![](\"https://cdn-images-1.medium.com/max/1600/1*0yFLIIK8W5rcPE-MPzGDwA.png\")
\nGenerative AI
\nđź”—Introduction
\nđź”—Foundation of Generative AI
\nđź”—Recent Development
\n - đź”—Year 2021
\n - đź”—Year 2022
\n - đź”—Year 2023
\nđź”—Future of Generative AI
\nđź”—Ethics in Generative AI
\nđź”—Overcoming disadvantages of Generative AI
\nđź”—Code
\nđź”—Conclusion
\nđź”—References
\n
\nAmidst this remarkable era of transformation, where the boundaries of human ingenuity are continuously pushed, we find ourselves at the precipice of a profound revolution. From transforming the way we search for information to revolutionizing the art of coding, this paradigm shift extends even further, transcending conventional boundaries to permeate the realm of creativity itself. Moreover, it has unveiled the potential to unlock groundbreaking discoveries in the field of medicine, redefining our approach to drug development and paving the way for transformative advancements.
\n\nGenerative AI is a magicbox that is trained on pre-existing data. Later, this magicbox is able to generate the new data similar to the trained-one according to the set of instructions provided to it.\n
\n\nExample of Text-to-Image generation
\n\nFoundation of Generative AI
\n \nIn the pursuit of advancing generative AI, several influential papers were introduced, each building upon the accomplishments of its predecessors.
\n![](\"https://cdn-images-1.medium.com/max/1600/1*J0FbB84vc3On8U7pBKOGHw.jpeg\")
\nSource: Author
\n\nIt all began with the groundbreaking paper on Boltzmann Machines, \"Learning representations by back-propagating errors\" [1].The paper introduced Boltzmann Machines, which are stochastic neural networks with binary units. It demonstrated how to train Boltzmann Machines using backpropagation, allowing them to learn complex representations of data. The model's energy-based formulation and Gibbs sampling procedure enable it to capture dependencies among variables, making it suitable for tasks like feature learning and dimensionality reduction.
\n\n\nAlthough it opened up new possibilities in learning complex representations, certain limitations needed to be addressed. To tackle these insufficiencies, the Neural Autoregressive Distribution Estimation (NADE) [2] proposed a novel neural network architecture for density estimation. NADE efficiently estimated high-dimensional probability distributions by exploiting the chain rule of probability, overcoming the limitations faced by Boltzmann Machines.
\n\n\n As the field progressed, the Generative Stochastic Networks (GSN) [3] emerged, combining stochastic hidden units with generative modeling. GSNs introduced a layer-wise training algorithm, enabling the learning of deep representations. GSNs addressed the need for deep generative models.
\n\n\nHowever, further advancements were needed to overcome the challenges in generating high-quality samples. That's when the Masked Autoencoder for Distribution Estimation (MADE) [4] entered the scene. MADE introduced masked connections to efficiently model conditional distributions, addressing the limitations of GSNs and improving training and sampling.
\n\n\nMeanwhile, in the realm of variational inference, the Variational Autoencoders (VAEs) paper, \"Auto-Encoding Variational Bayes\"(2013) [5], made significant strides. VAEs combined deep learning with variational inference, learning a probabilistic latent space representation. This breakthrough enabled efficient generation and manipulation of complex data, pushing the boundaries of generative modeling.The VAE architecture consists of an encoder and a decoder neural network, connected through a latent space representation.
\n\n\nThe encoder network takes an input data point and maps it to a latent space distribution. It learns to encode the data into a set of mean and variance parameters, which define the distribution in the latent space. The mean and variance parameters are typically computed using multilayer perceptrons (MLPs) or convolutional neural networks (CNNs).The latent space is a low-dimensional representation of the input data that captures its underlying structure. It acts as a bottleneck in the architecture, forcing the encoder to learn a compact and meaningful representation.
\n\n\nThe decoder network takes a sample from the latent space and reconstructs the original input data. It learns to decode the latent space representation back into the original data space, such as images, text, or audio. The decoder network is typically structured as an MLP or a CNN, with layers that gradually upsample or decode the latent features.
\n\n![](\"https://miro.medium.com/v2/resize:fit:1400/1*nLLC3nrS42oCdzz4hRDmGA.png\")
\n\nIn the quest for realistic image synthesis, the Generative Adversarial Networks (GANs) paper, \"Generative Adversarial Networks\"(2014) [6], revolutionized the field. The GAN framework introduced a generator-discriminator setup, where the generator learned to produce synthetic data while the discriminator aimed to distinguish between real and generated samples. This adversarial training process resulted in increasingly realistic samples.\nThe goal of the generator is to produce synthetic samples that are indistinguishable from real data samples.
\n\n\nThe discriminator network, on the other hand, acts as a binary classifier. It takes as input both real data samples and synthetic samples generated by the generator and learns to discriminate between them. The discriminator is trained to assign high probabilities to real data samples and low probabilities to synthetic samples.
\n\n\nThe training process of GANs involves a competitive interplay between the generator and the discriminator. The generator aims to produce samples that can fool the discriminator, while the discriminator strives to accurately distinguish between real and synthetic samples. \n\n
\n
![](\"https://miro.medium.com/v2/resize:fit:1400/format:webp/1*M54Ehz6C-WjMDe0L3ddX_A.png\")
\nGANsformers: Generate complex scenes using GANs and Transformers by Louis Bouchard
\n\nTo tackle the challenges of generating high-fidelity natural images, the BigGAN paper(2018) [7] proposed an architecture for large-scale GANs. BigGAN incorporated self-attention mechanisms and truncation techniques, leading to improvements in image quality and control, and pushing the boundaries of image synthesis.
\n\n\nIn the realm of natural language understanding, the GPT-2 paper, Language Models are Unsupervised Multitask Learners\" and \"Better Language Models and Their Implications\"(2019) [8], introduced a large-scale language model based on the Transformer architecture. By employing unsupervised learning, GPT-2 generated coherent and contextually relevant text, showcasing the potential of unsupervised learning in language tasks.
\n\n\nIn the realm of image generation, the groundbreaking StyleGAN paper, \"A Style-Based Generator Architecture for Generative Adversarial Networks\" (2019) [9], introduced an architecture that revolutionized the field. By separating the generation of image content and style, StyleGAN allowed for unprecedented control over the synthesized images. It introduced the concept of style vectors that could be manipulated to change various attributes, such as facial expressions, hair color, and more. StyleGAN produced high-resolution images with remarkable detail and fidelity, setting a new benchmark for image synthesis.
\n\nThe year 2018 saw NVIDIA's Progressive Growing of GANs (PGGAN) [10] taking center stage. This groundbreaking technique unfolded like a symphony, layer by layer, starting with low-resolution images and progressively refining them to achieve unparalleled levels of detail and realism. PGGAN tackled the problem of limited resolution in GANs, providing a visual feast that pushed the boundaries of what was deemed possible.
\n\nBuilding upon the success of StyleGAN, the StyleGAN2 paper, \"Analyzing and Improving the Image Quality of StyleGAN\"(2020) [11], aimed to address certain limitations and further improve the quality of generated images. It introduced several key enhancements to the architecture and training process. One notable contribution was the introduction of path length regularization, which encouraged smoother and more diverse image samples. Additionally, progressive growing, a technique introduced in the original StyleGAN, was refined to achieve even better stability and control over the image synthesis process.
\n\nThe development of generative AI has been driven by a series of influential papers, each building upon the achievements of its predecessors. From Boltzmann Machines to GANs, VAEs, and advanced architectures like StyleGAN and StyleGAN2, these papers have revolutionized image synthesis, language modeling, and probabilistic modeling. Authored by visionaries such as Rumelhart, Bengio, Goodfellow, and Karras, these seminal papers have set new benchmarks, inspired further research.
\n\n\nRecent Development
\n\n\n###Year 2021
\n\n1. DALL·E: Zero-Shot Text-to-Image Generation from OpenAI [12]
OpenAI has achieved a remarkable feat with DALL·E, a network capable of generating captivating images from text captions. Building upon the success of GPT-3 and Image GPT, this cutting-edge model produces astonishing results in the realm of visual synthesis.
![](\"https://cdn-images-1.medium.com/max/720/1*flCzuobeBgyW8nXObIdjjg.png\")
A. Ramesh et al., Zero-shot text-to-image generation, 2021. arXiv:2102.12092 [cs.CV]
2. VOGUE: Try-On by StyleGAN Interpolation Optimization [13]
Google introduces VOGUE, an innovative online fitting room that leverages a modified StyleGAN2 architecture. By simply using an image of oneself, users can effortlessly try on various pants and shirts, revolutionizing the virtual fitting experience.
![](\"https://miro.medium.com/v2/resize:fit:1400/format:webp/1*Oc0NQCzyndSHAxsIrVX-yA.png\")
Segmentation map generated by VOGUE’s StyleGAN2 model
3. Taming Transformers for High-Resolution Image Synthesis [14]
Researchers have successfully combined the efficiency of GANs and convolutional approaches with the expressive power of transformers. This breakthrough approach enables powerful and time-efficient semantically-guided image synthesis, showcasing impressive results in terms of quality and fidelity.
![](\"https://miro.medium.com/v2/resize:fit:1400/format:webp/1*NFbPrVd5eYiUiYI5bBPv6g.png\")
Chen et al., Generative Pretraining from Pixels, (2020)
4. ShaRF: Shape-conditioned Radiance Fields from a Single View [15]
This research introduces ShaRF, a method for estimating detailed 3D neural scene representations using only a single image. By incorporating a geometric scaffold to guide the reconstruction of the radiance field, this approach achieves impressive results in rendering and shape estimation, even extending to images beyond the training domain.
![](\"https://miro.medium.com/v2/resize:fit:858/format:webp/1*XQN6trPZebaO16JEIuzXmA.png\")
![](\"https://miro.medium.com/v2/resize:fit:512/1*uYIjSxYH9urYp7Eavw8x2Q.gif\")
5. Generative Adversarial Transformers [16]
The fusion of transformers' attention mechanism with the powerful StyleGAN2 architecture leads to the development of Generative Adversarial Transformers. This novel combination enhances the capabilities of image generation models, pushing the boundaries of visual synthesis.
![](\"https://miro.medium.com/v2/resize:fit:1400/format:webp/1*AJ3fKrHWHG-08av-nKXvbA.png\")
Results examples on generating bedroom scenes with its attention maps. Image from: Drew A. Hudson and C. Lawrence Zitnick, Generative Adversarial Transformers, (2021).
6. Swin Transformer: Hierarchical Vision Transformer using Shifted Windows [17]
Introducing Swin Transformer, a versatile vision backbone designed to excel in computer vision tasks. Overcoming challenges associated with applying transformers to vision, this hierarchical model with shifted windows achieves remarkable performance on various benchmarks, surpassing previous state-of-the-art methods.
![](\"https://miro.medium.com/v2/resize:fit:1400/format:webp/1*zI2WxslE1X61fMWl4Iuy_Q.png\")
Image generated by: Louis Bouchard
7. IMAGE GANS MEET DIFFERENTIABLE RENDERING FOR INVERSE GRAPHICS AND INTERPRETABLE 3D NEURAL RENDERING [18]
The GANverse3D model is an exciting development that can create customizable and animated 3D figures from a single image. This innovative technology opens up possibilities in inverse graphics and interpretable 3D neural rendering, allowing for greater creativity and flexibility.
![](\"https://cdn-images-1.medium.com/max/800/1*JcU_W4nMedDEeI6H7vUeOw.gif\")
GANverse3D. GANverse3D: Knight Rider KITT Re-created with AI by NVIDIA
8. Deep nets: What have they ever done for vision? [19]
In this thought-provoking opinion paper, the strengths and weaknesses of Deep Nets in the field of computer vision are explored. While acknowledging their significant contributions to AI advancements, the paper highlights the need to better understand their limitations and calls for reevaluation of benchmarking methods to ensure their practicality in real-world scenarios.
![](\"https://cdn-images-1.medium.com/max/800/1*VEuSascsbQgHB9Uv8_icdQ.png\")
9. DefakeHop: A Light-Weight High-Performance Deepfake Detector [20]
As the threat of deepfakes continues to grow, researchers have developed DefakeHop, an advanced deepfake detection system. Leveraging artificial intelligence, this breakthrough technology aims to combat the spread of manipulated media, providing solutions to identify and mitigate the impact of deepfakes in various contexts.
![](\"https://www.louisbouchard.ai/content/images/2021/06/Deepfake_example_1.gif\")
10. GitHub Copilot & Codex: Evaluating Large Language Models Trained on Code [21]
GitHub Copilot, powered by the Codex language model, revolutionizes code-writing capabilities. This groundbreaking technology, trained on publicly available code from GitHub, achieves remarkable success in program synthesis from docstrings, outperforming previous models and showcasing its potential for enhancing developer productivity.
![](\"https://i.pinimg.com/474x/71/63/55/7163554827bb53e7ef3240d28499005e.jpg\")
11. Skillful Precipitation Nowcasting using Deep Generative Models of Radar [22]
DeepMind introduces a state-of-the-art generative model for accurate precipitation nowcasting. Outperforming conventional methods in 89% of cases, this model leverages deep learning to predict and generate future radar data, providing valuable insights for meteorologists and weather forecasting.
![](\"https://assets-global.website-files.com/621e749a546b7592125f38ed/6227e31239003803b4eb74db_Fig%201.gif\")
Image taken from DeepMind's Blog post
12. The Cocktail Fork Problem: Three-Stem Audio Separation for Real-World Soundtracks [23]
Mitsubishi and Indiana University address the common issue of inaudible actors or imbalanced soundtracks in movies and videos. With their innovative model and dataset, they tackle the complex task of isolating specific sound sources, allowing for precise adjustment of audio tracks and enhancing the overall auditory experience.
![](\"https://cdn-images-1.medium.com/max/800/1*GBCD9tAbl2Jz9AyiNmotUg.png\")
Separating a soundtrack into music, speech, and sound effects. Image from Petermann, D. et al., (2021).
\nNote: Above mentioned list is inspired by the blog written by Louis Bouchard.
\n\n\n\n###Year 2022
\n\n1. Hierarchical Text-Conditional Image Generation with CLIP Latents (DALL-E 2) [24]
\nIn a groundbreaking research paper, a two-stage model is proposed to leverage the robust representations of images captured by contrastive models like CLIP. By generating CLIP image embeddings based on text captions and using them as conditioning inputs for image generation, the model achieves enhanced image diversity while maintaining photorealism and caption similarity. The decoders, guided by the image representations, generate variations of images that preserve both semantics and style, while allowing for creative manipulation of non-essential details. With the joint embedding space of CLIP, language-guided image transformations become possible in a zero-shot manner. The study explores diffusion models for the decoder and finds them to be computationally efficient, yielding high-quality samples.
![](\"https://d2e931syjhr5o9.cloudfront.net/Artboard_1_copy_4_100_be885935c3.jpg\")
NimbleBox.ai blog post
2. High-Resolution Image Synthesis with Latent Diffusion Models (Stable Diffusion) [25]
\nIn an exciting research development, diffusion models (DMs) are utilized to achieve exceptional synthesis results on image data by breaking down the image formation process into sequential denoising autoencoders. Unlike previous approaches, the application of DMs in the latent space of pretrained autoencoders allows for optimal balance between complexity reduction and detail preservation, resulting in remarkable visual fidelity. By incorporating cross-attention layers, the model architecture transforms diffusion models into versatile generators capable of handling diverse conditioning inputs like text or bounding boxes. This breakthrough enables high-resolution synthesis in a convolutional manner, leading to state-of-the-art performance in image inpainting, unconditional generation, semantic scene synthesis, and super-resolution tasks. Notably, these latent diffusion models (LDMs) significantly reduce computational requirements compared to pixel-based DMs while maintaining competitive quality.
![](\"https://miro.medium.com/v2/resize:fit:1400/format:webp/0*wfDSSWbAbPpFNhTP.png\")
Lexica Art
3. LAION-5B: An Open Large-Scale Dataset for Training Next Generation Image-Text Models [26]
\nThe breakthrough language-vision models CLIP and DALL-E demonstrated the power of training on large-scale noisy image-text data, without relying on expensive accurate labels. To enable further research on these models, we introduce LAION-5B, a dataset with 5.85 billion CLIP-filtered image-text pairs. It allows successful replication and fine-tuning of foundational models like CLIP, GLIDE, and Stable Diffusion. LAION-5B fosters experiments with large-scale multi-modal models and includes valuable resources such as nearest neighbor indices, an improved web interface, and detection scores for watermark, NSFW, and toxic content. This dataset unlocks the potential of exploring language-vision architectures on an unprecedented scale.
![](\"https://production-media.paperswithcode.com/datasets/775155ce-ba59-4220-949e-02426f37a001.jpeg\")
Image taken from Schuhmann et al.(2022)
4. An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion [27]
\nText-to-image models provide unprecedented freedom for generating images based on natural language. However, it remains unclear how this freedom can be harnessed to create specific and unique concepts, modify appearances, or compose new scenes. In this research, a simple approach is introduced to enable such creative freedom. By using a small set of 3-5 images representing a user-provided concept, the model learns to represent it through new \"words\" in the embedding space of a pre-trained text-to-image model. These \"words\" can be used to compose natural language sentences, allowing personalized and intuitive image generation. The effectiveness of this approach is demonstrated by comparing it to various baselines, showing its ability to faithfully portray concepts across different applications and tasks.
![](\"https://textual-inversion.github.io/static/images/editing/elephant.JPG\")
5. DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation [28]
\nLarge text-to-image models have revolutionized AI by enabling the synthesis of high-quality and diverse images from textual prompts. However, these models face limitations in mimicking the appearance of subjects from a given reference set and generating novel renditions in different contexts. This research introduces a novel approach for personalizing text-to-image diffusion models. By fine-tuning a pretrained model with a few subject images, a unique identifier is associated with the specific subject. Leveraging the model's semantic prior and a class-specific prior preservation loss, this technique enables the synthesis of photorealistic images of the subject in diverse scenes, poses, views, and lighting conditions not present in the reference images. The approach demonstrates its efficacy in various tasks such as subject recontextualization, text-guided view synthesis, and artistic rendering, while preserving the subject's distinctive features. A new dataset and evaluation protocol are also provided for this subject-driven generation task.
![](\"https://dreambooth.github.io/DreamBooth_files/high_level.png\")
![](\"https://dreambooth.github.io/DreamBooth_files/system.png\")
6. Make-A-Video: Text-to-Video Generation without Text-Video Data [29]
\nIntroducing Make-A-Video, a groundbreaking approach in text-to-video generation. By leveraging the advancements in text-to-image models and unsupervised video footage, Make-A-Video achieves remarkable results without the need for paired text-video data. With its innovative spatial-temporal modules and high-resolution pipeline, it sets a new state-of-the-art in faithful and diverse video synthesis from text prompts. Prepare to be captivated by the seamless fusion of text and motion, as Make-A-Video opens up new possibilities for dynamic storytelling.
![](\"https://makeavideo.studio/assets/i2vsingle1input.webp\")
⬇
![](\"https://makeavideo.studio/assets/i2vsingle1.webp\")
Developed at Meta AI Studio
7. FILM: Frame Interpolation for Large Motion [30]
\nIntroducing a breakthrough frame interpolation algorithm that revolutionizes the synthesis of intermediate frames between two input images with significant motion. Unlike previous methods, our unified network eliminates the need for separate networks for optical flow or depth estimation, making it simpler and more efficient. By leveraging a multi-scale feature extractor with shared weights and optimizing with the Gram matrix loss, we achieve crisp and visually appealing frames. Our approach surpasses state-of-the-art methods on benchmark datasets such as Xiph, Vimeo-90K, Middlebury, and UCF101. Moreover, we showcase the effectiveness of our model in generating high-quality and temporally coherent videos on a challenging near-duplicate photos dataset.
Feature Extraction Process:
![](\"https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi0SmdFNEGohaKfz2VoKw15mX92A_62xLRmPB7DAMWZqRGFdV9EYkTQpGZo6gIgTHstm2Bxd_suOyFZtAf6o6MF3esTx6jJ_gWf4-1E7EoHnDPx7RkgaozqKBSBja8uKgHkbXUVV4uZ3FdPFjwicqqI7rhGNGPtDomn1o4lYdiMZe8HtFnV9cjF42FjMA/s16000/image9.gif\")
Output:
![](\"https://cdn-images-1.medium.com/max/1600/1*fA9GhcAVxhBQJqmMwFfV2g.gif\")
\nFILM blog post
\n \n8. YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors [31]
\nIntroducing YOLOv7, the ultimate object detector that shatters speed and accuracy records in real-time detection. With an impressive range of 5 FPS to 160 FPS, it outperforms all existing detectors. Notably, YOLOv7-E6 takes the lead with an exceptional 56 FPS on GPU V100 and an astonishing accuracy of 55.9% AP. Surpassing transformer-based and convolutional-based detectors by staggering margins, YOLOv7-E6 achieves unmatched speed, outperforming its competitors by 509% and 551%, respectively, while maintaining superior accuracy. Leaving no room for doubt, YOLOv7 emerges triumphant over renowned detectors such as YOLOR, YOLOX, Scaled-YOLOv4, YOLOv5, DETR, Deformable DETR, DINO-5scale-R50, ViT-Adapter-B, and more. What's even more impressive is that YOLOv7 is trained solely on the MS COCO dataset, without relying on any other datasets or pre-trained weights. Prepare to witness the next level of object detection prowess with YOLOv7.
![](\"https://github.com/WongKinYiu/yolov7/raw/main/figure/performance.png\")
WongKinYiu GitHub Repositiory
9. A ConvNet for the 2020s [32]
\nGet ready to witness a revolution in computer vision! We're diving deep into the fascinating world of ConvNeXt models, a remarkable fusion of Convolutional Neural Networks (ConvNets) and Transformers. While Vision Transformers stole the show, their limitations in tasks beyond image classification left a gap. But fear not! Hierarchical Transformers like Swin Transformers brought back the ConvNet priors, and now, we're taking it to the next level. Brace yourself for the mind-blowing results: ConvNeXt models are here to dominate! With an incredible 87.8% ImageNet top-1 accuracy, ConvNeXts outshine Swin Transformers in COCO detection and ADE20K segmentation. It's the perfect blend of simplicity, efficiency, and mind-boggling accuracy. Welcome to a new era of computer vision possibilities!
![](\"https://miro.medium.com/v2/resize:fit:1400/format:webp/0*Pq7dwTVRCbKZrJve.png\")
10. A Generalist Agent (Gato) [33]
\nEmbark on a journey into the realm of multi-modal intelligence with the extraordinary agent known as Gato. Inspired by advancements in large-scale language modeling, Gato represents a breakthrough in generalist AI. This versatile agent possesses the remarkable ability to perform a multitude of tasks across various modalities, from playing Atari games to captioning images and even manipulating physical objects with a robot arm. Operating on a single network with shared weights, Gato dynamically adapts its outputs, whether they be text, torques, or other tokens, based on the context at hand. Join us as we unveil the intricacies of the model, the data, and the awe-inspiring capabilities of Gato in this illuminating report.\n
![](\"https://assets-global.website-files.com/621e749a546b7592125f38ed/627d13d743dc353a184da8d4_data_sequences.png\")
11. MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge [34]
\nImagine a groundbreaking world of versatile autonomous agents! Forget about agents limited to specific tasks; this paper introduces MineDojo, an incredible framework inspired by human adaptability. This cutting-edge approach empowers agents to excel across diverse challenges with ease. How does it work? With an expansive environment, a vast knowledge database, and a flexible agent architecture, MineDojo lets agents conquer open-ended tasks naturally, just like us! By utilizing large pre-trained video-language models as learned reward functions, manual reward design becomes a thing of the past. Excitingly, all these resources are open-source, driving research towards the development of truly capable embodied agents. Dive into the future of AI exploration at the MineDojo Link now!
![](\"https://www.nack.ai/blog/tree-of-thoughts/minedojo.png\")
12. Human-level Play in the Game of Diplomacy by Combining Language Models with Strategic Reasoning (Cicero) [35]
\nIntroducing Cicero, an extraordinary AI agent breaking new ground in the realm of interactive communication. While significant progress has been made in language modeling, the challenge of building agents capable of intentional language-based communication with humans in dynamic environments persists. Cicero emerges as a trailblazing AI agent, achieving human-level performance in Diplomacy, a complex strategy game known for its emphasis on natural language negotiation and tactical coordination among seven players. By seamlessly integrating language models, planning algorithms, and reinforcement learning techniques, Cicero not only comprehends players' beliefs and intentions through conversations but also generates strategic dialogue aligned with its plans. In a rigorous anonymous online Diplomacy league, Cicero's prowess shone through, surpassing human players with an impressive average score more than double that of its human counterparts. Furthermore, it secured a place in the top 10% among participants who engaged in multiple games. Cicero's accomplishments open up exciting possibilities for the future of AI-driven intentional communication and strategic decision-making.
![](\"https://miro.medium.com/v2/resize:fit:1400/format:webp/0*2geaS8fR_VkiKtEe.png\")
Meta AI
13. Training Language Models to Follow Instructions with Human Feedback (InstructGPT and ChatGPT) [36]
\nIn this groundbreaking research paper, the authors tackle the challenge of aligning large language models with user intent. They introduce InstructGPT, a model fine-tuned using human feedback to ensure it generates helpful and truthful outputs. Through a two-step process involving supervised and reinforcement learning, InstructGPT shows remarkable improvements in aligning with user intent, outperforming the larger GPT-3 model in human evaluations. Notably, InstructGPT exhibits reduced toxic output generation while maintaining high performance on public NLP datasets. This research highlights the potential of fine-tuning with human feedback as a promising avenue for creating language models that truly understand and cater to user needs.
![](\"https://miro.medium.com/v2/resize:fit:1200/1*j50ySUfctJC20LjDTRG3EA.png\")
14. LaMDA: Language Models for Dialog Applications [37]
\nIntroducing LaMDA, a remarkable advancement in language models designed specifically for dialog applications. With an impressive parameter count and extensive pre-training on dialog data and web text, LaMDA strives to excel not only in quality but also in safety and factual grounding. Through fine-tuning with annotated data and the ability to consult external knowledge sources, LaMDA addresses the challenges of ensuring safe and value-consistent responses while generating factually grounded answers. The researchers employ innovative metrics to measure safety and factual grounding, showcasing the model's enhanced capabilities. Furthermore, they explore LaMDA's potential in education and content recommendations, evaluating its helpfulness and role consistency in these domains.
![](\"https://storage.googleapis.com/gweb-uniblog-publish-prod/original_images/1_Welcome_GenerativeMeena_CL_V02_150521_v2_720_25fps.gif\")
15. Instant Neural Graphics Primitives with a Multiresolution Hash Encoding [38])
\nGet ready for a game-changer in neural graphics! This research introduces an efficient and powerful approach to train and evaluate neural graphics primitives. By using a clever input encoding technique, they reduce the computational cost without compromising on quality. They achieve this by combining a small neural network with a multiresolution hash table that optimizes feature vectors through gradient descent. This simple yet parallelizable architecture takes full advantage of modern GPUs. The researchers implement the system using fully-fused CUDA kernels, minimizing wasted resources. The results? Mind-blowing! They achieve incredible speedups, enabling lightning-fast training and rendering of high-quality neural graphics primitives. Prepare to be amazed by the future of graphics technology!
16. Block-NeRF: Scalable Large Scene Neural View Synthesis [39]
\nPrepare to be amazed by Block-NeRF, a groundbreaking advancement in Neural Radiance Fields! This research tackles the challenge of rendering large-scale environments, such as city scenes. They cleverly decompose the scene into individually trained NeRFs, revolutionizing the rendering process. This approach breaks free from the limitations of scene size and allows for updates on a per-block basis. To ensure robustness, they introduce appearance embeddings, pose refinement, and controllable exposure to each NeRF. They even developed a procedure to seamlessly combine adjacent NeRFs for a seamless scene. With 2.8 million images, they have created the largest neural scene representation ever. Get ready for a new era of rendering massive environments!
![](\"https://www.unite.ai/wp-content/uploads/2022/02/Day-toNight-block-NeRF.gif\")
17. DreamFusion: Text-to-3D using 2D Diffusion [40])
\nImagine bringing text-to-image synthesis to a whole new dimension – 3D! In this exciting research, they break the barriers by using a 2D text-to-image diffusion model to perform text-to-3D synthesis. No need for large-scale labeled 3D datasets or complex architectures! They cleverly optimize a 3D model (NeRF) through a DeepDream-like procedure, allowing it to render 2D images from various angles with impressive realism. The best part? This approach requires no 3D training data and no modifications to the image diffusion model! It's like magic – creating 3D models from text, ready to be viewed, relit, and composited into any 3D environment. The future of 3D synthesis is here!
![](\"https://cdn-images-1.medium.com/max/1600/1*dH2NQVXP7uhNZmxnXozZLg.gif\")
18. Point-E: A System for Generating 3D Point Clouds from Complex Prompts [41]
\nRevolutionizing 3D object generation, this paper introduces a lightning-fast method to create intricate 3D models in just 1-2 minutes on a single GPU! While others take hours to generate one sample, this novel approach leverages text-to-image diffusion models to first produce a synthetic view and then generates a 3D point cloud based on the image. Sure, it might not match the absolute state-of-the-art in sample quality, but it's a game-changer in speed – one to two orders of magnitude faster! For practical use cases, this trade-off between speed and quality is pure gold. Plus, they're sharing their pre-trained models and evaluation code, unlocking endless possibilities!
![](\"https://i0.wp.com/syncedreview.com/wp-content/uploads/2022/12/image-64.png?w=1205&ssl=1\")
Synced blog post
Year 2023
\n\n1. 3D Neural Embedding Likelihood for Robust Probabilistic Inverse Graphics [42]
Enter the realm of 3D scene understanding with an innovative research paper. Discover the power of probabilistic modeling in inverse graphics, enhancing 6D pose estimation tasks with robustness and uncertainty quantification. Experience the brilliance of 3D Neural Embedding Likelihood (3DNEL), a unified model blending RGB and depth information for precise object pose estimation. Breakthroughs include handling heavy occlusion, multi-object scenes, and principled uncertainty quantification. Unleash the potential to incorporate prior knowledge and extend to camera pose tracking from video. Prepare for a transformative journey in 3D scene perception.\n![](\"https://cdn-images-1.medium.com/max/1600/1*uWuxPJzTWtyxXqyZ8-VXZA.png\")
2. Unsupervised melody-guided lyrics generation [43]
Delve into the fascinating world of automatic songwriting with a groundbreaking research paper. Overcoming copyright limitations and the creative complexity of the task, this work introduces a novel approach to generate highly listenable lyrics. By disentangling training and inference processes, the proposed hierarchical framework achieves remarkable results in melody-guided text generation. With an emphasis on cross-modal correlation between melody and lyrics, the model produces singable, intelligible, coherent, and rhyming lyrics. Notably, it empowers users with limited music background through effective content control, democratizing song creation. Prepare to be captivated by the harmonious fusion of technology and creativity in the realm of automatic songwriting.
\n![](\"https://cdn-images-1.medium.com/max/1600/1*MY7L4jpzxt2cz8UP0ROh2A.png\")
3. A Hybrid Generator Architecture for Controllable Face Synthesis [44]
Embark on a journey into the realm of data-driven image generation as we explore a groundbreaking approach to control and enhance the synthesis of human faces. While modern generative models excel in producing realistic images, the challenge lies in endowing them with interpretable controls over key attributes such as head shape, expression, identity, background, and illumination. In this research, a unique philosophy is adopted, treating the problem as an unconditional synthesis task. By combining learned neural networks with fixed-function blocks, the model leverages interpretable inductive biases to achieve precise control and semantic understanding. This enables independent sampling of image aspects and empowers traditional animation tools for facial animation. Experience the fusion of photorealism and explicit control in the captivating world of data-driven image generation.\n
![](\"https://cdn-images-1.medium.com/max/1600/1*fMYpyTj37dsrSBLkXlDNcg.png\")
4. Capabilities of GPT-4 on Medical Challenge Problems [45]
Witness the groundbreaking evaluation of GPT-4, a cutting-edge large language model (LLM), in the realm of medicine. Unveiling its remarkable capabilities, GPT-4 showcases its prowess on medical competency exams and benchmark datasets without any specialized medical training. Exceeding passing scores on the USMLE exams by over 20 points, GPT-4 outperforms previous general-purpose models and even those fine-tuned specifically on medical knowledge. Notably, GPT-4 demonstrates superior probability calibration, a vital aspect in high-stakes medical applications. Delve into captivating case studies that highlight GPT-4's ability to explain medical reasoning, personalize explanations, and craft new scenarios. Explore the transformative potential of GPT-4 in medical education, assessment, and clinical practice, while addressing challenges of accuracy and safety.\n
\n![](\"https://cdn-images-1.medium.com/max/1600/1*39XpdPWTr-h_pFOpGYe1VQ.png\")
5. Generative Agents: Interactive Simulacra of Human Behavior [46]
Prepare to be captivated by the introduction of generative agents, software entities that simulate realistic human behavior. These agents live dynamic lives, engaging in daily activities like cooking breakfast, pursuing artistic endeavors, forming opinions, and initiating conversations. The architecture behind these generative agents extends large language models, allowing them to store and synthesize their experiences, reflect on them, and dynamically plan their behavior. In an interactive sandbox environment inspired by The Sims, users can interact with a town populated by these agents using natural language. Evaluation results demonstrate the agents' ability to produce believable individual and social behaviors, showcasing their autonomy in spreading invitations, making acquaintances, organizing events, and more. Through the fusion of large language models and interactive agents, this research opens up new possibilities for creating immersive simulations of human behavior.\n![](\"https://cdn-images-1.medium.com/max/1600/1*Mz1M4YmwwyL5q_TmuVToNQ.png\")
\n![](\"https://cdn-images-1.medium.com/max/1600/1*7TKzn3Oif4ZHE8QIZuzNng.png\")
\n
Joon Sung Park et. al.,(2023)\n
\n\n\n#Future of Generative AI
\n\nThe future of generative AI holds immense potential, promising a captivating journey into uncharted realms of creativity and limitless possibilities. With each stride in AI advancement, the horizons of artistic expression expand, enabling the creation of breathtaking visuals, mesmerizing melodies, and captivating narratives that defy the confines of human imagination. This extraordinary evolution, fueled by cutting-edge algorithms, neural networks, and computational prowess, paves the way for generative AI to rival the masterpieces of revered artists throughout history. However, as we embark on this exhilarating path, we must remain steadfast in upholding ethical principles. Privacy safeguards, fairness considerations, and the uplifting of humanity must be woven into the fabric of generative AI's future.
\n\nGenerative AI has the power to reshape gaming, education, and medicine. Gaming experiences could become highly personalized, with AI generating unique worlds and interactions for each player, including dynamic dialogue from non-playable characters. In education, generative AI can offer tailored learning experiences, adapting materials and assessments to individual students' needs. In medicine, AI algorithms can analyze vast amounts of medical data to improve diagnosis, treatment planning, and drug discovery. However, ethical considerations must be prioritized to ensure privacy, mitigate bias, and maintain human oversight. With responsible development, generative AI holds exciting prospects for the future of these fields.
\n\n\n\n#Ethics in Generative AI [47][48]
\n\nGenerative AI technologies have raised important ethical considerations in several areas, including copyright ambiguities, misuse, data privacy violations, data provenance, and the amplification of existing biases.
\n\nCopyright Ambiguities emerge as generative AI can produce content that closely resembles copyrighted material, leading to uncertainties around ownership and legality. Determining the boundaries of fair use becomes challenging when AI-generated content blurs the lines between original and reproduced works.\n
\n
![](\"https://dreambooth.github.io/DreamBooth_files/art.png\")
\nMisuse of generative AI raises concerns about deceptive practices, such as deepfakes and fabricated information. These technologies can be exploited for malicious purposes, including spreading disinformation, impersonation, or defamation. Safeguarding against such misuse becomes crucial to maintaining trust and the integrity of information.\n\n
\n
![](\"https://static.simonwillison.net/static/2023/chatgpt-lawyer-screenshot-2.jpg\")
\nLawyer cites fake cases invented by ChatGPT, judge is not amused
\nData privacy violations are a significant concern as generative AI models often rely on extensive datasets for training. If these datasets contain sensitive or personal information that is inadequately protected or anonymized, privacy breaches and security risks can arise. Stricter regulations and robust data protection measures are necessary to mitigate these concerns.\n\n
\n
![](\"https://d2u3dcdbebyaiu.cloudfront.net/uploads/atch_img/761/6518686dac016e3c49de29c30ed84e36_res.jpeg\")
\nSamsung Employees Use ChatGPT at Work, Unknowingly Leak Critical Source Codes
\nThe amplification of existing biases is a critical ethical issue as generative AI models can perpetuate and even magnify biases present in the training data. If the datasets used for training are biased or unrepresentative, the generated content may reinforce stereotypes and discrimination. Addressing this challenge involves ensuring diverse and unbiased training data and continuously evaluating and mitigating biases in algorithms.
\n\nGenerative AI poses ethical considerations for workforce roles and morale. With AI systems capable of automating tasks and generating content previously done by humans, there is a risk of job displacement and uncertainty about future employment opportunities. This can lead to decreased job security and lower morale among employees. To address these concerns, organizations must prioritize transparent communication, provide opportunities for reskilling, and foster a supportive work environment that values human creativity and collaboration. Human-AI collaboration should be emphasized, seeking ways to augment human work with AI technology and identifying new roles where human expertise can be leveraged alongside generative AI systems. By involving employees in decision-making and offering growth opportunities, organizations can mitigate workforce anxieties and promote ethical integration of generative AI technology.
\n\n\"Ethics of Artificial Intelligence and Robotics\" by Vincent C. MĂĽller (2020) investigates the ethical implications of AI and robotics, focusing specifically on generative AI systems. It addresses the distinctive ethical challenges associated with these systems and explores potential risks related to their implementation. Privacy, accountability, bias, and the societal impact on employment and social structures are discussed as key concerns. The paper emphasizes the significance of ethical frameworks and guidelines in guiding the responsible development and utilization of generative AI technologies. Its findings contribute valuable insights to the ongoing discourse on the ethical considerations surrounding generative AI, benefiting policymakers, researchers, and practitioners in the field.
\n\n\n#Overcoming disadvantages of Generative AI[49][50]
\n\nTo address these ethics concerns, it is crucial to establish clear legal frameworks for copyright issues, develop safeguards to prevent misuse and deception, implement robust data privacy policies and practices, ensure transparency in data sources and generation processes, and actively work towards mitigating biases in training data and algorithms.
\n\n### AI Fairness 360 \n\"An Extensible Toolkit for Detecting and Mitigating Bias in Machine Learning\" (Varshney et al., 2018) introduces the AI Fairness 360 toolkit, which provides a comprehensive set of tools and algorithms for measuring and reducing bias in machine learning models. It offers various bias detection algorithms, like disparate impact analysis and equal opportunity analysis, to identify biases in data and model predictions. The toolkit also includes bias mitigation algorithms, such as reweighing and disparate impact remover, to address biases in models.
\n\n### Model cards\n\"Model Cards for Model Reporting\" (Mitchell et al., 2019) proposes the use of model cards as a standardized way to report important information about machine learning models. Model cards provide details about training data, model performance, and potential biases, enhancing transparency and aiding users in making informed decisions. They facilitate discussions about fairness, bias, and ethical considerations by documenting aspects such as demographic distribution of training data and performance on specific subgroups. Model cards promote accountability and responsible deployment of machine learning models.
\n\n### ZeroGPT\nZeroGPT, developed by Edward Tian, is an innovative tool that distinguishes between AI-generated and human-authored text. It analyzes textual complexity and various variables to determine the source of language, be it AI or human. With advanced AI algorithms, ZeroGPT Detector accurately detects patterns and predicts word choices, enhancing its identification accuracy. It offers user-friendliness, free personal usage, multilingual support, organizational integration, and prioritizes privacy protection, making it an impressive choice for discerning AI-generated content.
\n\n### Copyleaks\nCopyleaks AI Content Detector accurately distinguishes between AI-generated and human-written content. It detects material produced by AI models like ChatGPT, GPT-4, and Jasper, providing a probability score for authorship identification. Supporting multiple languages, including English, Spanish, French, and more, Copyleaks offers a comprehensive solution for ensuring content originality.
\n\n### Human in the loop\nGenerative AI tools have limitations in understanding emotional and business context and recognizing their own errors or potential harm. Human involvement is essential to review outputs, address biases, and ensure models operate as intended. Generative AI should be viewed as a means to enhance human capabilities, rather than replace them. Companies must responsibly adopt generative AI, integrating these tools to improve employee and customer experiences while upholding accuracy, safety, honesty, empowerment, and sustainability. This requires mitigating risks, eliminating bias, and embracing ethical AI practices that extend beyond immediate corporate interests to encompass broader societal responsibilities.
\n\n","metadata":{}},{"cell_type":"markdown","source":"\n\n#Code
\nHere are the examples that demonstrates the applications of Generative AI in the field of Image Processing and Text-Generation.
","metadata":{}},{"cell_type":"code","source":"from kaggle_secrets import UserSecretsClient\nuser_secrets = UserSecretsClient()\nsecret_value_0 = user_secrets.get_secret(\"API\")\nsecret_value_1 = user_secrets.get_secret(\"API_URL\")\nsecret_value_2 = user_secrets.get_secret(\"text_api\")","metadata":{"execution":{"iopub.status.busy":"2023-07-16T05:33:12.093884Z","iopub.execute_input":"2023-07-16T05:33:12.094612Z","iopub.status.idle":"2023-07-16T05:33:12.576707Z","shell.execute_reply.started":"2023-07-16T05:33:12.094572Z","shell.execute_reply":"2023-07-16T05:33:12.575744Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import requests\n\nAPI_URL = secret_value_1\nheaders = {\"Authorization\": secret_value_0}\n\ndef query(payload):\n response = requests.post(API_URL, headers=headers, json=payload)\n return response.content\nimage_bytes = query({\n \"inputs\": \"Astronaut riding a horse\"\n})\n# You can access the image with PIL.Image for example\nimport io\nfrom PIL import Image\nimage = Image.open(io.BytesIO(image_bytes))","metadata":{"execution":{"iopub.status.busy":"2023-07-16T05:33:17.676642Z","iopub.execute_input":"2023-07-16T05:33:17.677032Z","iopub.status.idle":"2023-07-16T05:33:17.865039Z","shell.execute_reply.started":"2023-07-16T05:33:17.677002Z","shell.execute_reply":"2023-07-16T05:33:17.864003Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"image","metadata":{"execution":{"iopub.status.busy":"2023-07-16T05:33:19.577322Z","iopub.execute_input":"2023-07-16T05:33:19.577750Z","iopub.status.idle":"2023-07-16T05:33:19.718565Z","shell.execute_reply.started":"2023-07-16T05:33:19.577716Z","shell.execute_reply":"2023-07-16T05:33:19.717776Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"This example illustrates how generative AI models can be used to generate images based on given inputs. By leveraging these models, you can create new and imaginative visual content that aligns with the provided input, offering endless creative possibilities.
\n\n\n\n\n\n\n","metadata":{}},{"cell_type":"code","source":"API_URL = \"https://api-inference.huggingface.co/models/deepset/roberta-base-squad2\"\n\n\ndef query(payload):\n response = requests.post(secret_value_2, headers=headers, json=payload)\n return response.json()\n\noutput = query({\n \"inputs\": {\n \"question\": \"What's my name?\",\n \"context\": \"My name is Clara and I live in Berkeley.\"\n },\n})","metadata":{"execution":{"iopub.status.busy":"2023-07-16T05:33:23.684525Z","iopub.execute_input":"2023-07-16T05:33:23.685545Z","iopub.status.idle":"2023-07-16T05:33:23.816174Z","shell.execute_reply.started":"2023-07-16T05:33:23.685510Z","shell.execute_reply":"2023-07-16T05:33:23.814949Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"output","metadata":{"execution":{"iopub.status.busy":"2023-07-16T05:33:44.379333Z","iopub.execute_input":"2023-07-16T05:33:44.379760Z","iopub.status.idle":"2023-07-16T05:33:44.386735Z","shell.execute_reply.started":"2023-07-16T05:33:44.379727Z","shell.execute_reply":"2023-07-16T05:33:44.385551Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"In this specific example, the generative AI model is given a question (\"What's my name?\") and a context (\"My name is Clara and I live in Berkeley.\"). The model processes this input and generates an answer. The generated answer is \"Clara\" with a score of 0.932, indicating high confidence in the generated answer.\n
Overall, this code showcases how you can use generative AI models to generate text-based answers based on given questions and contextual information.
Note:
The code examples demontrated above are inspired from the official resource examples provided by the popular open-source organization: Hugging Face🤗
\nText-to-Image example is taken from cloudqi/cqi_text_to_image_pt_v0
\nQuestion and Answer example is taken from deepset/roberta-base-squad2
Conclusion
\n\nIn conclusion, generative AI has transformed the landscape of artificial intelligence, ushering in a realm of boundless creativity and innovation. From its early foundations to recent advancements, generative AI has demonstrated remarkable capabilities in language generation and image synthesis, propelling various domains like gaming, education, and healthcare forward.
\n\nHowever, the responsible development and deployment of generative AI require us to prioritize ethics. Safeguarding privacy, ensuring fairness, and mitigating biases are essential aspects that demand our attention and commitment.
\n\nLooking to the future, generative AI presents unparalleled opportunities to push the boundaries of human imagination. By striking a harmonious balance between technological progress and ethical responsibility, we can harness the potential of generative AI to enhance human capabilities and shape a future filled with extraordinary creativity and progress.
\n\nIn summary, generative AI heralds a new era of innovation, where collaboration with intelligent machines expands the frontiers of human imagination. It is our collective duty to foster its development while upholding ethical principles, paving the way for a future where generative AI unlocks infinite possibilities for the betterment of society.
\n\n\n\n#References
\n\n\n\n-[1] Learning representations by back-propagating errors\"by David E. Rumelhart et al. (1986) Link(↗️)
\n\n\n\n-[2] Neural Autoregressive Distribution Estimation (NADE) paper by Benigno Uria et al. (2010) Link(↗️)
\n\n\n\n-[3] Generative Stochastic Networks (GSN) paper by Yoshua Bengio et al. (2014) Link(↗️)
\n\n\n\n-[4] Masked Autoencoder for Distribution Estimation (MADE) paper by Mathieu Germain et al. (2015) Link(↗️)
\n\n\n\n-[5] Auto-Encoding Variational Bayes\" by Diederik P. Kingma and Max Welling (2013) Link(↗️)
\n\n\n\n-[6] Generative Adversarial Networks\" by Ian Goodfellow et al. (2014) Link(↗️)
\n\n\n\n-[7] BigGAN paper by Andrew Brock et al. (2018) Link(↗️)
\n\n\n\n-[8] Language Models are Unsupervised Multitask Learners\" and \"Better Language Models and Their Implications\" by Alec Radford et al. (2019) Link(↗️)
\n\n\n\n-[9] A Style-Based Generator Architecture for Generative Adversarial Networks\" by Tero Karras et al. (2019) Link(↗️)
\n\n\n\n-[10] Progressive Growing of GANs (PGGAN) by Tero Karras et al. (2018) Link(↗️)
\n\n\n\n-[11] Analyzing and Improving the Image Quality of StyleGAN\" by Tero Karras et al. (2020) Link(↗️)
\n\n\n\n-[12] DALL·E: Zero-Shot Text-to-Image Generation from OpenAI, Aditya Ramesh 1 et. al., (2021) Link(↗️)
\n\n\n\n-[13] VOGUE: Try-On by StyleGAN Interpolation Optimization, Kathleen M Lewis at. al.,(2021) Link(↗️)
\n\n\n\n-[14] Taming Transformers for High-Resolution Image Synthesis, PATRICK ESSER et. al.,(2021) Link(↗️)
\n\n\n\n-[15] ShaRF: Shape-conditioned Radiance Fields from a Single View Konstantinos Rematas et. al.,(2021) Link(↗️)
\n\n\n\n-[16] Generative Adversarial Transformers, Drew A. Hudson et. al.,(2021) Link(↗️)
\n\n\n\n-[17] Swin Transformer: Hierarchical Vision Transformer using Shifted Windows, Ze Liu et. al.,(2021) Link(↗️)
\n\n\n\n-[18] IMAGE GANS MEET DIFFERENTIABLE RENDERING FOR INVERSE GRAPHICS AND INTERPRETABLE 3D NEURAL RENDERING, Yuxuan Zhang et. al.,(2021) Link(↗️)
\n\n\n\n-[19] Deep Nets: What have they ever done for Vision?, Alan L. Yuille et. al.,(2021) Link(↗️)
\n\n\n\n-[20] DefakeHop: A Light-Weight High-Performance Deepfake Detector, Hong-Shuo Chen et. al.,(2021) Link(↗️)
\n\n\n\n-[21] GitHub Copilot & Codex: Evaluating Large Language Models Trained on Code, Mark Chen et. al.,(2021) Link(↗️)
\n\n\n\n-[22] Skilful precipitation nowcasting using deep generative models of radar, Suman Ravuri et. al.,(2021) Link(↗️)
\n\n\n\n-[23] THE COCKTAIL FORK PROBLEM: THREE-STEM AUDIO SEPARATION FOR REAL-WORLD SOUNDTRACKS, Darius Petermann et. al.,(2021) Link(↗️)
\n\n\n\n-[24] Hierarchical Text-Conditional Image Generation with CLIP Latents, Aditya Ramesh et. al.,(2022) Link(↗️)
\n\n\n\n-[25] High-Resolution Image Synthesis with Latent Diffusion Models, Robin Rombach et. al.,(2022) Link(↗️)
\n\n\n\n-[26] LAION-5B: An open large-scale dataset for training next generation image-text models, Christoph Schuhmann et. al.,(2022) Link(↗️)
\n\n\n\n-[27] An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion, Rinon Gal et. al., Yuval Alaluf et. al.,(2022) Link(↗️)
\n\n\n\n-[28] DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation, Nataniel Ruiz et. al.,(2022) Link(↗️)
\n\n\n\n-[29] Make-A-Video: Text-to-Video Generation without Text-Video Data, Uriel Singer et. al.,(2022) Link(↗️)
\n\n\n\n-[30] FILM: Frame Interpolation for Large Motion, Fitsum Reda et. al.,(2022) Link(↗️)
\n\n\n\n-[31] YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors, Chien-Yao Wang et. al.,(2022) Link(↗️)
\n\n\n\n-[32] A ConvNet for the 2020s, Zhuang Liu et. al.,(2022) Link(↗️)
\n\n\n\n-[33] A Generalist Agent, Scott Reed et. al.,(2022) Link(↗️)
\n\n\n\n-[34] MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge, Linxi Fan et. al.,(2022) Link(↗️)
\n\n\n\n-[35] Human-level play in the game of Diplomacy by combining language models with strategic reasoning, Anton Bakhtin et. al.,(2022) Link(↗️)
\n\n\n\n-[36] Training language models to follow instructions with human feedback, Long Ouyang et. al.,(2022) Link(↗️)
\n\n\n\n-[37] LaMDA: Language Models for Dialog Applications, Romal Thoppilan et. al.,(2022) Link(↗️)
\n\n\n\n-[38] Instant Neural Graphics Primitives with a Multiresolution Hash Encoding, Thomas Müller et. al.,(2022) Link(↗️)
\n\n\n\n-[39] Block-NeRF: Scalable Large Scene Neural View Synthesis, Matthew Tancik et. al.,(2022) Link(↗️)
\n\n\n\n-[40] DreamFusion: Text-to-3D using 2D Diffusion, Ben Poole et. al.,(2022) Link(↗️)
\n\n\n\n-[41] Point-E: A System for Generating 3D Point Clouds from Complex Prompts, Alex Nichol et. al.,(2022) Link(↗️)
\n\n\n\n-[42] 3D Neural Embedding Likelihood for Robust Probabilistic Inverse Graphics, Guangyao Zhou et. al.,(2023) Link(↗️)
\n\n\n\n-[43] Unsupervised melody-guided lyrics generation, Yufei Tian et. al.,(2023) Link(↗️)
\n\n\n\n-[44] A Hybrid Generator Architecture for Controllable Face Synthesis, Dann Mensah et. al.,(2023) Link(↗️)
\n\n\n\n-[45] Capabilities of GPT-4 on Medical Challenge Problems, Harsha Nori et. al.,(2023) Link(↗️)
\n \n \n-[46] Generative Agents: Interactive Simulacra of Human Behavior, Joon Sung Park et. al.,(2023) Link(↗️)\n
\n\n\n-[47] Article: Generative AI ethics: 8 biggest concerns, By George Lawton Link(↗️)
\n\n\n\n-[48] Article: Generative AI Ethics: Top 6 Concerns, By Cem Dilmegani Link(↗️)
\n\n-[49] Article: Best 5 AI detection tools, June 29, 2023 by Marek Link(↗️) \n\n-
[50] Article: Managing the Risks of Generative AI, by Baxter and Yoav Schlesinger, June 06, 2023 \n Link(↗️)
\n\n\n## Special Thanks\n\n Special thanks to Louis Bouchard for creating the amazing blog and giving us permission to use his content.
","metadata":{}}]}