π‘οΈTechnology of Midway AI
How does it work?
Last updated
How does it work?
Last updated
Now that youβre familiar with what Midway is capable of, letβs take a look at the different types of AI technology used in Midway Ai image generation aspect.
The core base layer of our technology is the Diffusion model integrated with our very own deep learning with custom training data and Fine tuning.
What is Deep Learning?
Deep Learning: Deep learning is a type of machine learning that uses neural networks to create models that can learn from data. The Bigger the Data the Better the Ai and the smarter it is, Deep learning is used in Midway to create complex models that can generate unique artworks.
There's Different types of Models existing at this very moment:
1.GAN
2.VAE
3.Flow-based models:
4.Diffusion models:
The Ai undergoes a heavy processing cycle which uses Neural libraries and Big Data mining analysis to produce what the user requests. The Ai needs to have a lot of training data for this to work and the more training data the model has the better the Ai is.
Below shows the steps using Math and scientific explanations the Models use to produce what the user requests.
Forward diffusion (noising) π₯0 β π₯1 β β― π₯π
β’ Take a data distribution π₯0~π(π₯), turn it into noise by diffusion π₯π~π© (0, π 2 πΌ)
Reverse diffusion (denoising) π₯π β π₯πβ1 β β― π₯0
β’ Sample from the noise distribution π₯π~π©(0, π 2 πΌ), reverse the diffusion process to generate data π₯0~π(π₯)
For a forward diffusion process:
Backward diffusion(repeat) process that reverse the time
We can reverse the diffusion process if we know the time-dependent score function βπ₯ log π(π,π‘), which is the gradient of the log probability of a state π at a given time π‘.
The reverse diffusion process involves taking the noise distribution π₯π~π© (0, π 2 πΌ) as an input, and then applying a backward diffusion differential equation of the form
to it. This equation takes into account the time-dependent score function βπ₯ log π(π,π‘), which is the gradient of the log probability of a state π at a given time π‘. By using this equation, the reverse diffusion process can effectively reverse the direction of the diffusion process, reconstructing the original data distribution π₯0π(π₯) from the noise distribution π₯π~π© (0, π 2 πΌ).
The Ai uses a function called the Score function as the layer two judgement to produce an accurate Data prediction.
If we can learn a score model ππ( π₯,π‘) β β log π(π₯,π‘)
Then we can denoise samples, by running the reverse diffusion equation. π₯π‘ β π₯π‘β1 Score model ππ: π³ Γ [0,1]β π³ . A time dependent vector field over π₯ space. Training objective: Infer noise from a noised sample π₯ βΌ π (π₯) , π βΌ π© (0,πΌ ),π‘ β [0,1] min ||π + ππ (π₯ + π π‘ π,π‘ )||2 base 2 Add Gaussian noise π to an image π₯ with scale π π‘ , learn to infer the noise π.
The score function is a mathematical tool used to measure how well a model is able to predict the output of a given input. It is often used to evaluate the performance of data mining algorithms, such as decision trees, neural networks, and support vector machines. The score function measures the accuracy of the model on a given problem. It is calculated by comparing the modelβs prediction to the true output of the given input.
The score function can be used to evaluate any type of model, from simple linear models to complex, non-linear models. In general, the higher the score, the better the model is at predicting the output of an input.
Equation
The score function is defined by the following equation:
Score = (Observed Output - Predicted Output)^2
where Observed Output is the true output of the input and Predicted Output is the output predicted by the model.
The above equation is derived from the concept of mean squared error (MSE). MSE is an important concept in machine learning, and it measures the difference between the predicted response of a model and the true response of an input. The higher the MSE, the worse the model is at predicting the output of an input.
Applications
The score function can be applied to any type of machine learning problem. It is a powerful tool for evaluating the performance of different models. For example, the score function can be used to compare the accuracy of different decision trees on a given dataset. Additionally, it can be applied to compare the accuracy of different neural networks on a given problem.
Conclusion
The score function is an important tool for evaluating the performance of machine learning models. It is calculated by comparing the modelβs predicted output to the true output of an input. The higher the score, the better the model is at predicting the output of an input. The score function can be applied to any type of machine learning problem, from simple linear models to complex, non-linear models. This paper has provided an in-depth explanation of the score function and its equation, as well as a few examples and applications.
References
Dong, Y. (2021, January 4). What is the Score Function and Why is it Important for Machine Learning? Retrieved from https://yang-song.net/blog/2021/score/
McGee, J. (2019, October 23). Mean Squared Error (MSE) in Machine Learning: Definition and Examples. Retrieved from https://towardsdatascience.com/mean-squared-error-in-machine-learning-definition-and-examples-f18e47af11d5
β’ One shot generation. Fast.
β’ Harder to control in one pass.
β’ Adversarial min-max objective.
β’ Multi-iteration generation. Slow.
β’ Easier to control during generation.
β’ Simple objective, no adversary in training.
Generative Adversarial Networks (GANs): GANs are a type of deep learning algorithm that uses two neural networks to create models that can generate realistic artworks. GANs are used to create lifelike images, such as landscapes and portraits. Reinforcement Learning: Reinforcement learning is a type of machine learning that uses rewards and punishments to teach models to take the best action in a given situation. Reinforcement learning is used in Midway art to create models that can generate artworks that adhere to certain rules or criteria.
In recent years, Artificial Intelligence has become increasingly prevalent in our lives. From self-driving cars to smart home assistants, AI technology is being used to automate various tasks and make our lives easier. One of the most fascinating areas of AI research is its application to natural language processing (NLP). This has enabled AI to generate text answers and image prompts, a process known as Prompt Engineering. Prompt engineering is an AI-driven technology that can generate answers to text or image prompts. The technology works by using a set of algorithms to identify patterns in the data and then using those patterns to create a response.
The proposed approach consists of a domain-agnostic training method, which is formulated as follows:
min L(ΞΈ) = Ls(ΞΈ) + Ξ»D(ΞΈ) + Ξ³F(ΞΈ), where Ls(ΞΈ) is the supervised loss,
D(ΞΈ) is the domain adaptation regularizer, and F(ΞΈ) is the feature selection regularizer
For example, if a user were to ask a question about a particular topic, the AI would use its algorithms to identify keywords in the question and then generate a response based on those keywords. The technology has already been used to great effect in applications such as chatbots, customer service bots, and virtual assistants. These applications can provide users with a more personalized experience by providing tailored answers to their queries.
Prompt fed to Midway
A text-to-image generation system, unCLIP, is presented that combines a CLIP image encoder, a prior model and a decoder (diffusion or autoregressive). unCLIP is used to generate images from captions or to manipulate, interpolate and apply text diffs to images. It is found to have comparable performance to GLIDE, with greater diversity in generations.
The generative stack of unCLIP is given by:
P(x|y) = P(x|zi, y)P(zi|y), where P(x|zi, y) is the decoder and P(zi|y) is the prior model.
The decoder uses classifier-free guidance to improve sample quality. Two diffusion up samplers are used to generate high-resolution images and the prior model is conditioned on the caption and a dot product between the text embedding and image embedding.
The bipartite latent representation of unCLIP (zi, xT) enables image manipulations such as variations, interpolations and text diffs. Variations are produced by applying the decoder to the latent representation with Ξ· > 0. Interpolations are produced by rotating between the CLIP embeddings of two images, while text diffs are produced by rotating between the image CLIP embedding and a text diff vector.
Prompt engineering can also be used to generate captions for images, allowing users to better understand the context of the image. Prompt engineering is also being used to create more engaging interactive experiences. For example, AI-powered virtual assistants such as Amazon Alexa and Google Assistant can now provide users with more natural conversations by using prompt engineering to generate responses to spoken questions. Similarly, AI-powered games can use prompt engineering to generate more dynamic and engaging gameplay experiences.
Prompt engineering is also being used in the medical field to provide more accurate diagnoses and treatments. For example, AI-powered medical chatbots can use prompt engineering to generate more accurate diagnoses based on symptoms provided by the user. The technology can also be used to generate more personalized treatments based on a patientβs medical history. In addition to the medical field, prompt engineering is being used in the legal field to automate legal document production. AI-powered legal bots can use prompt engineering to generate legal documents such as contracts, wills, and leases. This technology can also be used to generate more accurate legal opinions based on the facts of the case. Overall, prompt engineering is a powerful tool that is being used in a variety of applications. It is being used to create more personalized experiences, automate document production, and provide more accurate diagnoses and treatments. As AI technology continues to advance, prompt engineering will only become more powerful and more widely used.
Midway Ai uses the same concept of this highly sophisticated Ai technology to generate whatever the user requests in return for the userβs requested Art.
we took it a step further by integrating this sophisticated technology with blockchain and crypto to offer the user utility and genius ways to monetize and verify ownership of the art created.
With Midway, users can create NFTs and sell them on the Midway marketplace or any other NFT marketplace such as Opensea.
Midway also offers the option to Buy and Sell Physical products by attaching the NFTS backed by the blockchain to products like clothes, bags, and accessories.
Midway Ai chat Bot is a conversational AI platform powered by advanced Artificial intelligence technology that provides automated conversations and interactions between a Ai bot and its user . The technology used by Midway Ai to generate AI chat responses to user requests is a combination of natural language understanding (NLU) and natural language generation (NLG).
NLU is the process of understanding human language, allowing a machine to interpret and respond to user input. NLU is used to break down and interpret requests from users to identify the intent and meaning behind the userβs request. This process involves the use of machine learning algorithms to identify the meaning of each word and the semantic relationships between them. Once the NLU process is completed, the machine can then generate an appropriate response to the userβs request.
The transformer architecture USED in the Midway Ai -LARA is based on the equation:
y = f(x, weights)
Where: x: the input to the model weights: the weights of the model y: the output of the model f: a non-linear transformation function.
Midway Ai also uses a hierarchical transformer-based architecture, which is based on the equation:
y = F(F1(x, w1), F2(x, w2))
Where: x: the input to the model w1 and w2: the weights of the model y: the output of the model F1 and F2: two different non-linear transformation functions.
Midway Ai also uses a unique training procedure to learn the parameters of the model. It uses a combination of supervised, unsupervised, and reinforcement learning. This allows the model to learn from both labeled and unlabeled data. The training procedure is based on the equation:
loss = loss_supervised + loss_unsupervised + loss_reinforcement
NLG is the process of generating human-like conversations that are tailored to the userβs specific request. NLG is used to generate natural language responses to user requests that include the correct syntax, grammar and structure. This process involves the use of deep learning algorithms that are trained on large datasets of conversations. This allows the machine to generate responses that are tailored to the userβs specific request, while still sounding natural and human-like.
In addition to NLU and NLG, Midway Ai also employs a variety of other technologies to generate AI chat responses to user requests. These technologies include natural language processing (NLP), natural language understanding (NLU), natural language generation (NLG), and artificial intelligence (AI). All of these technologies work together to provide automated conversations and interactions between a company and its customers.
Midway Ai also utilizes a variety of other technologies to improve the user experience. These technologies include natural language understanding (NLU) and natural language processing (NLP) to better understand user requests and generate more accurate responses. Additionally, Midway Ai leverages AI technologies such as natural language generation (NLG) and deep learning algorithms to generate more natural and human-like conversations between a company and its customers.
Overall, Midway Ai is a powerful conversational AI platform that enables companies to automate conversations and interactions with their customers. The technology used by Midway Ai to generate AI chat responses to user requests is a combination of NLU, NLG, NLP, and AI. All of these technologies work together to provide automated conversations and interactions that are tailored to the userβs specific request.
