Gradient AI is a fast-growing startup, bringing state-of-the-art Artificial Intelligence (AI) and Machine Learning (ML) solutions to the trillion-dollar insurance industry

Gradient AI

A little prologue from the author: It all begun with Transformers — not the robots — a deep learning technique released by google for predicting words in a very good sequence. Suddenly we have achieved artificial intelligence, at least some form of it. But the true question remains: Where the hell are my robots! Well, fear not dear roboticist. MachinaScript is here to help you bring next-gen robotics to your garage. You can start making incredibly intelligent robots right away. Anakin built C3PO when he was 9 years old. How about you? - babycommando, made with love for the maker community. Introducing MachinaScript For Robots A set of tools and a LLM-JSON-based language designed to empower humans in the creation of their own mechanical companions. It facilitates the , the integration of , and with a high degree of autonomy. animation of generative movements motion personality teaching a set of instructions (skills) With MachinaScript, you can control a wide range of electronics like Arduinos, Raspberry Pis, servo motors, cameras, sensors, and much more. Our goal is to make a wide modular generative format more accessible, as no robot design is the same for anyone. How does it work, and some tips A robot is naturally built on top of computers, motors and other cool electronical components. They are also built around a physical design. There’s a lot of “ ” out there already. They work pretty much like remote-controller toys: robot dogs, humanoids, R2D2 clones… how to arduino robots We are just . And trust me, they perform well on this. Motion design can display a lot of personality. switching the controls to a multimodal large language model very Consider for example a robot that have a have a pair of cat ears. LLMs knows incredibly accurate ways to move them to express feelings when talking. You just need a way to tell the robot they have cat ears… Enter The MachinaScript Language The MachinaScript language is built on a LLM-JSON-based synthax with incredibly modular capailities because it is generative. As long as your “Brain” code is adapted to understand it, you literally have no ending for your creativity. Hook up all your components and limit a set of rules on how the robot may use them to fulfill the future inputs. The current version of MachinaScript was built in this format: At the top layer there are a set of actions. Inside actions we have a set of movements to be executed to achieve a goal in an animated motion way. Movements contain the set of motors, degrees for each to move and additional configuration like movement speed. Also inside of an action, we have the “useSkill” — a set of skills to be used in an order to also achieve a goal. They are similar to function calling, with some code ready to be activated. Take a picture, text to speech, blink a led, etc. Sudo make me a sandwich? Let’s go. Note you can even change the complete synthax for the language structure for your needs! You see, MachinaScript is more of a philosophy for building generative robots than anything else. This is the true power of natural language processing. Additionally, you may want to finetune a llm to make even better/faster generations for your own machines and specs. Training local models are pretty easy nowadays with tools like or even the official openAi one if you are using their models. gradient.ai A Brain and a Body Robot design patterns for MachinaScript to work are pretty straight forward: a computer represents brain, a microcontroller represents the body. Motors, sensors and other dispositives can be hooked to them in different setups and controlled from the MachinaScript generative set of instuctions and skills. The actions, movements and skills are then serially encoded in a sequence to be executed — in deep specification if needed. For example: Move slowly for taking a whole-night long exposure picture of the milky way center. Start now at 45 degrees north and only stop at 5 am. This set of orders comes from the LLM encoded in the MachinaScript JSON format. For arduino serial communication to be very fast, we must serialize this data and pass from the “Brain” to the “Body”. Engineers and hackers, behold the for a full working example ready to be customized. github repo here How to Make a Robot One of the wildests capabilities of the MachinaScript is the ability to metamorph into any robotic body shape. It learns in no-time how to control your design, being it two or four legged, aerial, terrestrial or submarine. As long as the capabilities are well prompted, mapped and parsed in the and the Body/Brain codes, you are good to go. Grab some scrap metal, some very cheap servos and start playing around. Find an enclosure, make it have a personality. Or maybe ask it who it wants to be. Who knows. And suddenly the dream of building intelligent robots is not a dream anymore. Join the , share your designs, skills, 3D models, hacks and crafts with us! We really wanna see your robots :] discord community MachinaScript for Robots is and always will be free and open source for everyone. Contribute to the . You can find some early code examples to start making your robots right now. github repo here MachinaScript for Robots is in very early beta. Use at your own risk. Also published . here

Introducing LLM-Powered Robots: MachinaScript for Robots

ChatGPT has . None more so, perhaps, than in the consulting world. Strategy consultants, especially at the “lower” level of , are expected to find information and offer scenarios to solve issues within companies (this is a wild over-simplification, but tracks based on experience). changed the game for many professions the pyramid This becomes much easier to do when you have a generative AI tool that can answer questions, act as a sparring partner, or organize large amounts of data. Of course, there may come a time when the role of strategy consultant altogether disappears because of large language models. But in the meantime, ; they do not replace them. tools like ChatGPT augment our skills Below are a few tips and tricks to use ChatGPT like a strategy consultant and provide value in your role. ChatGPT prompts for consultants Just as an Analyst needs to learn PPT and Excel shortcuts, one now needs to know the easiest and best ways to use ChatGPT to optimize their work. Obviously, this in no way replaces the grunt work that needs to be done for clients… but it can often be a helpful start. Highlight obvious areas of improvements “ ]. I wrote the text below for [ ]. Please point out possible areas of improvement to make it more impactful.” I am a consultant. I am working on a project to [define project’s nature audience : Always say please to the AI; just in case HINT Summarize meeting / interview minutes (ex: in bullet-points) [define project] “I am a consultant. I am working on a project to . Summarize the below notes in easy-to-understand bullet points, highlighting the pain points and key take-aways.” Mass title production “ [Stream 1] [Stream 2] [Stream 3] ” I am working on a sub-project with 3 streams: , , . Give me 20 possible titles for my project : ask for longer lists, more professional lists… HINT Categorization “ [factor 1], [Factor 2], [Factor n] ” I have hereafter listed 20 success factors for my project: … Categorise them in 3 different categories, and write a short definition of each category. Find something you’ve missed “ [topic] [problem statement] [Paste existing ideas as bullet points].” I am working on . Below is a list of 10 ideas I’ve had to answer . Please give me 10 more ideas. Find quotes and examples “ [Paste statement that supports your analysis] ” Can you give me some data and quotes from business leaders to support the below statement? . : only works for Bing, not ChatGPT WARNING Find metaphors / ELI5 “ [complicated concept] ” Please explain like I’m 5 years old. Use metaphors if possible : ask for more than one example, and use different ages HINT ChatGPT for consultants: 10 commandments While ChatGPT is very useful, there are easy mistakes that should be avoided. The most obvious one is that . They are paying good money for a service, and your shortcuts are for you alone to know about. Below are 10 more things to keep in mind. you should not tell your client you are using ChatGPT Thou shalt — don’t use it to search for information; that’s not the point not use ChatGPT like Google Thou shalt — it’s a “good enough” type of tool not use it for specific examples Thou shalt — ChatGPT ” all the time (hint: use to spot errors) validate and verify “lies chain of thought prompting Thou shalt — no anthropomorphizing the AI! understand the underlying statistical technology Thou shalt — Use the tools at your disposal… and keep the hard analysis for yourself use it early for brainstorms and workshops Thou shalt — Give the model relevant background information provide context Thou shalt — Use ChatGPT’s responses as a starting point and iterate on them iterate and refine Thou shalt — human expertise, judgment, and experience are paramount prioritize human expertise Thou shalt — it’s the closest we may ever get to humanity hive-experiencing itself; feel the magic have fun with it Thou shalt — Don’t share sensitive or confidential information with ChatGPT (!!!) be cautious with sensitive data Generative AI reading list for Management Consultants Below is a list of the best reads on the topic. I’ve advised all my analysts read them before talking to clients. You should do the same. Relaxing reads — The New Yorker Will A.I. Become the New McKinsey? — The New York Times OMG! What Will Happen When A.I. Makes BuzzFeed Quizzes? — The Atlantic ChatGPT Is a Blurry JPEG of the Web — The Atlantic Prepare for the Textpocalypse — Neoma GPT’s Very Inhuman Mind — Vice ChatGPT Can Be Broken by Entering These Strange Words, And Nobody Is Sure Why — The New Yorker What’s the Point of Reading Writing by Humans? — Quartz Here’s how 10 industries are experimenting with ChatGPT — Undark.org ChatGPT Isn’t ‘Hallucinating.’ It’s Bullshitting Serious reads — Cornell University Talking About Large Language Models — Medium Who are we talking to when we talk to these bots? — Samuel Bowman Eight Things to Know about Large Language Models — McKinsey What every CEO should know about generative AI — World Economic Forum These are the jobs that AI can’t replace — Prompt Engineering Guide Prompting Techniques Good AI newsletters One Useful Thing The Road to AI We Can Trust AI: A Guide for Thinking Humans The Gradient Import AI Courses & Books — Forbes The 29 Best (And Free) ChatGPT And Generative AI Courses And Resources — S. Russel & P. Norvig Artificial Intelligence: A Modern Approach — M. Mitchell Artificial Intelligence: A Guide for Thinking Humans — K. Crawdord Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence — J. Shane You Look Like a Thing and I Love You In 1962, in his book “ ”, science fiction writer Arthur C. Clarke famously wrote that “a .” Profiles of the Future: An Inquiry into the Limits of the Possible ny sufficiently advanced technology is indistinguishable from magic This is true more than ever today. The future is uncertain, and we may not have a job in 5 years. We may as well enjoy the magic while it still has some shine on it. But we must do it right. Call it professional conscience. Good luck out there. Also published . here

The Ten Commandments of ChatGPT

ChatGPT এর দশটি আদেশ

Die zehn Gebote von ChatGPT

Los Diez Mandamientos de ChatGPT

Les dix commandements de ChatGPT

चैटजीपीटी की दस आज्ञाएँ

ChatGPT の十戒

ChatGPT의 십계명

Os Dez Mandamentos do ChatGPT

Десять заповедей ChatGPT

ChatGPT'nin On Emri

Mười điều răn của ChatGPT

ChatGPT 的十诫

Backpropagation - The Most Fundamental Training Systems Algorithm in Modern Generative AI

Implementing different variants of Gradient Descent Optimization Algorithm in Python using Numpy

Ferrofluid is a suspension of tiny iron nanoparticles in mineral oil that behaves like a moving liquid magnet when placed in a magnetic field. It can now be inexpensively at magnet supply stores like Apex magnets. It has some fascinating and beautiful behavior. You can use it make a liquid piston with no moving parts, unless you count the liquid moving. Since this could be made very small, researchers have been interested in trying to make small pumps with Ferrofluid for the purpose of manipulating tiny samples for chemical analysis, perhaps by making a “ ”. purchased lab on a chip In part due to interest from a young student, Huy, I started experimenting with ferrofluid. This is the story of a miserable failure; as a , it is my ethos to publish failures as well as successes. Public Inventor Although Huy was most interested in build a soft actuator, I developed the idea that this could be accomplished with ferrofluid most basically by building a ferrofluid pump — that is, a pump which had no moving parts except ferrofluid manipulated with electromagnets. If we could pump ferrofluid inside a bladder or membrane, we would have the beginning of a “hydrostat”, an object that could change shape base on internal pressure changes. In theory we might be able to make a model of the human tongue or a snail’s body or an octopus arm. But before we did that, I insisted on trying to build a pump, and the basis of a pump is a piston and valves. In particular, “check valves”, or one-way valves. Valves that allow air (or water) to flow in one direction only. With two check valves and piston you an make an oscillating pump, like a bicycle pump for example. If you put a blob of ferrofluid inside a tube and then apply a strong magnetic field to the blob, it “seals” the tube. That is, it forms a somewhat airtight seal. If the air pressure on one side becomes high enough, it will explosively spurt away for the magnet and thus break the seal. But until you reach that much pressure it is airtight. You can easily move the blob in the tube by moving the magnet. I used powerful 1/2" diameter wide by 1" long neodymium magnets, but any magnet will do — the stronger the better, up to the point of staying safe. You can easily observe that this moving blob acts as a piston, pushing air around inside the tube. A standard way to play with ferrofluid is to put it in a covered petridish and then apply a magnet beneath it. Then slowly remove the petridish cover. Why slowly? Because ferrofluid stains like ink and tends to splash. It is best to wear goggles and use gloves and old clothes you don’t mind being stained when playing with it. It is very messy. I was doing this and playing around, more or less without purpose, and decided to inject air into the ferrofluid with a hypodermic syringe. The result was interesting — the bubbles that were produced always came out in the direction of lowest magnetic gradient. That is, the bubbles fled the magnetic field where it was strongest. This of course makes sense. In the presence of a magnetic field, the ferrofluid becomes “solid” or semi-solid. Perhaps “solid” is not the right word — but ferrofluid in a magnetic field resists deformation of its shape. In some sense, the ferrofluid in a stronger field has more internal pressure. The affect with a syringe was very clear: bubbles travel in the opposite direction of the gradient of magnetic flux. This led me to an idea: this asymmetric behavior should allow us to create a one-way valve. If we could place ferrofluid into an magnetic field that has a strong gradient, we can imagine making a blob that lets air pass in one direction but doesn’t let air pass in the other direction. Another way of saying this is that if you put air pressure on the surface of a blob in direction of increasing gradient, you move the whole blob. If you put air inside a blob, it forms a bubble that flows outward. So, I reasoned (erroneously) that I could easily build a geometry that would let me inject a bubble into a point of high gradient and it would flow to a point of low gradient (that is, air would pass easily in this direction.) But air in the other direction would push the whole blob, and thus be sealed and not flow. So, I build six distinct apparatuses to test this theory, pictured above. The first was with hypdermic needles. It seemed to work, but didn’t. Over about 48 hours the ferrofluid tends to get sticky and clog the needle. Silicone sealant seals urethane pressure tubes to acrylic better (but slower) than hot-melt glue So I build another similar apparatus made purely out of acrylic tubing. That is, a small acrylic tube took the place of the steel needle. This made it clear that the idea does work — with a catch. Once there is air (not ferrofluid) in the tube so that the air really does get into the point of decreasing gradient, then it works. I could accomplish this by moving my blob by hand over the acrylic inject tube (which started with air in it.) However, I could not “blow” air into the blob. Once ferrofluid was in the needle, it took high pressure to clear it. That is, this defeats the idea of allowing air to pass at low pressure. I then decided the problem was that I had to overcome this increasing gradient before I got to the decreasing gradient. That is, if I injected the air from the same direction, without going through the center, high-flux part of the blob maybe it would work. Perhaps, gentle reader, you now see my mistake, but let me continue. At considerable trouble, I laser cut some sheets of acrylic to make an easy-to-see two-dimensional version. Then, at the cost of even more trouble, I cemented them together and made them airtight, sealing them to urethane tubes with hot-melt glue. As usually happens, I had to do each of these operations twice to get it to work. Magent with steel yoke to channel flow lines But it didn’t work. I just couldn’t understand it. I tried an even simpler experiment, of using an iron yoke (actually, mild steel) to divert magnetic flux from the end of the single hollow cylindrical magnet. This definitely produces a magnetic gradient of weak (where the yoke is) to strong (where there is not yoke.) At least, if the tube is full of air. But it didn’t work. Using $40 gauge that measures air pressure in column inches of water and carefully sealed urethane tubes, it was clear that the blob sealed well and then broke down at approximately 14 column-inches of water on both sides. Air pressure gauge use to measure pressures (sealed to apparatus.) Confusion is to be cherished because it precedes enlightenment. Once my experiments dragged me kicking and screaming into the light, I finally get it: The surface of any blob of ferrofluid tends to have the same magnetic flux at any point on the surface, and the surface will be lower flux than points deeper in the interior. It is not, therefore, possible to blow air into a blob of ferrofluid, not matter how you shape the injection needle or where you put it, without first moving fluid in a low-flux position to a higher-flux position. As we have seen, this will not a allow a small bubble to pass, the whole blob will move. Yes, you can inject air into a ferrofluid blob and have the air flow out easily — once you have used enough pressure to move the fluid in the needle out of the way. In other words, I conjecture that it is not possible to make a one-way passive ferrofluid valve based on any geometry that involves simple air interfaces. This does not mean that we can’t “smuggle” air into a blob some other way, but any active measure ceases to be a passive valve. So here are some challenges to mathematically/physically inclined: Prove or disprove that the surface of a ferrofluid blob in the presence of a magnetic field (discounting gravity) is a surface of equal flux density per unit area. Prove or disprove that the magnetic flux gradient at a blob surface always points inward to the blob. Prove or disprove that an blob physically contained within a tube, having two air interfaces (at either end of the blob) will have equal flux density at each side. Prove or disprove that is not possible to build a passive ferrofluid geometry, no matter what magnets you use or what physical shape you use, which presents an air surface of decreasing flux gradient, and therefore it is not possible to build a simple passive one-way valve with ferrofluid as I attempted. I claim that all of these assertions are true, but do not claim to have produced compelling proofs of these statements. Public Invention is a non-profit dedicated to creating free-libre open-source inventions without seeking intellectual property monopolies for the betterment of all humanity. To learn more or get involved, contact Robert L. Read , or check out or GitHub or our read.robert@gmail.com repos YouTube channel.

Failed Experiments with Ferrofluid

Generating an immeasurable amount of data has become a need to develop more advanced and sophisticated machine learning techniques. Boosting machine learning is one such technique that can be used to solve complex data-driven real-world problems. Want to Understand Why boosting is used? What does boosting means in machine learning How does the boosting algorithm work? What are the different types of boosting? Adaptive boosting Gradient boosting XGBoost Understand how boosting machine learning algorithms can be used to improve the accuracy of a model? Why correctly are we using boosting machine learning techniques? Let's understand what led to the need for boosting machine learning. To solve complex and convoluted problems, we require more advanced techniques right now. Three classes of boosting this Adaptive Boosting, Gradient Boosting and XGBoost Adaptive Boosting is implemented by combining several weak learners into a single strong learn. Adaptive boosting starts by assigning equal weight edge to all of your data points and you draw out a decision stump for a unique input feature, so the next step is the results that you get from the first decision stump which are analyzed If any observations are misclassified, then they are assigned higher weights this correctly. After that new decision stump is drawn by considering the representations of higher pressures as more significant. So whichever data point was misclassified they are given a higher weight it in the next step you'll draw another decision stump that tries to classify the data points by giving more importance to the data points with more upper weight age. will keep looping until all the observations fall into the right class. The end goal here is to make sure that all your data points are classified into the correct courses Adaptive Boosting is also based on the sequential and symbol learning model. The base learners are generated sequentially in such a way that the present based learner is always more effective than the previous one. The overall model improves sequentially with each iteration now. Gradient boosting The difference in this boosting is that the weights for misclassified outcomes are not incremented. Instead, in gradient increasing what you do is you try to optimize the loss function of the previous learner by adding a new adaptive model that combines weak learners. This happens to reduce loss function. The main idea here is to overcome the errors in the previous learner's prediction . The loss function is the one that needs to be optimized (Reduce the error) You have to keep adding a model that will regularize the loss function from the previous learner. Just like adaptive boosting gradient boosting can also be used for both classification and regression. Gradient Boosting has three main components has the tendency to fill in the missing values. This Method is mentioned in the following code XGBoost xgboost xgb
model=xgb.XGBClassifier(random_state= ,learning_rate= )
model.fit(x_train, y_train)
model.score(x_test,y_test) import as 1 0.01 0.82702702702702702 Parameters: thread eta min_child_weight max_depth max_depth max_leaf_nodes gamma subsample colsample_bytree XGBoost is an advanced version of gradient boosting It means extreme gradient boosting. Boosting falls under the category of the . XGBoost is a more advanced version of the gradient boosting method. The main aim of this algorithm is to increase speed and to increase the efficiency of your competitions distributed machine learning community Why this model? The following code is for XGBoost. # importing required libraries pandas pd xgboost XGBClassifier sklearn.metrics accuracy_score
 
# read the train and test dataset
train_data = pd.read_csv( )
test_data = pd.read_csv( )
 
# shape the dataset
print( ,train_data.shape)
print( ,test_data.shape)
 
# Now, we need to predict the missing target variable the test data
# target variable - Survived
 
# seperate the independent and target variable on training data
train_x = train_data.drop(columns=[ ],axis= )
train_y = train_data[ ]
 
# seperate the independent and target variable on testing data
test_x = test_data.drop(columns=[ ],axis= )
test_y = test_data[ ] import as from import from import 'train-data.csv' 'test-data.csv' of 'Shape of training data :' 'Shape of testing data :' in 'Survived' 1 'Survived' 'Survived' 1 'Survived' Create the object of the XGBoost model You can also add other parameters and test your code here Some settings are : and max_depth n_estimators t Read: Documentation of xgboos model = XGBClassifier()
 
# fit the model the training data
model.fit(train_x,train_y)
 
 
# predict the target on the train dataset
predict_train = model.predict(train_x)
print( ,predict_train) 
 
# Accuray Score on train dataset
accuracy_train = accuracy_score(train_y,predict_train)
print( , accuracy_train)
 
# predict the target on the test dataset
predict_test = model.predict(test_x)
print( ,predict_test) 
 
# Accuracy Score on test dataset
accuracy_test = accuracy_score(test_y,predict_test)
print( , accuracy_test) with '\nTarget on train data' '\naccuracy_score on train dataset : ' '\nTarget on test data' '\naccuracy_score on test dataset : ' XGBoost was introduced because the gradient boosting algorithm was computing the output at a prolonged rate right because there's a sequential analysis of the data set and it takes a longer time XGBoost focuses on your speed and your model efficiency. To do this, XGBoost has a couple of features. It supports parallelization by creating decision trees. There's no sequential modeling in computing methods for evaluating any large and any complex modules One question that comes up again and again in my classes is, "Where can I get data?" There are a few answers to this question, but the best solution depends on what you are trying to learn. Data comes in all shapes and sizes. Remember, some of the best learning comes from playing with the data. Having a question in mind that you are trying to answer with the data is a good start. Machine learning is built up from a diverse set of tools, languages, and techniques. It's fair to say that no one solution fits most projects Back Propagation Algorithm- Robust Mechanism For a neural network to learn, you have to adjust the weights to get rid of most of the errors. This can be done by performing backpropagation of the error. When it comes to a simple neuron that uses the Sigmoid function as its activation function, you can demonstrate the fault as we did below. We can consider that a general case where the weight is termed as W and the inputs as X. With this equation, the weight adjustment can be generalized, and you would have seen that this will only require the information from the other neuron levels. This is why this is a robust mechanism for learning, and it is known as the back-propagation algorithm. Often in practice, examples of some class will be underrepresented in your training data. This is the case; for example, when your classiﬁer has to distinguish between genuine and fraudulent e-commerce transactions: the patterns of actual sales are much more frequent. If you use SVM with soft margin, you can deﬁne a cost for mis-classiﬁed examples. Because noise is always present in the training data, there are high chances that many instances of genuine transactions would end up on the wrong side of the decision boundary by contributing to the cost. Other Methods without Splitting training data Boosting Instead of splitting training data into multiple data models, we can use another method like encouraging to optimize the best weighting scheme for a training set. Given a binary classification model like SVMs, decision trees, Naive Bayesian Classifiers, or others, we can boost the training data to improve the results. Assuming that you have a similar training set to what we just described with 1,000 data points, we usually operate under the premise that all data points are necessary or that they are of equal importance. Boosting takes the same idea and starts with the assumption that all data points are equal. But we intuitively know that not all training points are the same. What if we were able to weight each input based on what is most relevant optimally? That is what boosting aims to do. Many algorithms can do promoting, but the most popular is XGBoost.

A Complete Guide to XGBoost Model in Python using scikit-learn

Have you ever wished you could bring your imagination to life with the help of AI-generated images? Well, now you can! In this blog post, I'll introduce you to an AI model that can generate mesmerizing images from your text prompts. This guide will walk you through using the Stable Diffusion Aesthetic Gradients model, ranked 409th on . We'll also see how we can use Replicate Codex to find similar models and decide which one we like. Let's begin. Replicate Codex About the Stable Diffusion Aesthetic Gradients Model This AI model, called Stable Diffusion Aesthetic Gradients, is created by and is designed to generate captivating images from your text prompts. You can find the model's details on its . The model offers a wide range of customization options to help you create the perfect image for your creative project. cjwbw detail page What are aesthetic gradient embeddings? Aesthetic gradient embeddings are a fascinating aspect of AI models that involve the use of textual inversion. But first, let's understand what an embedding is. An embedding is the outcome of textual inversion, which is a method used to define new keywords in a model without modifying it. This approach has gained popularity because it can introduce new styles or objects to a model using as few as 3-5 sample images. What is textual inversion? Textual inversion works in a unique way. Its most impressive feature is not the ability to add new styles or objects, as other fine-tuning methods can do that too, but the fact that it can achieve this without changing the model. The process involves defining a new keyword that's not in the model for the new object or style. This new keyword gets tokenized (represented by a number) like any other keywords in the prompt. Each token is then converted into a unique embedding vector used by the model for image generation. Textual inversion finds the embedding vector of the new keyword that best represents the new style or object, without modifying any part of the model. Essentially, it's like finding a way within the language model to describe the new concept. How can you use embeddings? Embeddings can be used for new objects, such as injecting a toy cat into an image. The new concept (toy cat) can be combined with other existing concepts (boat, backpack, etc.) in the model. Embeddings can also represent a new style, allowing the transfer of that style to different contexts. If you're looking for a repository of custom embeddings, Hugging Face hosts the Stable Diffusion Concept Library, which contains a large number of them. Understanding the Inputs and Outputs of the Stable Diffusion Aesthetic Gradients Model Before diving into the step-by-step guide, let's first understand the inputs and outputs of this model. Inputs (string): The text prompt to render. Default value: "a painting of a rvirus monster playing guita" prompt (integer): How many samples to produce for each given prompt. A.k.a. batch size. Allowed values: 1, 4. Default value: 1 n_samples (integer): Width of the output image. Scale down if run out of memory. Allowed values: 128, 256, 512, 768, 1024. Default value: 512 width (integer): Height of the output image. Scale down if run out of memory. Allowed values: 128, 256, 512, 768, 1024. Default value: 512 height (number): Unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty)). Default value: 7.5 scale (integer): Number of ddim sampling steps. Default value: 50 ddim_steps (boolean): Set to true to use plms sampling. Default value: false plms (string): Aesthetic embedding file. Allowed values: gloomcore, flower_plant, sac_8plus, cloudcore, aivazovsky, glowwave, fantasy, laion_7plus. Default value: sac_8plus aesthetic_embedding (integer): Number of steps for the aesthetic personalization. Default value: 10 aesthetic_steps (integer): The seed (for reproducible sampling). seed Outputs The output of the model is an array of URI strings, each representing an image generated based on the input parameters provided. Now that we understand the inputs and outputs, let's move on to the step-by-step guide for using the model. A Step-by-Step Guide to Using the Stable Diffusion Aesthetic Gradients Model If you're not up for coding, you can interact directly with this model's "demo" on Replicate via their UI. You can use to interact directly with the interface and try it out! This is a nice way to play with the model's parameters and get some quick feedback and validation. If you do want to use coding, this guide will walk you through how to interact with the model's Replicate API. this link Step 1: Install the Node.js client First, install the Node.js client by running the following command: npm install replicate Step 2: Set up your API token Next, copy your API token and authenticate by setting it as an environment variable: export REPLICATE_API_TOKEN=[token] Step 3: Run the model Now, you can run the model with the following code: import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

const output = await replicate.run(
  "cjwbw/stable-diffusion-aesthetic-gradients:e2589736f21fd0479499a6cd55002f75085f791c0780c987dc0925f8e2bcb070",
  {
    input: {
      prompt: "Roman city on top of a ridge, sci-fi illustration by Greg Rutkowski #sci-fi detailed vivid colors gothic concept illustration by James Gurney and Zdzislaw Beksiński vivid vivid colorsg concept illustration colorful interior
"
    }
  }
); You can also specify a webhook URL to be called when the prediction is complete. Take a look at the webhook docs for details on setting that up. For example: const prediction = await replicate.predictions.create({
  version: "e2589736f21fd0479499a6cd55002f75085f791c0780c987dc0925f8e2bcb070",
  input: {
    prompt: "a painting of a rvirus monster playing guita"
  },
  webhook: "https://example.com/your-webhook",
  webhook_events_filter: ["completed"]
}); To learn more, take a look at the Node.js library documentation. Taking it Further - Finding Other Text-to-Image Models with Replicate Codex Replicate Codex is a fantastic resource for discovering AI models that cater to various creative needs, including image generation, image-to-image conversion, and much more. It's a fully searchable, filterable, tagged database of all the models on Replicate, and also allows you to compare models and sort by price or explore by the creator. It's free, and it also has a digest email that will alert you when new models come out so you can try them. If you're interested in finding similar models to Stable Diffusion Aesthetic Gradients... Step 1: Visit Replicate Codex Head over to to begin your search for similar models. Replicate Codex Step 2: Use the Search Bar Use the search bar at the top of the page to search for models with specific keywords, such as "anime style" or "selfie." This will show you a list of models related to your search query. Step 3: Filter the Results On the left side of the search results page, you'll find several filters that can help you narrow down the list of models. You can filter and sort by models by type (Image-to-Image, Text-to-Image, etc.), cost, popularity, or even specific creators. By applying these filters, you can find the models that best suit your specific needs and preferences. For example, if you're looking for an that's the cheapest or most popular, you can search and then sort by the relevant metric. image restoration model Conclusion In this guide, we walked through the process of using the Stable Diffusion Aesthetic Gradients model to generate captivating images from text prompts. We also discussed how to leverage the search and filter features in Replicate Codex to find similar models and compare their outputs, allowing us to broaden our horizons in the world of AI-powered image generation. I hope this guide has inspired you to explore the creative possibilities of AI and bring your imagination to life. Don't forget to subscribe for more tutorials, updates on new and improved AI models, and a wealth of inspiration for your next creative project. Happy image enhancing and exploring the world of AI with , check out additional guides and resources at , and follow me on for more updates and insights. Replicate Codex Notes by Replicate Twitter Also published . here

Stable Diffusion Aesthetic Gradients: A Comprehensive Guide

Liquidity provisioning in Uniswap V3 presents a stochastic optimal control problem with a well-defined utility function to maximize. This article introduces an innovative framework for intelligent liquidity provisioning, utilizing a combination of agent-based modeling and reinforcement learning. Our framework provides a robust and adaptive solution for optimizing liquidity provisioning strategies. The Uniswap V3 model mimics real-world market conditions, while the agent-based model (ABM) creates an environment for simulating agent interactions with Uniswap V3 pools. The reinforcement learning agent, trained using deep deterministic policy gradients (DDPG), learns optimal strategies, showcasing the potential of machine learning in enhancing DeFi participation. This approach aims to improve liquidity providers’ profitability and understanding of CFMM markets. Content Overview Introduction Intelligent liquidity provisioning framework Components of Intelligent Liquidity Provisioning Framework Agent-based simulator Reinforcement Learning Model Limitations Future Work Conclusion Resources References Introduction In my previous article on market making we explored the mechanics and strategies of market making in traditional financial markets. Building upon those insights, this article introduces an innovative framework for intelligent liquidity provisioning in the context of Uniswap V3. As mentioned in our prior research, our goal was to extend our understanding of market dynamics and liquidity management in decentralized finance ( , specifically through the development of the Intelligent Liquidity Provisioning Framework. [Market Making Mechanics and Strategies], DeFi) Decentralized finance (DeFi) has undergone remarkable growth, introducing innovative financial products and services accessible to a global audience. Uniswap V3, at the forefront of this innovation, has revolutionized liquidity provisioning with its concentrated liquidity feature. However, this advancement brings forth complex decision-making challenges for liquidity providers. This article introduces a comprehensive framework designed to address these challenges, offering a simulated environment for studying and optimizing liquidity provisioning strategies. Our framework comprises three key components: the Uniswap V3 model, an agent-based model (ABM), and a reinforcement learning agent. The Uniswap V3 model provides a representation of the pool, enabling the deployment and interaction with tokens and pools. The ABM introduces complexity by simulating agent interactions and market dynamics, creating a rich environment for strategy evaluation. The reinforcement learning agent, operating within this environment, adopts a deep deterministic policy gradient approach to learn and adapt strategies, aiming for optimal performance in liquidity provisioning. This research aims to develop an intelligent liquidity provisioning (ILP) mechanism using reinforcement learning (RL) to autonomously manage and optimize liquidity within the Uniswap V3 environment. The mechanism seeks to maximize the utility function, considering fees earned, impermanent loss, and other metrics based on liquidity providers’ preferences while adapting to the complex dynamics of the CFMM market. Intelligent Liquidity Provisioning Framework In the RL framework, the liquidity provisioning problem is formulated as a Markov Decision Process (MDP). The MDP consists of states, actions, and rewards. States represent the current market conditions, including asset prices, trading volumes, and other relevant variables. States: Actions correspond to the decisions made by the liquidity provider, such as adjusting liquidity allocations, rebalancing portfolios, etc. Actions: Rewards quantify the desirability of the outcomes based on the liquidity provider’s objective function, preferences, and constraints. The rewards can be positive for desirable outcomes (e.g., high returns) and negative for undesirable outcomes (e.g., high risk or underperformance). Rewards: The objective function represents the liquidity provider’s desired outcome, which can be a combination of factors like maximizing returns, minimizing risks, or achieving a specific trade-off between the two. Constraints can include limitations on liquidity allocations, capital utilization, risk tolerance levels, or other restrictions defined by the liquidity provider. Objective Function: RL training is an iterative process where the agent continuously updates its policy based on feedback. The agent learns from its experiences and refines its decision-making over time, gradually converging to more optimal liquidity provisioning strategies. Once the RL agent has been trained, it can be tested and evaluated using historical data or simulated environments to assess its performance against the liquidity provider’s objective function and constraints. The agent’s performance can be measured using metrics like returns, risk measures, or other relevant performance indicators. By applying RL algorithm, the liquidity provisioning mechanism can learn and adapt to changing market conditions, identify optimal liquidity provision strategies, and balance constraints and preferences specified by the liquidity provider. RL enables the mechanism to find solutions that maximize the liquidity provider’s objective function, considering various trade-offs and constraints autonomously and dynamically. Components of Intelligent Liquidity Provisioning Framework The framework comprises three major components: UniswapV3 Model The Uniswap V3 model implemented in Python offers a detailed and functional simulation of the Uniswap V3 protocol, capturing its nuanced mechanics and providing users with a comprehensive toolset for interacting with the protocol. The UniswapV3_Model class handles the deployment of tokens and pools, initializes pools, and provides an interface for pool actions and pool state retrieval. Overview The Uniswap Model serves as the foundation of the Intelligent Liquidity Provisioning Framework, encapsulating the core mechanics of Uniswap V3. It leverages compiled smart contracts from Uniswap’s V3-Core, deployed in a local Ganache environment using brownie, to create a realistic and interactive simulation. Contract Compilation and Deployment The framework integrates with Brownie, a Python-based development and testing framework for smart contracts, to compile and deploy the Uniswap V3 smart contracts. These contracts are then deployed to a local Ganache environment, providing a sandbox for testing and development. This setup ensures that users can interact with the Uniswap environment without the need for real assets or network transactions, fostering a safe and controlled experimentation space. Agent-Based Simulator is used to simulate the Uniswap V3 environment, agent policies are defined to incorporate the dynamics of Uniswap market participants. Different types of agents are used to simulate the dynamic Uniswap environment Tokenspice agent-based simulator Introduction simulates the actions and interactions of individual agents within the Uniswap V3 ecosystem. By modeling the complex behaviors of different participants, the ABM provides a comprehensive interface of Uniswap V3 dynamic environment, enabling the analysis and optimization of liquidity provisioning strategies. Tokenspice Agent-Based Model (ABM) Agent Types and Behaviors The ABM includes various agent types, each representing a specific role within the Uniswap V3 ecosystem. The two main agents are the Liquidity Provider Agent and the Swapper Agent, which interact with the Uniswap pools to provide liquidity and perform token swaps, respectively. The behavior of these agents is dictated by policies defined in the file, ensuring that their actions are aligned with real-world strategies and market conditions. agents_policies.py This agent adds and removes liquidity from the Uniswap pools. It follows a set of policies that dictate its actions based on the current state of the market and the agent’s preferences. Liquidity Provider Agent: The Swapper Agent performs token swaps within the Uniswap pools, taking advantage of price discrepancies and arbitrage opportunities. Its behavior is guided by policies that assess the potential profitability of trades, considering transaction fees and slippage. Swapper Agent: Simulation Configuration and Execution The file is central to the ABM, configuring how agents interact with each other and with the Uniswap pools. It defines the relationships between agents, policies, and the simulation environment. netlist.py The , , and modules provide the foundational elements for running simulations. The SimEngine orchestrates the simulation, managing the flow of time and the execution of agent actions. The SimStateBase maintains the current state of the simulation, storing data on agent holdings, pool states, and other relevant variables. The SimStrategyBase defines the overarching strategies that guide agent behavior throughout the simulation. SimEngine.py SimStateBase.py SimStrategyBase.py Reinforcement Learning Model Introduction The Reinforcement Learning (RL) Agent is a pivotal component of the Intelligent Liquidity Provisioning Framework, designed to interact with the Uniswap V3 ecosystem through Uniswap Model an agent-based model. This section delves into the RL Agent, its environment, and the DDPG (Deep Deterministic Policy Gradient) algorithm used for training. RL Agent Environment The RL Agent operates in a custom environment, , which interfaces with the Uniswap model and the agent-based model to simulate the DeFi market. This environment facilitates the agent’s interaction with Uniswap pools, allowing it to add and remove liquidity, and observe the consequences of its actions. The RL Agent interacts with the Uniswap model and ABM to simulate real-world liquidity provisioning in Uniswap V3. It chooses actions that result in adding or removing liquidity, with policies and simulation configuration defined in the ABM, ensuring realistic interactions. DiscreteSimpleEnv The environment’s state space includes various market indicators such as the current price, liquidity, and fee growth. These parameters are normalized and provided to the agent at each timestep. State Space: The agent’s action space consists of continuous values representing the price bounds for adding liquidity to a Uniswap pool. These actions are translated into interactions with the Uniswap pools, affecting the state of the environment. Action Space: The reward function is crucial for training the RL Agent. It takes into account the fee income, impermanent loss, portfolio value, and potential penalties, providing a scalar reward signal to guide the agent’s learning process. Reward Function: DDPG Agent The DDPG Agent is a model-free, off-policy actor-critic algorithm using deep function approximators. It can handle high-dimensional state spaces and continuous action spaces, making it well-suited for our Uniswap V3 environment. This network is responsible for providing the best-believed action given a state. It has a sigmoid output layer, outputting relative values for and which are then scaled to the desired range in agent env, representing the price bounds for adding liquidity. Actor Network: price_lower price_upper This network evaluates the action value function, estimating the expected return of taking an action in a given state. Critic Network: DDPG employs target networks for both the actor and critic, which are slowly updated to stabilize training. Target Networks: This technique is used to store a replay buffer of past experiences, allowing the agent to learn from a diverse set of samples, breaking correlations in observations, and smoothing out learning. Experience Replay: Interaction with Uniswap Model and ABM The RL Agent leverages the Uniswap model and agent-based model to simulate real-world liquidity provisioning in Uniswap V3. It interacts with the Uniswap pools through the , performing actions that result in adding or removing liquidity. The agent’s policies and the simulation configuration are defined in the ABM component, ensuring a realistic and coherent dynamic environment. DiscreteSimpleEnv The agent is trained over a series of episodes, each representing a different market scenario (different pool). The agent’s performance is evaluated based on its ability to maximize returns while minimizing risks associated with liquidity provisioning. The effectiveness of the Intelligent Liquidity Provisioning Framework is assessed through the evaluation of the reinforcement learning (RL) agent’s performance. Train and Evaluate Agent: To evaluate the RL agent, we set up a specialized evaluation environment, , which extends the base environment, . This environment is tailored for the evaluation of agent policies. Environment Setup: DiscreteSimpleEnvEval DiscreteSimpleEnv In our evaluation setup, we compare the RL agent’s performance against that of a baseline agent. The baseline agent’s actions are determined by a baseline policy that relies on the current state of the liquidity pool. This agent aims to provide a reference point for evaluating the RL agent’s performance. Baseline Agent: Results Training Evaluation Limitations Currently, the framework does not fully capture the real-time synchronization of pools, which can lead to discrepancies in modeling real Uniswap V3 dynamics. Future work should focus on incorporating mechanisms for better pool synchronization, potentially utilizing tick/position data or events to enhance realism. Pools Synchronization: The agent policies employed in the current framework are relatively simple and naive. To achieve more accurate simulations, future iterations should aim to define more comprehensive agent policies. These policies could model various types of Uniswap agents, such as noise traders, informed traders, retail liquidity providers, and institutional liquidity providers. Alternatively, statistical models trained on historical pool data can inform agent policies for more realistic behavior. Naive Agent Policies: The observation space provided to the agents lacks comprehensive information about the state of the pool. To improve decision-making capabilities, future enhancements should include tick and position data, along with engineered features that offer agents a more comprehensive understanding of the pool’s status. Sparse Observation Space: The action space for agents is currently constrained, with fixed liquidity amounts and restricted price range bounds. Expanding the action space to allow for more flexibility in liquidity provision, as well as considering multiple positions per step, can enhance the fidelity of the simulations. Limited Action Space: Future Work Agent Environment: Implement mechanisms to synchronize pools, possibly using tick/position data or events, to create more realistic dynamics in the Uniswap V3 environment. Synced Pools: Actor/Critic Network Architecture, alpha, beta, tau, batch size, steps, episodes, scaling parameters (rewards, actions, observation space) Hyperparameter Tuning: Define more sophisticated analytical policies that accurately model various Uniswap agents or utilize statistical models trained on historical pool data to inform agent behavior. Comprehensive Agent Policies: Enhance the observation space by including tick and position data, and engineer features that provide agents with a comprehensive view of the pool’s state. Informative Observation Space: Develop an improved reward function that accounts for a wider range of factors, leading to more effective agent training. Improved Reward Function: Instead of one position with a fixed budget at each timestep, implement a more comprehensive mechanism in which the agent is allocated a budget once at the start of the simulation and then learns to use this budget optimally in subsequent steps. Multiple Positions: Define more comprehensive baseline policies to evaluate the performance of the RL agent Baseline Policies: Agent Algorithm Further refine and optimize the hyperparameters of the reinforcement learning agent for better training performance. Hyperparameter Tuning: Explore alternative RL agent models, such as Proximal Policy Optimization (PPO) or Soft Actor-Critic (SAC), to determine if they offer advantages in specific scenarios. Experimentation with Other RL Agents: Investigate the application of multi-agent reinforcement learning techniques, which can be beneficial for modeling interactions among multiple liquidity providers and swappers. Multi-Agent RL (MARL): Implement online learning strategies that allow agents to adapt to changing market conditions in real time, providing a more dynamic and adaptive liquidity provisioning solution. Online Learning: Conclusion In the rapidly evolving landscape of decentralized finance (DeFi), liquidity provisioning plays a pivotal role in enabling efficient and secure trading. Uniswap V3, with its innovative concentrated liquidity feature, has pushed the boundaries of what is possible in DeFi liquidity management. However, the complexities of optimizing liquidity provisioning strategies within this dynamic ecosystem require innovative solutions. Our Intelligent Liquidity Provisioning Framework represents a significant step forward in addressing these challenges. By combining agent-based modeling and reinforcement learning, we have created a powerful toolkit for liquidity providers and market participants. This framework offers a robust and adaptive solution for optimizing liquidity provisioning strategies, with a focus on maximizing utility functions that encompass fees earned, impermanent loss mitigation, and other metrics tailored to individual preferences. Resources Github: https://github.com/idrees535/Intelligent-Liquidity-Provisioning-Framework-V1 References https://kth.diva-portal.org/smash/get/diva2:1695877/FULLTEXT01.pdf https://arxiv.org/pdf/2305.15821.pdf https://github.com/KodAgge/Reinforcement-Learning-for-Market-Making/tree/main https://arxiv.org/ftp/arxiv/papers/2211/2211.01346.pdf https://arxiv.org/pdf/2004.06985.pdf https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9682687 https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0277042 https://deliverypdf.ssrn.com/delivery.php?ID=104119098102026014120072084014107007042068069003049020126088025087121115103007084028042013055035009000054122074096068089064070102052026003014069082076098016080066026088066039027093020006122067093104092065070020126069068106118079127088008098077106031120&EXT=pdf&INDEX=TRUE https://medium.com/blockapex/market-making-mechanics-and-strategies-4daf2122121c https://www.gauntlet.xyz/resources/uniswap-user-cohort-analysis https://gov.uniswap.org/t/uniswap-incentive-design-analysis/21662 https://arxiv.org/pdf/2108.07806.pdf https://www.researchgate.net/publication/341848292_Market_makers_activity_behavioural_and_agent_based_approach https://fruct.org/publications/volume-29/fruct29/files/Struc.pdf https://www.arxiv-vanity.com/papers/1911.03380/ https://insights.glassnode.com/the-week-onchain-week-31-2023/ Also published . here

A Detailed Framework for Intelligent Liquidity Provisioning in Uniswap V3 

Uniswap V3-এ বুদ্ধিমান তারল্য বিধানের জন্য একটি বিস্তারিত কাঠামো

Ein detailliertes Framework für die intelligente Liquiditätsbereitstellung in Uniswap V3

Un marco detallado para el aprovisionamiento inteligente de liquidez en Uniswap V3

Un cadre détaillé pour l'approvisionnement intelligent en liquidités dans Uniswap V3

Uniswap V3 में बुद्धिमान तरलता प्रावधान के लिए एक विस्तृत रूपरेखा

Uniswap V3 におけるインテリジェントな流動性プロビジョニングの詳細なフレームワーク

Uniswap V3의 지능형 유동성 프로비저닝을 위한 세부 프레임워크

Uma estrutura detalhada para provisionamento inteligente de liquidez no Uniswap V3

Подробная структура интеллектуального предоставления ликвидности в Uniswap V3

Uniswap V3'te Akıllı Likidite Tedariği için Ayrıntılı Bir Çerçeve

Khung chi tiết để cung cấp thanh khoản thông minh trong Uniswap V3

Uniswap V3 智能流动性配置的详细框架

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