Pytchify Image Image patching is, in my opinion, the hardest part of the project. In order to have well-structured code, I move image patching to its own transform class: PyTorch Tensor has a method called unfold(dimension, size, step) that does exactly what we need. It creates a sliding window along the given dimension and unfolds it to a new dimension. Let's take apart __call__ method. Its first row: The tensor img has size 3 x 32 x 32 and self.patch_size equals to 4. unfold method goes through all patches of size 4 with a step 4 along the dimension 1 that has 32 elements and put found patches into a new dimension. So, now we have a new tensor res with a size 3 x 8 x 32 x 4. For easier understanding, I follow this logic: Discard color dimension for a moment. The tensor is 8 x 32 x 4;This tensor can be seen as an 8 x 32 matrix of four-element vectors, for example, [0.3, 0.01, 0.4, 0.7];Now add color dimension. There is 8 x 32 four-element vectors for each RGB color channel. In the second row, we unfold the second dimension of the image: Now, the size of res is 3 x 8 x 8 x 4 x 4 which may seem insane but it isn't. You may understand it as follows: Discard color dimension, for now, having 8 x 8 x 4 x 4 tensor;Each element in 8 x 8 matrix contains 4 x 4 patch;Bring back color dimension and you have three 8 x 8 matrices of 4 x 4 patches. What is remained to do is to reshape tensor res back to 2D matrix. It is achieved with the reshape method: After reshape operation is done, res is a matrix of size64 x 48. If you worked with NLP tasks you may notice how similar it is to a sentence embedding: the first dimension corresponds to a word and the second dimension corresponds to a context vector of a given word.


Build and Train Vision Transformer from Scratch

Model We build a model as a set of standard PyTorch modules, except the main ViT module. ViT inherits LightningModule. The model diagram is displayed in the figure below. The full code of the model can be found at src/basic.py. Let's take a detailed look at each module.


InputEmbedding Input Embedding accepts a batch of patchified images and returns a full embedding of patches with the [class] token prepended. An interesting thing to look at is how we create class_token and position embedding parameters: Tensors that are created via nn.Parameter are added to the graph and trained during the fit process. class_token is a tensor of size 1 x hidden_size which we later repeat for each batch. position tensor has size 1 x num_patches + 1 x hidden_size and is also repeated for each batch. The first dimension of position is num_patches + 1 because [class] token is taken into consideration by position as well. Let's take a closer look at the forward method: First of all, we accept inp tensor that has size batch_size x 64 x 48. Then we pass it through a linear projection layer. Context size 48 of the input tensor is too small. Our model will barely be able to catch dependencies between the input and the target. So, we should expand it to a hidden_size. Projectionres has size batch_size x 64 x hidden_size. In the next operation, we repeat class_token parameter for each element in a batch and concatenate it with res The size of class_token tensor is batch_size x 1 x hidden_size. Above operations repeat position tensor for each image in batch, sum res and position tensor, and pass the result to the dropout layer.


AttentionHead After we've got input embeddings, they are sent to each AttentionHead. AttentionHead module looks like this: size argument here is our hidden size. Let's set values of our parameters to which we'll refer later as an example: Now let's take a look at the code of forward method: At first, we create query, key, value projections for input_tensor. input_tensor has size batch_size x sequence_size x hidden_size or 64 x 64 x 512. In the next set of operations, we calculate attention scores using the famous formula: The size of scores is sequence_size x sequence_size  64 x 64 meaning that each element of the sequence has an attention score to each other element in a sequence. Note, that we do not apply any masking. After scores are calculated, we multiply them with value tensor. The size of output is the same as the size of the input_tensor - batch_size x sequence_size x hidden_size  64 x 64 x 512.


MultiHeadAttention The intent of MultiHeadAttention module is to unite attention heads. size argument in this module is the hidden size. We calculate the output of each attention head and concatenate them into dimension 2. The resulting size of s is batch_size x sequence_size x num_heads * hidden_size. Regarding the example above, the size is 64 x 64 x 4096. Then we pass s through the linear layer. The size of output is batch_size x sequence_size x hidden_size  64 x 64 x 512, the same as the input size.


Encoder The encoder module contains multi-head attention and a feed-forward network. Also, it provides normalization to the data. Feed-forward network is created as a Sequential module: We make the feed-forward network four times bigger than attention to make it more expressive and to capture more complex dependencies between input and the target. It helps mitigate the vanishing gradients problem as well. We use Gaussian Error Linear Units (GELU) activation function. forward method is more than expressive: Note, that we apply the normalization layer before we pass tensors further to the network in contrast to Attention Is All You Need. This process is called pre-normalization. The paper Understanding the Difficulty of Training Transformers analyzes both approaches.


ViT Finally, we are ready to proceed with the main module of the Vision Transformer ViT. This class is kind of big to include the full code at once. I split it into several parts and cover them all individually. ViT not only embeds all parts of the model but also provides training functionality. Note, that ViT inherits pl.LightningModule, not nn.Module. We create the model modules in the constructor: Input Embedding layer;Set of encoders;MLP head that does the final classification. The forward step is seen in forward method: At first, we convert the input tensor into the model's inner state with added [class] token and positional encoding. emb tensor has size batch_size x sequence_size x hidden_size. Regarding the values defined above, its size is 64 x 64 x 512. Then we pass emb into a sequential set of encoders. attn tensor has the same size as emb: batch_size x sequence_size x hidden_size or 64 x 64 x 512. The first element in the sequence attn corresponds to the [class] token. So, we pass only this element to the mlp_head and return the value from the function. attn[:, 0, :] has size batch_size x hidden_size  64 x 512. The function outputs a tensor of logits with a size batch_size x num_classes  64 x 10. Now we can use the output of the model to organize the training process.


Training The training process starts in ViT class. There are a few more methods that participate in the training: configure_optimizersconfigure_parameterstraining_stepvalidation_step Let's run through each of them. We should create our optimizers and schedulers in configure_optimizers method. Read more at Lightning Optimization docs. We use AdamW with the CosineAnnealingLR scheduler. There is also a nice article that visualizes various learning rate schedulers A Visual Guide to Learning Rate Schedulers in PyTorch. Note that we don't pass all parameters of the model to AdamW. We configure them in our custom method configure_parameters: The code of the above method is taken from Andrej Karpathy's minGPT. LayerNorm module has its own regularization so, we should disable weight decay for this module. configure_parameters prepares two groups of parameters: the ones with weight decay enabled and the ones with weight decay disabled. training_step should return the loss of the particular training step. We use cross_entropy loss function. While logits tensor has size batch_size x hidden_size, the target tensor size is (batch_size,). These are exact arguments that cross_entropy function expects from us. Also, for each period of time, we log loss and model accuracy to tensorboard. The code of validation_step is the same as of training_step: A function that calculates accuracy looks like this: logit_accuracy function takes two tensors as arguments: logits tensor is the output of the model. It has a size of batch_size x 10 (10 is because we have 10 possible classes);target is the target tensor for the batch. It has a size of batch_size,. At the first row of the logit_accuracy, we take the class index with the maximum logit value, i.e. the element with the highest value along the 1 dimension: idx tensor now has the same size as target - batch_size,. Then we create a tensor acc whose elements are 0 or 1, where 1 shows that the model output and target are equal for the specific element in a batch. 0, obviously, means that the prediction isn't correct. At the return statement, we calculate the ratio of correctly predicted classes to the total amount of elements.


Lightning Trainer The script that runs model training is located in train.py. First of all, there we import all required objects: Then we set constants and hyperparameters of the model: where BASE_DIR is the base directory of the project;LIGHTNING_DIR is a directory where lightning stores models and logs;MODELS_DIR is a directory where lightning stores models;LOG_EVERY_N_STEPS defines how often to log statistics into tensorboard;MAX_EPOCHS is the maximum amount of epochs to run;SIZE is the size of the context vector of each element in the input sequence;HIDDEN_SIZE is the size of the context after embedding is applied;NUM_PATCHES is the total amount of patches of an image;NUM_HEADS is the number of attention heads;NUM_ENCODERS is the amount of sequential encoders;DROPOUT is dropout percentage to apply in encoders;EMB_DROPOUT is dropout percentage to apply in embedding. Such as we're training the model with a GPU device, we can speed up the training process by using mixed precision floating points. We do it by the command: Under if __name__ == '__main__' section, we create a data module and instantiate the model: Then we create several useful callbacks: ModelCheckpoint callback saves a model after each epoch into MODELS_DIR directory. So, if the training process breaks we'll not lose the progress;EarlyStopping monitors validation loss and stops training if it wasn't improved in the last 10 epochs;LearningRateMonitor adds the visualization of the learning rate to tensorboard. Eventually, we see the trainer: We create an instance of pytorch_lightning.Trainer and run it for model and data. For GPU training we should set cuda as an accelerator. Also, we want to set the precision to bf16. Read more about PyTorch Lightning precision management in N-bit Precision. One of the biggest advantages of pytorch_lightning (I suppose) is that you don't need to pass .to(device) to every tensor you have, you just pass accelerator to the trainer. We run the model training with the command: In the terminal, you may see the training progress. Note, that if you don't have last.ckpt model saved, you should remove resume_from_checkpoint argument from Trainer creation. Run this command to visualize training in Tensorboard:


CPU Training If there is only a CPU or MPS device available, you can train a smaller model. Set the following hyperparameters in train.py: update the accelerator value in the Trainer: and remove AutoAugment transform in src/dataset.py, having: The model with the above parameters can be trained to ~80% accuracy for around 68 hours on an MPS device.


Training Results I trained the model on RTX 4090. 200 epochs of training took almost 3 hours. At the end of the 200th epoch, the model is ~83% accurate on validation data. Below you may see charts of the training progress. The model is neither on plateau nor overfitting. We can continue training and get even higher accuracy.


Evaluate Model Now, it's time to evaluate the model by running classify.py. Follow the code on GitHub to see the full implementation of the script. First of all, we create CIFAR10 validation dataset and load the trained model by command: Then we can use the model to classify the images. If you run the script, you should see a nice 32 x 32 frog. And the output in the terminal:


Summary As you see there is nothing complicated in the vision transformer. It utilizes the same self-attention mechanism as any other transformer model. Despite the production models should be pre-trained on a huge set of data, it was shown that the vision transformer can be trained even with the CIFAR10 dataset. However powerful computation device is required: 200 epochs of training of the above model were running for almost 3 hours on RTX 4090. Also, we fulfilled our secondary goal of the tutorial and showed how well the code is organized when we use a bundle of pytorch and pytorch_lightning. This approach can be used to, actually, prepare any production-ready model.


References [1] Mikhail Kravets, vision_transformer (2023). GitHub repository containing the full code of the tutorial; [2] Ashish Vaswani, Llion Jones, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Aidan N. Gomez, Łukasz Kaiser, Illia Polosukhin, Attention Is All You Need (2017); [3] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby, An Image Is Worth 16x16 Words: Transformers For Image Recognition At Scale (2021); [4] Liyuan Liu, Xiaodong Liu, Jianfeng Gao, Weizhu Chen, Jiawei Han, Understanding the Difficulty of Training Transformers (2020); [5] Jay Alammar, The Illustrated Transformer (2018); [6] Ekin D. Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, Quoc V. Le, AutoAugment: Learning Augmentation Strategies from Data (2019); [7] Leonie Monigatti, A Visual Guide to Learning Rate Schedulers in PyTorch (2022).


Foundation Models: Scaling Large Language Models

New Moore's Laws Achieving Zettascale Computing As the traditional Moore's Law reaches its twilight, new laws are emerging to define the evolution of computing performance. The exponential growth of GPU performance and supercomputer systems has accelerated AI's advancements, with LLMs as a prime example. Despite their extensive training times, these LLMs are benefiting from the rapid growth of computational power. Moore's Law, famously predicting the number of transistors on a microchip would double approximately every two years, is now being replaced by new performance-based laws: GPU performance doubles every 2.2 years, while supercomputer system performance doubles every 1.2 years. These advancements are shaping the way AI/ML technologies progress. Despite the rapidly increasing performance, the training of LLMs still takes anywhere from days to months. This extended duration speaks to the complexity and vast potential of these models. As computational power continues to soar, it will unlock new possibilities for AI research and development. In the coming decade, the AI landscape is set to enter the era of Zettascale Computing. As a result of the new Moore's Laws, AI performance is expected to dramatically outpace other computing advancements. This shift to Zettascale Computing will provide unprecedented processing capabilities, enabling further breakthroughs in AI and other domains. The new Moore's Laws, focusing on GPU and supercomputer performance, herald a new era for AI research and development. With the advent of Zettascale Computing, we can expect even more rapid growth in AI capabilities, impacting various industries and shaping the future of technology.


Generative AI Journey with State-of-the-Art LLMs Generative AI (GAI) has experienced rapid advancements in text-to-images, videos, and 3D. But ChatGPT and GPT-4 took the world by storm. These are LLMs-based GAI like other state-of-the-art LLMs: Claude, Bard, LLaMA, ToolFormer, Google USM, PaLM, NeMo, Databricks Dolly, etc. These have revolutionized NLP, enabling a myriad of applications once thought to be unattainable. Despite their impressive capabilities and increasing computing power, LLMs face common challenges such as scalability, training efficiency, and the need for high-quality training data. It reportedly required over 3 million GPU hours across 3072 GPUs to train GPT-3's 175 billion parameters over a period of several months. To address these common challenges, foundation models are emerging as a potential solution. These models aim to provide a solid base for AI development, enabling researchers and developers to build upon them and adapt them for various tasks more efficiently. By focusing on foundation models, the AI community can tackle the limitations posed by scalability, performance, training efficiency, and data quality, ultimately unlocking the full potential of LLMs and other large-scale models (LSMs) in diverse applications.


The Era of Foundation Models Foundation models are pre-trained AI models serving as a basis for building diverse applications and tasks. Designed to be versatile, adaptable, and robust, they offer strong leverage across a wide range of use cases. The concept of foundation models was introduced by Stanford with two significant points: emergence and homogenization. Emergence: Referring to the implicit induction of a system's behavior, emergence is a source of both scientific excitement and concern regarding unforeseen consequences. Foundation models learn from vast amounts of data, developing intricate patterns and relationships that can exhibit surprising behaviors.Homogenization: Foundation models consolidate methodologies for building ML systems across various applications. While this homogenization provides strong leverage for many tasks, it also creates single points of failure, raising concerns about resilience and reliability. The astounding success of GAI and human-like ChatGPT has ushered in a new era of foundation models, laying the groundwork for large-scale models and the rise of artificial general intelligence (AGI). Foundation models have emerged to transform the digital world. Their impact is comparable to other milestones in digital evolution, such as the invention of electricity, the advent of the internet, and the rise of cloud computing. By bridging the gap between narrow AI and AGI, foundation models are shaping the future of AI research and development, opening up new possibilities and opportunities in the rapidly evolving digital landscape.


Key Characteristics of Foundation Models Foundation models have rapidly become the core of AI. They share several key characteristics, highlighting their potential and significance in shaping the future of AI. Pre-trained and Adaptable: A defining characteristic of foundation models is their pre-trained nature, allowing them to serve as a starting point for various applications and tasks. Through transfer learning and fine-tuning, these models can be adapted to address specific challenges and requirements, significantly reducing development time and resources.Scalability: Designed to be scalable, foundation models can handle vast amounts of data and grow in complexity as required. This scalability enables them to tackle a broad range of tasks and accommodate the ever-increasing demands of the AI landscape.Versatility: Foundation models boast remarkable versatility, as they can be employed across multiple domains and industries. From language and vision to healthcare and finance, these models serve as a basis for a wide range of applications.Self-Supervised Learning: A key aspect of foundation models is their ability to utilize self-supervised learning techniques. By leveraging large-scale, unlabeled data, these models can learn complex representations and features, greatly improving their performance on various tasks and reducing dependence on labeled data.Robustness: Foundation models are known for their robustness, demonstrating resilience in the face of noisy, incomplete, or even adversarial data. This robustness allows them to maintain high levels of performance and accuracy across different contexts and challenges.Interoperability: Interoperability is another critical characteristic of foundation models, as they can be easily integrated with existing systems and frameworks. This seamless integration facilitates collaboration between different AI models and components, streamlining the development process and fostering innovation.Generalization: The ability to generalize is a hallmark of foundation models, enabling them to perform well on unseen data and novel tasks. This characteristic allows them to adapt to a variety of challenges, making them an invaluable asset in AI research and development. By understanding the key characteristics of foundation models, such as their pre-trained nature, adaptability, scalability, versatility, self-supervised learning capabilities, robustness, interoperability, and generalization, we can better appreciate their potential and impact on the future of AI.


Capabilities of Foundation Models Beyond LLMs Foundation models have made a significant impact beyond LLMs, offering a versatile and powerful approach to solving complex problems across various domains in language, vision, robotics, reasoning and search, interaction, and philosophy of understanding. Language: Foundation models excel in language, demonstrating human-like comprehension and generation of text. From machine translation and sentiment analysis to summarization and question-answering, these models are unlocking new possibilities in language-related applications and enhancing communication between humans and machines.Vision: In the realm of computer vision (CV), foundation models are transforming the way we analyze and interpret visual data. By effectively recognizing objects, detecting patterns, and segmenting images, these models are enabling advancements in fields such as autonomous vehicles, medical imaging, and surveillance systems.Robotics: By incorporating self-supervised learning and reinforcement learning techniques, foundation models are empowering robots to learn from their environments, adapt to new tasks, and interact more effectively with humans.Reasoning and Search: Foundation models are enhancing our ability to reason and search through vast amounts of data, extracting valuable insights and uncovering hidden connections. Their capabilities extend to logical reasoning, pattern recognition, and knowledge graph exploration, enabling more informed decision-making and efficient problem-solving across numerous industries.Interaction: The interactive capabilities of foundation models facilitate more natural and intuitive communication between humans and machines. By understanding and generating human-like responses, these models pave the way for seamless collaboration and improved user experiences in applications such as chatbots, virtual assistants, and customer support systems.Philosophy of Understanding: At the core of foundation models lies the philosophy of understanding, aiming to uncover the underlying principles and mechanisms that enable machines to comprehend and interpret complex data. The capabilities of foundation models span across language, vision, robotics, reasoning and search, interaction, and philosophy of understanding, highlighting their potential to reshape the AI landscape. By exploring these capabilities, we can foster responsible innovation and unlock the full potential of foundation models in addressing the world's most pressing challenges.


AI Engineering AI engineering is a burgeoning discipline combining software engineering principles with AI techniques to design, build, and scale intelligent systems. As large-scale foundation models continue to revolutionize the AI landscape, AI engineering plays a pivotal role in their development and deployment. AI engineering offers the tools and techniques necessary to scale out large-scale models while maintaining their performance and adaptability. Some aspects of scaling out these models through AI engineering include: Distributed Training: AI engineers harness the power of distributed computing to train large-scale models on vast amounts of data, accelerating the training process and improving model performance.Data Management: AI engineers ensure that the data used for training and fine-tuning foundation models is well-organized, clean, and representative of the target domain.Resource Management: AI engineers optimize the use of computational resources, such as GPUs and TPUs, ensuring that large-scale models can be trained and deployed efficiently and cost-effectively.Model Compression and Pruning: AI engineers employ model compression and pruning techniques to reduce the size and complexity of large-scale models, making them more accessible and deployable across various platforms.Monitoring and Maintenance: AI engineers continuously monitor the performance of large-scale models, identifying potential issues and implementing necessary updates and improvements to ensure their ongoing success. AI engineering is an essential discipline for building and scaling foundation models, providing the necessary expertise and techniques to ensure their robustness, efficiency, and adaptability. As we continue to push AI boundaries, AI engineering will play a crucial role in unlocking the full potential of foundation models and shaping the future of AI research and development.


TL;DR In closing, foundation models represent a critical milestone in the advancement of AI, providing a versatile and adaptable approach to solving complex problems across multiple domains. From language and vision to robotics and reasoning, these models are unlocking new possibilities and driving innovation across various industries. As we continue to explore the full potential of foundation models and their role in the evolution towards AGI, it is crucial to foster responsible and ethical AI development, ensuring these models are used to benefit humanity and address the most pressing challenges of our time. With foundation models as a solid basis, we can accelerate AI research and development, unlocking new frontiers and shaping the future of intelligent systems.


LLMs Papers GPT-4 Technical Report: https://arxiv.org/abs/2303.08774GPT-3: Language Models are Few-Shot Learners: https://arxiv.org/abs/2005.14165Toolformer: Language Models Can Teach Themselves to Use Tools: https://arxiv.org/abs/2302.04761LLaMA: Open and Efficient Foundation Language Models: https://arxiv.org/abs/2302.13971Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages: https://arxiv.org/abs/2303.01037Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model: https://arxiv.org/abs/2201.11990


Foundation Models Resources Reflections on Foundation Models: https://hai.stanford.edu/news/reflections-foundation-modelsOn the Opportunities and Risks of Foundation Models: https://arxiv.org/abs/2108.07258


Better Together - Four Examples of How Rust Makes Python Better

Introduction Python is a popular programming language known for its ease of use, flexibility, and readability. However, its dynamic nature and interpreted execution make it comparatively slower than statically-typed compiled languages like C and Rust. To overcome this limitation, Python developers have been leveraging Rust, a systems programming language, to improve their code's performance and safety. In this article, we'll explore some of the popular Python libraries that are either fully or partially written in Rust, discussing their features, benefits, and how they can help Python developers optimize their codebase. Lets get started!


Polars - Fast DataFrames Polars is a blazingly fast data manipulation library for Python and Rust that is fully implemented in Rust. It provides a DataFrame API that is similar to the pandas library and can handle large datasets in memory, outperforming pandas in terms of speed and memory usage. Polars is designed to provide a familiar and user-friendly interface while also taking advantage of Rust's memory safety and performance features. Here's an example of how Polars can be used to filter rows from a DataFrame: In addition to its performance benefits, Polars also provides a number of memory management features that can help prevent memory leaks and improve the overall stability of your Python codebase. By leveraging Rust's ownership and borrowing system, Polars ensures that memory is managed safely and efficiently without the need for manual memory management by the user. Overall, Polars is an excellent choice for data-intensive Python applications that require high-speed data manipulation and memory management. Its intuitive API and full compatibility with the pandas library make it easy to integrate into existing codebases and start seeing immediate performance improvements.


TikToken- OpenAI's Tokenizer TikToken is a Python library developed by OpenAI that provides fast and efficient tokenization of natural language text. The library is partially implemented in Rust, which helps to improve its performance and memory management. Here's an example of how to use the TikToken library to tokenize a piece of text: TikToken also provides a number of other tokenization models, including WordPiece, BytePairEncoding (BPE), and Unigram. It also allows for custom tokenization models to be defined and trained. By leveraging Rust's performance and memory management features, Tokenizers can handle large datasets efficiently, making it an excellent choice for natural language processing (NLP) applications that require fast and accurate tokenization. In addition to its speed and efficiency, TikToken also provides a number of features for fine-tuning the tokenization process, such as configurable padding and truncation options, token-level and input-level masking, and support for multiple encoding formats. Overall, TikToken is a powerful library for Python developers working with natural language text, offering both performance and flexibility for a variety of NLP applications.


River - Online Machine Learning River is a Python library for online machine learning developed by Online-ML. The library is fully implemented in Rust, making it one of the fastest online machine-learning libraries available in Python. One of the key benefits of River is its ability to train machine learning models on data streams, which are continuous and potentially infinite sources of data. This is a valuable feature for applications such as fraud detection, anomaly detection, and other use cases that require real-time analysis of incoming data. Here's an example of how to use River to train a logistic regression model on a stream of data: By implementing the library in Rust, River is able to take advantage of the performance benefits of Rust's memory management and low-level optimizations. Rust's ownership system allows River to optimize memory usage by minimizing the number of copies of data and reducing the amount of time spent on garbage collection. Rust also provides high-level abstractions for low-level operations, such as SIMD instructions, which can improve the performance of machine learning algorithms. Overall, River is a powerful library for online machine learning in Python, offering both speed and flexibility for a variety of use cases. With its fast performance and support for data streams, River is an excellent choice for developers working on real-time machine learning applications.


HyperJSON - Hyper Fast JSON HyperJSON is a Python library for encoding and decoding JSON that is implemented in Rust. This library is designed to be faster and more efficient than the built-in json module in Python. Here's an example of how to use HyperJSON to encode and decode JSON: By implementing the library in Rust, HyperJSON is able to take advantage of Rust's performance benefits, including its memory safety and efficient handling of memory. This makes it possible for HyperJSON to encode and decode JSON faster than the built-in json module in Python. HyperJSON also provides some additional features, such as the ability to encode and decode JSON with support for NaN and infinity values, which are not supported by the built-in json module in Python. HyperJSON also provides a more flexible API for working with JSON data, allowing users to customize the encoding and decoding process to better fit their needs. Overall, HyperJSON is a powerful and efficient library for working with JSON data in Python. Its use of Rust provides significant performance benefits over the built-in json module, making it a great choice for applications that require fast and efficient JSON encoding and decoding.


Conclusion In this article, we've explored several Python libraries that are written in Rust, or partially written in Rust, and how they can improve the safety and performance of Python code. Polars is a data manipulation library that is implemented entirely in Rust, providing high performance and efficient memory management.TikToken is OpenAI's tokenizer, partially written in Rust, that provides a faster and more memory-efficient implementation than the built-in tokenizer in Python's tokenize module.River from Online-ML is a machine learning library that integrates Rust code to provide a faster and more efficient implementation of machine learning models in Python.Finally, HyperJSON is a JSON library implemented in Rust that provides faster and more efficient encoding and decoding of JSON data. Overall, these libraries demonstrate the power and flexibility of Rust as a language for implementing high-performance code that can integrate with Python. By leveraging Rust's strengths, these libraries provide faster and more efficient implementations of functionality that is critical to many Python applications. As such, these libraries are valuable tools for developers looking to optimize their Python code for speed and efficiency.


From Text to Conversation: How ChatGPT is Changing the Way We Communicate

Introduction In recent years, artificial intelligence (AI) has seen significant advancements, particularly in the field of natural language processing (NLP). One of the most notable developments in this field is ChatGPT, an advanced language model developed by OpenAI that uses the GPT architecture. ChatGPT is designed to generate human-like responses to text-based inputs, making it a useful tool for a variety of applications. With its ability to understand and generate natural language, ChatGPT has the potential to revolutionize the way we interact with technology and each other. But what exactly is ChatGPT, and why is it significant? At its core, ChatGPT is an AI language model that is trained on large amounts of text data to learn patterns and generate responses that are contextually appropriate and conversational in tone. This means that when given a text-based input, such as a question or statement, ChatGPT can generate a response that is similar to what a human would say. The GPT architecture, which stands for Generative Pre-trained Transformer, is a neural network-based model that uses self-attention mechanisms to process text data. This allows the model to understand the relationships between different words and phrases in a sentence, enabling it to generate more coherent and contextually appropriate responses. One of the primary uses of ChatGPT is in conversational AI, where it can be integrated into chatbots, virtual assistants, and other conversational interfaces to provide more natural and engaging interactions with users. By using ChatGPT, these interfaces can provide more personalized and relevant responses, improving the overall user experience. In addition to conversational AI, ChatGPT can also be used in a variety of other applications, such as language translation, text summarization, and content creation. Its ability to generate human-like responses and understand natural language makes it an asset for businesses and organizations looking to improve their interactions with customers and users. Overall, ChatGPT represents a significant advancement in the field of NLP and AI. Its ability to generate human-like responses and understand natural language has the potential to transform the way we interact with technology and has already shown promising results in a variety of applications. In the following chapters, we will explore ChatGPT in greater detail, including how it works, its applications, use cases, and potential future developments.


Understanding ChatGPT To understand ChatGPT and its uses, it's important to first dive into how the model works. At its core, ChatGPT is a neural network-based language model that is trained on large amounts of text data. This training process is broken down into two main stages: pre-training and fine-tuning. Pre-Training During pre-training, ChatGPT is fed large amounts of text data, such as books, articles, and other sources of written content. This allows the model to learn patterns and relationships between different words and phrases in the text, which it can then use to generate responses to new inputs. One of the key features of ChatGPT's pre-training process is the use of unsupervised learning. This means that the model is not explicitly taught how to respond to specific inputs or outputs but instead learns through exposure to large amounts of text data. This approach is more efficient and scalable than traditional supervised learning methods, which require large amounts of labeled data. Fine-Tuning After pre-training, ChatGPT is fine-tuned on a specific task or domain, such as conversational AI or content creation. During fine-tuning, the model is further trained on a smaller set of labeled data that is specific to the task at hand. This allows the model to adapt to the nuances of the task and generate more contextually appropriate responses. One of the benefits of using ChatGPT is that it can be fine-tuned on a wide range of tasks and domains. This means that the same underlying model can be used for a variety of applications, from chatbots to content creation to language translation. Generation Once ChatGPT has been trained and fine-tuned, it can be used to generate responses to text-based inputs. When given a prompt, such as a question or a statement, the model processes the input and generates a response based on the patterns it has learned from the pre-training and fine-tuning processes. One of the key advantages of ChatGPT's generation process is its ability to generate responses that are contextually appropriate and conversational in tone. This allows the model to provide more natural and engaging interactions with users, improving the overall user experience. Advantages and Limitations While ChatGPT has shown significant promise in a variety of applications, it is important to note that the model is not without its limitations. One of the primary challenges with ChatGPT is the potential for bias in the text data it is trained on, which can lead to biased responses. In addition, ChatGPT's generation process can sometimes result in responses that are repetitive or nonsensical. This can be mitigated through careful fine-tuning and input filtering, but it is still an area of active research. Overall, understanding the inner workings of ChatGPT is essential to understanding its potential uses and limitations. The pre-training and fine-tuning processes, as well as the generation process, all play important roles in the model's ability to generate contextually appropriate and conversational responses.


Applications of ChatGPT ChatGPT has a wide range of potential applications, thanks to its ability to generate natural language responses and understand the context. Here are a few of the most promising applications of ChatGPT: Conversational AI One of the primary applications of ChatGPT is conversational AI, which can be used to create chatbots and virtual assistants that can engage in natural language conversations with users. By using ChatGPT to generate responses, these interfaces can provide more personalized and relevant interactions, improving the overall user experience. 2. Content Creation ChatGPT can also be used for content creation, such as generating product descriptions, news articles, or even entire novels. By fine-tuning the model on a specific task, such as writing in a particular style or tone, ChatGPT can generate high-quality content that is indistinguishable from human-written content. 3. Language Translation Another promising application of ChatGPT is in language translation. By training the model on a large corpus of multilingual text data, ChatGPT can generate accurate translations of text inputs, even for languages it has not been explicitly trained on. 4. Customer Service ChatGPT can also be used in customer service applications, where it can be integrated into chatbots and other interfaces to provide more personalized and responsive support to customers. By using ChatGPT to generate responses, these interfaces can provide more natural and engaging interactions, improving customer satisfaction. 5. Education ChatGPT can also be used in education applications, such as generating personalized learning materials or answering student questions. By fine-tuning the model on a specific domain, such as mathematics or science, ChatGPT can generate contextually appropriate and informative responses to student queries. 6. Creative Writing ChatGPT can be used in creative writing applications, such as generating poetry or song lyrics. By fine-tuning the model on a particular style or genre, ChatGPT can generate high-quality and engaging creative writing that is indistinguishable from human-written content. These are just a few of the many potential applications of ChatGPT. As the technology continues to evolve, we can expect to see even more innovative uses for this advanced language model.


Use Cases and Success Stories In this chapter, we'll explore some of the use cases and success stories of ChatGPT in various industries. Customer Service One of the most successful use cases of ChatGPT is in the field of customer service. Many companies have implemented ChatGPT-powered chatbots to automate responses to customer inquiries. This has led to faster response times and improved customer satisfaction rates. For example, H&M implemented a chatbot powered by ChatGPT to assist customers with product inquiries and saw a 70% increase in customer satisfaction. 2. Education In the education field, ChatGPT has been used to provide personalized learning experiences for students. For example, OpenAI's GPT-3 was used to create a language-learning app that provides students with personalized quizzes and assessments. This has led to improved learning outcomes and increased engagement among students. 3. Healthcare In the healthcare industry, ChatGPT has been used to assist in diagnosis and treatment. For example, Babylon Health, a healthcare company in the UK, implemented a chatbot powered by ChatGPT to assist patients with symptom assessments. This has led to improved patient outcomes and reduced healthcare costs. 4. Language Translation ChatGPT has also been used for language translation, enabling seamless communication between individuals who speak different languages. For example, Microsoft implemented a chatbot powered by ChatGPT to provide real-time language translation for customers visiting its website. This has led to increased customer satisfaction and improved international business opportunities. 5. Creative Writing Finally, ChatGPT has been used for creative writing, such as generating news articles or product descriptions. For example, The Washington Post implemented a chatbot powered by ChatGPT to generate news articles on a variety of topics. This has led to increased efficiency and productivity among journalists. These are just a few examples of the many successful use cases of ChatGPT. As the technology continues to develop, we can expect to see even more innovative applications of this powerful language model.


Future Developments and Limitations of ChatGPT ChatGPT is a powerful language model with a wide range of potential applications, but there are also limitations to its capabilities. In this chapter, we'll explore some of the future developments and limitations of ChatGPT. Improved Accuracy As research in natural language processing (NLP) continues to advance, we can expect to see continued improvements in the accuracy and effectiveness of ChatGPT. This may include new techniques for training and fine-tuning the model, as well as advancements in the underlying architecture and algorithms. 2. Multimodal Input Currently, ChatGPT primarily relies on text input to generate responses. However, as technology advances, we may see the integration of other modalities, such as images or video, into the input. This could enable more advanced applications, such as generating image captions or video summaries. 3. Privacy Concerns As ChatGPT and other language models become more prevalent, there are growing concerns about privacy and security. The ability of these models to generate natural language responses raises concerns about the potential for misuse or manipulation, particularly in areas like disinformation or cyber-attacks. 4. Cultural Differences One limitation of ChatGPT is its potential to reflect and reinforce cultural biases present in the data used to train and fine-tune the model. As the model is used in different regions and languages, it will be important to carefully consider these cultural differences and adapt the model accordingly. 5. Resource Requirements ChatGPT is a computationally intensive model, that requires significant resources to train and fine-tune. As the size and complexity of the model increase, this resource requirement will only grow, potentially limiting the accessibility and scalability of the technology. Overall, the future of ChatGPT is bright, with many exciting developments on the horizon. However, it's important to carefully consider the potential limitations and ethical concerns associated with this powerful technology, to ensure it is used responsibly and for the benefit of society.


Conclusion In this book, we've explored the world of ChatGPT and its uses. We've seen how ChatGPT has the potential to revolutionize a wide range of industries, from customer service to healthcare, education, and more. We've also discussed some of the ethical considerations that must be taken into account when using ChatGPT, such as bias, privacy, misinformation, transparency, and access and equity. It's important to approach the use of ChatGPT with care and consideration for these ethical issues to ensure that the benefits of this powerful technology are enjoyed by all members of society. As ChatGPT continues to develop and improve, we can expect to see even more exciting and innovative uses of this technology. From personalized learning experiences to seamless language translation, ChatGPT has the potential to transform the way we live and work. However, it's important to remember that ChatGPT is only a tool, and it cannot replace human creativity, intuition, and empathy. Ultimately, it is up to us to decide how we use this technology to shape our world and to ensure that we use it responsibly and ethically. In conclusion, ChatGPT is a powerful tool that has the potential to revolutionize a wide range of industries. By carefully considering ethical issues and using ChatGPT responsibly, we can harness the full potential of this technology and create a better future for all.


The Power of Words: Mastering Prompt Engineering for AI Models

How the term "prompt engineering" evolved The term "prompt engineering" was coined to describe the process of crafting questions, phrases, and statements that prompt AI language models to generate specific outputs. This process involves carefully selecting the right words and providing context to ensure that the AI model understands what is being asked. By optimizing prompts, we can improve the accuracy and relevance of AI-generated responses, making it possible to use AI to solve complex problems and answer a wide range of questions. In the future, prompt engineering will be critical for enabling seamless communication between humans and machines. As AI becomes more sophisticated and integrated into everyday life, we will rely on AI language models to help us with everything from scheduling appointments to conducting research. With the right prompts, AI can provide quick and accurate answers to complex questions, saving time and increasing productivity.


The ABC of prompt engineering At its core, prompt engineering is the process of crafting questions or statements that prompt AI models to generate specific outputs. In other words, prompts serve as the "cue" for the AI model to understand what you are looking for and generate a relevant response. At the most basic level, prompts are inputs that are provided to an AI model to generate a response. However, the art of prompt engineering goes beyond simply asking a question. It involves providing clear and concise prompts that are easy for the AI model to understand. This is because AI models rely on algorithms to analyze prompts and generate responses, which means that providing a clear and specific prompt is critical for getting accurate and relevant responses. Clear and concise prompts are also important for saving time and increasing productivity. The more specific the prompt, the faster the AI model can generate an accurate response, which can be incredibly useful in many industries. For example, in the healthcare industry, an accurate and quick response from an AI model can mean the difference between life and death. In the financial industry, prompt responses can help traders make more informed decisions and stay ahead of the competition.


Understanding the factors influencing prompt effectiveness When it comes to prompt engineering, there are several factors that can influence the effectiveness of a prompt. These factors can have a significant impact on the AI model's ability to understand and generate accurate responses. One key factor is the length of the prompt. Generally, shorter prompts are more effective than longer ones. This is because shorter prompts are easier for the AI model to process, and they provide a clear and specific cue for the model to generate a response. Longer prompts, on the other hand, can be more difficult for the model to analyze and may lead to less accurate responses. Another important factor is the use of natural language in prompts. Natural language prompts, which are written in a conversational style, are typically more effective than prompts that use formal or technical language. because let's be real, no one likes a robot. Write your prompts in a conversational style that is easy for people to understand. This will help to ensure that the AI model generates responses that are relevant and useful. Another very underrated factor is providing the context like you would when telling a story to your friend. Including relevant information and background can help to clarify the intent of the prompt and ensure that the model generates accurate responses. In addition to these factors, the inclusion of examples in prompts can also be highly effective. Examples can help to provide context and clarify the intent of the prompt, which can lead to more accurate responses. For example, if you are asking an AI model to provide information about a particular product, including specific examples of that product can help to ensure that the model generates accurate and relevant responses. So, by being specific and concise, using natural language, providing context, and using examples, you can craft prompts that generate accurate and valuable responses. So, the next time you're writing prompts for an AI model, remember to keep it simple, conversational, and full of examples that even a robot would understand.


Are prompts industry specific too? Yes, In the start, I was testing this as a vague theory, but as we have standards and jargon for every industry, so do the industry specifics prompts are evolving too (please note that this is my personal research so far and general truth might contradict this) Industry-specific prompts are like tailored suits for AI models. Just like how a well-tailored suit is perfectly fitted to a person's body, industry-specific prompts are perfectly tailored to the unique needs of different industries. These prompts are optimized to extract relevant information that is specific to a particular industry. So go ahead and tailor those prompts like a pro!


Collaborating with AI language models to assist in prompt engineering Collaborating with AI language models is like having a partner in crime for prompt engineering. These models can assist in generating prompts that are optimized for specific tasks and industries, saving you time and effort. One way to collaborate with AI language models is to use them to generate multiple prompts at once. This can be done by providing a list of potential prompts and having the AI model rank them in order of effectiveness. It's like having your own personal prompt evaluator! Another way to collaborate with AI language models is to fine-tune their prompts. By providing feedback on the effectiveness of prompts, you can train the AI model to generate better prompts in the future. It's like having your own personal prompt coach!


Analyzing the effectiveness of prompts This is what I believe is also going to be a new science in the new AI realm. because, let's face it, we all need to know if our prompts are actually doing their job or if we're just talking to a brick wall. In this section, we'll explore how to analyze the effectiveness of your prompts and measure the performance of your AI model because who doesn't love a good data analysis session? First, you'll need to gather some data on how your AI model is performing with different prompts. This can involve running multiple tests with different prompts and recording the results or using tools like A/B testing to compare the performance of different prompts. Once you have your data, it's time to dive into the analysis. You can use metrics like accuracy, precision, and recall to evaluate the performance of your AI model and see how different prompts impact these metrics. Or, if you're feeling fancy, you can even use machine learning techniques like regression analysis or decision trees to uncover the most effective prompts for your specific use case. So, get ready to roll up your sleeves and dive into some data because, with the right analysis, you'll be able to optimize your prompts and take your AI model to the next level. And who knows, maybe you'll even become the next big data analysis guru - or maybe you'll just have some cool graphs to show off at your next team meeting.


Future trends in prompt engineering The future of prompt engineering is looking bright! With the rise of machine learning, we can expect to see even more advanced techniques for generating effective prompts. One exciting trend is the use of machine learning algorithms to automatically generate prompts. Now, you can sit back and let the AI do the work for you! Of course, you'll still need to provide some guidance and direction, but this can save a lot of time and effort in the long run. Another emerging trend is the development of new techniques for creating more effective prompts. We're talking about things like sentiment analysis, where AI models can analyze the emotions and attitudes conveyed in a prompt and adjust their responses accordingly. Who knew that machines could be so emotionally intelligent? :D So buckle up and get ready for a wild ride!


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What happened this week in AI by Louis With the surging demand for generative AI, this week saw preparatory developments for the next wave of AI. Companies are fast-tracking the development of AI products, and generative AI tools are closer to becoming consumer products than ever before. They are already becoming powerful assistants for writers and programmers and rapidly taking on more challenges. The open-source community is also making significant progress in running local LLMs. For instance, Facebook's LLama model has continued to be a focal point for building in the academic and open source community following the leaked weights on 4Chan. One outcome of this is the development of llama.cpp, authored by the creator of whisper.cpp. This new tool has demonstrated that it is possible to run an LLM on an M1 Mac. Following the recent Llama Alpaca model Databricks has also recently released Dolly, which can equip an outdated open-source large language model (LLM) with ChatGPT-like instruction-following abilities. This is done by training it on high-quality data for 30 minutes on a single machine. This ability doesn't require the latest or largest models, as Dolly only has 6 billion parameters, much less than GPT-3's 175 billion. The code for Dolly is open-sourced, and the paper outlines how to create it on Databricks. Models like Llama Alpaca and Dolly may help to democratize LLMs, making them an accessible commodity that can be tailored to every company's needs. With the pace of generative AI development, Nvidia has accelerated the rollout of new hardware as well as a new AI cloud business model. NVIDIA's latest product announcements at GTC have positioned the company's AI compute platforms, such as the NVIDIA H100 NVL, L4, L40, and Grace Hopper to meet the growing demand for AI and are tailored for specific training and inferencing tasks. OpenAI has continued its rapid execution of news and feature releases with the announcement of ChatGPT plugins. These plugins can significantly enhance the capabilities of GPT 3 and 4 with browsing, information retrieval, code execution, and also third-party plugins. This has fueled lots of speculation on whether these plugins will become the new "App store" for LLMs or even something more, for example, the excellent blog from Not Boring - OpenAI's ChatGPT Plugins and the emergence of an Apex Aggregator. The increased availability of generative AI-specific compute, the progress towards open-source tools, the increased capabilities of leading LLMs and a product-oriented approach to generative AI applications indicate that we have entered a new phase in the commercialization of AI. This is where AI is revealing more of its true power to revolutionize the way we work, interact, create, play, and engage with technology, information, and people. Although current leading LLM AI models may still be challenging to run on personal hardware, we might soon see a demo showcasing an open-source model running on a phone.


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Hottest News ChatGPT plugins OpenAI is currently testing new ChatGPT plugins with select users before launching them for widespread use. These plugins are tools that are specifically designed for language models, with safety as a core principle. They assist ChatGPT in accessing up-to-date information, running computations, and utilizing third-party services. 2. Stable Diffusion Reimagine - Stability AI Stability AI has recently launched Stable Diffusion Reimagine, a new Clipdrop tool that enables users to generate multiple variations of a single image without any limitations. However, the tool has certain limitations and biases, but the company has plans to open-source it soon to make further improvements. 3. GitHub Copilot X: The AI-powered developer experience GitHub has launched GitHub Copilot X, which leverages OpenAI's GPT-4 to provide a chat interface and voice-to-code AI for developers. This new developer experience enables Copilot to work with pull requests, command lines, and documentation to answer questions about your projects. 4. Meet Bard Bard, a Google AI experiment, aims to enhance creativity and productivity by collaborating with users. It employs generative AI technology to facilitate collaboration. Bard includes safety controls and feedback mechanisms; however, it may occasionally display inaccurate information or offensive statements. 5. Releasing Alpaca-30B A recent paper from Tatsu Lab introduced Alpaca, an "instruction-tuned" version of Facebook's Llama model. The post details the release of Alpaca-30B, the benefits of fine-tuning, usage instructions, and community involvement.


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Three 5-minute reads/videos to keep you learning AI trends in 2023: Graph Neural Networks GNNs, or graph neural networks, are powerful tools for processing graph data and understanding entity relationships. They have seen exponential growth in published research and are expected to continue to rise in popularity. This article will recap some of the highly impactful applications of GNNs and provide an overview of what to expect for the next big wave in AI. 2. Using AI to make teaching easier & more impactful AI has been shown to enhance learning in classrooms by offering tailored experiences, generating examples, providing feedback, creating study groups, and conducting diagnostic tests. This article provides a summary of the five effective teaching strategies discussed in the paper titled "Using AI to Implement Effective Teaching Strategies in Classrooms: Five Strategies, Including Prompts". 3. ChatGPT + Code Interpreter = Magic OpenAI's ChatGPT has expanded its capabilities with the integration of third-party tools, including a Code Interpreter that enables the generation and execution of code, data analysis, and spreadsheet graphing. This article explores the new features and expanded capabilities of ChatGPT, providing a range of examples for integrating tools such as the Code Interpreter. 4. The Age of AI has begun The advancements in AI are set to revolutionize various aspects of life, including work, education, healthcare, travel, and communication. This article delves into the opportunities and responsibilities that come with the Age of AI and discusses its impact on industries such as healthcare and education. 5. Getting started with Pytorch 2.0 and Hugging Face Transformers This blog post offers a comprehensive guide on how to leverage the new features of PyTorch 2.0, such as TorchDynamo, AOTAutograd, PrimTorch, and TorchInductor, to enhance performance. It also provides step-by-step instructions on how to use PyTorch 2.0 and Hugging Face Transformers to fine-tune a BERT model for text classification.


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Papers & Repositories Databricks' Dolly Dolly, a large language model trained on the Databricks Machine Learning Platform, shows that a two-year-old open-source model like GPT-J can exhibit high-quality instruction following behavior. Fine-tuning Dolly for just 30 minutes on a focused corpus of 50k records (Stanford Alpaca) demonstrates an impressive level of performance not characteristic of the foundation model on which it is based. 2. Sparks of Artificial General Intelligence This paper investigates the early version of GPT-4 during its active development at OpenAI. The focus of this report is on identifying its limitations and the challenges that lie ahead in advancing toward deeper and more comprehensive versions of AGI. It also explores the possibility of pursuing a new paradigm that goes beyond next-word prediction. 3. GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models This paper explores the potential implications of large language models (LLMs), specifically Generative Pre-trained Transformers (GPTs), on the U.S. labor market. The study found that GPTs could potentially displace tasks performed by up to 10% of workers, with 80% of workers facing some level of displacement, and 19% experiencing more than 50% displacement. 4. MM-ReAct: Prompting ChatGPT for Multimodal Reasoning and Action This paper presents MM-REACT, which integrates ChatGPT with a pool of vision experts to achieve multimodal reasoning and action. It explores advanced vision tasks that may exceed the capabilities of existing models and introduces a textual prompt design that represents text descriptions, textualized spatial coordinates, and dense visual signals, such as images and videos, as aligned file names. 5. Vid2Seq: a pretrained visual language model for describing multi-event videos Google has introduced a paper titled "Vid2Seq: Large-Scale Pretraining of a Visual Language Model for Dense Video Captioning" which will appear at CVPR 2023. The paper describes the Vid2Seq architecture, which adds time tokens to a language model, enabling it to predict event boundaries and textual descriptions in the same output sequence.


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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. NN Architecture Seminar, emptyshore on 3D- PMRNN This week, emptyshore will present the neural network architecture described in the paper "3D-PMRNN: Reconstructing three-dimensional porous media from two-dimensional images with recurrent neural network" during a seminar. The session will not be recorded, so join us live for the first seminar here or add it to your calendar here! Date & Time: 28th March, 9:00 pm EST Add our Google calendar to see all our free AI events!


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Meme of the week! Meme shared by Recruiter6061#8864


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Featured Community post from the Discord We're excited to share that FromCommunity has seen tremendous success since we enabled invite-only access just 2 months ago and received over 2,500 job applications from talented developers from amazing tech communities. FromCommunity helps companies hire directly from open-source and Tech communities. And now, with the help of ChatGPT, we're expanding our platform's capabilities by evaluating candidates' coding skills and automating our communication with them. Find us on ProductHunt, your support, and feedback would mean the world to us!


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


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Article of the week Drift Detection Using TorchDrift for Tabular and Time-series Data by Rahul Veettil Machine learning models are created to make predictions based on data. However, the data in the real world is constantly changing, and this can affect the model's accuracy. This phenomenon is known as data drift, which can cause incorrect predictions and poor performance. In this blog post, the author discusses how to detect data drift using the Python library TorchDrift.


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Our must-read articles Correlation and Causation: What are the Differences? by Cornellius Yudha Wijaya Unlocking New Insights with Vision Transformer by Anay Dongre 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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Python for Natural Language Processing: A Beginner's Guide

Introduction Natural Language Processing (NLP) is the study of making natural human language readable to computer programs. It is a fast-expanding field with important applications in banking, healthcare, and technology. Python is one of the most popular programming languages used for natural language processing (NLP) because of its ease of use, versatility, and abundance of open-source libraries. In this beginner's guide to NLP with Python, we will look at the fundamentals of NLP and its applications, as well as how to set up a Python environment for NLP, dive into preprocessing, and build a simple NLP application in Python. By the end of this guide, you will have a good knowledge of NLP in Python and be ready to tackle more advanced projects.


Setting up the Python environment for NLP The first step in getting started with Natural Language Processing (NLP) in Python is to configure your development environment. Here are some fundamental steps for getting your Python environment ready for NLP:


1. Install Python: Depending on your operating system, you can download and install Python from the official website. Be sure to download the most recent Python version. The steps for installing Python on Windows are as follows: Go to the Python official website: https://www.python.org/downloads/Click on the "Download Python" button, which will navigate you to the download page.Scroll down to the "Stable Releases" section and select the most recent Python version. You can use Python 3.x or Python 2.x, however, Python 3.x is preferred because it is the most recent stable version.Click on the download link that corresponds to your operating system. For example, if you have a 64-bit Windows operating system, you should download the "Windows x8664 executable installer."After downloading the installer, double-click the executable file to start the installation process.Select the "Add Python X.X to PATH" option on the first screen of the installer. This adds Python to your system's PATH, making it easier to execute Python from the command prompt.Complete the installation by following the remaining installation steps.To check if Python is correctly installed, open the command prompt and type "python - version" and press Enter. This will display the Python version that you have installed in your system.


2. Install a package manager: A package manager like pip will help you install and manage packages, which are the libraries used for NLP. Use the command 'pip - version' to see if pip is already installed. If not, you can follow the instructions below instructions to install it. Go to the official pip website: https://pip.pypa.io/en/stable/installation/Download the get-pip.py script by clicking on the "get-pip.py" link.Open a command prompt and navigate to the directory where you downloaded the get-pip.py script.Execute the following command: py get-pip.pyThis will download and install pip on your system. Once the installation is complete, execute the following line to see if pip is correctly installed: "pip - version"If pip is installed correctly, you will be able to see the version of pip that is installed on your system.


3. Install NLP Libraries: Many NLP libraries are available in Python, and the installation process varies depending on the library. This feature of python makes it very useful for artificial intelligence and machine learning applications. For more information on installation and usage, check the official documentation of the library you want to install. NLTK: NLTK is a powerful NLP library in Python that offers a wide range of features for text processing and analysis. To install NLTK, open a command prompt and run the following command: pip install nltk. You can find more information about NLTK and its features on the official NLTK website: https://www.nltk.org/spaCy: spaCy is another well-known NLP library that offers efficient text processing and language modeling features. To install spaCy, run the following command: pip install spacy. Once spaCy is installed, use the following command to download the language model for a specified language: python -m spacy download. For example, you can run python -m spacy download en to download the English language model. You can find more information about spaCy and its features on the official spaCy website: https://spacy.io/TextBlob: TextBlob is a simple and easy-to-use NLP library that provides basic NLP tasks such as sentiment analysis, part-of-speech tagging, and noun phrase extraction. To install TextBlob, run the following command: pip install textblob. You can find more information about TextBlob and its features on the official TextBlob website: https://textblob.readthedocs.io/en/dev/


4. Download language models: Some NLP libraries require language models to be downloaded separately. For example, spaCy requires a specific language model to be installed before you can use it for a particular language. You can download the required language models using the respective libraries' commands.


5. Set up a development environment: It is recommended to use an integrated development environment (IDE) such as PyCharm or Visual Studio Code while working on NLP projects in Python. You can download and install an IDE of your choice. Installing PyCharm Go to the official PyCharm website: https://www.jetbrains.com/pycharm/download/Select the version of PyCharm you want to download (Community or Professional) and click on the "Download" button.Once the download is complete, run the PyCharm installer.To finish the installation process, keep following the installation wizard.Once the installation is complete, you can open PyCharm and start using it. Installing Visual Studio Code Go to the official Visual Studio Code website: https://code.visualstudio.com/downloadClick on the "Download for Windows" button to download the Visual Studio Code installer.Once the download is complete, run the Visual Studio Code installer.To finish the installation process, keep following the installation wizard.Once the installation is complete, you can open Visual Studio Code and start using it.


Working with Text Data in Python Once you have set up your Python environment and installed the necessary packages and libraries, you can start working with text data in Python for NLP projects. Here are some basic operations you can perform on text data using Python:


Reading text Data To read a text file in Python, you can use the built-in open() function. Here's an example: This code reads the contents of the data.txt file and stores it in the text variable.


Cleaning text Data Before you can perform any NLP analysis on text data, you need to clean the data by removing any unnecessary characters, punctuation, and stopwords. Here are some common operations you can perform to clean text data: Remove punctuation: You can remove all punctuation characters using regular expressions. Here's an example: This code removes all non-word and non-space characters from the text variable. Convert to lowercase: You can convert all text to lowercase to ensure consistency. Here's an example: This code converts all uppercase characters in the text variable to lowercase. Remove stopwords: You can remove common stopwords (such as "a", "an", "the", "is", "of", etc.) using the NLTK library. Here's an example: This code removes all stopwords from the text variable and stores the result in the filtered_words variable.


Tokenizing text Data Tokenization is the process of splitting text into individual words or tokens. Here's an example of how to tokenize text using the NLTK library: This code tokenizes the text variable and stores the result in the words variable.


Stemming and Lemmatizing text data Stemming and lemmatization are techniques used to reduce words to their root form (or lemma) for analysis. Here's an example of how to stem and lemmatize text using the NLTK library: This code stems and lemmatizes the text variable and stores the results in the stemmed_words and lemmatized_words variables, respectively.


Preprocessing text Data for NLP Preprocessing text data is an important step in NLP as it prepares the data for analysis and modeling. Here are some common preprocessing techniques used in NLP:


Tokenization Tokenization is the process of dividing a string of text into distinct tokens, typically words. This is an important stage in NLP because most analyses rely on individual words as building blocks for subsequent processing. The NLTK library includes a range of tokenizers for different languages and use cases. # Output:


Stop word removal Stop words, such as "the," "and," "is," and "an," are common words that appear frequently in a language. These terms are frequently irrelevant to the analysis and can be removed to reduce the noise in the data. The NLTK library includes a list of English stop words for this purpose. # Output:


Stemming Stemming is the process of reducing words to their base or stem form, by removing any prefixes or suffixes. This is a common technique for reducing the dimensionality of the data, as it groups similar words together. # Output:


Lemmatization Lemmatization is a similar process to stemming, but it reduces words to their base form by using a dictionary or knowledge of the language. This can result in more accurate base forms than stemming. # Output:


Building a simple NLP application with Python Creating an NLP application is an excellent method to study NLP and apply your knowledge. Here's an example of a simple NLP program that analyses text data using Python and some famous NLP libraries.


Problem Statement In this example, we want to build a simple sentiment analysis application. The goal is to classify a given text into positive, negative, or neutral sentiments.


Solution We can use the following steps to solve this problem: Load the dataPreprocess the dataFeature extraction and text representationTrain a machine-learning modelAnalyze the modelUse the model to make predictions on new data Here's the code for creating the sentiment analysis application using Python and some popular NLP libraries. In this example, we use a dataset with text and sentiment labels. We preprocess the text by converting it to lowercase, removing stop words and punctuation, and lemmatizing the words. We then use TF-IDF for feature extraction and text representation. We train a Naive Bayes classifier on the data and evaluate the model using accuracy. Finally, we use the model to make predictions on new text data.


Conclusion Python is a popular programming language used for Natural Language Processing (NLP) due to its simplicity, flexibility, and a large number of libraries and tools available.Setting up a development environment with the right tools and packages is essential for efficient and effective NLP work.Preprocessing text data is a crucial step in NLP and involves tasks like tokenization, stemming, lemmatization, and removing stop words and punctuation.NLP applications can be built using a variety of machine-learning techniques, such as classification, clustering, and topic modeling.By understanding these key concepts and tools in Python for NLP, beginners can start building their own NLP applications and exploring the vast potential of this field.


Reference : Stackoverflow, Chegg, NLP by Gizem


Chain-of-Thought Reasoning

What is CoT? Chain-of-Thought (CoT) prompting is a type of language prompting technique used in natural language processing (NLP) that involves the generation and refinement of chains of reasoning to facilitate better language understanding and generation. In CoT prompting, a language model (e.g., a neural network-based model such as GPT-3) is presented with a prompt to perform a given task (e.g., answer a question). The model is then given a set of rationales (or reasons) for why a given word or phrase might be the correct answer. The model can then use these rationales to generate a more accurate and contextually appropriate response. CoT prompting has been proven to be effective in improving the performance of language models in a variety of tasks. Chain-of-Thought prompting is like playing a game of "Twenty Questions". You think of an object, and your friend has to ask you questions to figure out what it is. When your friend asks a question, you give them a reason why the answer to the question might help them guess the object. For example, if your friend asks "Is it a vegetable?", you might say "Yes, because it grows in the ground." Your friend can then use this reason to make a better guess. CoT prompting works in a similar way. A computer program is given a prompt to perform a task, like answering a question. It is then given a set of reasons why a certain answer might be correct. The program can use these reasons to come up with a better answer. This technique has been shown to improve the performance of computer programs that process language in a variety of tasks. Theoretically, a pretty straightforward and convincing model, wouldn't you agree? Give it a try next time you use ChatGPT and see if the answers improve in accuracy and desirability. On the more practical side, CoT prompting can be computationally expensive, especially when dealing with large datasets and complex reasoning tasks. The process involves generating and refining chains of reasoning, which can require significant computing resources. Additionally, creating the set of rationales to present to the language model may require manual annotation, which can be time-consuming and costly. However, the benefits of using CoT prompting, such as improved performance in language tasks, may outweigh the computational cost for certain applications. Moreover, advancements in hardware and software technologies, as well as the development of more efficient algorithms, may help to reduce the computational cost of CoT prompting in the future.


Chain-of-Thought Reasoning

Multimodal-CoT: A Two-Step Framework The Multimodal CoT framework, as proposed by Zhang et al. (2023), consists of two distinct stages. The first stage involves the generation of rationales, which is achieved by feeding the model with both speech and vision inputs. The second stage then focuses on response inference, where they combine the initial speech input with the rationale generated in the first stage. The updated language input, along with the original vision input, is then fed into the model to infer the answer (Zhang et al., 2023).By combining speech and visual input in this way, Multimodal CoT allows for a more comprehensive understanding of the task at hand, resulting in more accurate and informative responses. Zhang et al. (2023) believe that this framework has the potential to revolutionize the field of multimodal learning, and I am excited to see where it goes from here. Zhang et al. evaluated their method using the "ScienceQA" benchmark dataset (Lu et al., 2022), which is a large-scale multimodal science question dataset that annotates answers with detailed lectures and explanations. The dataset contains 21k multimodal multiple-choice questions with rich diversity across subjects, topics, categories, and skills. Zhang et al.'s (2023) most sophisticated model (i.e., Multimodal-CoT_Large) outperformed the previous state-of-the-art LLM GPT-3.5 by an average of 17 percentage points, achieving an accuracy rate of 92%, and even surpassed human performance on the "ScienceQA" benchmark. Overall, the results confirm the effectiveness of multimodality and the potential for high accuracy through the use of CoT and a two-step framework as proposed by Zhang et al. (2023).


Chain-of-Thought Reasoning

Conclusion In summary, Chain-of-Thought (CoT) prompting is a technique that can be used to improve the reasoning and accuracy performance of large language models (LLM) by providing rationales for a given the word or phrase. The method shows strong capabilities to be effective in improving the performance of language models in various tasks. In addition, research by Zhang et al. presents an exciting new approach to Chain-of-Thought reasoning by incorporating both language and vision modalities into a two-step framework that separates reasoning generation and response inference. Their proposed method achieves high accuracy, outperforming existing LLMs and human-level performance. This type of research opens up promising avenues for future studies and AI applications. In addition, the concept behind CoT prompting can also help users of LLMs, such as ChatGPT, to improve their prompting and achieve better, more accurate results. I believe we will see much more of this approach in the near future, and iterations of this model will push the already very impressive capabilities of current large language models even further.


Chain-of-Thought Reasoning

References Lu, P., Mishra, S., Xia, T., Qiu, L., Chang, K.-W., Zhu, S.-C., Tafjord, O., Clark, P., and Kalyan. A. (2022). Learn to explain: Multimodal reasoning via thought chains for science question answering. ArXiv preprint, arXiv:2209.09513v2. Zhang, Zhuosheng, Zhang, Aston, Li, Mu, Zhao, Hai, Karypis, George, & Smola, Alex. (2023). Multimodal Chain-of-Thought Reasoning in Language Models. arXiv:2302.00923v4.


Multimodal Deep Multipage Document Classification using both Image and Text

Distinctive Features of Text and Images Document AI models don't just read? they can see too Text and images have distinct features that can bring different perspectives to the task of document classification. Analyzing the words and phrases in the text is a traditional way of gaining semantic insight into a document. For instance, technical jargon and terms in a scientific article can indicate its domain. Moreover, analyzing the structure of the text can provide a high-level comprehension of the document. For example, the presence of particular sections such as item description, quantity, and price can indicate the type of invoice. Images play a critical role in providing a visual understanding of the document by analyzing elements like charts, tables, and diagrams. Furthermore, images can give context to the text by locating it in the document. For instance, a signature in a legal document can indicate the type of document.


Why Use Both Text and Image for Document Classification? Why choose one? when we can use both Using both text and image data can lead to a more comprehensive understanding of the document than using only one type of data. For example, a medical report may provide diagnosis and treatment details in the text, while the images may provide visual evidence like X-rays or MRI scans. The multimodal model can achieve higher accuracy than unimodal models by combining the information from both data types. Additionally, using both text and image data can enhance the model's robustness. There are cases where text or image data may be incomplete or inaccurate, such as in scanned documents with errors due to scanning artifacts or poor OCR performance. In such situations, the image data can complement the text data by providing additional information. Deep learning models can handle both text and image data, and it is possible to build multimodal models that can leverage both data types. Combining information from text and images can help achieve higher accuracy than unimodal models that only utilize text or images.


Multimodal Deep Learning Models for Document Classification Methodology  Lets go Multimodal! There are several approaches to building multimodal deep-learning models for document classification. We will use Deep CNN to extract visual features from the document page. For text, we will use BERT ( for the document-specific task; other fine-tuned versions can also be used, e.g., BioBert for Medical/Healthcare related documents. Below diagram shows how this strategy will work. In this diagram, the input document page is the starting point, which is connected to the image input. The image input is then passed through the CNN-based image feature extractor to generate the image feature vector. The image input is also passed through Tesseract OCR to generate the text input, which is then passed through the BERT-based text feature extractor to generate the text feature vector. Both feature vectors are then passed through the multimodal fusion layer, dense layer, and output layer to produce the final classification output.


Tl:dr, Give me the code already? :O Like the approach discussed in my previous article . For text, the approach remains mostly the same, I have introduced BERT here instead of Doc2Vec. But you can use either. It doesn't change much.


Evaluation - Moment of truth! Does it really work? In order to test this strategy, I evaluated the multimodal method with singular text or image-based models. Below is how i formulated this test Dataset: A collection of 1000 multipage documents, each with 5 pages. Each document belongs to one of 10 classes.Data Split: 80% training, 10% validation, 10% testingText Model: BERT-based text feature extractor with 2 feedforward layers. Trained on the text content of each page of the document.Image Model: CNN model with 4 convolutional layers and 2 fully connected layers. Trained on the image content of each page of the document.Multimodal Model: Combines the output from the text and image models using a concatenation method before passing the output to a 2-layer feedforward neural network.Evaluation Metrics: Accuracy, Precision, Recall, F1-score, and AUC-ROC score. Here are the results of the evaluation:


Empirical Analysis Although this wasn't a very big dataset to test, practically, I evaluated the results to review our intuition and the arguments we made above. Since i cannot share the dataset, I will share my findings in detail.


Where do Text-based and Image-based Models fail? It was seen that the more wordy documents, e.g., Letters, were classified well by the text-based model. But image-based models suffered in such documents. Where it shined was the document with structure, e.g., complex forms. The main reason was that for such documents, Tesseract OCR wasn't detecting the text properly as the documents were scanned and had noise. Moreover, OCR suffered in knowing what phrase comes after the other. It had weird line breaks, which in turn, broke the sentence structure. Since Image-based models can learn the pattern, they worked well here.


Does Multimodal actually work? I purposefully added a few classes in my dataset where the model would have to use both Image and Text guidance to determine document differences. I had two classes where the difference was the title/subtitle/form number of the document and where the difference was pretty slight, e.g., In one document, the form name was W9-EBY2, and the other one was W9-EBYK. The title also included the same sentence and the only difference was the state name, e.g. [Text] - New york and the other was [Text] - New Jersey. Visually, both document pages of the classes had stamps, which were also slightly different. This brought it a challenge for the multimodal model as the above separate models were not working well individually. In these classes, multimodal strategy specifically worked well. This proves the fact that a multimodal strategy is better. Now that we have proved it, this strategy can be used in the overall methodology explained in this article


Conclusion Overall, this study demonstrates the importance of using a multimodal approach for document classification tasks. The combination of image and text data can capture more comprehensive and diverse features, leading to better classification performance. In future work, we aim to explore the effectiveness of incorporating other modalities to explore this further. Follow me on medium to be the first to know and help me reach my goal of 100+ subscribers! also on github for further updates. Also, check out some of my other projects ;)


The Intersection of Technology & Mental Health: Advantages & Concerns

Introduction Technology has been rapidly advancing in recent years and has started to play a significant role in mental health. With mental health disorders on the rise, the intersection of technology and mental health has become increasingly important. Technology can provide more accessible and effective mental healthcare services to individuals worldwide. In this article, we will explore the intersection of technology and mental health, its advantages, and concerns.


Advantages 1.Remote Care One of the most significant advantages of technology in mental health is the ability to provide remote care. With the rise of telehealth, individuals can receive mental health services from the comfort of their own homes, which has been especially useful during the COVID-19 pandemic. Telehealth can reduce barriers to mental healthcare such as transportation, geographical location, and accessibility. Telehealth can provide several forms of remote care such as videoconferencing, phone calls, and online chat. These forms of remote care can also be useful in providing real-time support to individuals who may be struggling with mental health conditions. 2.Personalization Technology can provide personalized mental healthcare services to individuals. Machine learning algorithms can analyze data from an individual's behavior, preferences, and demographics to provide personalized treatment plans. Additionally, mobile apps can offer self-care tips and personalized treatment recommendations based on the user's symptoms. This personalization can also increase patient engagement and motivation. Individuals are more likely to engage with mental health services that are tailored to their needs and preferences. 3.Early Intervention Technology can also help with early intervention for mental health conditions. For example, digital mental health tools can help identify symptoms of mental health disorders and provide self-care tips to manage symptoms before they become severe. This early intervention can be especially helpful in preventing the onset of mental health disorders. Early intervention can also help prevent individuals from experiencing a mental health crisis. 4.Data Collection Technology can be used to collect data on mental health conditions. Wearable devices and mobile apps can collect data on a person's behavior, sleep, and physical activity, which can help identify patterns that may be indicative of mental health conditions. This data collection can provide valuable insights into the onset and progression of mental health conditions. This information can also help healthcare providers develop more personalized and effective treatment plans for individuals.


Concerns 1.Privacy One of the main concerns with technology in mental health is privacy. Mental health data is sensitive, and there is a risk that it could be used for nefarious purposes. Additionally, there is a risk that data breaches could expose mental health information, which could lead to discrimination or stigmatization. It is crucial to ensure that the data collected is stored and managed securely. It is also essential to ensure that individuals are informed about how their data is being used and have the right to control their data. 2.Bias Another concern with technology in mental health is bias. Machine learning algorithms are only as unbiased as the data they are trained on. If the data is biased, the algorithm will also be biased. This could lead to inaccurate diagnoses or treatment recommendations. It is essential to ensure that the data used to train machine learning algorithms is diverse and representative. Additionally, it is crucial to regularly audit these algorithms to identify and address any bias that may arise. 3.Access While technology has the potential to increase access to mental healthcare, it could also exacerbate the digital divide. Individuals without access to the internet or technology may not be able to benefit from remote mental healthcare services. It is essential to ensure that mental health services are accessible to all individuals, regardless of their technological capabilities. This could involve providing individuals with access to technology or offering alternative forms of mental healthcare services. 4.Ethical Concerns Finally, there are ethical concerns surrounding the use of technology in mental healthcare.One ethical concern is the potential for replacing human therapists with automated technology. While technology can provide personalized treatment plans and support, it cannot replace the human connection and empathy that is essential in mental healthcare. Additionally, there is a risk that individuals may become overly reliant on technology for mental healthcare services. This could lead to a decrease in social support and human connection, which are essential for mental well-being. It is essential to ensure that technology is used as a supplement to human therapists, rather than a replacement. It is also crucial to maintain ethical principles, such as informed consent and confidentiality, when using technology in mental healthcare.


Conclusion In conclusion, the intersection of technology and mental health has the potential to revolutionize mental healthcare services. Technology can provide remote care, personalization, early intervention, and data collection for mental health conditions. However, there are also concerns surrounding privacy, bias, access, and ethical issues. It is crucial to address these concerns and ensure that technology is used ethically and responsibly in mental healthcare. Overall, the integration of technology and mental health provides an exciting opportunity for improving mental healthcare services and increasing access to mental health resources for individuals worldwide. Furthermore, as technology continues to advance, it is important for mental healthcare professionals to stay up-to-date with the latest developments in the field. This will allow them to provide the most effective and personalized treatment options to their patients. Additionally, it is important for individuals to understand and be informed about the technology used in mental healthcare services. This includes understanding how their data is being used and stored, as well as the potential benefits and risks of using technology in mental healthcare.


GPT-4 and the Next Frontier of Generative AI

Introducing GPT- 4 GPT-4 has burst onto the scene! Open AI officially released the larger and more powerful successor to GPT-3 with many improvements, including the ability to process images, draft a lawsuit, and handle up to a 25,000-word input.¹ During testing, Open AI reported that it was smart enough to find a solution for solving a CAPTCHA by hiring a human on taskrabbit to do it for GPT-4.² Yes, you read that correctly. When presented with a problem that it knew only a human could do, it reasoned it should hire a human to do it. Wow. This is just a taste of some of the amazing things that GPT-4 can do.