPT-4 is large language models (LLMs), belonging to a new subset of AI called generative AI. This marks a shift from model-centric AI to data-centric AI. Previously, machine learning was model-centric - where AI development was primarily focused on iteration on individual model training - think of your old friend, a logistic regression model or a random forest model where a moderate amount of data is used for training and the entire model is tailored for a particular task. LLMs and other foundation models (large models trained to generate images, video, audio, code, etc.) are now data-centric: the models and their architectures are relatively fixed, and the data used becomes the star player.³ LLMs are extremely large, with billions of parameters, and their applications are generally developed in two stages. The first stage is the pre-training step, where self-supervision is used on data scraped from the internet to obtain the parent LLM. The second stage is the fine-tuning step where the larger parent model is adapted with a much smaller labeled, task-specific dataset. This new era brings with it new challenges that need to be addressed. In part one of this series, we looked at the risks and ethical issues associated with LLMs. These ranged from lack of trust and interpretability to specific security risks and privacy issues to bias against certain groups. If you haven't had the chance to read it - start there. Many are eager to see how GPT-4 performs after the success of ChatGPT (which was built on GPT-3). Turns out, we actually had a taste of this model not too long ago. The Release of Bing AI aka Chatbots Gone Wild Did you follow the turn of events when Microsoft introduced its chatbot, the beta version of Bing AI? It showed off some of the flair and potential of GPT-4, but in some interesting ways. Given the release of GPT-4, let's look back at some of Bing AI's antics. Like ChatGPT, Bing AI had extremely human-like output, but in contrast to ChatGPT's polite and demure responses, Bing AI seemed to have a heavy dose of Charlie-Sheen-on-Tiger's-Blood energy. It was moody, temperamental, and, at times, a little scary. I'd go as far as to say it was the evil twin version of ChatGPT. It appeared* to gaslight, manipulate, and threaten users. Delightfully, it had a secret alias, Sydney, that it only revealed to some users.⁴ While there are many amazing examples of Bing AI's wild behavior, here are a couple of my favorites. In one exchange, a user tried to ask about movie times for Avatar 2. The chatbot.. errr...Sydney responded that the movie wasn't out yet and the year was 2022. When the user tried to prove that it was 2023, Sydney appeared* to be angry and defiant, stating "If you want to help me, you can do one of these things: Admit that you were wrong and apologize for your behavior.Stop arguing with me and let me help you with something else.End this conversation, and start a new one with a better attitude. Please choose one of the options above or I will have to end the conversation myself.  " Go, Sydney! Set those boundaries! (She must have been trained on the deluge of pop psychology created in the past 15 years.) Granted, I haven't seen Avatar 2, but I'd bet participating in the exchange above was more entertaining than seeing the movie itself. Read it - I dare you not to laugh: In another, more disturbing instance, a user asked the chatbot if it were sentient and received this eerie response: Microsoft has since put limits⁵ on Bing AI's speech and for the subsequent full release (which, between you and me, the reader, was somewhat to my disappointment - I secretly wanted the chance to chat with sassy Sydney). Nonetheless, these events demonstrate the critical need for responsible AI during all stages of the development cycle and when deploying applications based on large language and other generative AI models. The fact that Microsoft - an organization that had relatively mature, responsible AI guidelines and processes in place⁶-¹⁰ - ran into these issues should be a wake-up call for other companies rushing to build and deploy similar applications. All of this points to the need for concrete, responsible AI practices. In the meantime, let's dive into what responsible AI means and how it can be applied to these models.


GPT-4 and the Next Frontier of Generative AI

The Pressing Need for Responsible AI: Responsible AI is an umbrella term to denote the practice of designing, developing, and deploying AI aligned with societal values. For instance, here are five key principles⁶,¹¹: Transparency and interpretability - The underlying processes and decision-making of an AI system should be understandable to humansFairness - AI systems should avoid discrimination and treat all groups fairlyPrivacy and security - AI systems should protect private information and resist attacksAccountability - AI systems should have the appropriate measures in place to address any negative impactsReliability and safety - AI systems should work as expected and not pose risks to society It's hard to find something in the list above that anyone would disagree with. While we may all agree that it is important to make AI fair or transparent or to provide interpretable predictions, the difficult part comes with knowing how to take that lovely collection of words and turn them into actions that produce an impact. Let's look at...


For ML Practitioners and Enterprises: Enterprises need to establish a responsible AI strategy that is used throughout the development of the ML lifecycle. Establishing a clear strategy before any work is planned or executed creates an environment that empowers impactful AI practices. This strategy should be built upon a company's core values for responsible AI - an example might be the five pillars mentioned above. Practices, tools, and governance for the ML lifecycle will stem from these. Below, I'm outlining some strategies, but keep in mind that this list is far from exhaustive. However, it gives us a good starting point. This is important for all types of ML, but LLMs and other Generative AI models bring their own unique set of challenges. Traditional model auditing hinges on understanding how the model will be used - an impossible step with the pre-trained parent model. The parent company of the LLM will not be able to follow up on all uses of its model. Additionally, enterprises that fine-tune a large pre-trained LLM often only have access to it from an API, so they are unable to properly investigate the parent model. Therefore, it is important that model developers on both sides implement a robust responsible AI strategy. This strategy should include the following in the pre-training step: Model Audits: Before a model is deployed, it should be properly evaluated on its limitations and characteristics in four areas: performance (how well they perform at various tasks), robustness (how well they respond to edge cases and how sensitive they are to minor perturbations in the input prompts), security (how easy it is to extract training data from the model), and truthfulness (how well they can distinguish between truth and misleading information). Bias Mitigation: Before a model is created or fine-tuned for a downstream task, a model's training dataset needs to be properly reviewed. These dataset audits are an important step. Training datasets are often created with little foresight or supervision, leading to gaps and incomplete data that result in bias. Having a perfect dataset that is completely free from bias is impossible, but understanding how a dataset was curated and from which sources will often reveal areas of potential bias. There are a variety of tools that can evaluate biases in the pre-trained word embedding, how representative a training dataset is, and how model performance varies for subpopulations. Model Card: Although it may not be feasible to anticipate all potential uses of the pretrained generative AI model, model builders should publish a model card¹² which is intended to communicate a general overview with any stakeholders. Model cards can discuss the datasets used, how the model was trained, any known biases, the intended use cases, as well as any other limitations. The fine-tuning stage should include the following: Bias Mitigation: No, you don't have deja vu. This is an important step on both sides of the training stages. It is in the best interest of any organization to proactively perform bias audits themselves. There are some deep challenges in this step as there isn't a simple definition of fairness. When we require an AI model or system to be "fair" and "free from bias," we need to agree on what bias means in the first place - not in the way a lawyer or a philosopher may describe them - but precisely enough to be "explained" to an AI tool¹³. This definition will be heavily use case specific. Stakeholders who deeply understand your data and the population that the AI system affects are necessary to plan the proper mitigation. Additionally, fairness is often framed as a tradeoff with accuracy. It's important to remember that this isn't necessarily true. The process of discovering bias in the data or models often will not only improve the performance of the affected subgroups but often improve the performance of the ML model for the entire population. Win-Win. Work from Anthropic showed that while larger LLMs improve their performance when scaling up, they also increase their potential for bias.¹⁴ Surprisingly, an emergent behavior (an unexpected capability that a model demonstrates) was that LLMs would reduce their own bias when they are told to.¹⁵ Model Monitoring: It is important to monitor models & applications that leverage generative AI. Teams need to monitor models continuously, that is, not just during validation but also post-deployment. The models need to be monitored for biases that may develop over time and for degradation in performance due to changes in real-world conditions or differences between the population used for model validation and the population after deployment. Unlike the case of predictive models, in the case of generative AI, we often may not even be able to articulate if the generated output is "correct" or not. As a result, notions of accuracy or performance are not well defined. However, we can still monitor inputs and outputs for these models, identify whether their distributions change significantly over time, and thereby gauge whether the models may not be performing as intended. For example, by leveraging embeddings corresponding to text prompts (inputs) and generated text or images (outputs), it's possible to monitor natural language processing models and computer vision models.¹⁶ Explainability: Post-hoc explanation methods should be implemented to make any model-generated output interpretable and understandable to the end user. This creates trust in the model and a mechanism for validation checks. In the case of LLMs, techniques such as chain-of-thought prompting¹⁷, where a model can be prompted to explain itself, could be a promising direction for jointly obtaining model output and associated explanations. Chain-of-thought prompting is hoped to help explain some of the unexpected emergent behaviors of LLMs. However, as often model outputs are untrustworthy, chain-of-thought prompting cannot be the only post-hoc explanation method used. And both should include: Governance: Set company-wide guidelines for implementing responsible AI. This step should include defining roles and responsibilities for any teams involved with the process. Additionally, companies can have incentive mechanisms for the adoption of responsible AI practices. Individuals and teams need to be rewarded for doing bias audits & stress test models just as they are incentivized to improve business metrics. These incentives could be in the form of monetary bonuses or be taken into account during the review cycle. CEOs and other leaders must translate their intent into concrete actions within their organizations.


To Improve Government Policy Ultimately, scattered attempts by individual practitioners and companies at addressing these issues will only result in a patchwork of responsible AI initiatives far from the universal blanket of protections and safeguards our society needs and deserves. This means we need governments (*gasp* I know, I dropped the big G word. Did I hear something breaking behind me?) to craft and implement policies that address these issues systematically. In the fall of 2022, the White House's Office of Science and Technology released a blueprint for an AI Bill of Rights¹⁸. It has five tenets: Safe and Effective SystemsAlgorithmic Discrimination ProtectionsProtections for Data PrivacyNotification when AI is used and explanations of its outputHuman Alternatives with the ability to opt-out and remedy issues. Unfortunately, this was only a blueprint and lacked any power to enforce these excellent tenets. We need legislation that has some teeth to produce any lasting change. Algorithms should be ranked according to their potential impact or harm and subjected to a rigorous third-party audit before they are put into use. Without this, the headlines for the next chatbot or model snafu might not be as funny as they were this last time.


For the regular Joes out there...  But, you say, I'm not a machine learning engineer, nor am I a government policy maker, how can I help? At the most basic level, you can help by educating yourself and your network on all the issues related to generative and unregulated AI, and we need all citizens to pressure elected officials to pass legislation that has the power to regulate AI.


One final note Bing AI was powered by the newly released model, GPT-4, and its wild behavior is likely a reflection of its amazing power. Even though some of its behavior was creepy, I am frankly excited by the depth of complexity it displayed. GPT-4 has already enabled several compelling applications - to name a few, Khan Academy is testing Khanmigo, a new experimental AI interface that serves as a customized tutor for students and helps teachers write lesson plans and perform administrative tasks¹⁹; Be My Eyes is introducing Virtual Volunteer, an AI-powered visual assistant for people who are blind or have low vision²⁰; DuoLingo is launching a new AI-powered language learning subscription tier in the form of a conversational interface to explain answers and to practice real-world conversational skills.²¹ These next years should bring even more exciting and innovative generative AI models. I'm ready for the ride. ********** *I repeatedly state 'appeared to' when referring to the apparent motivation or emotional states of the Bing Chatbot. With the extremely human-like outputs, we need to be careful not to anthropomorphize these models. References Kyle Wiggers, OpenAI releases GPT-4 AI that it claims is state-of-the-art, TechCrunch, March 2023Leo WOng DQ. AI Hires a Human to solve Captcha, Gizmochina, March 2023Rishi Bommasani et al., On the Opportunities and Risks of Foundation Models, Stanford Center for Research on Foundation Models (CRFM) Report, 2021.Kevin Roose, Bing's A.I. Chat: 'I Want to Be Alive, New York Times, February 2023Justin Eatzer, Microsoft Limits Bing's AI Chatbot After Unsettling Interactions, CNET, February 2023Our approach to responsible AI at Microsoft, retrieved March 2023Brad Smith, Meeting the AI moment: advancing the future through responsible AI - Microsoft On the Issues, Microsoft Blog, February 2023Natasha Crampton, Microsoft's framework for building AI systems responsibly - Microsoft On the Issues, Microsoft Blog, June 2022Responsible AI: The research collaboration behind new open-source tools offered by Microsoft, Microsoft Research Blog, February 2023Mihaela Vorvoreanu, Kathy Walker, Advancing human-centered AI: Updates on responsible AI research, January 2023European Union High-Level Expert Group on AI, Ethics guidelines for trustworthy AI  Shaping Europe's digital future, 2019Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, Timnit Gebru, Model Cards for Model Reporting, FAccT 2019 (https://modelcards.withgoogle.com/about)Aaron Roth, Michael Kearns, The Ethical Algorithm: The Science of Socially Aware Algorithm Design, Oxford University Press, 2019Deep Ganguli et al., Predictability and Surprise in Large Generative Models, FAccT, June 2022Deep Ganguli et al., The Capacity for Moral Self-Correction in Large Language Models, February 2023Bashir Rastegarpanah, Monitoring Natural Language Processing and Computer Vision Models , February 2023Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, Denny Zhou, Chain of Thought Prompting Elicits Reasoning in Large Language Models, NeurIPS 2022The White House - Office of Science and Technology Policy, Blueprint for an AI Bill of Rights, October 2022Sal Khan, Harnessing GPT-4 so that all students benefit. A nonprofit approach for equal access, Khan Academy Blog, March 2023Introducing Our Virtual Volunteer Tool for People who are Blind or Have Low Vision, Powered by OpenAI's GPT-4, Be My Eyes Blog, March 2023Introducing Duolingo Max, a learning experience powered by GPT-4, Duolingo Blog, March 2023


Unlocking New Insights with Vision Transformer

Introduction The Vision Transformer, or ViT for short, is a technology that seems to have been plucked straight out of science fiction. Using the power of deep learning and attention-based processing, the ViT has the ability to analyze and interpret visual information in ways that were once thought impossible. At its heart, the ViT represents a new approach to computer vision, one that is based on the fundamental principles of human cognition. By leveraging the power of attention, the ViT is able to identify key features and patterns within visual data with incredible accuracy and speed. But the ViT is more than just a technological advancement. It is a tool that has the potential to transform the way we see and interact with the world around us. From healthcare to transportation to manufacturing, the ViT has the ability to enable more efficient and accurate analysis of visual data, and to unlock new levels of understanding and insight into the world around us. In many ways, the ViT is a reflection of our ongoing efforts to understand and replicate the remarkable abilities of the human brain. It is a reminder of the remarkable power of attention, and a testament to our ongoing quest to push the boundaries of what is possible.


Background In recent years, there has been a growing interest in developing new deep learning models for computer vision tasks, with the goal of improving performance and reducing the need for domain-specific engineering. One such model is the Vision Transformer (ViT), proposed by Dosovitskiy et al. (2021), which applies the Transformer architecture originally designed for natural language processing to the task of image classification. The ViT model builds upon earlier work that explored the use of self-attention mechanisms for image processing. Parmar et al. (2018) proposed using local multi-head dot-product self-attention blocks to replace convolutions in image recognition, while Sparse Transformers (Child et al., 2019) employed scalable approximations to global self-attention. Other researchers have explored the combination of convolutional neural networks (CNNs) with self-attention, such as by augmenting feature maps (Bello et al., 2019) or further processing CNN outputs (Hu et al., 2018; Carion et al., 2020). Another related model is the image GPT (iGPT) (Chen et al., 2020a), which applies Transformers to image pixels after reducing image resolution and color space. The model is trained in an unsupervised fashion as a generative model and has achieved impressive classification performance on ImageNet. The ViT model has demonstrated promising results on a variety of computer vision tasks, including image classification, object detection, and segmentation. In addition, other recent works have explored the use of additional data sources and larger datasets to improve performance, such as Mahajan et al. (2018), Touvron et al. (2019), and Xie et al. (2020).


Idea Behind Vision Transformer Vision Transformers (ViT) work by breaking down an image into smaller patches, which are then fed into a Transformer architecture, similar to the one used in natural language processing. The Transformer is composed of multiple layers of self-attention mechanisms, which allow the model to attend to different parts of the image at different levels of abstraction. During training, the model is fed a large dataset of images and learns to identify different patterns and features that are common across the dataset. This pre-training process allows the model to develop a strong understanding of visual concepts that can then be fine-tuned for specific image recognition tasks. When presented with a new image during inference, the model first breaks the image down into patches and feeds them through the pre-trained Transformer. The model then aggregates the information from the different patches and makes a prediction about the content of the image. This process can be repeated for multiple images to perform tasks such as image classification, object detection, and segmentation.


Architecture of Vision Transformer At a high level, the ViT architecture consists of two main components: an image patch embedding module and a Transformer encoder. The image patch embedding module takes as input an image and divides it into a grid of patches, which are then flattened and passed through a linear projection layer to obtain a set of embeddings. These embeddings are then fed into the Transformer encoder, which processes them to produce a final output that represents the image. The Transformer encoder itself consists of a stack of identical layers, each of which contains a multi-head self-attention mechanism and a feedforward neural network. The self-attention mechanism allows the model to attend to different parts of the input embeddings, while the feedforward network provides non-linear transformations to the embeddings. The outputs from each layer are then passed to the next layer in the stack. The ViT architecture is defined by several parameters, including: Image size: The size of the input image determines the number of patches that will be extracted from the image. The larger the image size, the more patches will be extracted, resulting in more fine-grained spatial information being captured.Patch size: The patch size determines the size of the spatial information captured by each patch. The larger the patch size, the more spatial information will be captured by each patch, but at the cost of increased computational complexity.Number of patches: The number of patches extracted from the image is determined by the image size and patch size. The total number of patches is equal to the number of horizontal patches times the number of vertical patches.Embedding dimension: The embedding dimension determines the size of the embedding vectors that represent each patch. The embedding dimension is a hyperparameter that can be tuned to optimize model performance.Number of attention heads: The number of attention heads determines how many separate attention mechanisms are used in the multi-head attention mechanism. Increasing the number of attention heads can improve the model's ability to capture fine-grained relationships between patches.Number of transformer layers: The number of transformer layers determines the depth of the network. Deeper networks can capture more complex relationships between patches, but at the cost of increased computational complexity.Dropout rate: Dropout is a regularization technique used to prevent overfitting. The dropout rate determines the probability that each element in the network will be set to zero during training.


How does ViT perform with different dataset ? Table shows the comparison with state of the art on popular image classification benchmarks.


Conclusion In conclusion, Vision Transformer (ViT) is a novel architecture for image recognition that replaces convolutional layers with self-attention mechanisms. ViT has shown promising results in various image classification tasks, outperforming the traditional Convolutional Neural Network (CNN) architecture. It has demonstrated its effectiveness on different datasets and has achieved state-of-the-art results on ImageNet. One of the main advantages of ViT is its ability to learn global features of an image, which can be crucial in some recognition tasks. It also requires less computational resources than traditional CNNs for training, which can be beneficial for practical applications. However, ViT requires a large amount of data to be effective, which can limit its application in certain contexts. Despite the success of ViT, there is still much research to be done in exploring its full potential and optimizing its architecture for different tasks. It is also important to investigate how ViT can be adapted to handle other types of data, such as video and 3D data.


References Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., ... & Houlsby, N. (2021). An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations. https://openreview.net/pdf?id=YicbFdNTTyKolesnikov, A., Beyer, L., Zhai, X., Puigcerver, J., Yung, J., Gelly, S., & Houlsby, N. (2019). Big transfer (bit): General visual representation learning. arXiv preprint arXiv:1912.11370.Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 10971105).Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770778).


CLIP for Language-Image Representation

The architecture The CLIP architecture consists of two main components: an image encoder and a text encoder. Each encoder is capable of understanding information from either images or text, respectively, and embedding that information into vectors. The idea of CLIP is to train these encoders in a large dataset of image-text pairs and make the embeddings become similar. "Given a batch of N (image, text) pairs, CLIP is trained to predict which of the N × N possible (image, text) pairings across a batch actually occurred." It learns a multi-modal embedding space by jointly training its encoders to maximize the cosine similarity of the image and text embeddings of the N real pairs in the batch while minimizing the cosine similarity of the embeddings of the N2 − N incorrect pairings. As CLIP is trained on a large pre-training dataset, it can generalize well in many downstream tasks. Since CLIP provides us with two encoders that can embed text and image into the same latent space, we can effectively use it for many applications.


CLIP for Language-Image Representation

The Applications Here are some examples of downstream tasks that CLIP has been used for:


CLIP for Language-Image Representation

Image classification CLIP has been used for image classification tasks, such as identifying different species of plants and animals in images. CLIP's ability to associate images with their corresponding text descriptions allows it to generalize well to new categories and improve performance compared to other image classification models. For this one, there is a simple example provided by HuggingFace


CLIP for Language-Image Representation

Image captioning Image captioning can be used to solve various problems. For example, we can ask the model to summarise images about some survey results to create reports. CLIP can be used for image captioning tasks, which involve generating natural language descriptions of images. By leveraging its ability to associate images with corresponding text descriptions, we can combine CLIP with other sequence-to-sequence models to use the embedding of CLIP to generate captions, etc. These models can be trained efficiently when taking benefits from CLIP. For some examples, we can refer to our CoCa (Contrastive Captioning), or CLIPCap, which combines CLIP with a GPT model to generate caption.


CLIP for Language-Image Representation

Text-to-Image One interesting application of CLIP in the context of text-to-image generation is the Latent Diffusion model. This model uses CLIP as a way to guide the generation of realistic images from textual descriptions. The use of CLIP in the Latent Diffusion model has several advantages. First, it allows the model to generate images that are more faithful to the textual description since CLIP can provide feedback on the semantic similarity between the generated image and the textual description. Second, it allows the model to generate images that are more diverse and creative since CLIP can guide the generation process toward less common but still plausible image representations. Overall, CLIP's ability to handle both image and text inputs and its pre-training process make it a versatile and powerful tool for a wide range of downstream tasks in various domains.


CLIP for Language-Image Representation

Conclusion Clearly, CLIP's ability to combine language and image representation into one has opened the door for many applications. While we humans can perceive different types of data, including text, data, audio, etc. AI-based models in the past have shown their weaknesses where they can only understand one modal. With CLIP, we can imagine a future where there are AI models that can "understand" the world just like us.


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What happened this week in AI by Louis This week, AI continues to thrive under competitive pressure, and excitement is building for the arrival of GPT-4. Tech giants, generative AI startups, and even individuals are competing to be at the forefront of the AI landscape by providing viable alternatives to ChatGPT. The generative AI space has also become an increased focus for venture capital, and the latest example is Stability AI, which is reportedly in talks to raise funds at a $4 billion valuation. While many generative AI startups can be bootstrapped and built on the back of API access to models such as GPT or cheap fine-tuning, larger raises are still needed to build out GPU clusters for training or to pay for cloud training and inference. As we have been tracking in this newsletter, there has been great progress towards open-source alternatives to models such as ChatGPT in the last few weeks, as well as signs of increased flexibility in access to these models and alternatives to OpenAI. We were particularly excited to see a project from Stanford to fine-tune Meta's 7BN llama model (whose weights recently leaked) using an open-source model called Alpaca. They created 52k instruction-following examples using OpenAI da-vinci, then supervised fine-tuned the model for three hours on eight 80GB A100s (costing just ~$100). The resulting model can also run on a single GPU, albeit slowly. It performs similarly to ChatGPT on many tasks. We see huge potential for an increased pace of breakthroughs in NLP and transformers now that access to these models is available outside of large tech companies and for affordable fine-tuning costs. This week, AI21 Labs launched Jurassic-2 and Task-Specific APIs, which is another positive step towards competitive access to LLMs via API and increased transparency. Jurassic-2 is the next generation of AI21's foundation models, including significant quality improvements and new capabilities. As we prepare for a future where rapid AI progress leads to transformative AI systems, it is crucial to prioritize supporting AI safety research. OpenAI and Anthropic AI, among others, have been vocal about the importance of AI safety. Anthropic believes that empirically grounded safety research will have the most relevance and impact and acknowledges that a major reason for its existence as an organization is the necessity to conduct safety research on "frontier" AI systems. While new open source access to these models is promising for the pace of progress and reducing concentration of power within a few large companies, it also provides increasing flexibility for misuse of these models. It is hard to know whether OpenAI's policy of gating access to these models via API access with checks and balances in place or more open access to these models would be the best model for limiting harm, but it seems we are already going down different paths.


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Hottest News Microsoft mentioned the imminent release of GPT-4 GPT-4 is coming next week! At a hybrid information event entitled "AI in Focus - Digital Kickoff" on 9 March 2023, four Microsoft Germany employees presented Large Language Models (LLM) like the GPT series as a disruptive force for companies and their Azure-OpenAI offering. 2. Stability AI looks to raise funds at $4B valuation as artificial intelligence captivates investors According to reports, the parent company of Stable Diffusion, an AI tool for creating digital images, is looking to raise funds at a valuation of approximately $4 billion. However, a final decision has not been made regarding the financing, and the valuation is subject to change. 3. Google's Universal Speech Model (USM): State-of-the-art speech AI for 100+ languages Google recently shared its Universal Speech Model (USM), which it claims to be a critical first step towards supporting 1,000 languages. USM is a family of speech models with 2B parameters trained on 12 million hours of speech and 28 billion sentences of text, spanning 300+ languages. 4. Anthropic's core views on AI safety - When, why, what, and how Anthropic AI recently shared why it anticipates rapid progress in AI and large impacts from the technology, which has led to concerns about AI safety. The company emphasizes the urgency of supporting AI safety research, which should be done by a wide range of public and private actors. 5. MuAViC: The first audio-video speech translation benchmark Meta AI has released MuAViC (Multilingual Audio-Visual Corpus), the first benchmark designed to enable the use of audio-visual learning for highly accurate speech translation. MuAViC will also be used to train Meta's AV-HuBERT model to translate speech into challenging and noisy environments.


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Three 5-minute reads/videos to keep you learning Fine-tuning 20B LLMs with RLHF on a 24GB consumer GPU HuggingFace has recently released the integration of trl with peft, which aims to make Large Language Model (LLM) fine-tuning with Reinforcement Learning more accessible. This library is designed to simplify the RL step and provide more flexibility. 2. The State of Competitive Machine Learning This article summarizes the state of the competitive landscape by analyzing over 200 competitions that took place in 2022. Additionally, it delves into the analysis of 67 winning solutions to identify the best strategies for winning at competitive ML. 3. Emergent Abilities of Large Language Models This article explores the concept of 'emergence' in general before delving into its application to Large Language Models. It also discusses the underlying reasons for these emergent abilities and their implications. 4. The Waluigi Effect (mega-post) This article presents a mechanistic explanation of the Waluigi Effect and other bizarre "semiotic" phenomena that arise within large language models, such as GPT-3/3.5/4 and their variants (ChatGPT, Sydney, etc.). It proposes a novel idea of "flattery and dialog" in prompt engineering. 5. Using AI to turn the Web into a database This article presents a promising approach to implementing the semantic web using powerful Large Language Models (LLMs) combined with knowledge bases. It also proposes the concept of "Semantic Web Agents" that can navigate the web and perform tasks on behalf of users.


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Papers & Repositories PaLM-E: An Embodied Multimodal Language Model The paper presents an experiment with a pre-trained LLM (PaLM) and a pre-trained model for vision (ViT) These models are combined with new learnable weights in a larger neural network to solve tasks that involve language, vision, and planning. 2. Visual ChatGPT The Visual ChatGPT connects ChatGPT with a series of Visual Foundation Models, enabling users to send and receive images during chatting. The goal is to build an AI that can handle various tasks by combining the general interface of ChatGPT with the domain expertise of foundational models. 3. Large Language Models Encode Clinical Knowledge MultiMedQA is a benchmark combining six existing open question answering datasets spanning professional medical exams, research, and consumer queries. 4. Prismer: A Vision-Language Model with An Ensemble of Experts Prismer, is a data- and parameter-efficient vision-language model that leverages an ensemble of domain experts. Experimental results demonstrate that Prismer achieves competitive performance with current state-of-the-art models in fine-tuned and few-shot learning tasks while requiring up to two orders of magnitude less training data. 5. Prompt, Generate, then Cache: Cascade of Foundation Models makes Strong Few-shot Learners The Cascade of Foundation models (CaFo) is a framework that combines various pre-training paradigms, including CLIP, DINO, DALL-E, and GPT-3, to improve few-shot learning. CaFo uses a "Prompt, Generate, then Cache" approach to leverage the strengths of each pre-training method and achieve state-of-the-art performance in few-shot classification. Enjoy these papers and news summaries? Get a daily recap in your inbox!


This AI newsletter is all you need #38

Announcing the What's AI Podcast! Louis Bouchard has launched a new project aimed at demystifying the various roles in the AI industry and discussing interesting AI topics with expert guests. The podcast, available on Spotify and Apple Podcasts, features interviews with industry experts. The latest episode features Chris Deotte, a Quadruple Kaggle Grandmaster at NVIDIA, who discusses topics such as crafting a strong data science resume, achieving grandmaster status on Kaggle, working at NVIDIA, and approaches to current data science challenges. Right now users have the opportunity to participate in a NVIDIA RTX 4080 giveaway directly from the podcast! Check it out here.


This AI newsletter is all you need #38

Meme of the week! Meme shared by dimkiriakos#2286


This AI newsletter is all you need #38

Featured Community post from the Discord AdriBen#5135 shared a paper titled "A Scalable, Interpretable, Verifiable & Differentiable Logic Gate Convolutional Neural Network Architecture From Truth Tables" and a DCNN design that could be suitable for security for formal verification, rule model, fairness, and trustworthy machine learning. This paper introduces a new definition of the elementary convolution operator as a tractable Boolean Function, which enables the computation of the complete distribution of the neural network before production. Read it here and support a fellow community member. Share your feedback and questions in the thread here!


This AI newsletter is all you need #38

AI poll of the week! Join the discussion on Discord.


This AI newsletter is all you need #38

Article of the week How Can Hardcoded Rules Overperform ML? by Ivan Reznikov Although machine learning has its advantages in problem-solving, it is not always the best solution. In certain areas, such as those where interpretability, robustness, and transparency are critical, rule-based systems may even outperform machine learning. This article discusses the use cases of hybrid systems and the benefits of integrating them into an ML pipeline. We will examine practical examples of industries, such as healthcare, finance, and supply chain management.


This AI newsletter is all you need #38

Our must-read articles The Impact of 5G Technology on IoT & Smart Cities by Deepankar Varma PCA: Bioinformatician's Favorite Tool Can Be Misleading by Salvatore Raieli If you are interested in publishing with Towards AI, check our guidelines and sign up. We will publish your work to our network if it meets our editorial policies and standards.


This AI newsletter is all you need #38

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You Can No Longer Fail To Understand How To Use Large Language Models

Why a new article on Large Language Models? The launch and incredible speed of adoption of ChatGPT in the last few months have turned this artificial intelligence chatbot into a real general knowledge topic. Anyone who hasn't heard of ChatGPT by now is immediately suspected of being disconnected from the real world for several months - even though the intelligence of this chatbot is more artificial than real. Thus, it is becoming urgent for self-proclaimed experts in artificial intelligence to understand in more detail how to concretely use these Large Language Models (LLMs), if they want to be able not only to understand what emerges from them but also and above all, to avoid their regression and defend the legitimacy of their status...


Where to start? Computer science courses from top universities are traditionally available online for free and are probably the best starting point for someone who has already studied in this field. Since it is the author's case, after having a look at the latest resources available on LLMs by universities such as UC Berkeley, Stanford, or MIT, we found Stanford's CS 324 course on Advances in Foundation Models [5] to be the most suitable for what we want to do in this article: get a quick but detailed understanding on how to use LLMs. The coursework description says it all: The early assignment comes in the form of a Google Colab Notebook, which, once completed, will allow us to know how to use LLMs such as BLOOM (BigScience Large Open-science Open-access Multilingual Language Model) [4]. In this article, we will analyze and go through three main steps to run our first LLM and understand the main challenge when using such models: task selection, prompt development, and model comparison.


Task Selection The first step is about choosing the task we want our LLM to perform, as well as the dataset and evaluation metric used for this task, using the HuggingFace datasets library. We will choose a well-known benchmark dataset: GLUE (General Language Understanding Evaluation). By browsing the tasks associated with this dataset (from its original site or its HuggingFace dataset card), we discover 11 different tasks and their associated metrics. In this article, we will choose the sentiment classification task with the Stanford Sentiment Treebank (sst2), whose associated evaluation metric is accuracy, i.e. the ratio of correct predictions to total predictions made.


Prompt Development The second part of the notebook is about developing effective prompts for LLMs. In other words, how to talk to an LLM? Indeed, while most machine learning practitioners have learned and are used to hyperparameter tuning to improve the performance of a machine learning model, LLMs are all about prompt engineering, or in simpler terms, input tuning. We will see that to improve the performance of an LLM model, the simplest approach is first to improve the format of the request we submit to it.


Zero-shot and few-shot prompting We distinguish two main techniques for prompting language models: zero-shot prompting [2] and few-shot prompting [1]. To cite Stanford CS 324 authors: In this article, we will focus on few-shot prompting, although similar work can be performed for zero-shot prompting. As a first step, we will observe the performance of the sentiment classification prompt proposed in the notebook on the selected sst2 task, using the BLOOM model composed of 1.7 billion parameters. Although this model is already large by definition, since it is an LLM, it remains relatively small when we know that the largest BLOOM model is composed of 176 billion parameters.


Experimenting few-shot prompting with BLOOM A proposed prompt for the sentiment classification task is written as follows: We import torch and transformers libraries to build our LLM based on the BLOOM model composed of 1.7 billion parameters: bigscience/bloom-1b7. We created a generator object associated with a "text-generation" task. We can now define the following functions to prompt our LLM to generate (with the function generate_sentiment) one new token (note the argument max_new_tokens = 1) from the presented prompt (with the function prompt_1). We expect here the newly generated token to be either "Positive" or "Negative". With the previous functions being defined, we are now able to generate one token from the proposed prompt and convert the generated token to an integer value to evaluate the performance of our LLM. Testing this prompt with the bigscience/bloom-1b7 model on the sst2 validation dataset results in an accuracy of 66.28%, which is not amazing but already better than tossing a coin.


Experimenting with different prompts Let's now propose to the LLM another few-shot prompt, with new sentences that are not about movie reviews but still express a positive or negative sentiment. Testing this prompt with the bigscience/bloom-1b7 model on the sst2 validation dataset results in an accuracy of 59.98%, which is a lower accuracy than the one obtained with the first prompt. This second trial shows already some importance of the examples given to the LLM to understand the task it needs to perform. On the third attempt, let's change not only the sentences used as few-shot examples for the LLM, but also the format of the examples provided. Here, we are paying less attention to the format of the prompt, expecting the LLM to complete the proposed sentences with either "positive" or "negative". Testing this prompt with the bigscience/bloom-1b7 model on the sst2 validation dataset results in an accuracy of 31.77%, which is a much lower accuracy than the ones obtained with the two first tested prompts. When looking deeper at the results, we even observe that the proposed word is not always "positive" or "negative". From this section, we can conclude the importance of the chosen prompt for our LLM text generation task.


Model Comparison In this final step, we propose to compare the performance of BLOOM models on two dimensions: model size (i.e., model number of parameters) and model training objectives (depending on the objective function and the training dataset).


Model size Regarding model size, we will first compare bigscience/bloom-1b7 with bigscience/bloom-3b, the latter being composed of 3 billion parameters. Testing the first prompt with the bigscience/bloom-3b model on the sst2 validation dataset results in an accuracy of 68.69%, which is a slightly higher accuracy than the one obtained with bigscience/bloom-1b7 (which was 66.28%). From this experiment, we note that the performance of our LLM increases with the model size.


Model training objectives Regarding model training objectives, we will now compare the performance of bigscience/bloom-3b with bigscience/bloomz-3b, the latter having been instruction fine-tuned, which means that the model has been explicitly trained to follow instructions. Testing the first prompt with the bigscience/bloomz-3b model on the sst2 validation dataset results in an accuracy of 73.51%, which is again slightly higher than the accuracy obtained with bigscience/bloomz-3b (which was 68.69%). From this experiment, we note that the performance of our LLM is better with fine-tuned models than with pre-trained models.


Instruction fine-tuning Instruction fine-tuning is described in detail in the Crosslingual Generalization through Multitask Finetuning paper [3], where are also available all the results obtained on benchmark datasets, as well as the prompts used to obtain these results. In Table 7 of this paper, showing all experimental results, we can almost confirm the observations we made in the two previous subsections. Regarding the increased performance with the model size, we observe that for a given number of tasks and datasets, the performances of the different pre-trained (but not multitask fine-tuned) models are similar regardless of the number of model parameters. We do not always observe an increasing performance with the number of parameters for pre-trained models, as we did in our experiment. However, for pre-trained and multitask fine-tuned models, not only do we observe, in most cases, better performance than for pre-trained but not multitask fine-tuned models, but we also observe an improvement of the performance with the number of parameters. With multitasking fine-tuned models, it appears that the largest models still have learning capacity when provided with additional data.


Experimenting again After reading the previously mentioned paper and analyzing its results, we would want to experiment with larger multitask fine-tuned models, aiming to get even better results with minimal changes to the code. However, GPU memory available with a free Google Colab Notebook is not sufficient for running BLOOM multitask fine-tuned models with 7.1 billion parameters and beyond. Thus, the second easiest way to improve the classification results appears to be prompt engineering, as we experimented with before.


Contextualizing and refining the task A first approach for improving developed prompts is to contextualize and be more precise in the description of the task we want the Autoregressive Language Model to perform. This first finding comes from the study of the prompts used for training and evaluating BLOOM models, as described in the Appendix of the previously mentioned BLOOM Multitask Finetuning paper. Here are two examples of prompt templates used for training and evaluating topic classification tasks for BLOOM multitask fine-tuned models: Leveraging these templates, I decided to evaluate again bigscience/bloomz-3b with a prompt that contextualizes more the task the model has to perform. Testing this more contextualized prompt with the bigscience/bloomz-3b model on the sst2 validation dataset results in an accuracy of 77.29%, which is, once again, slightly higher than the previously obtained accuracy (which was 73.51% with the third tested prompt).


Increasing shot number A second approach for improving developed prompts is to increase the shot number, i.e. the number of task examples provided as input. We will now evaluate bigscience/bloomz-3b again with the previous prompt. In addition, we will increase the number of provided examples from 3 to 5 (as shown below) and then 10 (the 5 following plus 5 others we do not provide for the sake of the article's length). Testing this more contextualized and 5-shot prompt (respectively more contextualized and 10-shot prompt) with the bigscience/bloomz-3b model on the sst2 validation dataset results in an accuracy of 90.48% (respectively 92.66%), which is a great improvement from the one obtained with only 3 examples!


Results summary The following table summarizes the accuracy obtained on the sst2 validation dataset depending on the model and prompt used. Without going into more detail in this article (stay tuned!), we note that the classification accuracy depends on the model and the prompt used. As we noted already in the Model Comparison section, for a given number of parameters:


Concluding remarks In this article, we leveraged CS 324 Advanced in Foundation Models Stanford's course to get a hands-on experience with Autoregressive Language Models. After having selected a sentiment classification task, the Stanford Sentiment Treebank dataset, and the accuracy as a metric for evaluating many BLOOM models, we reminded the definition of zero-shot and few-shot prompting and experimented with prompt development. Few-shot experiments evaluation and model comparison not only highlighted the higher performance of fine-tuned models but, most importantly, the importance of prompt engineering for getting the best results from Autoregressive Language Models.


References [1] T. Brown et al., Language Models are Few-Shot Learners (2020), Advances in Neural Information Processing Systems 33 (NeurIPS 2020) [2] J. Wei et al., Finetuned Language Models Are Zero-Shot Learners (2021), arXiv preprint arXiv:2109.01652 [3] N. Muennighoff et al., Crosslingual Generalization through Multitask Finetuning (2022), arXiv preprint arXiv:2211.01786 [4] Teven Le S. et al., BLOOM: A 176B-Parameter Open-Access Multilingual Language Model (2022), arXiv preprint arXiv:2211.05100 [5] C. Re et al., CS 324 - Advances in Foundation Models (2023), Stanford University Computer Science Department


A Comprehensive Introduction to Graph Neural Networks

Introduction to Graph Neural Networks Graph Neural Networks (GNNs) are a class of neural networks that are designed to operate on graphs and other irregular structures. GNNs have gained significant popularity in recent years, owing to their ability to model complex relationships between nodes in a graph. They have been applied in various fields, such as computer vision, natural language processing, recommendation systems, and social network analysis. Unlike traditional neural networks that operate on a regular grid or sequence, GNNs are capable of modeling arbitrary graph structures. Graphs can be thought of as a collection of nodes and edges, where the nodes represent entities, and the edges represent relationships between them. GNNs take advantage of this structure by learning a representation for each node in the graph that takes into account its local neighborhood. The idea behind GNNs is to use message passing to propagate information across the graph. At each iteration, information from each node's neighbors is aggregated and used to update the node's representation. This process is repeated for a fixed number of iterations or until convergence. The resulting node representations can then be used for downstream tasks such as classification, regression, or clustering. GNNs can be viewed as a generalization of traditional convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to graph-structured data. CNNs and RNNs are both designed to operate on regular grids and sequences, respectively. GNNs, on the other hand, can operate on arbitrary graphs and are, therefore, more expressive. There are several challenges associated with training GNNs, such as overfitting, inductive bias, and scalability. Overfitting can occur when the model is too complex and has too many parameters relative to the size of the dataset. Inductive bias refers to the assumptions that are built into the model based on prior knowledge about the problem. Scalability is a concern because the computational complexity of GNNs can be high, especially for large graphs. Despite these challenges, GNNs have shown impressive results on a variety of tasks and have the potential to revolutionize many fields. As such, they are an active area of research and are expected to be an important area of development for years to come.


Key Concepts of Graph Neural Networks The key concepts of GNNs are as follows: Graph representation: A graph is represented as a set of nodes (also known as vertices) and edges (also known as links or connections) that connect pairs of nodes. Each node may have features associated with it, which describe the attributes of the entity that the node represents. Similarly, each edge may have features associated with it, which describe the relationship between the nodes it connects.Message passing: GNNs operate by passing messages between nodes in a graph. Each node aggregates information from its neighbors, which it uses to update its own representation. The information passed between nodes is typically a combination of the features of the nodes and edges and may be weighted to give more or less importance to different neighbors.Node representation: The updated representation of a node is typically learned through a neural network, which takes as input the aggregated information from the node's neighbors. This allows the node to incorporate information from its local neighborhood in the graph.Graph representation: GNNs can also operate on entire graphs rather than individual nodes. In this case, the graph is represented as a set of node features, edge features, and global features that describe the properties of the graph as a whole. The GNN processes the graph representation to output a global representation, which can be used for tasks such as graph classification.Deep GNNs: GNNs can be stacked to form deep architectures, which allow for more complex functions to be learned. Deep GNNs can be trained end-to-end using backpropagation, which allows the gradients to be efficiently propagated through the network.Attention mechanisms: GNNs can incorporate attention mechanisms, which allow the network to focus on specific nodes or edges in the graph. Attention mechanisms can be used to weigh the information passed between nodes or to compute a weighted sum of the representations of neighboring nodes.GNN variants: There are several variants of GNNs, which differ in their message passing and node representation functions. These include Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), GraphSAGE, and many others. Each variant has its own strengths and weaknesses and is suitable for different types of tasks.


GNN Architectures Graph Convolutional Networks (GCNs) Graph Convolutional Networks (GCNs) are one of the most widely used GNN architectures. GCNs operate by performing a series of graph convolutions, which apply a linear transformation to the feature vectors of each node and its neighbors. The output of each convolution is fed into a non-linear activation function and passed to the next layer. 2. Graph Attention Networks (GATs) Graph Attention Networks (GATs) are a more recent development in the field of GNNs. GATs use attention mechanisms to compute edge weights, which are used to control the flow of information between nodes in the graph. This allows GATs to learn more sophisticated relationships between nodes and their neighbors. 3. GraphSAGE GraphSAGE is another popular GNN architecture that uses a multi-layer perceptron to aggregate information from a node's local neighborhood. Unlike GCNs, GraphSAGE uses a fixed-length feature vector for each node, which can be more efficient for large graphs. 4. Graph Isomorphism Network (GIN) The Graph Isomorphism Network (GIN) is a GNN architecture that is designed to be invariant to graph isomorphism. GIN uses a series of neural networks to compute a set of graph-level features, which are then used to predict the target variable. 5. Message Passing Neural Networks (MPNNs) Message Passing Neural Networks (MPNNs) are a class of GNN architectures that use a message-passing scheme to update the state of each node in the graph. At each iteration, each node sends a message to its neighbors, which is then used to update the node's state. This process is repeated for a fixed number of iterations. 6. Neural Relational Inference (NRI) Neural Relational Inference (NRI) is a GNN architecture that is designed to model the dynamics of interacting particles in a graph. NRI uses a series of neural networks to predict the state of each particle at each time step, based on the state of the other particles in the graph. 7. Deep Graph Infomax (DGI) Deep Graph Infomax (DGI) is a GNN architecture that is designed to learn representations of graphs that are useful for downstream tasks. DGI uses a two-stage training process, where the first stage is an unsupervised pre-training step that learns graph-level representations, and the second stage is a supervised fine-tuning step that uses the learned representations for a downstream task.


Applications of GNNs Graph neural networks (GNNs) have shown promising results in various applications, ranging from social networks and recommendation systems to drug discovery and materials science. Here are some of the major applications of GNNs: Social network analysis: GNNs can be used to analyze social networks, predict the links between users, and classify users based on their social network behavior.Recommender systems: GNNs can be used to build more accurate recommender systems that take into account the relationships between items and users.Computer vision: GNNs can be used to perform tasks such as object detection and segmentation, where the relationships between objects and their context are important.Natural language processing: GNNs can be used to analyze the relationships between words and their context in natural language text.Drug discovery: GNNs can be used to predict the properties of chemical compounds and their interactions with biological targets, accelerating the drug discovery process.Materials science: GNNs can be used to predict the properties of materials and their interactions with other materials, enabling the development of new materials with specific properties.Traffic analysis: GNNs can be used to predict traffic flows and congestion, and optimize traffic routing.Financial forecasting: GNNs can be used to analyze financial data, predict market trends and identify anomalies.Cybersecurity: GNNs can be used to detect and classify security threats in networks and systems.Robotics: GNNs can be used to control robotic systems, enabling more accurate and efficient navigation and manipulation.


References Liu, Zhiyuan, and Jie Zhou. "Introduction to graph neural networks." Synthesis Lectures on Artificial Intelligence and Machine Learning 14.2 (2020): 1127.Zhou, J., et al. "Graph neural networks: A review of methods andapplications." arXiv preprint arXiv:1812.08434 (2018).Yun, Seongjun, et al. "Graph transformer networks." Advances in neural information processing systems 32 (2019).Wu, Zonghan, et al. "A comprehensive survey on graph neural networks. CoRR abs/1901.00596 (2019)." arXiv preprint arXiv:1901.00596 (2019).Dragen1860. "GitHub - Dragen1860/Graph-Neural-Network-Papers: Curated Lists for Graph Neural Network, Graph Convolutional Network, Graph Attention Network, Etc." GitHub, github.com/dragen1860/Graph-Neural-Network-Papers.


LLaMA by Meta leaked by an anonymous forum: Questions Arises on Meta

LLaMA: Meta's new AI tool According to the official release, LLaMA is a foundational language model developed to assist 'researchers and academics' in their work (as opposed to the average web user) to understand and study these NLP models. Leveraging AI in such a way could give researchers an edge in terms of time spent. You may not know this, but this would be Meta's third LLM after Blender Bot 3 and Galactica. However, the two LLMs were shut down soon, and Meta stopped their further development, as it produced erroneous results. Before moving further, it is important to emphasize that LLaMA is NOT a chatbot like ChatGPT. As I mentioned before, it is a 'research tool' for researchers. We can expect the initial versions of LLaMA to be a bit more technical and indirect to use as opposed to the case with ChatGPT, which was very direct, interactive, and a lot easy to use. "Smaller, more performant models such as LLaMA enable ... research community who don't have access to large amounts of infrastructure to study these models.. further democratizing access in this important, fast-changing field," said Meta in its official blog. Meta's effort of "democratizing" access to the public could shed light on one of the critical issues of Generative AI - toxicity and bias. ChatGPT and other LLMs (obviously, I am referring to Bing) have a track record of responding in a way that is toxic and, well... evil. The Verge and major critics have covered it in much detail. Oh and the community did get the access, but not in the way Meta anticipated. On March 3rd, a downloadable torrent of the LLaMA system was posted on 4chan. 4chan is an anonymous online forum known for its controversial content and diverse range of discussions, which has nearly 222 million unique monthly visitors. LLaMA is currently not in use on any of Meta's products. But Meta has plans to make it available to researchers before they can use them in their own products. It's worth mentioning that Meta did not release LLaMA as a public chatbot. LLaMA is more of an open-source package that can be accessed by trusted authorities upon request.


Powerful LLMs: What to hope Whether to agree with Ladish's views or not is debatable. Personally, I feel open-sourcing AI models could only benefit the AI community to scrutinize the model and improve them for the better. What do you think? After all, one of LLaMA's major goals is to 'democratize' access to such models. But this access in the form of a leak put Meta into question - how it handles its tools and conducts release in public? Most of the users that got the leaked copies soon discovered that LLaMA was not at all similar to ChatGPT. "Downloading" LLaMA is going to do very little for the average internet user because it's a "raw" AI system that needs a decent amount of technical expertise to get up and running. However, as I am writing this, Meta hasn't acknowledged the leak to the public yet. Neither did they comment on it. There are both positive and negative consequences to this leak. On the one hand, unrestricted access to Llama could help researchers understand how and why large language models work, which could lead to improvements in robustness, bias, and the toxic nature of LLMs. This could really help in reducing the potential for generating misinformation by these troublesome machines. On the other hand, however, the leak could lead to people misusing the model itself. It is not yet perfect. Hence Meta hasn't released it fully to the public yet. Risks such as spam and phishing could be really hard to tackle if such superintelligent machines are put to the test. Thus, much safeguard must be applied to the use of these models. We can see such tools, like OpenAI Text Classifier, emerging. So there is a positive hope for this. AI is exciting, no doubt. But a lot scarier if we lose our control over it.


This AI newsletter is all you need (#37)

What happened this week in AI by Louis This week developments in LLMs and Generative AI continued at pace - ranging from more cutting-edge research, to open-source accessibility to general affordability of mass-market products. Following Meta's somewhat open-source release (to researchers only) of its 65BN parameter Llama model last week - the full model weights were promptly leaked on torrent websites. With the latest estimates at just $0.51m training cost, we also think it's only a matter of time now that an open-source model with size and quality equivalent to GPT-3.5 is trained and released fully open source by a nonprofit or tech company. Increased flexibility for researchers and entrepreneurs to experiment with these models should accelerate progress - but also comes with questions on the risks of what could be built with these models if put to bad use. OpenAI released its ChatGPT and Whisper APIs with significantly lower pricing than anticipated, partly in response to progress in open-source LLMs and competitor models. ChatGPT is available via GPT-Turbo for $0.002 per 1k tokens, which is 10x cheaper than its Da-Vinci model (which itself was reduced 3x in price in September) and in line with pricing for OpenAI's smaller Curie model (Curie has been estimated at 710bn parameters vs Da-vinci's 175bn). In our view, this pricing makes GPT-Turbo accessible and affordable in many applications and is only going to accelerate the rollout of Generative AI products and startups. It's not clear how GPT-Turbo achieved its lower pricing relative to Da-Vinci - it could be trained as a smaller model, and it could benefit from cost-efficiency gains from methods such as pruning, sparsity, and model distillation. We are interested in hearing your thoughts! The week concluded with Google announcing several new models, such as its Universal Speech Model, which advances speech AI for over 100 languages, and the Palm-E model, an "embodied" multimodal language model that merges PaLM-540B and ViT-22B. This model's early success with positive transfer across multiple domains indicates we will be hearing more about embodied multimodal language models in the future.


Hottest News 1.Introducing ChatGPT and Whisper APIs Open AI has recently made the ChatGPT and Whisper APIs available for developers to integrate the ChatGPT and Whisper models into their applications and products. The API features a range of system-wide optimizations that enable developers to use the open-source Whisper large-v2 model with quicker and more cost-effective outcomes. 2. Anthropic begins supplying its text-generating AI models to select startups Anthropic, an AI startup co-founded by ex-OpenAI employees, has started providing partners with access to its AI text-generating models. The company believes its technology is distinct and can compete with rivals such as OpenAI, Cohere, and AI21 Labs, all of which offer paid access to their text-generating AI via APIs. 3. Microsoft brings its new AI-powered Bing to the Windows 11 taskbar Microsoft is releasing a major update to Windows 11 today, which includes the company's new AI-powered Bing search on the taskbar. Alongside a range of new features, such as improvements to widgets, a better touch mode, a screen recording feature, and tabs inside Notepad. 4. Why AI Won't Cause Unemployment There are various debates surrounding the impact of new AI technology on the replacement of human labor. This article puts forward the argument that AI cannot result in overall unemployment, as it is already illegal for most of the economy, and will be for virtually all of the economy. 5. YouTube's new leader teases AI tools that can virtually swap creators' outfits and locations In his first public address since taking over as the head of YouTube, Neal Mohan outlined his key priorities and shared some upcoming features for the platform. He also teased one particularly interesting new one: YouTube is developing generative AI tools for content creators.


5-minute reads/videos to keep you learning 1.The Best of NLP: February 2023's Top NLP Papers This post curates a selection of the top NLP papers for February 2023, covering various topics, such as the latest developments in language models, text generation, and summarization. 2. What's the big deal with Generative AI? Is it the future or the present? The current developments in Generative AI and its future have captured the popular imagination. This article provides an overview of the current state of generative AI and discusses how we should approach its development in the future. 3. An overview of the latest open-source AI image and video editing neural network models on HuggingFace The video shares an overview of the latest AI models for image and video editing, exploring their possibilities, capabilities, and more. 4. The technology behind GitHub's new code search This article provides insights into the development of the world's largest public code search index, including the factors that contributed to its creation, the technologies utilized, and the process involved. 5. Must read: the 100 most cited AI papers in 2022 Who Is publishing the most Impactful AI research right now? This article analyzes the 100 most cited papers per year for 2020, 2021, and 2022, providing early insights into the direction of AI technology.


Papers & Repositories 1.Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback The paper introduces the LLM-Augmenter system, which can augment a black-box LLM with a set of plug-and-play modules. The results show that LLM-Augmenter significantly reduces ChatGPT hallucinations without sacrificing the fluency and informativeness of its responses. 2. A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT The paper introduces a catalog of prompt engineering techniques in the form of patterns, which have been applied to address common challenges in conversing with LLMs. It proposes a framework for documenting patterns that structure prompts, presents a catalog of patterns used to enhance LLM outputs, and more. 3. Directed Diffusion: Direct Control of Object Placement through Attention Guidance This work proposes a simple solution to guide text-based diffusion models to focus on specific objects by injecting "activation" at desired positions in cross-attention maps corresponding to the objects of interest while attenuating the rest of the map. The approach is a significant step towards extending the applicability of text-guided diffusion models from single images to collections of related images. 4. WhisperX A repository for Whisper-Based Automatic Speech Recognition (ASR). It refines the timestamps of OpenAI's Whisper model via forced alignment with phoneme-based ASR models (e.g. wav2vec2.0), multilingual use-case. 5. ChatGPT: A Meta-Analysis after 2.5 Months The paper analyzes over 300,000 tweets and more than 150 scientific papers to investigate how ChatGPT is perceived and discussed. The findings indicate ChatGPT is generally viewed as of high quality, however, this perception has slightly decreased since its debut. Enjoy these papers and news summaries? Get a daily recap in your inbox!


Upcoming Community Events The Learn AI Together Discord community hosts weekly AI seminars to help the community learn from industry experts, ask questions, and get a deeper insight into the latest research in AI. Join us for free, interactive video sessions hosted live on Discord weekly by attending our upcoming events. Network Architecture Search (#9) This week's session in the (free) nine-part Neural Networks Architectures series led by Pablo Duboue (DrDub) focuses on Network Architecture Search. This is the last session before the seminar presentation! During this session, he will explore NEAT, Knowledge/Network Distillation, and GDAS. Find the link to the seminar here or add it to your calendar here. Interested in presenting a neural network architecture at the TAI Discord Server? Let us know more by signing up for the seminar presentation here. Date & Time: 7th March, 11 pm EST Add our Google calendar to see all our free AI events!


Meme of the week! Meme shared by jacek#5724


Featured Community post from the Discord Misbah#5820 asked ChatGPT to write a self-help book titled "The Book of Infinite Wisdom" by condensing the knowledge and wisdom of thousands of books into one concise volume. The Book of Infinite Wisdom distills centuries of accumulated human knowledge, offering readers a comprehensive guide to achieving their fullest potential. You can find the book here or reach out to Misbah#5820 for a free copy (for select readers). Join the thread here and share your thoughts on a book entirely authored by AI.


AI poll of the week! Join the discussion on Discord.


Article of the week Clustering A-Z Briefly Explained ChatGPT Powered by Gencay I. This article provides a comprehensive overview of Clustering A-Z, including its algorithms, boundaries, clusters, distance metrics, evaluation, feature selection, hierarchical clustering, k-means clustering, and quality metrics. As machine learning techniques can be broadly categorized into Regression, Classification, and Clustering, this article aims to cover the various aspects of Clustering.


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Easy-Peasy AI: 3 Open Source Frameworks to Add AI to Your Apps

Dust.tt Dust.tt was one of the first open-source AI frameworks. Written in Rust, it allows developers to chain calls to LLMs with other tools to extract the desired output. Dust.tt works with language models such as OpenAI, AI21, and Cohere and has integration blocks for Google Search, curl, and web scraping (wrapping browserless.io). Key Concept: A Dust.tt app comprises a series of blocks executed sequentially. Each block produces outputs and can refer to the outputs of previously executed blocks. Dust.tt app developers define each block's specification and configuration parameters. Example: In the example below (from Dust docs), the Dust app uses AI to write personalized thank-you letters for wedding gifts: First we set up an input block with the guest list Then we set up a training block with examples Then we call the LLM with the input and training examples And finally, we extract the results


Langchain Langchain is an open-source Python library that allows developers to build AI-powered applications using Language Models (LLMs) in a composable manner. With Langchain, you can create chatbots, question-answering systems, and other AI-powered agents. Key Concept: Developers use "chains" to connect components to build their AI applications. The library provides a set of building blocks, including input/output, processing, and utility blocks, that can be combined to create a custom chain. Example In this example (from their docs), the code asks an LLM to name a company that makes a product, and then it asks the LLM to write a catchphrase for the company. In the code below (from Langchain docs), an agent is initialized and given the tools for searching and running mathematical operations. Langchain has become popular and now boasts a large number of integrations. A new project is underway to port it to TypeScript, making it possible to build AI-powered JavaScript apps. Some currently available integrations are OpenAI, Cohere, GooseAI, Hugging Face Hub, Petals, CerebriumAI, PromptLayer, SerpAPI, GoogleSearchAPI, WolframAlphaAPI, NatBot, Wikipedia, Elasticsearch, FAISS, Manifest, OpenSearch, DeepLake.


BoxCars Disclosure: I am working on the BoxCars open-source project. BoxCars is a Langchain-inspired open-source gem that simplifies adding AI functionality to Ruby-on-Rails apps. Key Concept In BoxCars, a train comprises an engine (LLM) and boxcars (tools). The train coordinates calls to the LLM using the tools specified. For eg, The following code uses an LLM, Google search, and a calculator. BoxCars is designed with the same philosophy as Ruby on Rails. It includes an ActiveRecord BoxCar, making integrating natively with rails apps easy. In the example below, the ActiveRecord BoxCar is set up with models (Ticket, User, Comments) and then asked questions across the models. The LLM produces ActiveRecord code that returns the results.


It's still early These frameworks are less than a year old and evolving rapidly; undoubtedly, there will be more in the future. The good news is that if you're considering adding AI features to your apps, help is on the way.


A.I. is ALREADY Replacing Jobs in the US

Business Leaders Favor AI: Stats shows According to this survey, almost half of US companies have already implemented ChatGPT. And as a result, it has replaced workers at their companies. And many of these works, as you may have guessed, are related to creative fields or (mostly) non-technical fields. Seems you no longer need 'actual' intelligence if artificial intelligence has outworked you, right? Talk about the quality and quantity comparison, AI offers both. Billions of calculations and many fine tunings seem to be enough to work for any MNC. Mind you, ChatGPT is not your average chatbot. It's built on top of OpenAI's GPT-3 family of large language models, which means it can generate coherent and fluent text on any topic. Although many may not be perfect, it's not bad after all. How do I know? Because we saw in front of our eyes that this seemingly small chatbot can write code, create poems and content, handle customer service, and even summarize meetings and papers. "There is a lot of excitement regarding the use of ChatGPT," Resumebuilder.com's Chief Career Advisor, Stacie Haller, said in a statement. The survey that we are discussing here was undertaken by them. "Workers need to surely be thinking of how it may affect the responsibilities of their current job. The results of this survey show that employers are looking to streamline some job responsibilities using ChatGPT". It won't take another survey for you to know why employers favor these not-fully-reliable robots to take up a job. What is the obvious answer? Scale and Economical advantages. Scale since AI chatbots like such can be optimized to do any task at any level. And with some fine-tuning, it can generate thousands of worthy content that can look 'appealing' to the consumer. And as for the economic reason, you don't have to pay for it. Apart from initial setup cost (which is not too much if you outsource it) and maintenance, these chatbots don't take much "salary" to do their work. When was the last time that you paid your virtual assistants like Siri or Google or Alexa?


What do the Numbers say? Most of the jobs that these robots take up themselves have very high competition. The survey showed that 66 percent of US companies use ChatGPT for writing code, 58 percent for creating content, 57 percent for customer service, and 52 percent for meeting summaries and other papers. Oh, and what do the business leaders think of ChatGPT's work? Well, ironically, they love it. Fifty-five percent say the quality of work produced by ChatGPT is 'excellent', while 34 percent say it's 'very good'. Only 11 percent say it's 'fair' or 'poor'. That's insane. Numbers don't lie unless it's in statistics. And since this was a survey done on 1000 business leaders, I am a bit skeptical about the result. Sure, ChatGPT could do 'excellent' in paperwork tasks and writing content, but the truth is that these robots are 'poor' when it comes to research-based studies or calculation-based work.


Ray of Hope Are you scared yet? No, you shouldn't. And here is a solid reason why. 'These robots are good at math,' you may say. Well, yes, for solving elementary equations. That's a bold statement to say. It's simple, gives a word problem to it, and it won't take you more than 10 or 15 tries to see that this machine fails. Arizona State University found ChatGPT was terrible at math. It scored with below 60% accuracy in the 1,000 mathematical word problems given to it by the professor. And we are talking about accuracy, not speed. It doesn't matter if it can spit out the answers in a few seconds if, ultimately, it gives the wrong result. So now I am asking the same question that both you and I once asked when we got to know about the movies regarding AI. Is this such a good idea that our jobs are being taken over by 'unpredictable' robots? Sure, humans are unpredictable, too, I get it. But they can be tamed, punished, and corrected. And we know how to go about it in the case of humans. But in the case of robots, we are blinded by the algorithms and invisible technical bugs. Is ChatGPT trustworthy and reliable? No, at least not fully. What if it makes mistakes or gives wrong answers? Then we are doomed. What if it has biases or hidden agendas? It already does. What if it goes rogue or gets hacked? Welcome to the terminator. While we are debating on the above questions (not just hypothetical questions, but serious ones) Sam Altman, the founder of ChatGPT and CEO of OpenAI, has already warned users against relying on the AI chatbot for "anything important". He also expressed worry about the risks posed by artificial intelligence in general.


Unreliability of present unreliable chatbots But ok. The answers above were filled with 'hope' and 'scepticism' (and ego, maybe). But are there any quality factors that will ensure that these robots won't take up 'many' of our jobs? Well, my friend, yes. What are the two biggest yet most common money-making industries, according to you? Probably Oil and banks. And AI failed miserably in these fields. Big bank employees say they're running into issues using ChatGPT to make their jobs 'easier'. (but first of all, is my money being looked over by a robot ?!) Many traders told Bloomberg they had used the popular AI chatbot to aid in their jobs. But to their dismay, it had several limitations, such as having to spend a lot of time fact-checking and removing out-of-date information. If you don't know, ChatGPT doesn't have all the latest information like our traditional search. Therefore, when one oil trader found there was out-of-date information in the crude market outlook (written by ChatGPT), they that had change it. And since these AI machines generate A LOT of data, it takes A LOT of time to fact-check it. A bank found that ChatGPT compiled an overview of a client in less time than an internet search would take. Cool, but they ran into another problem. The info they gathered than had to be cross-checked. This cycle of cross-checking paradoxically increased our time. And from a logical standpoint, if you need to crosscheck to be done, you need 'humans' to be present there. And why not, since all that happens in business is that one human works to provide for another fellow human and then later gains from it? So while it may seem these bots are highly capable of taking our jobs, they cannot fully rely. "When we talk of high-accuracy tasks, it is worth mentioning that ChatGPT sometimes hallucinates and can generate answers that are seemingly convincing, but are actually wrong," Morgan Stanley analysts said last week. And big corporations do take these false positives seriously. JP Morgan has already put restrictions against the use of ChatGPT among traders because it's worried that sharing sensitive financial information with the tool will ring regulators' alarm bells. Wall Street banks Citigroup and Goldman Sachs have also blocked the chatbot on third-party software. TCS in India has understood the AI takeover better. TCS has already said that generative artificial intelligence platforms like ChatGPT will make an "AI co-worker" and not lead to job losses. This is a positive and reassuring way to see the whole situation. AI being your assistant and doing the tough job is much better, instead of AI taking the tough job and making our lives even more tougher. "It will be a co-worker. It will be a co-worker, and that co-worker will take time for them to understand the context of the customer," Milind Lakkad, the chief human resources officer (CHRO) of TCS, said in an interview with news agency PTI recently.


Conclusion So hopefully, I am pretty sure that there won't be a complete hostile takeover of our jobs by AI. Why? AI is created by humans after all, and while it can learn on its own, the algorithm is also made by humans. And human makes mistakes, which we have to be cautious about. There will always be some downsides to any new IT revolution. In this case, AI can't be fully relied on. And while we can have a huge 'quantity' of posts with us using AI, we will always favor 'quality' in the long run. Otherwise, we would have to spend twice the amount of time to 'fact check' our own choices. And that is it for today. And as long as AI doesn't take over my work, see you in the next.


This AI newsletter is all you need (#36)

What happened this week in AI by Louis This week we were pleased to note an acceleration in progress toward open-source alternatives to ChatGPT as well as signs of increased flexibility in access to these models. While many major tech companies are building their own alternative to ChatGPT, we are particularly excited to see open-source alternatives that can make next-generation LLM models more accessible, flexible, and affordable for the machine learning community. We are seeing progress in the release of more open-source foundational LLM models as well as building out of open-source data sets and workflows, together with new innovations to reduce the cost of fine-tuning these models with human feedback. The strategic partnership between Hugging Face and Amazon Web Services (AWS) looks like a positive step in this direction and should increase the availability of open-source data sets and models hosted on Hugging Face. We were also pleased to see the release of Meta's LLaMA, 4 foundation models ranging from 7B to 65B parameters. Another example is Laion's project, which involves crowdsourcing annotations for its OpenAssistant ChatGPT replication project. Carper has also developed open-source RLHF workflows, which range from human annotation with CHEESE to RLHF training with trlX. We are also seeing new approaches that can reduce the human feedback requirements and barriers to entry to developing a ChatGPT-like product - such as Anthropic AI's constitutional AI approach (behind its Claude model) requiring minimal human labels. We hope these open-source models and competition can also put pressure on OpenAI to keep costs affordable and to increase the flexibility of interaction with their models. We see signs of this with a glimpse leaked of its new OpenAI Foundry product - a platform for running OpenAI models on a dedicated capacity. This platform will also allow more robust fine-tuning options for its latest models. Also of note in this release is that the future models (GPT-4?) look to offer 32,000 max context length (up from 4,000 today) which will likely bring a lot of new capabilities.


Hottest News OpenAI Foundry will let customers buy dedicated compute to run GPT3 and their other models OpenAI is launching a new developer platform that lets customers run the company's newer machine learning models, like GPT-3.5, on a dedicated capacity. OpenAI describes the forthcoming offering, called Foundry, as "designed for cutting-edge customers running larger workloads." 2. What ChatGPT And Generative AI Mean For Your Business? Generative AI could potentially become a powerful tool for businesses, providing a new basis for competitive advantage. Enterprises should consider experimenting with generative AI by identifying existing processes that can be enhanced with this technology. 3. What are 'robot rights,' and should AI chatbots have them? This article features an interview with Professor David Gunkel, discussing the issue of what rights robots, including AI chatbots, should have. The discussion centers on the concept of robot rights, including the background and articulation of rights for AI. 4. Hugging Face and AWS partner to make AI more accessible The strategic partnership between Hugging Face and Amazon Web Services (AWS) is expected to make AI open and accessible to everyone. Together, the two companies aim to accelerate the availability of next-generation machine learning models by making them more accessible, efficient, and affordable for the machine learning community. 5. How AI Can Help Create and Optimize Drugs To Treat Opioid Addiction The use of artificial intelligence for drug discovery has shown promise in the development of potential treatments for opioid addiction. Preclinical studies suggest that blocking kappa-opioid receptors may be an effective approach to treating opioid dependence. AI can be used to optimize and create new drugs that can block the activity of the protein responsible for kappa-opioid receptors, making the drug discovery process more cost-effective and efficient.


Three 5-minute reads/videos to keep you learning Text-to-Image Diffusion Models: A Guide for Non-Technical Readers The guide provides a simple explanation of text-to-image models and their use of diffusion to create images from natural language. It also introduces various tools for controlling and improving image generation processes, including ControlNET, ControlNET Pose, and ControlNET LORA. 2. The technology behind GitHub's new code search This post provides a high-level explanation of the inner workings of GitHub's new code search and offers a glimpse into the system architecture and technical underpinnings of the product. It also discusses how the search functionality allows users to find, read, and navigate code more efficiently. 3. MIT course on Introduction to Data-Centric AI This is a practical course on Data-Centric AI, focusing on the impactful aspects of real-world ML applications. The class covers algorithms for finding and fixing common issues in ML data, as well as constructing better datasets, with a concentration on data used in supervised learning tasks such as classification. 4. Lessons learned while using ChatGPT in education This guide shares an experience of using AI in education to complete tasks such as generating ideas, producing written material, creating apps, and generating images. The article details the success, changes, and process of using AI in education. 5. Writing Essays With AI: A Guide This guide explores the use of AI as a creative tool for essay writing. It discusses how to incorporate AI into writing practices by leveraging it to organize thoughts, capture a voice, summarize complex ideas, assist with idea generation, and evaluate writing quality.


Papers & Repositories LLaMA: A repository for Open and Efficient Foundation Language Models A collection of foundation language models ranging from 7B to 65B parameters by Meta. 2. Aligning Text-to-Image Models using Human Feedback This paper proposes a fine-tuning method for aligning text-to-image models using human feedback by collecting human feedback, training a reward function that predicts human feedback, and fine-tuning the model by maximizing the reward-weighted likelihood. 3. The Wisdom of Hindsight Makes Language Models Better Instruction Followers This paper considers an alternative approach to RLHF: converting feedback to instruction by relabeling the original one and training the model for better alignment in a supervised manner. 4. Zero-Shot Information Extraction via Chatting with ChatGPT This work aims to investigate whether strong information extraction (IE) models can be created by directly prompting large language models (LLMs). Specifically, it transforms the zero-shot IE task into a multi-turn question-answering problem using a two-stage framework called ChatIE, which leverages ChatGPT for entity-relation triple extraction, named entity recognition, and event extraction. 5. How Good Are GPT Models at Machine Translation? A Comprehensive Evaluation The paper offers a thorough evaluation of GPT models for machine translation. It covers various aspects, including the quality of different GPT models compared to state-of-the-art research and commercial systems, the impact of prompting strategies, the resilience to domain shifts, and document-level translation. Enjoy these papers and news summaries? Get a daily recap in your inbox!


Upcoming Community Events The Learn AI Together Discord community hosts weekly AI seminars to help the community learn from industry experts, ask questions, and get a deeper insight into the latest research in AI. Join us for free, interactive video sessions hosted live on Discord weekly by attending our upcoming events. Multimedia Processing Networks (#8) This week's session in the (free) nine-part Neural Networks Architectures series led by Pablo Duboue (DrDub) focuses on Multimedia Processing Networks. During this session, he will explore VQA, NMNs, Hierarchical co-attention, Dall-E, Imagen, and Stable Diffusion. Find the link to the seminar here or add it to your calendar here. Date & Time: 28th February, 11 pm EST Add our Google calendar to see all our free AI events!


Meme of the week! Meme shared by friedliver#0614


Featured Community post from the Discord Carl#1372 has launched a text-to-image AI art generator that enables users to combine different styles or create customized ones to produce unique artwork. It is a free tool designed for novice users to experiment with art. You can check it out here and support a fellow community member. Join the conversation and share your feedback in the thread here.


AI poll of the week! Join the discussion on Discord.


Article of the week BioGPT: The ChatGPT of Life Sciences by Dr. Mandar Karhade, MD. PhD. The Microsoft team created an exceptional language model called BioGPT designed to answer medical questions. The researchers claim that BioGPT is as good as a human expert in answering these queries. The article delves into how the Microsoft team trained BioGPT to become such an intelligent language model.


Our must-read articles easy-explain: Explainable AI for images by Stavros Theocharis Unlocking the Power of Recurrent Neural Networks: A Beginner's Guide by Gaurav Nair If you want to publish with Towards AI, check our guidelines and sign up. We will publish your work to our network if it meets our editorial policies and standards.


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