AI/ML

As technology professionals, we are already aware that our world is increasingly data-driven. This is especially true in the realm of financial markets, where algorithmic trading has become the norm, leveraging complex algorithms to execute trades at speeds and frequencies that far outstrip human capabilities. In this world where milliseconds can mean the difference between profit and loss, algorithmic trading provides an edge by making trading more systematic and less influenced by human emotional biases. But what if we could take this a step further? What if our trading algorithms could learn from their mistakes, adapt to new market conditions, and continually improve their performance over time? This is where reinforcement learning, a cutting-edge field in artificial intelligence, comes into play. Reinforcement learning (RL) is an area of machine learning that's focused on making decisions. It is about learning from interaction with an environment to achieve a goal, often formulated as a game where the RL agent learns to make moves to maximize its total reward. It is the technology that now being applied to a variety of problems, from self-driving cars to resource allocation in computer networks. But reinforcement learning's potential remains largely untapped in the world of algorithmic trading. This is surprising, given that trading is essentially a sequential decision-making problem, which is exactly what reinforcement learning is designed to handle. In this article, we will delve into how reinforcement learning can enhance algorithmic trading, explore the challenges involved, and discuss the future of this exciting intersection of AI and finance. Whether you're a data scientist interested in applying your skills to financial markets, or a technology enthusiast curious about the practical applications of reinforcement learning, this article has something for you. Understanding Algorithmic Trading Algorithmic trading, also known as algo-trading or black-box trading, utilizes complex formulas and high-speed, computer-programmed instructions to execute large orders in financial markets with minimal human intervention. It is a practice that has revolutionized the finance industry and is becoming increasingly prevalent in today's digital age. At its core, algorithmic trading is about making the trading process more systematic and efficient. It involves the use of sophisticated mathematical models to make lightning-fast decisions about when, how, and what to trade. This ability to execute trades at high speeds and high volumes offers significant advantages, including reduced risk of manual errors, improved order execution speed, and the ability to backtest trading strategies on historical data. In addition, algorithmic trading can implement complex strategies that would be impossible for humans to execute manually. These strategies can range from statistical arbitrage (exploiting statistical patterns in prices) to mean reversion (capitalizing on price deviations from long-term averages). An important aspect of algorithmic trading is that it removes emotional human influences from the trading process. Decisions are made based on pre-set rules and models, eliminating the potential for human biases or emotions to interfere with trading decisions. This can lead to more consistent and predictable trading outcomes. However, as powerful as algorithmic trading is, it is not without its challenges. One of the primary difficulties lies in the development of effective trading algorithms. These algorithms must be robust enough to handle a wide range of market conditions and flexible enough to adapt to changing market dynamics. They also need to be able to manage risk effectively, a task that becomes increasingly challenging as the speed and volume of trading increase. This is where reinforcement learning can play a critical role. With its ability to learn from experience and adapt its strategies over time, reinforcement learning offers a promising solution to the challenges faced by traditional algorithmic trading strategies. In the next section, we will delve deeper into the principles of reinforcement learning and how they can be applied to algorithmic trading. The Basics of Reinforcement Learning Reinforcement Learning (RL) is a subfield of artificial intelligence that focuses on decision-making processes. In contrast to other forms of machine learning, reinforcement learning models learn by interacting with their environment and receiving feedback in the form of rewards or penalties. The fundamental components of a reinforcement learning system are the agent, the environment, states, actions, and rewards. The agent is the decision-maker, the environment is what the agent interacts with, states are the situations the agent finds itself in, actions are what the agent can do, and rewards are the feedback the agent gets after taking an action. One key concept in reinforcement learning is the idea of exploration vs exploitation. The agent needs to balance between exploring the environment to find out new information and exploiting the knowledge it already has to maximize the rewards. This is known as the exploration-exploitation tradeoff. Another important aspect of reinforcement learning is the concept of a policy. A policy is a strategy that the agent follows while deciding on an action from a particular state. The goal of reinforcement learning is to find the optimal policy, which maximizes the expected cumulative reward over time. Reinforcement learning has been successfully applied in various fields, from game playing (like the famous AlphaGo) to robotics (for teaching robots new tasks). Its power lies in its ability to learn from trial and error and improve its performance over time. In the context of algorithmic trading, the financial market can be considered as the environment, the trading algorithm as the agent, the market conditions as the states, the trading decisions (buy, sell, hold) as the actions, and the profit or loss from the trades as the rewards. Applying reinforcement learning to algorithmic trading means developing trading algorithms that can learn and adapt their trading strategies based on feedback from the market, with the aim of maximizing the cumulative profit. However, implementing reinforcement learning in trading comes with its own unique challenges, which we will explore in the following sections. The Intersection of Algorithmic Trading and Reinforcement Learning The intersection of algorithmic trading and reinforcement learning represents an exciting frontier in the field of financial technology. At its core, the idea is to create trading algorithms that can learn from past trades and iteratively improve their trading strategies over time. In a typical reinforcement learning setup for algorithmic trading, the agent (the trading algorithm) interacts with the environment (the financial market) by executing trades (actions) based on the current market conditions (state). The result of these trades, in terms of profit or loss, serves as the reward or penalty, guiding the algorithm to adjust its strategy. One of the key advantages of reinforcement learning in this context is its ability to adapt to changing market conditions. Financial markets are notoriously complex and dynamic, with prices affected by a myriad of factors, from economic indicators to geopolitical events. A trading algorithm that can learn and adapt in real-time has a significant advantage over static algorithms. For example, consider a sudden market downturn. A static trading algorithm might continue executing trades based on its pre-programmed strategy, potentially leading to significant losses. In contrast, a reinforcement learning-based algorithm could recognize the change in market conditions and adapt its strategy accordingly, potentially reducing losses or even taking advantage of the downturn to make profitable trades. Another advantage of reinforcement learning in trading is its ability to handle high-dimensional data and make decisions based on complex, non-linear relationships. This is especially relevant in today's financial markets, where traders have access to vast amounts of data, from price histories to social media sentiment. For instance, a reinforcement learning algorithm could be trained to take into account not just historical price data, but also other factors such as trading volume, volatility, and even news articles or tweets, to make more informed trading decisions. Challenges and Solutions of Implementing Reinforcement Learning in Algorithmic Trading While the potential benefits of using reinforcement learning in algorithmic trading are significant, it's also important to understand the challenges and complexities associated with its implementation. Overcoming the Curse of Dimensionality The curse of dimensionality refers to the exponential increase in computational complexity as the number of features (dimensions) in the dataset grows. For a reinforcement learning model in trading, each dimension could represent a market factor or indicator, and the combination of all these factors constitutes the state space, which can become enormous. One approach to mitigating the curse of dimensionality is through feature selection, which involves identifying and selecting the most relevant features for the task at hand. By reducing the number of features, we can effectively shrink the state space, making the learning problem more tractable. Python from sklearn.feature_selection import SelectKBest, mutual_info_regression # Assume X is the feature matrix, and y is the target variable k = 10 # Number of top features to select selector = SelectKBest(mutual_info_regression, k=k) X_reduced = selector.fit_transform(X, y) Another approach is dimensionality reduction, such as Principal Component Analysis (PCA) or t-distributed Stochastic Neighbor Embedding (t-SNE). These techniques transform the original high-dimensional data into a lower-dimensional space, preserving as much of the important information as possible. Python from sklearn.decomposition import PCA # Assume X is the feature matrix n_components = 5 # Number of principal components to keep pca = PCA(n_components=n_components) X_reduced = pca.fit_transform(X) Handling Uncertainty and Noise Financial markets are inherently noisy and unpredictable, with prices influenced by numerous factors. To address this, we can incorporate techniques that manage uncertainty into our reinforcement learning model. For example, Bayesian methods can be used to represent and manipulate uncertainties in the model. Additionally, reinforcement learning algorithms like Q-learning and SARSA can be used, which learn an action-value function and are known to handle environments with a high degree of uncertainty. Preventing Overfitting Overfitting happens when a model becomes too specialized to the training data and performs poorly on unseen data. Regularization techniques, such as L1 and L2 regularization, can help prevent overfitting by penalizing overly complex models. Python from sklearn.linear_model import Ridge # Assume X_train and y_train are the training data alpha = 0.5 # Regularization strength ridge = Ridge(alpha=alpha) ridge.fit(X_train, y_train) Another way to prevent overfitting is through the use of validation sets and cross-validation. By regularly evaluating the model's performance on a separate validation set during the training process, we can keep track of how well the model is generalizing to unseen data. Python from sklearn.model_selection import cross_val_score from sklearn.linear_model import LinearRegression # Assume X and y are the feature matrix and target variable model = LinearRegression() cv_scores = cross_val_score(model, X, y, cv=5) # 5-fold cross-validation Balancing Exploration and Exploitation Striking the right balance between exploration (trying out new actions) and exploitation (sticking to known actions) is a key challenge in reinforcement learning. Several strategies can be used to manage this tradeoff. One common approach is the epsilon-greedy strategy, where the agent mostly takes the action that it currently thinks is best (exploitation), but with a small probability (epsilon), it takes a random action (exploration). Python import numpy as np def epsilon_greedy(Q, state, n_actions, epsilon): if np.random.random() < epsilon: return np.random.randint(n_actions) # Exploration: choose a random action else: return np.argmax(Q[state]) # Exploitation: choose the action with the highest Q-value Another approach is the Upper Confidence Bound (UCB) method, where the agent chooses actions based on an upper bound of the expected reward, encouraging exploration of actions with high potential. Python import numpy as np import math def ucb_selection(plays, rewards, t): n_arms = len(plays) ucb_values = [0] * n_arms for i in range(n_arms): if plays[i] == 0: ucb_values[i] = float('inf') else: ucb_values[i] = rewards[i] / plays[i] + math.sqrt(2 * math.log(t) / plays[i]) return np.argmax(ucb_values) Future Perspectives The intersection of reinforcement learning and algorithmic trading is a burgeoning field, and while it's already showing promise, there are several exciting developments on the horizon. One of the most prominent trends is the increasing use of deep reinforcement learning, which combines the decision-making capabilities of reinforcement learning with the pattern recognition capabilities of deep learning. Deep reinforcement learning has the potential to handle much more complex decision-making tasks, making it especially suited to the intricacies of financial markets. We can also expect to see more sophisticated reward structures in reinforcement learning models. Current models often use simple reward structures, such as profit or loss from a trade. However, future models could incorporate more nuanced rewards, taking into account factors such as risk, liquidity, and transaction costs. This would allow for the development of more balanced and sustainable trading strategies. Another intriguing prospect is the use of reinforcement learning for portfolio management. Instead of making decisions about individual trades, reinforcement learning could be used to manage a portfolio of assets, deciding what proportion of the portfolio to allocate to each asset in order to maximize returns and manage risk. In terms of research, there's a lot of ongoing work aimed at overcoming the challenges associated with reinforcement learning in trading. For instance, researchers are exploring methods to manage the exploration-exploitation tradeoff more effectively, to deal with the curse of dimensionality, and to prevent overfitting. In conclusion, while reinforcement learning in algorithmic trading is still a relatively new field, it holds immense potential. By continuing to explore and develop this technology, we could revolutionize algo-trading, making it more efficient, adaptable, and profitable. As technology professionals, we have the exciting opportunity to be at the forefront of this revolution.

As part of my involvement at LeadDev NYC, I had the opportunity a short video message that would be part of a montage played for folks between the live talks. I decided to speak about the way engineers are enabling the future of products (you can watch it here). It seems to me that questions like “how can engineers affect the future of (whatever)” sometimes come from a place of anxiety. And these days, there’s no greater source of that anxiety than the advances — and the impacts we imagine coming from those advances — in large language models (LLM), more broadly billed as artificial intelligence (AI). But LLM and AI are techniques. Nobody in tech ever lost their job because of a new technique, but plenty of folks become anxious when techniques grow to full-on implementations that take the world by storm. I’m speaking, of course, of tools like Bard, DALL-E, and ChatGPT. It’s inarguable that the things these tools can accomplish are both impressive and diverse. But we — both as tech practitioners and humans moving through the world — encounter a lot of tools that are both impressive and versatile. My argument about AI-driven tools isn’t that they’re worthless. My frustration is with the statements of how they’ll change the world, replacing the work done by entire swaths of professionals. This point of view is entirely unfounded in experience, if not fact. Nathan Hamiel recently dug into this on ModernCISO.com, and honestly I can’t provide better examples than he does. What he drills down to time and time again is that LLM-based tools can accomplish wondrous things, but only when given highly specific, tightly bounded directions that require the operator to have intimate knowledge of both the subject and the desired outcome, and often took multiple attempts to perfect before it “just worked like magic.” In fact, in reading his essay I was reminded of the scene in the movie “Sully.” Every time I read a blog breathlessly proclaiming “I used ChatGPT to accomplish this really hard thing,” I’m left wondering, like Sully, “How many times? How many practice attempts did they make before successfully pulling it off.” I’m not saying these tools are invalid or the claims overblown. I simply want to put their achievements into a realistic context. And that context should be familiar to us, because we’ve already seen a similar thing happen — on both a larger and yet also smaller scale — in our lifetimes. The first example I encountered was as simple and unassuming as it was revolutionary: the calculator. The first pocket calculator came onto the market in 1971. Advances in what that tiny yet powerful device could do were both rapid and impressive. It wasn’t just the improvements in speed, or size, or interface. There were massive leaps in the types of operations that could be performed. “This is going to change the world!” proclaimed a chorus of voices from supporters. Ironically, the detractors were shouting the same thing, although with a decidedly different intonation. In what should sound eerily familiar to us in 2023, this tool was banned from schools, job interviews, and other settings for fear it would diminish children’s ability to learn; that it would make identifying truly skilled individuals impossible; that it would upset the balance of merit in the school, in the workplace, and in society at large. With the benefit of over 50 years of perspective, we can see how foolish such fears were. Calculators proved themselves to be a useful and versatile tools, but they were limited by the math skills of the operator. To put it plainly, no matter how powerful the calculator, if I am using the square root function while balancing my checkbook, something has gone horribly wrong, and it’s probably not my finances which are to blame. In the hands of a novice, a scientific calculator is far more likely to be used to spell out “fart” than it is to find the proof to Fermat’s theorem. (Use hex mode; put in “46415254.” Don’t ask me why I know this.) Like the calculator, the spreadsheet, and the internet, AI-driven LLM tools are likely to change HOW we do our work, but not the fact that humans will still be the ones doing the work in the first place. The shift will come in people learning how to use the tool to its best advantage: translating from our context into the context we know it needs to be in. Which brings me back to the beginning. What is it, then, that we as engineers, developers, designers, and creative people can do to affect the future of products. And I think I’ve laid out an answer both satisfying and filled with hope: We should endeavor to be fully present as thinking, feeling, context-seeking humans in all of our work. To embrace new tools and use them to their best advantage, while also being clear about their limitations. We affect the future of products when the bulk of the work we do is not with our brains or our hands, but with our hearts.

In today's rapidly evolving technological landscape, Artificial Intelligence (AI) is transforming industries across the board, and software development is no exception. AI can significantly enhance developer experience by streamlining processes, automating repetitive tasks, and empowering developers to build innovative applications. Improving developer experience with AI involves providing tools, frameworks, and resources that make it easier for developers to build, test, and deploy AI models. Here, we will explore several ways to leverage AI and improve developer experience, fostering efficiency, creativity, and growth within the software development community. 1. Intelligent Code Completion One of the most time-consuming aspects of coding is writing repetitive lines of code. AI-powered intelligent code completion tools, such as GPT-based models, can analyze existing codebases and suggest relevant code snippets, making development faster and more efficient. By harnessing the power of AI, developers can spend less time on boilerplate code and focus on crafting unique and high-quality logic. 2. Automated Testing and Debugging Testing and debugging are critical stages in the software development lifecycle. AI can help automate these processes, reducing human effort and increasing accuracy. Machine learning algorithms can analyze code patterns, identify potential bugs, and suggest fixes. Moreover, AI-powered test generation tools can automatically generate test cases, ensuring comprehensive coverage and freeing up developers to concentrate on complex problem-solving. 3. Natural Language Processing (NLP) for Documentation Developers often struggle with deciphering complex documentation. NLP algorithms can be employed to build intelligent documentation systems that understand developers' queries and provide contextual information in a user-friendly manner. By enabling developers to find relevant information quickly, AI-powered documentation systems enhance productivity, improve code comprehension, and accelerate learning. 4. Continuous Integration and Deployment Optimization AI can optimize CI/CD pipelines by predicting the impact of code changes, analyzing test results, and automatically triggering deployments. Machine learning algorithms can identify potential bottlenecks, optimize resource allocation, and suggest improvements for faster and more efficient deployment cycles. By leveraging AI in CI/CD processes, developers can ensure stable, reliable, and seamless software releases. 5. Automated Issue Tracking and Triage Managing and prioritizing software issues is a challenging task. AI can help automate issue tracking and triage processes by analyzing bug reports, categorizing issues, and assigning appropriate developers. Natural language processing techniques can extract relevant information from bug reports and suggest possible solutions or similar resolved issues, reducing the time spent on issue resolution and enabling faster software iterations. 6. Intelligent Data Analytics AI can augment data analytics capabilities for developers by providing insights into code performance, user behavior, and system health. Machine learning algorithms can process large volumes of data to identify patterns, detect anomalies, and optimize code for better performance. With AI-powered analytics tools, developers can make informed decisions, improve application efficiency, and enhance the overall user experience. 7. Simplified APIs and SDKs Offering intuitive application programming interfaces (APIs) and software development kits (SDKs) can greatly enhance the developer experience. These tools should have well-defined interfaces with streamlined and easy-to-use functions, allowing developers to interact with AI components effortlessly. 8. Pre-trained Models and Model Marketplaces Providing pre-trained models or access to model marketplaces allows developers to leverage existing AI models, saving them time and effort in training models from scratch. These resources should have a wide range of models applicable to various domains, making it easier for developers to incorporate AI capabilities into their applications. 9. Visual Development Interfaces User-friendly visual development interfaces, such as drag-and-drop editors or visual flow builders, can simplify the process of designing, training, and deploying AI models. These interfaces enable developers with limited AI expertise to create and experiment with AI models more easily. 10. Collaboration and Version Control Collaboration features and version control systems specifically tailored for AI development help multiple developers work together seamlessly. These tools allow for efficient collaboration, code sharing, and version management in AI projects. 11. Performance Optimization Optimizing AI models for speed, memory usage, and efficiency is crucial for real-world applications. Providing tools and techniques to optimize model performance, such as model quantization, pruning, or hardware acceleration, can significantly improve the developer experience. 12. Community Support and Forums Establishing online communities, forums, and chat platforms dedicated to AI development fosters collaboration, knowledge sharing, and support among developers. These platforms enable developers to seek help, share experiences, and learn from each other. 13. Training and Learning Resources Providing comprehensive training materials, tutorials, online courses, and workshops can help developers upskill in AI development. These resources should cover both foundational concepts and advanced techniques, empowering developers to expand their AI expertise. By focusing on these areas, developers can have a more seamless and efficient experience when working with AI, leading to increased productivity and innovation in the field. Embrace AI, and Unlock a World of Possibilities for Enhanced Developer Experience Artificial Intelligence is revolutionizing the software development landscape by enabling developers to overcome challenges, streamline workflows, and boost productivity. By harnessing the power of AI, developers can focus on innovation and creativity while tedious and repetitive tasks are automated. The possibilities for improving developer experience with AI are vast, and as technology advances, we can expect even more exciting developments that will empower developers and shape the future of software development.

Traditional machine-learning models can be stored on standalone machines or local hard drives when working with small datasets and simple algorithms. However, as deep learning advances, teams increasingly encounter storage bottlenecks when handling larger datasets and more complex algorithms. This highlights the importance of distributed storage in the artificial intelligence (AI) field. JuiceFS, an open-source, high-performance distributed file system, provides a solution to this problem. In this article, we’ll discuss the challenges AI teams face, how JuiceFS can speed up model training, and common strategies for faster model training. Challenges Faced by AI Teams AI teams often encounter the following challenges: Large datasets: As both data and model size grow, standalone storage is unable to meet application demands. Therefore, distributed storage solutions become imperative to address these issues. Archiving historical datasets in full: In certain scenarios, large amounts of new datasets are generated on a daily basis and must be archived as historical data. This is particularly crucial in the field of autonomous driving, where data collected by road test vehicles, such as radar and camera data, are highly valuable assets for the company. Standalone storage proves inadequate in these cases, making distributed storage a necessary consideration. Too many small files and unstructured data: Traditional distributed file systems struggle with managing a large number of small files, resulting in a heavy burden on metadata storage. This is especially problematic for visual models. To address this issue, we need a distributed storage system that is optimized for storing small files. This ensures efficient upper-layer training tasks and easy management of a large volume of small files. POSIX interfaces for training frameworks: In the initial stages of model development, algorithm scientists often rely on local resources for research and data access. However, when scaling to distributed storage for larger training needs, the original code typically requires minimal modifications. Therefore, distributed storage systems should support POSIX interfaces to maximize compatibility with code developed in the local environment. Sharing public datasets and data isolation: In some fields, such as computer vision, authoritative public datasets need to be shared across different teams within a company. To facilitate data sharing between teams, these datasets are often integrated and stored in a shared storage solution to avoid unnecessary data duplication and redundancy. Low data I/O efficiency in cloud-based training: Cloud-based model training often uses object storage as the underlying storage for a storage-compute separation architecture. However, the poor read-and-write performance of object storage can result in significant bottlenecks during training. How JuiceFS Helps Improve Model Training Efficiency What Is JuiceFS? JuiceFS is an open-source, cloud-native, distributed file system compatible with POSIX, HDFS, and S3 API. Using a decoupled architecture that stores metadata in a metadata engine and uploads file data to object storage, JuiceFS delivers a cost-effective, high-performance, and elastic storage solution. JuiceFS has users in more than 20 countries, including leading enterprises in AI, internet, automotive, telecommunications, and fintech industries. The architecture of JuiceFS in the model training scenario. JuiceFS in the model training scenario The diagram above shows the architecture of JuiceFS in a model training scenario, which consists of three components: Metadata engine: Any database, such as Redis or MySQL, can serve as the metadata engine. Users can make a choice based on their own needs. Object storage: You can use any supported object storage services provided by public cloud or self-hosted. JuiceFS Client: To access the JuiceFS file system just like a local hard drive, users need to mount it on each GPU and computing node. The underlying storage relies on the raw data in object storage, and each computing node has some local cache, including metadata and data cache. JuiceFS design allows for multiple levels of local cache on each computing node: The first level: a memory-based cache The second level: a disk-based cache Object storage is accessed only upon cache penetration. For a standalone model, in the first round of training, the training set or dataset usually doesn't hit the cache. However, from the second round onwards, with sufficient cache resources, it's almost unnecessary to access object storage. This can accelerate data I/O. The Read and Write Cache Process in JuiceFS We previously compared using or not using cache to training efficiency when accessing object storage. The results showed that JuiceFS could achieve more than 4 times performance improvement compared to object storage on average and up to nearly 7 times performance increase due to JuiceFS’ metadata cache and data cache. The diagram below shows the process of reading and writing cache in JuiceFS: JuiceFS’ read and write cache process For the "chunk cache" in the figure above, a chunk is a logical concept in JuiceFS. Each file is divided into multiple chunks of 64 MB to improve the read performance of large files. This information is cached in the memory of the JuiceFS process to accelerate metadata access efficiency.The read cache process in JuiceFS: 1. The application, which can be an AI model training application or any application initiating a read request, sends the request. 2. The request enters the kernel space on the left. The kernel checks whether the requested data is available in the kernel page cache. If not, the request goes back to the JuiceFS process in the user space, which handles all read and write requests. By default, JuiceFS maintains a read buffer in memory. When the request cannot retrieve the data from the buffer, JuiceFS accesses the block cache index, which is a cache directory based on local disks. JuiceFS divides files into 4 MB blocks for storage, so the cache granularity is also 4 MB. For example, when the client accesses a part of a file, it only caches the 4 MB block corresponding to that part of the data to the local cache directory rather than the entire file. This is a significant difference between JuiceFS and other file systems or caching systems. 3. The block cache index quickly locates the file block in the local cache directory. If it finds the file block, JuiceFS reads from the local disk, enters the kernel space, and returns the data to the JuiceFS process, which in turn returns it to the application. 4. After the local disk data is read, it’s also cached in the kernel page cache. This is because if direct I/O is not used, Linux systems store data in the kernel page cache by default. The kernel page cache speeds up cache access. If the first request hits and returns data, the request does not enter the user space process through the filesystem in the userspace (FUSE) layer. If not, the JuiceFS Client will go through the cache directory for this data. If not found locally, a network request is sent to the object storage, and the data is fetched and returned to the application. 5. When JuiceFS downloads data from the object storage, data is asynchronously written to the local cache directory. This ensures that when accessing the same block next time, it can be hit in the local cache without needing to retrieve it from the object storage again. Unlike data cache, the metadata cache time is shorter. To ensure strong consistency, Open operations are not cached by default. Considering that metadata traffic is low, its impact on overall I/O performance is small. However, in small file-intensive scenarios, the overhead of metadata also occupies a certain proportion. Why Is AI Model Training Too Slow? When you use JuiceFS for model training, performance is a critical factor you should consider because it directly affects the speed of the training process. Several factors may impact JuiceFS’ training efficiency: Metadata Engine The choice of metadata engine, such as Redis, TiKV, or MySQL, can significantly impact performance when processing small files. Generally, Redis is 3-5 times faster than other databases. If metadata requests are slow, try a faster database as the metadata engine. Object Storage Object storage affects the performance and throughput of data storage access. Public cloud object storage services provide stable performance. If you use self-built object storage, such as Ceph or MinIO, you can optimize the components to improve performance and throughput. Local Disk The location of the cache directory storage has a significant impact on overall read performance. In cases of high cache hit rates, the I/O efficiency of the cache disk affects overall I/O efficiency. Therefore, you must consider factors such as storage type, storage medium, disk capacity, and dataset size. Network Bandwidth After the first round of training, if the dataset is not sufficient to be fully cached locally, network bandwidth or resource consumption will affect data access efficiency. In the cloud, different machine models have different network card bandwidths. This also affects data access speed and efficiency. Memory Size Memory size affects the size of the kernel page cache. When there is enough memory, the remaining free memory can be used as JuiceFS’ data cache. This can further speed up data access. However, when there is little free memory, you need to obtain data access through local disks. This leads to increased access overhead. Additionally, switching between kernel mode and user mode affects performance, such as context switching overhead of system calls. How to Troubleshoot Issues in JuiceFS JuiceFS offers many tools to optimize performance and diagnose issues. Tool #1: The juicefs profile Command You can run the juicefs profile command to analyze access logs for performance optimization. After each filesystem is mounted, an access log is generated. However, the access log is not saved in real-time and only appears when it’s viewed. Compared to viewing the raw access log, the juicefs profile command aggregates information and performs sliding window data statistics, sorting requests by response time from high to low. This helps you focus on requests with slower response times, further analyzing the relationship between the request and the metadata engine or object storage. Tool #2: The juicefs stats Command The juicefs stats command collects monitoring data from a macro perspective and displays it in real time. It monitors CPU usage, memory usage, buffer usage in memory, FUSE read/write requests, metadata requests, and object storage latency for the current mount point. These detailed monitoring metrics make it easy to view and analyze potential bottlenecks or performance issues during model training. Other Tools JuiceFS also provides performance analysis tools for CPU and heap profiling: The CPU profiling tool analyzes bottlenecks in JuiceFS process execution speed and is suitable for users familiar with source code. The heap profiling tool analyzes memory usage, especially when the JuiceFS process occupies a large amount of memory. It’s necessary to use the heap profiling tool to determine which functions or data structures are consuming a lot of memory. Common Methods for Accelerating AI Model Training Metadata Cache Optimization You can optimize the metadata cache in two ways, as follows. Adjusting the Timeout of the Kernel Metadata Cache The parameters --attr-cache, --entry-cache, and --dir-entry-cache correspond to different types of metadata: attr represents file attributes such as size, modification time, and access time. entry represents files and related attributes in Linux. dir-entry represents directories and the files they contain. These parameters respectively control the timeout of the metadata cache. To ensure data consistency, the default timeout value of these parameters is only one second. In model training scenarios, the original data is not modified. Therefore, it's possible to extend the timeout time of these parameters to several days or even a week. Note that the metadata cache cannot be invalidated actively and can only be refreshed after the timeout period has expired. Optimizing the User-Level Metadata Cache of the JuiceFS Client When opening a file, the metadata engine typically retrieves the latest file attributes to ensure strong consistency. However, since model training data is usually not modified, the --open-cache parameter can be enabled, and a timeout can be set to avoid repetitive access to the metadata engine every time the same file is opened. Additionally, the --open-cache-limit parameter controls the maximum number of cached files. The default value is 10,000, meaning that the metadata of the 10,000 most recently opened files will be cached in memory at most. This value can be adjusted based on the number of files in the dataset. Data Cache Optimization JuiceFS data cache includes kernel page cache and local data cache: The kernel page cache cannot be tuned by parameters. Therefore, enough idle memory should be reserved on the computing nodes so that JuiceFS can make full use of it. If the resources on the computing node are tight, JuiceFS doesn’t cache data in the kernel. The local data cache is controllable by users, and the cache parameters can be adjusted according to specific scenarios. --cache-size adjusts the cache size, with a default value of 100 GB, which is sufficient for most scenarios. However, for datasets that occupy particularly large storage space, the cache size needs to be adjusted appropriately. Otherwise, the 100 GB cache space may be filled up quickly, making it impossible for JuiceFS to cache more data. Another parameter that can be used with --cache-size is --free-space-ratio. It determines the amount of free space on the cache disk. The default value is 0.1, which allows up to 90% of the disk space to be used for caching data. JuiceFS also supports using multiple cache disks at the same time. It’s recommended to use all available disks as much as possible. Data will be evenly distributed to multiple disks through polling to achieve load balancing and maximize the storage advantages of multiple disks. Cache Warm-Up To improve training efficiency, you can use cache warm-up to speed up training tasks. JuiceFS supports warming up the metadata cache and local data cache in the client. The juicefs warmup command builds cache in advance so that the cache is available at the beginning of the training task to improve efficiency. Increasing Buffer Size Buffer size also affects read performance. By default, the buffer size is 300 MB. But in high-throughput training scenarios, this may not be enough. You can adjust the buffer size according to the memory resources of the training node. Generally speaking, the larger the buffer size, the better the read performance. But do not set a value too large, especially in container environments where the maximum memory is limited. It’s necessary to set the buffer size based on the actual workload and find a relatively reasonable value. You can use the juicefs stats command introduced earlier in the article to monitor the buffer usage in real-time.

Last week, I decided to see the capabilities of OpenAI's image generation. However, I noticed that one has to pay to use the web interface, while the API was free, even though rate-limited. Dall.E offers Node.js and Python samples, but I wanted to keep learning Rust. So far, I've created a REST API. In this post, I want to describe how you can create a Web app with server-side rendering. The Context Tokio is a runtime for asynchronous programming for Rust; Axum is a web framework that leverages the former. I already used Axum for the previous REST API, so I decided to continue. A server-side rendering Web app is similar to a REST API. The only difference is that the former returns HTML pages, and the latter JSON payloads. From an architectural point of view, there's no difference; from a development one, however, it plays a huge role. There's no visual requirement in JSON, so ordering is not an issue. You get a struct; you serialize it, and you are done. You can even do it manually; it's no big deal - though a bit boring. On the other hand, HTML requires a precise ordering of the tags: if you create it manually, maintenance is going to be a nightmare. We invented templating to generate order-sensitive code with code. While templating is probably age-old, PHP was the language to popularize it. One writes regular HTML and, when necessary, adds the snippets that need to be dynamically interpreted. In the JVM world, I used JSPs and Apache Velocity, the latter, to generate RTF documents. Templating in Axum As I mentioned above, I want to continue using Axum. Axum doesn't offer any templating solution out-of-the-box, but it allows integrating any solution through its API. Here is a small sample of templating libraries that I found for Rust: handlebars-rust, based on Handlebars liquid, based on Liquid Tera, based on Jinja, as the next two askama MiniJinja etc. As a developer, however, I'm lazy by essence, and I wanted something integrated with Axum out of the box. A quick Google search lead me to axum-template, which seems pretty new but very dynamic. The library is an abstraction over handlebars, askama, and minijinja. You can use the API and change implementation whenever you want. axum-template in Short Setting up axum-template is relatively straightforward. First, we add the dependency to Cargo: Shell cargo add axum-template Then, we create an engine depending on the underlying implementation and configure Axum to use it. Here, I'm using Jinja: Rust type AppEngine = Engine<Environment<'static>>; //1 #[derive(Clone, FromRef)] struct AppState { //2 engine: AppEngine, } #[tokio::main] async fn main() { let mut jinja = Environment::new(); //3 jinja.set_source(Source::from_path("templates")); //4 let app = Router::new() .route("/", get(home)) .with_state(AppState { //5 engine: Engine::from(jinja), }); } Create a type alias. Create a dedicated structure to hold the engine state. Create a Jinja-specific environment. Configure the folder to read templates from. The path is relative to the location where you start the binary; it shouldn't be part of the src folder. I spent a nontrivial amount of time to realize it. Configure Axum to use the engine. Here are the base items: Engine is a facade over the templating library Templates are stored in a hashtable-like structure. With the MiniJinja implementation, according to the configuration above, Key is simply the filename, e.g., home.html The final S parameter has no requirement. The library will read its attributes and use them to fill the template. I won't go into the details of the template itself, as the documentation is quite good. The impl Return It has nothing to do with templating, but this mini-project allowed me to ponder the impl return type. In my previous REST project, I noticed that Axum handler functions return impl, but I didn't think about it. It's indeed pretty simple: If your function returns a type that implements MyTrait, you can write its return type as -> impl MyTrait. This can help simplify your type signatures quite a lot! - Rust By Example However, it has interesting consequences. If you return a single type, it works like a charm. However, if you return more than one, you either need a common trait across all returned types or to be explicit about it. Here's the original sample: Rust async fn call(engine: AppEngine, Form(state): Form<InitialPageState>) -> impl IntoResponse { RenderHtml(Key("home.html".to_owned()), engine, state) } If the page state needs to differentiate between success and error, we must create two dedicated structures. Rust async fn call(engine: AppEngine, Form(state): Form<InitialPageState>) -> Response { //1 let page_state = PageState::from(state); if page_state.either.is_left() { RenderHtml(Key("home.html".to_owned()), engine, page_state.either.left().unwrap()).into_response() //2 } else { RenderHtml(Key("home.html".to_owned()), engine, page_state.either.right().unwrap()).into_response() //2 } } Cannot use impl IntoResponse; need to use the explicit Response type Explicit transform the return value to Response Using the Application You can build from the source or run the Docker image, available at DockerHub. The only requirement is to provide an OpenAI authentication token via an environment variable: Shell docker run -it --rm -p 3000:3000 -e OPENAI_TOKEN=... nfrankel/rust-dalle:0.1.0 Enjoy! Conclusion This small project allowed me to discover another side of Rust: HTML templating with Axum. It's not the usual use case for Rust, but it's part of it anyway. On the Dall.E side, I was not particularly impressed with the capabilities. Perhaps I didn't manage to describe the results in the right way. I'll need to up my prompt engineering skills. In any case, I'm happy that I developed the interface, if only for fun. The complete source code for this post can be found on GitHub. To Go Further: axum-template Image generation API

Machine learning is a complex field, with one of its biggest challenges is building models that can predict outcomes for new data. Building a model that fits the training data perfectly is easy, but the real test is whether it can accurately predict outcomes for new data. This article delves into the concepts of overfitting and generalization and explores how they relate to the bias vs. variance trade-off. We will also discuss techniques for avoiding overfitting and finding the optimal balance between bias and variance in our models. Overfitting In machine learning, overfitting is a common problem that occurs when a model becomes too complex and starts to fit the training data too closely. This means that the model may not generalize well to new, unseen data because it has essentially memorized the training data instead of truly learning the underlying patterns or relationships. In technical terms, think about a regression model that requires a linear relationship, but instead is represented using a polynomial one. Overfitting happens when the model is too good at learning from the training data but not so good at generalizing to new data. This can be a particular issue with deep learning models, which have many parameters that can be adjusted to fit the training data. Underfitting Underfitting is the opposite of overfitting in machine learning. In the case of underfitting (see leftmost graph below), we're essentially referring to a situation where the model is just too simple for the task at hand. In other words, the model doesn't have the necessary complexity to capture the underlying patterns in the data. In technical terms, think about a regression model that requires a polynomial equation, but instead is represented using a linear relationship. Another way to think about underfitting is to consider the example of predicting housing prices. If we were to create a model that only takes into account the size of a house and ignores other important factors like the number of bedrooms, then this model might underfit the data. This occurs because the model is not taking into account all of the relevant information and thus is unable to accurately predict housing prices. An underfit model tends to have high bias and low variance, which means that it makes a lot of errors in both the training and testing data. This is because the model is not able to capture the relationships between the data and is, therefore, unable to make accurate predictions. Goldilocks Zone and Optimum Model Complexity The optimum model complexity is the sweet spot where the machine learning model is neither too simple nor too complex, but just right for the data it's working with. If a model is too simple, it may not capture all the important patterns and relationships in the data and can lead to underfitting. On the other hand, if the model is too complex, it may start to memorize the training data instead of learning the underlying patterns, which can lead to overfitting. The goal of finding the optimum model complexity is to strike a balance between model fit and model complexity, where the model is simple enough to generalize well to new data but complex enough to capture the important patterns in the training data. In the rest of this article, we will focus on different techniques that can be used to find the optimum model complexity, such as starting with a simple model and gradually increasing its complexity, cross-validation to evaluate the model on different subsets of the data, and using regularization techniques to prevent overfitting. But first, let's start by explaining two very important concepts in machine learning, which are bias and variance. Difference Between Bias and Variance in Machine Learning Imagine trying to create a model to predict the price of a house based on its size. We have a dataset of 100 houses with their corresponding prices and sizes. To make predictions, we decide to use a linear regression model that only takes into account the size of the house. Now, there are two problems that can occur when creating a model: bias and variance. Bias happens when the model is too simple and can't accurately capture the patterns in the data. In this case, if we use a linear model with only one feature (size), the model would likely not accurately predict the prices of the houses, leading to high bias. On the other hand, variance occurs when the model is too complex and overfits the data, meaning it fits the training data too closely but doesn't perform well on new, unseen data. In this case, if we were to use a high-order polynomial model with many features (e.g. size squared, size cubed, etc.), it could overfit the data, resulting in high variance. As shown in the above image, a high variance tends to disperse the model’s output, as the model is overly complex and fits the training data too closely. Essentially, the model captured the noise in the training data instead of the underlying patterns. While in the case of high bias, the model tends to produce a similar output for almost all input values, which is far from the true relationship between the input and output. An optimum model complexity lies in the balance between these two errors, as we will see in the trade-off section, where the model has enough flexibility to capture the underlying patterns in the data but not so much that it overfits the noise or idiosyncrasies of the training data. What Is the Bias-Variance Tradeoff? The bias-variance tradeoff refers to the balance that is needed between bias and variance to build a model that can generalize well to new data. A model that is too simple will have high bias but low variance, while a model that is too complex will have low bias but high variance. The goal is to find the right level of complexity that minimizes both bias and variance, resulting in a model that can accurately generalize to new data. To strike a balance between bias and variance, we want to find the optimal level of model complexity that allows it to accurately predict the prices of the houses while also generalizing well to new data. This can be done by creating an error vs. model complexity graph, which shows the performance of the model at different levels of complexity. By analyzing this graph, we can identify the optimal level of model complexity where the bias and variance trade-off is balanced. Plotting a graph of error versus model complexity starts by building a series of models with varying levels of complexity. For instance, if we’re creating a linear regression model, we might begin with a simple model that has just one feature and gradually include more features to make the models more complex. We would then train each model on a portion of our data and evaluate its performance on a separate test set. To measure the prediction error on the test set, we could use a metric like mean squared error (MSE) or mean absolute error (MAE). After we have trained and evaluated each model, we can plot the test error against model complexity. The resulting graph typically shows a U-shaped curve, where error decreases as model complexity increases, reducing bias. However, the error eventually starts to increase again as the model becomes too complex and starts overfitting the data, which increases variance. To determine the optimal complexity for bias and variance, look for the point on the graph where the test error is the lowest(as depicted by the dotted line in the middle of the graph). This point represents the optimal balance between bias and variance for this specific problem. Understanding Generalization in Machine Learning Generalization is the ability of a model to perform well on new data. A model that generalizes well is able to make accurate predictions on new data, which is important if we want to use the model in the real world. On the other hand, a model that doesn't generalize well may perform well on the data that it was trained on but may not make accurate predictions on new data. This is a problem because it means the model may not be useful in practice. When we train a machine learning model, we want it to be able to make accurate predictions not just on the data that we use to train it but on new data that it has never seen before. This is because, in the real world, we don't always have access to the exact same data that we used to train the model but new first-time-seen data points. Therefore, it's important to train models that not only fit the training data well but also generalize well to new data. Regularization Techniques to Address Overfitting Various regularization technique helps to prevent overfitting by adding a penalty term to the loss function, which discourages the model from becoming too complex. There are two types of regularization that are commonly used: L1(Lasso) and L2(Ridge) regularization. Lasso Regression (L1 Regularization) While all three approaches do add a penalty term to the loss function, in the case of Lasso Regression, the regularization approach adds a penalty term to the loss function that is proportional to the absolute(modules) value of the model parameters. This approach encourages the model to give less weight to unimportant features as it has the effect of driving some of the parameters to zero, which can help with feature selection. This means that it can help to identify which features are the most important and discard the rest. This can be really useful when working with high-dimensional datasets, where there are many features to choose from. Lasso Regression can be particularly useful in high-dimensional datasets where the number of independent variables is much larger than the number of samples. In these cases, Lasso Regression can help to identify the most important variables and reduce the impact of noise. Lasso Regression can be particularly useful in high-dimensional datasets where the number of independent variables is much larger than the number of samples. In these cases, Lasso Regression can help to identify the most important variables and reduce the impact of noise. Ridge Regression (L2 Regularization) Ridge Regression is another type of linear regression that can be used to deal with overfitting in machine learning models. It's similar to Lasso Regression in that it adds a penalty term(regularization term) to the loss function, but instead of using the absolute value of the coefficients like Lasso Regression, it uses the square of the coefficients. This has the effect of shrinking the coefficients of the less important variables toward zero, but unlike Lasso Regression, Ridge Regression doesn't set them exactly to zero. This means that Ridge Regression can't perform feature selection as well as Lasso Regression does, but it's better suited for cases where all the features are important to some degree. Ridge Regression is particularly useful when dealing with datasets that have a high degree of collinearity (correlation between the features). In such cases, the model may have trouble determining which features are important and which are not, leading to overfitting. By adding a penalty term to the loss function, Ridge Regression can help to reduce overfitting and make the model more accurate. Elastic Net Regression Elastic Net Regression combines the best of both worlds by using techniques from both Ridge Regression and Lasso Regression. By adding both the Ridge Regression and Lasso Regression penalty terms to the loss function, Elastic Net Regression can perform both feature selection and feature shrinkage, which makes it more flexible and powerful than either technique alone. The L1 regularization term tries to set some of the coefficients in the model to zero, which is useful for feature selection. This means it can identify the most important features that help to predict the target variable and exclude the less important features. On the other hand, the L2 regularization term helps to control the magnitude of the coefficients in the model. This is useful for feature shrinkage, which means it reduces the impact of less important features on the model's performance. Elastic Net Regression is particularly useful when working with datasets that have a large number of features and a high degree of multicollinearity, where the model may have difficulty distinguishing between important and unimportant features. By identifying and shrinking the less important features, Elastic Net Regression can help to reduce overfitting and improve the generalization performance of a model. Cross-Validation Techniques to Address OverfittingCross-validation is a technique that can be used to address overfitting by evaluating the performance of a machine-learning model on a limited amount of data. The basic idea is to split the data into training and testing sets and then fit the model on the training set and evaluate its performance on the testing set. K-Fold Cross-Validation One popular cross-validation technique is k-fold cross-validation, where the data is divided into k equally sized parts. The model is trained on k-1 of the parts and tested on the remaining part. This process is repeated k times, with each part being used for testing once, and the performance is averaged across all iterations. Leave-One-Out Cross-Validation Leave-one-out cross-validation is another technique where a single data point is left out as the testing set, and the model is trained on the remaining data points. Note that the leave-one-out cross-validation is a special case of k-fold cross-validation, where k is equal to the number of data points in the dataset. For each iteration, a single data point is left out as the testing set, and the model is trained on the remaining data points. This process is repeated for each data point, and the average performance is computed. How to Choose the Right Model Complexity Choosing the right complexity for a machine learning model is crucial to its performance. A model that's too simple won't capture the data's complexity and will underfit, while a model that's too complex will overfit the data and won't perform well on new data. To choose the optimal model complexity, start with a simple model and gradually increase complexity until you get satisfactory results. Split the data into training, validation, and testing sets, and use the validation set to select the best model complexity. Use cross-validation to evaluate the model's performance on different data subsets. Finally, use regularization techniques like L1, L2, and Elastic Net to prevent overfitting. The key is to balance model fit and complexity, evaluate performance, and prevent overfitting, so the model can generalize well to new data.

When Celina John finalized her college project “Spam Classification through Machine Learning Algorithms,” we didn’t expect the scope of the work to be so huge since the domain has been constantly evolving and we had to firm up the scope and key deliverables. As technology grows, spam is growing exponentially in all electronic communication channels, be it email, short message service, or social media. No matter how unethical and illegal, the number of spammers is increasing day by day, and sending unsolicited, unwanted, malicious messages sent in bulk to a large number of recipients. We realized that it’s not about spam or not spam anymore. This article briefs about the impact of spam and how it can be addressed with emerging machine-learning technology based on our journey in this domain. Why Should We Bother About Spam? The impact of spam is literally in every area such as finance, security, healthcare, advertisements, business, and so on. Spam emails are beyond junk mail that create an impact when we inadvertently deal with it. The intent of spam has gone beyond a business opportunity. These days spam can be anything that tries to steal valuable information, money, and credibility too. Financial Impact Nowadays money theft has become a serious issue, different scams are devised by spammers to expropriate credentials and all valuable assets without the knowledge of the user. This further leads to another chain of causes described below. Impact on Security Security is much more than simply protecting your important credentials; it is also about protecting one’s privacy and valuable possessions. Spammers attempt to imitate and resemble authorized entities to infiltrate your computer. The attacker constantly changes his invasion techniques to deceive potential users. Psychological Impact Receiving too many spam messages requesting sensitive or important information can cause stress and depression. It is important to highlight that this harms a person's physical health. Spam with pornographic content is another significant side effect. Another significant problem that threatens people's societal reputations is this one. Machine Learning Approaches and Algorithms Traditionally, supervised learning was considered in identifying spam messages. However, as the technology grows, the complexity of classifying spam is increasing rapidly. Hence, it is important to understand different learning from the aspects of spam. Supervised Machine Learning Algorithm In supervised learning, the machine learns under supervision. It contains a model that can predict with the help of a labeled dataset. A labeled dataset is one where you already know the target answer. To make it more simple, supervised learning is more like a student learning new things with the help of a teacher, where a student gets more inference about the domain with the supervision of a teacher. This is an effective method, where you don’t get lost as you know the expert is with you. In summary, supervised learning is training the machine with previously known information. For spam classification, we can use different machine learning models to classify the mail as spam and ham. Logistic Regression Logistic regression is a machine learning model which is used to categorize or classify the data, also called a discriminative model. This model is a type of statistical model that is used to estimate the occurrence of an event. You can use these to classify the emails as spam and non-spam based on the spamicity (a measure of how likely a page/message is spam) of the words. This is mostly used for binary classification. Even though the model classifies the data properly, there's a chance that the model may be overfitting. Naïve Bayes The most often utilized machine learning model is utilized for both multinomial and binary classification. This model predicts based on the likelihood that a word will be spam or classified based on probability. Why is this referred to as naïve if it is based on the Bayes theorem? This is so because a variable or attribute in the model is supposed to be independent of every other variable. This concept is also known as class conditional independence. Consider that you enjoy both cupcakes and chili oil ramen to better appreciate why the model is so naïve. Mr. Naïve will tell you that you like chili oil ramen cupcakes when you serve this to your model. It just sounds strange, doesn't it? This is the issue, and when you are knowledgeable about your field, you can also resolve it. Naïve Bayes is utilized primarily for classification-based problems. Support Vector Machine This is one of the most often used machine learning models, both for classification and regression. The model, as the name suggests, attempts to draw a line or a boundary in the data using the points that support the class labels, such as the points that help determine the spamicity of the emails in the spam classification in n-dimensional data. The support vectors are these points. A hyperplane is a term used to describe the decision boundary that the model generates. The data can be viewed in n dimensions using this model. To simplify things even further, the model looks at the data from all directions, taking in every nook and cranny. Unsupervised Machine Learning Algorithms Unsupervised learning is the training of a machine using information that is neither classified nor labeled and allowing the algorithm to act on that information without guidance. Unsupervised learning helps in grouping unsorted information according to similarities, patterns, and differences without any prior training of data. Clustering These algorithms group similar data points based on some similarity metric, such as distance or density. We can apply clustering to group similar emails together based on their content and other features such as sender, subject, and attachments. Spam emails often have similar characteristics such as keywords, URLs, and email addresses. By clustering these emails together, we can identify groups of messages that are likely to be spam. Topic Modeling Topic modeling algorithms identify common themes and topics in the emails. Spam emails often have similar themes such as promotions, scams, or phishing attempts. By identifying these topics, we can flag emails that are likely to be spam. Rule-Based Systems Rule-based systems use a set of predefined rules to identify spam. These rules can be based on known patterns or characteristics of spam emails such as certain keywords or phrases, specific email addresses or domains, or other attributes. Rule-based systems can be effective, but they require frequent updates to keep up with evolving spam tactics. Anomaly Detection Anomaly detection algorithms identify unusual patterns in the data that do not fit with the normal pattern. Spam emails often have features that are unusual or abnormal, such as a high number of links, unusual formatting, or a mismatch between the sender and subject line. Anomaly detection algorithms can flag emails with these features as potential spam. We also can combine the power of multiple algorithms such as rule-based anomaly detection to classify spam effectively. Reinforcement Learning Algorithms Reinforcement learning is a feedback-based machine learning technique in which an agent learns to behave in an environment by performing the actions and seeing the results of actions. For example, if the email is correctly classified as spam, the agent receives a reward of +1 and if the email is incorrectly classified as spam, the agent receives a penalty of -1. Markov Decision Process While the Markov Decision Process is primarily used for optimization problems, a spam filter agent can be modeled as a Markov Decision Process (MDP), where the states represent the content/body of an email, and the actions represent whether to classify the email as spam or not spam. The agent receives a reward for each correctly classified email (spam as spam and spam as not spam) and a penalty for each incorrectly classified email (spam as not spam and not spam as spam). The objective of the agent is to maximize the cumulative reward over time. Q-Learning Finally, how about a model-free algorithm? Q-learning comes as a rescue since it learns an optimal action-value function that maps states to actions. It does not require a model of the environment, and it can handle problems with stochastic transitions and rewards without requiring adaptations. Since spam may not be able to fit into a model, Q-learning may be one of the suitable algorithms for effective classification. To train the spam filter agent using Q-learning, we can define the state, action, and reward function as below. The state space can include features such as the email subject, sender, recipient, and message content. The action space includes two possible actions: classify the email as spam or not spam. The reward function can be defined as mentioned earlier, as rewarding for correct classification and penalizing for incorrect classification). Conclusion Combining the power of machine learning algorithms with rule engines will provide a recommendation to end users on how to identify spam messages and can ignore or take actions appropriately. Since the challenges associated with spam are constantly evolving, we must take advantage of reinforcement learning. Technology for that matter any good thing always comes with a caveat. We need to be careful about certain aspects while applying machine learning algorithms to spam. The following lists a few of them: Privacy and confidentiality: At all levels of machine learning algorithm application, we need to ensure the confidentiality and privacy of the data are maintained. The consent of the user should be acquired as applicable. Transparency: The algorithm should be transparent and explainable to the end users so that the users can understand how it works, how it makes decisions, and what data it uses while maintaining privacy and confidentiality. This is one main reason why we need to rely on explicable models more.

You’ve probably heard that Generative AI has demonstrated the potential to disrupt a huge number of industries and jobs—and web3 is no exception. But how well can ChatGPT create smart contracts? Can using ChatGPT to code smart contracts make you a 10x developer? In this article, we’ll try it out and see. We’ll walk through writing and deploying an Ethereum smart contract using MetaMask, Infura, and Truffle - and we will ask ChatGPT for instructions on how to do everything from the code to the deployment. The goal is to see if we can break down the task of creating a smart contract into a set of simple prompts. Ideally, ChatGPT can parse those prompts and give good (and accurate) answers to them, making us a faster, and better, developer. Let’s see what we can do! A Note on Accuracy ChatGPT is a Large Language Model (LLM) extremely proficient in explaining blockchain concepts, providing advice, and even generating code snippets related to web3 development. However, keep in mind that some of the code or answers it generates may be partially or completely incorrect. Access to ChatGPT does not remove the need to know the basics of blockchain development. However, even a beginner can use artificial intelligence to dramatically increase productivity. The second point to note is that the free, public version of ChatGPT isn’t very aware of events, updates, and practices that have emerged after 2021. This can be an issue in a field such as web3 development, where everything is improving and changing at a breakneck pace. However, it is possible to get good answers if something that you’re asking is fundamental and has been stable over the years (for instance, Truffle and Solidity). Create an Escrow Contract With Truffle and ChatGPT To see what ChatGPT can do, we’re going to ask it to create an escrow smart contract that allows a buyer and a seller to exchange money, and we’ll have ChatGPT establish an arbiter to resolve disputes. Let’s see what Generative AI can do! Step 1: Install MetaMask First, we need to set up a wallet. We’ll use the MetaMask wallet—the most popular Ethereum option—and add the Sepolia test network. MetaMask is secure and easy to use. You can download the MetaMask extension for your browser here. Then, just install and set up the wallet. You’ll be given a “secret phrase”—keep this private and safe! Never store it anywhere public. Note: If you need background or a primer on web3, check out this previous article I wrote on learning web3/smart contracts/Solidity. Next, click on the “Network” tab in the top-right. Click on the option to show test networks. You should now see the Sepolia test network in the dropdown. Select that. Step 2: Get SepoliaETH To deploy and interact with a smart contract, you need ETH. Since we are using the Sepolia test network, we’ll need to get some Sepolia test ETH. Don’t worry—it’s free! You can obtain free SepoliaETH from Infura’s recently released faucet here. This faucet gives up to 0.5 SepoliaETH each day for free. Step 3: Install Node and NPM In order to build an escrow system using Truffle, we will need Node and NPM. To check to see if it’s installed, run: $ node -v You should see the Node version. Step 4: Sign Up for Infura Infura is an RPC provider. They provide easy API-based access to everything we need to connect to—and work with—the Ethereum blockchain (and quite a few others). Sign up for a free account. Then go to the dashboard and select Create New Key. Choose Web3 API as the network and name the new key Escrow (though you can name it whatever you want). Click Create, and Infura will generate an API key. This key is what you need to access your RPC endpoint. For our purposes, we just want the Sepolia endpoint (https://sepolia.infura.io/v3/<your API key>). Step 5: Create the Node Project Next, we need to create our Node project and then complete our setup with any necessary packages. $ mkdir escrow && cd escrow $ npm init -y To deploy and test our contract, we’ll use Truffle. Truffle is a suite of tools that give you everything you need to develop, test, and deploy smart contracts. Let’s install it: $ npm install —save truffle You can create a project skeleton using Truffle by running: $ npx truffle init To check that everything ran correctly: $ npx truffle test We now have Truffle successfully configured. Let’s next install the OpenZeppelin contracts package. This package will give us access to a few more helpful functionalities that we may require. $ npm install @openzeppelin/contracts We’re almost done with the setup! Next, we have to allow MetaMask and Truffle to work together. We need Truffle to be able to sign transactions, pay gas, etc. For this, we’ll use hdwalletprovider. Install it: $ npm install @truffle/hdwallet-provider And finally, let’s use dotenv to keep our wallet information secure. $ npm install dotenv Step 6: Sign Up for ChatGPT We won’t be writing the escrow smart contract code from scratch. Instead, we’ll see what ChatGPT can do. In order to use ChatGPT, sign up for a free account with OpenAI. Once you’ve signed up for an account, visit this page. You should see an interface that looks something like this: Step 7: Create the Escrow Contract Next, we’ll have ChatGPT create an extremely simple escrow contract that allows a buyer and a seller to exchange a certain amount of money, and an arbiter to resolve disputes. Let’s use the following prompt in ChatGPT. As an expert smart contract developer, create a smart contract using Solidity that implements an escrow. There will be three parties: the buyer, the seller, and the arbiter. The constructor will set the buyer, seller, and arbiter. It will also set the value which will be equal to the amount sent to the constructor. There will be two functions. The first function allows the buyer or the arbiter to release funds to the seller. The second function allows the buyer or the arbiter to refund the amount back to the buyer. Implement any other functions that you may think are relevant. Note the “As an expert smart contract developer” beginning of the prompt. This type of “role-playing” with ChatGPT works very well. Now ChatGPT will generate a unique response for you. And this response will change every time. So you may not get the same output that is shown below, but it should be something similar. Sure, here's an example implementation of an escrow smart contract in Solidity: ``` pragma solidity ^0.8.0; contract Escrow { address payable public buyer; address payable public seller; address public arbiter; uint public value; bool public released; bool public refunded; constructor(address payable _buyer, address payable _seller, address _arbiter) { buyer = _buyer; seller = _seller; arbiter = _arbiter; value = msg.value; } function release() public { require(!released, "Funds have already been released."); require(msg.sender == buyer || msg.sender == arbiter, "Only buyer or arbiter can release funds."); released = true; seller.transfer(value); } function refund() public { require(!refunded, "Funds have already been refunded."); require(msg.sender == buyer || msg.sender == arbiter, "Only buyer or arbiter can refund funds."); refunded = true; buyer.transfer(value); } function getBalance() public view returns (uint) { return address(this).balance; } function setArbiter(address _arbiter) public { require(msg.sender == arbiter, "Only arbiter can set arbiter."); arbiter = _arbiter; } function setSeller(address payable _seller) public { require(msg.sender == seller, "Only seller can set seller."); seller = _seller; } } ``` The `Escrow` contract has a constructor that takes three arguments: the addresses of the buyer, seller, and arbiter, and sets the `value` equal to the amount sent to the constructor. The `release()` function allows the buyer or the arbiter to release the funds to the seller. It first checks that the funds haven't already been released, and that the caller is either the buyer or the arbiter. If both conditions are met, the `released` variable is set to `true`, and the `value` is transferred to the seller's address. The `refund()` function allows the buyer or the arbiter to refund the funds back to the buyer. It first checks that the funds haven't already been refunded, and that the caller is either the buyer or the arbiter. If both conditions are met, the `refunded` variable is set to `true`, and the `value` is transferred back to the buyer's address. The `getBalance()` function returns the balance of the contract. The `setArbiter()` function allows the arbiter to set a new arbiter. The `setSeller()` function allows the seller to set a new seller. Note that this is a very basic implementation of an escrow contract and should not be used in production without thorough testing and auditing. Also, there may be additional features that you may want to add to suit your specific use case. Upon inspection, the snippet provided by ChatGPT is pretty good! But it does have an error—the constructor is not marked payable even though we’re accessing msg.value and expecting Ether to be sent to it. Again, an important note: Never expect ChatGPT to give you code that is 100% correct. More often than not, you will have to make small corrections like we did here. Also, notice that ChatGPT uses an earlier version of Solidity, and it uses a practice of fund transfer that is not considered best practice anymore. For the purpose of this tutorial, we’ll just let it be. In the contracts folder of your project, create a new file called Escrow.sol and add the code provided by ChatGPT with the corrections. // SPDX-License-Identifier: MIT pragma solidity ^0.8.0; contract Escrow { address payable public buyer; address payable public seller; address public arbiter; uint public value; bool public released; bool public refunded; constructor(address payable _buyer, address payable _seller, address _arbiter) payable { buyer = _buyer; seller = _seller; arbiter = _arbiter; value = msg.value; } function release() public { require(!released, "Funds have already been released."); require(msg.sender == buyer || msg.sender == arbiter, "Only buyer or arbiter can release funds."); released = true; seller.transfer(value); } function refund() public { require(!refunded, "Funds have already been refunded."); require(msg.sender == buyer || msg.sender == arbiter, "Only buyer or arbiter can refund funds."); refunded = true; buyer.transfer(value); } function getBalance() public view returns (uint) { return address(this).balance; } function setArbiter(address _arbiter) public { require(msg.sender == arbiter, "Only arbiter can set arbiter."); arbiter = _arbiter; } function setSeller(address payable _seller) public { require(msg.sender == seller, "Only seller can set seller."); seller = _seller; } } Make sure the contract is compiling correctly by running: $ npx truffle compile Step 8: Update Your Truffle Config Next, we’ll do a little configuring without ChatGPT. This is a standard setup and will depend on the environment we’re using. Create a new .env file in your project’s root directory and add the following: INFURA_API_KEY = "https://sepolia.infura.io/v3/<insert API key here>" MNEMONIC = "<insert metamask secret phrase here>" Now let’s configure the Infura endpoint. In truffle.config.js replace the existing code with this standard config: require('dotenv').config(); const HDWalletProvider = require('@truffle/hdwallet-provider'); const {INFURA_API_KEY, MNEMONIC} = process.env; module.exports = { networks: { development: { host: "127.0.0.1", port: 8545, network_id: "*" }, sepolia: { provider: () => new HDWalletProvider(MNEMONIC, INFURA_API_KEY), network_id: '11155111', } } }; Step 9: Deploy the Contract Now it’s time to deploy! Instead of giving you the script to deploy our contract using Truffle, let’s ask ChatGPT for the relevant instructions. In the same window that you used to ask for the escrow contract code, we’ll enter the following prompt: Give me a migration script that deploys the Escrow contract using Truffle. I need to send 0.001 ETH to the constructor during deployment. And I need the address that deploys the contract to be the buyer. We get this response: Sure, here's an example migration script that deploys the `Escrow` contract and sends 0.001 ETH to the constructor during deployment. In this script, the address that deploys the contract will be set as the buyer. 1. Create a new JavaScript file named `2_deploy_escrow.js` (the number at the beginning indicates the order in which the migrations will be executed). 2. Copy the following code into `2_deploy_escrow.js`: ``` const Escrow = artifacts.require("Escrow"); module.exports = async function(deployer, network, accounts) { const buyer = accounts[0]; // set the buyer as the address that deploys the contract const seller = "0x2222222222222222222222222222222222222222"; // replace with seller address const arbiter = "0x3333333333333333333333333333333333333333"; // replace with arbiter address const value = web3.utils.toWei('0.001', 'ether'); // change to the value you want to send await deployer.deploy(Escrow, seller, arbiter, { from: buyer, value: value }); }; ``` 3. Replace the seller and arbiter addresses with the actual Ethereum addresses of the parties involved in the escrow transaction. 4. Save the file. 5. Make sure you have a network configured in your `truffle-config.js` file. If you want to deploy to a testnet, you'll need to configure your Truffle project to use the network you want to deploy to. 6. Run `truffle migrate --network <network-name>` to deploy the contract to the specified network. Replace `<network-name>` with the name of the network you want to deploy to, e.g. `ropsten` for the Ropsten testnet. This will deploy the contract and send 0.001 ETH to the constructor with the address that deploys the contract set as the buyer. Once again, the instructions provided by ChatGPT are mostly correct. It gets the naming wrong (we want it to be prefixed with 1_ and not 2_) and makes a minor mistake in setting the arguments of the deploy function. Let’s create a new 1_deploy_contract.js file in the migrations folder. Add the following, slightly modified code. const Escrow = artifacts.require("Escrow"); module.exports = async function (deployer, network, accounts) { const buyer = accounts[0]; // set the buyer as the address that deploys the contract const seller = accounts[1]; // replace with seller address const arbiter = accounts[2]; // replace with arbiter address const value = web3.utils.toWei('0.001', 'ether'); // change to the value you want to send await deployer.deploy(Escrow, seller, arbiter, { from: buyer, value: value }); }; And finally, let’s deploy our contract: $ truffle migrate --network sepolia You should see something similar to this: Compiling your contracts... =========================== > Everything is up to date, there is nothing to compile. Migrations dry-run (simulation) =============================== > Network name: 'sepolia-fork' > Network id: 11155111 > Block gas limit: 30000000 (0x1c9c380) 1_deploy_contract.js ==================== Deploying 'Escrow' ------------------ > block number: 3400252 > block timestamp: 1682974543 > account: 0xc361Fc33b99F88612257ac8cC2d852A5CEe0E217 > balance: 0.506876109994053108 > gas used: 849556 (0xcf694) > gas price: 2.500000007 gwei > value sent: 0.001 ETH > total cost: 0.003123890005946892 ETH ------------------------------------- > Total cost: 0.003123890005946892 ETH Summary ======= > Total deployments: 1 > Final cost: 0.003123890005946892 ETH Starting migrations... ====================== > Network name: 'sepolia' > Network id: 11155111 > Block gas limit: 30000000 (0x1c9c380) 1_deploy_contract.js ==================== Deploying 'Escrow' ------------------ > transaction hash: 0x7a7804d92a6b6e805991eeb8249d79a2b8c5da43cffe633a31f987e9fe596654 > Blocks: 1 Seconds: 33 > contract address: 0x64ccE52898F5d61380D2Ec8C02F2EF16F28436de > block number: 3400258 > block timestamp: 1682974584 > account: 0xc361Fc33b99F88612257ac8cC2d852A5CEe0E217 > balance: 0.506876109994053108 > gas used: 849556 (0xcf694) > gas price: 2.500000007 gwei > value sent: 0.001 ETH > total cost: 0.003123890005946892 ETH > Saving artifacts ------------------------------------- > Total cost: 0.003123890005946892 ETH Summary ======= > Total deployments: 1 > Final cost: 0.003123890005946892 ETH To validate deployment, you can find your contract’s address on Sepolia Etherscan: Congratulations! You’ve deployed your contract. And you did this using ChatGPT to generate more than 95% of your code. Tips and Tricks Here are a few tips and tricks for using ChatGPT when coding: ChatGPT has limited knowledge and has a knowledge-base cutoff of September 2021. So some of those best practices and the latest developments in smart contracts are going to be missing from its knowledge. There are several versions of ChatGPT. GPT-4 tends to give better answers but is a paid service. GPT-3.5 (which we used here) is free, and does pretty well. Make your questions as specific as possible. Don’t stop with writing code. ChatGTP can debug your code, audit it, document it, make it pretty, analyze it, find vulnerabilities, and more. Conclusion ChatGPT was an extremely useful co-developer. But it wasn’t perfect. I wouldn’t use it to create unmodified code that I pushed to mainnet—especially if I was a new developer. But as a coding partner, and as a place to start, it was helpful. It saved me significant time and created surprisingly good code. In the hands of an experienced developer, I believe it can increase productivity by many times. I plan on using it quite heavily on future projects, alongside MetaMask, Infura, and Truffle. Have a really great day!

As AI technology continues to evolve, it is creating a vast array of job opportunities that were once unheard of. While traditional AI roles such as data scientists, machine learning engineers, and AI researchers are widely recognized, there are several lesser-known careers that are equally important in driving change in the tech industry. In this article, we will explore 21 hidden careers in the AI revolution. These hidden careers in the AI revolution are becoming increasingly vital as AI technology becomes more pervasive in various industries. From AI psychologists who understand the human impact of AI to AI knowledge engineers who ensure AI systems have the necessary information to perform their tasks, these roles are critical to creating AI systems that are effective and ethical. Moreover, the rise of AI in the industry has created a need for AI professionals who can develop and implement AI tools and solutions tailored to this niche market. This highlights the versatility of AI professionals and the diverse range of applications for this technology. Here are 21 hidden careers in the AI revolution that are driving change in the tech industry. AI Ethicist: An AI Ethicist is a professional who specializes in the ethical considerations and implications of Artificial Intelligence (AI). They work to identify and address potential ethical issues that may arise from the development and deployment of AI technologies. This includes topics such as bias, privacy, safety, and transparency. AI Ethicists collaborate with various stakeholders, including developers, policymakers, and end-users, to ensure that AI systems are developed and used in a responsible and ethical manner. Their work is essential in shaping the future of AI and ensuring that it aligns with the values and principles of society. AI Psychologist: An AI Psychologist is a professional who specializes in the intersection of artificial intelligence and human psychology. They work to develop and implement AI technologies that can enhance mental health and well-being, as well as improve the diagnosis and treatment of psychological disorders. AI Psychologists use machine learning algorithms and natural language processing to analyze large amounts of data and provide personalized interventions and recommendations to individuals. They collaborate with other mental health professionals to ensure that AI technologies are used in an ethical and responsible manner and that they align with the values and principles of the mental health profession. AI Business Strategist: In AI Business, my role is to help organizations leverage artificial intelligence to improve their operations and gain a competitive advantage. This involves identifying areas where AI can be applied, developing AI strategies, and overseeing the implementation of AI solutions. One of the key benefits of AI is its ability to automate repetitive tasks and analyze large amounts of data quickly and accurately. This can lead to increased efficiency, improved decision-making, and cost savings for businesses. However, implementing AI solutions also requires careful consideration of ethical and privacy concerns, as well as ensuring that employees are trained to work alongside AI systems. As an AI business strategist, I work to ensure that these issues are addressed and that AI is used in a responsible and effective way. AI Language Specialist: As an AI language specialist, my focus is on developing natural language processing (NLP) technologies that enable machines to understand and generate human language. This involves working with large datasets and using machine learning algorithms to teach machines to recognize patterns and understand language structures. NLP technologies have many practical applications, from chatbots and virtual assistants that can understand and respond to human queries to sentiment analysis tools that can analyze customer feedback and identify areas for improvement. One of the biggest challenges in NLP is developing systems that can understand the nuances of human language, such as sarcasm, irony, and metaphor. As an AI language specialist, I work to develop systems that can accurately interpret these nuances and generate appropriate responses. Overall, my goal as an AI language specialist is to create technologies that enable machines to communicate with humans in a natural and intuitive way, ultimately making our interactions with technology more seamless and productive. AI Trainer: An AI Trainer is a professional who specializes in training and developing artificial intelligence systems. They work to design and implement machine learning algorithms that can learn and adapt to new data over time. AI Trainers also work to identify and address potential biases or inaccuracies in AI models, ensuring that they are accurate, reliable, and effective. They collaborate with other professionals, such as data scientists and software engineers, to develop and test AI systems across various industries, including healthcare, finance, and transportation. Their work is essential in shaping the future of AI and ensuring that it aligns with the needs and expectations of society. AI Translator: An AI Translator is a professional who specializes in developing and implementing artificial intelligence technologies to facilitate communication between people who speak different languages. They work to design and implement machine learning algorithms that can accurately translate speech and text in real time. AI Translators use natural language processing and other advanced techniques to identify nuances in language and ensure that translations are accurate and culturally appropriate. They collaborate with other professionals, such as linguists and software engineers, to develop and test AI translation systems across various industries, including travel, hospitality, and international business. Their work is essential in breaking down language barriers and facilitating global communication. AI Infrastructure Architect: An AI Infrastructure Architect is a professional who specializes in designing and implementing the technological infrastructure that supports AI systems. They work to develop and optimize the hardware and software components that are required for running large-scale AI applications. AI Infrastructure Architects also work to ensure that AI systems are secure, reliable, and scalable and that they can handle large amounts of data in real time. They collaborate with other professionals, such as data scientists and software engineers, to develop and implement AI systems across various industries, including healthcare, finance, and transportation. Their work is essential in enabling the growth and advancement of AI technology. AI DevOps Engineer: An AI DevOps Engineer is a professional who specializes in the development and deployment of artificial intelligence systems. They work to streamline the process of building, testing, and deploying AI applications, ensuring that they are efficient, reliable, and scalable. AI DevOps Engineers also work to identify and address potential issues with AI systems, such as performance bottlenecks and security vulnerabilities. They collaborate with other professionals, such as data scientists and software engineers, to develop and implement AI systems across various industries, including healthcare, finance, and transportation. Their work is essential in ensuring that AI systems are optimized for performance and reliability. AI Data Analyst: An AI Data Analyst is a professional who specializes in analyzing and interpreting large amounts of data using artificial intelligence technologies. They work to develop and implement machine learning algorithms that can identify patterns and insights in complex data sets. AI Data Analysts also work to ensure the accuracy and validity of data used in AI systems and to optimize data processing and storage methods. They collaborate with other professionals, such as data scientists and software engineers, to develop and implement AI systems across various industries, including healthcare, finance, and transportation. Their work is essential in enabling data-driven decision-making and unlocking the potential of AI technology. AI User Experience (UX) Designer: An AI User Experience (UX) Designer is a professional who specializes in designing user interfaces and experiences for artificial intelligence systems. They work to create intuitive and user-friendly interfaces that enable users to interact with AI systems in a natural and intuitive way. AI UX Designers also work to optimize the performance and usability of AI systems, ensuring that they are accessible to users of all skill levels. They collaborate with other professionals, such as data scientists and software engineers, to develop and implement AI systems across various industries, including healthcare, finance, and transportation. Their work is essential in creating engaging and effective AI applications that can be easily adopted by users. AI Product Manager: An AI Product Manager is a professional who specializes in managing the development and launch of artificial intelligence products. They work to identify market opportunities for AI products and define product requirements based on customer needs and market trends. AI Product Managers also work to ensure the successful launch and adoption of AI products by developing marketing and sales strategies, managing product pricing and distribution, and providing customer support. They collaborate with other professionals, such as data scientists and software engineers, to develop and implement AI systems across various industries, including healthcare, finance, and transportation. Their work is essential in enabling businesses to leverage the potential of AI technology and stay competitive in the market. AI Security Specialist: An AI Security Specialist is a professional who specializes in ensuring the security and privacy of artificial intelligence systems. They work to identify potential security risks in AI systems and implement measures to protect them from cyber-attacks and other threats. AI Security Specialists also work to ensure the confidentiality of sensitive data used in AI systems, such as personal information and financial data. They collaborate with other professionals, such as data scientists and software engineers, to develop and implement AI systems across various industries, including healthcare, finance, and transportation. Their work is essential in ensuring the safety and reliability of AI technology and preventing potential security breaches. AI Data Steward: An AI Data Steward is a professional who specializes in managing the quality, consistency, and security of data used in artificial intelligence systems. They work to ensure that data used in AI systems is accurate, complete, and up-to-date, and they implement measures to protect it from cyber threats and unauthorized access. AI Data Stewards also work to improve data governance practices, such as data lineage and data classification, to support the ethical and responsible use of AI systems. They collaborate with other professionals, such as data scientists and software engineers, to develop and implement AI systems across various industries, including healthcare, finance, and transportation. Their work is essential in enabling businesses to leverage the potential of AI technology while ensuring the quality and integrity of the data used to power AI systems. AI Human Enhancement Specialist: An AI Human Enhancement Specialist is a professional who specializes in using AI technology to enhance human capabilities, such as memory, vision, and decision-making. They work to develop and implement AI-powered tools and devices that can augment human abilities, such as brain-computer interfaces and neuroprosthetics. AI Human Enhancement Specialists also collaborate with healthcare professionals to develop AI-powered treatments for conditions such as paralysis, stroke, and Alzheimer's disease. They are experts in both AI technology and human physiology, and their work is essential in unlocking the full potential of AI technology in improving human health and performance. AI System Architect: An AI System Architect is a professional who designs and implements complex AI systems that integrate multiple technologies and data sources. They work to develop a comprehensive understanding of the business needs and objectives and then create a blueprint for the AI system architecture that will meet those needs. AI System Architects also work to identify and address potential technical challenges, such as data integration and security, and ensure that the AI system is scalable and adaptable to future changes in the business environment. They collaborate with other professionals, such as data scientists and software engineers, to implement and maintain the AI system, and their work is critical in enabling businesses to leverage the full potential of AI technology for competitive advantage. AI Process Engineer: An AI Process Engineer is a professional who specializes in using AI technology to optimize business processes and workflows. They work to identify opportunities for automation and streamlining of processes and then use AI tools and techniques to design and implement more efficient and effective processes. AI Process Engineers also monitor and analyze process data to identify areas for improvement and make data-driven recommendations to enhance performance. They collaborate with business stakeholders, such as operations managers and IT professionals, to ensure that the AI-powered processes are aligned with business objectives and requirements. Their work is essential in driving organizational efficiency and agility through the application of AI technology. AI Entertainment Producer: An AI entertainment producer utilizes AI tools and techniques to create and produce various forms of adult entertainment, such as Images, movies, TV shows, and video games. They can employ AI to generate scripts, characters, or music or to enhance the overall production process by automating tasks like editing and post-production. AI entertainment producers can help to streamline production timelines, reduce costs, and create new and innovative forms of entertainment that leverage the latest AI technology. AI Model Evaluator: An AI Model Evaluator is a professional who specializes in assessing the performance and reliability of AI models. They work to ensure that AI models are accurate, unbiased, and effective in solving specific business problems. AI Model Evaluators use a variety of tools and techniques to test the AI model's performance against relevant data sets and real-world scenarios. They also work to identify and mitigate potential risks associated with the use of AI, such as privacy violations or unintended consequences. AI Model Evaluators collaborate with data scientists and other stakeholders to make data-driven decisions about the design and implementation of AI models, and their work is critical in ensuring the responsible and ethical use of AI technology. AI Knowledge Engineer: An AI Knowledge Engineer is a professional who specializes in creating and managing the knowledge base for AI systems. They work to ensure that AI systems have access to relevant and accurate data and information, which is necessary for them to function effectively. AI Knowledge Engineers use a variety of tools and techniques to build, organize, and maintain the knowledge base, such as natural language processing, machine learning, and semantic web technologies. They collaborate with other AI professionals, such as data scientists and software developers, to design and implement AI systems that can effectively process and apply knowledge to real-world problems. The work of AI Knowledge Engineers is critical in ensuring the success of AI systems in various industries, from healthcare to finance to retail. AI Domain Expert: An AI Domain Expert is a professional who has deep knowledge and expertise in a specific domain or industry, such as healthcare, finance, or transportation. They work with AI teams to provide domain-specific insights and guidance, helping to ensure that AI systems are designed and implemented to meet the unique needs and challenges of their industry. AI Domain Experts work closely with AI developers, data scientists, and other professionals to develop and test AI models, ensuring that they are accurate, reliable, and aligned with industry best practices. They also play a key role in communicating the potential benefits and limitations of AI systems to stakeholders, such as policymakers, regulators, and customers. The work of AI Domain Experts is essential in enabling the successful deployment and adoption of AI technology in various industries. AI Solution Architect: An AI Solution Architect is a professional responsible for designing and implementing complex AI systems that meet the needs of their clients. They work with clients to understand their business requirements and design AI solutions that are efficient, scalable, and cost-effective. AI Solution Architects use their expertise in AI technologies, software engineering, and system integration to design and develop AI systems that can process and analyze large amounts of data, make accurate predictions, and automate tasks. They also ensure that AI systems are secure, reliable, and compliant with regulatory requirements. The work of AI Solution Architects is critical in enabling organizations to leverage the full potential of AI technology and gain a competitive edge in their respective industries. AI Education Specialist: An AI Education Specialist is an expert in developing and implementing AI-based educational programs and curricula. They design and create learning materials that incorporate AI technologies, such as machine learning and natural language processing, to enhance the learning experience. AI Education Specialists work with educators and institutions to help them integrate AI into their teaching methods and courses, and they provide training and support to ensure that educators have the skills and knowledge necessary to use AI effectively. They also conduct research on the impact of AI on education and work to develop new educational models that leverage AI to improve learning outcomes. The role of AI Education Specialists is becoming increasingly important as AI continues to transform the education industry and create new opportunities for students and educators alike. These are just a few examples of the hidden careers in the AI revolution that are driving change in the tech industry. As AI technology continues to advance, it is likely that new roles will emerge to support it. If you're interested in pursuing a career in the AI industry, it's worth exploring these lesser-known roles to find the perfect fit for your skills and interests.

In recent years, large language models (LLMs) like GPT-4 have gained significant attention due to their incredible capabilities in natural language understanding and generation. However, to tailor an LLM to specific tasks or domains, custom training is necessary. This article offers a detailed, step-by-step guide on custom training LLMs, complete with code samples and examples. Prerequisites Before diving in, ensure you have: Familiarity with Python and PyTorch. Access to a pre-trained GPT-4 model. Adequate computational resources (GPUs or TPUs). A dataset in a specific domain or task for fine-tuning. Step 1: Prepare Your Dataset To fine-tune the LLM, you'll need a dataset that aligns with your target domain or task. Data preparation involves: 1.1 Collecting or Creating a Dataset Ensure your dataset is large enough to cover the variations in your domain or task. The dataset can be in the form of raw text or structured data, depending on your needs. 1.2 Preprocessing and Tokenization Clean the dataset, removing irrelevant information and normalizing the text. Tokenize the text using the GPT-4 tokenizer to convert it into input tokens. Python from transformers import GPT4Tokenizer tokenizer = GPT4Tokenizer.from_pretrained("gpt-4") data_tokens = tokenizer(data_text, truncation=True, padding=True, return_tensors="pt") Step 2: Configure the Training Parameters Fine-tuning involves adjusting the LLM's weights based on the custom dataset. Set up the training parameters to control the training process: Python from transformers import GPT4Config, GPT4ForSequenceClassification config = GPT4Config.from_pretrained("gpt-4", num_labels=<YOUR_NUM_LABELS>) model = GPT4ForSequenceClassification.from_pretrained("gpt-4", config=config) training_args = { "output_dir": "output", "num_train_epochs": 4, "per_device_train_batch_size": 8, "gradient_accumulation_steps": 1, "learning_rate": 5e-5, "weight_decay": 0.01, } Replace <YOUR_NUM_LABELS> with the number of unique labels in your dataset. Step 3: Set Up the Training Environment Initialize the training environment using the TrainingArguments and Trainer classes from the transformers library: Python from transformers import TrainingArguments, Trainer training_args = TrainingArguments(**training_args) trainer = Trainer( model=model, args=training_args, train_dataset=data_tokens ) Step 4: Fine-Tune the Model Initiate the training process by calling the train method on the Trainer instance: Python trainer.train() This step may take a while depending on the dataset size, model architecture, and available computational resources. Step 5: Evaluate the Fine-Tuned Model After training, evaluate the performance of your fine-tuned model using the evaluate method on the Trainer instance: Python trainer.evaluate() Step 6: Save and Use the Fine-Tuned Model Save the fine-tuned model and use it for inference tasks: Python model.save_pretrained("fine_tuned_gpt4") tokenizer.save_pretrained("fine_tuned_gpt4") To use the fine-tuned model, load it along with the tokenizer: Python model = GPT4ForSequenceClassification.from_pretrained("fine_tuned_gpt4") tokenizer = GPT4Tokenizer.from_pretrained("fine_tuned_gpt4") Example input text: Python input_text = "Sample text to be processed by the fine-tuned model." Tokenize input text and generate model inputs: Python inputs = tokenizer(input_text, return_tensors="pt") Run the fine-tuned model: Python outputs = model(**inputs) Extract predictions: Python predictions = outputs.logits.argmax(dim=-1).item() Map predictions to corresponding labels: Python model = GPT4ForSequenceClassification.from_pretrained("fine_tuned_gpt4") tokenizer = GPT4Tokenizer.from_pretrained("fine_tuned_gpt4") # Example input text input_text = "Sample text to be processed by the fine-tuned model." # Tokenize input text and generate model inputs inputs = tokenizer(input_text, return_tensors="pt") # Run the fine-tuned model outputs = model(**inputs) # Extract predictions predictions = outputs.logits.argmax(dim=-1).item() # Map predictions to corresponding labels label = label_mapping[predictions] print(f"Predicted label: {label}") Replace label_mapping with your specific mapping from prediction indices to their corresponding labels. This code snippet demonstrates how to use the fine-tuned model to make predictions on the new input text. While this guide provides a solid foundation for custom training LLMs, there are additional aspects you can explore to enhance the process, such as: Experimenting with different training parameters, like learning rate schedules or optimizers, to improve model performance Implementing early stopping or model checkpoints during training to prevent overfitting and save the best model at different stages of training Exploring advanced fine-tuning techniques like layer-wise learning rate schedules, which can help improve performance by adjusting learning rates for specific layers Performing extensive evaluation using metrics relevant to your task or domain, and using techniques like cross-validation to ensure model generalization Investigating the usage of domain-specific pre-trained models or pre-training your model from scratch if the available LLMs do not cover your specific domain well By following this guide and considering the additional points mentioned above, you can tailor large language models to perform effectively in your specific domain or task. Please reach out to me for any questions or further guidance.