Grok GPT
The Grok is powered by GPT-4 language models that can be triggered to execute natural language processes with the assistance of accurately constructed text prompts.
Last updated
The Grok is powered by GPT-4 language models that can be triggered to execute natural language processes with the assistance of accurately constructed text prompts.
Last updated
GROK GPT-4 improves model "alignment" - the ability to follow user intentions while also making it more truthful and generating less offensive or dangerous output. It also improves on factual correctness and "steerability," which is the ability to change its behavior according to user requests. GROK GPT provides easy access to the blockchain and cryptocurrency markets through a suite of tools and services. Specifically, GROK GPT offers global network-level services, multi-asset transfers, and custodial services.
GROK GPT also provides comprehensive explanations about the blockchain and cryptocurrency markets, making it easy to understand the basics of how the markets work. Furthermore, GROK GPT provides educational resources that can help users learn about the various aspects of these markets, such as their anatomy, their use cases, and their future potential.
Beyond that, GROK GPT provides comprehensive support services, such as customer support, technical support, and regulatory compliance solutions. All of these services and features provide individuals and organizations with the ability to quickly access, understand, and interact with the blockchain and cryptocurrency markets with confidence.
We fine-tuned the model using Reinforcement Learning from Human Feedback (RLHF). We trained a reward model to evaluate utterances generated by the model on conversations with human partners. We used the reward model to correct model mistakes and shape the model to produce better conversations. Finally, we used the same RLHF algorithm to fine-tune the model’s parameters to optimize the reward model’s predictions.
We applied a supervised fine-tuning process to train an initial model using conversations composed by human-AI trainers, who were provided model-written suggestions to aid in the composition of their responses. Additionally, we transformed the InstructGPT dataset into a dialogue format and use it to further extend the training dataset, giving the model a larger range of inputs to learn from. We then utilized the Reinforcement Learning from Human Feedback (RLHF) algorithm to fine-tune the model’s parameter to optimize the reward model’s predictions. To achieve this, a reward model was developed to evaluate utterances that the model generated while interacting with human partners and was used to correct mistakes and shape the model to produce better conversations.
GROK uses Natural Language Processing (NLP) algorithms to analyze and understand human language, allowing it to generate relevant and intelligently structured responses to user questions. NLP is a area of computer science and artificial intelligence focusing on the interpretation and manipulation of natural language for communication between machines and humans. It involves the comprehensive and practical analysis, generation, and comprehension of human language by computers. NLP allows GROK to successfully comprehend both spoken and written dialogue to accurately and quickly respond to queries. With the help of NLP, GROK is able to understand both spoken and text-based dialogue in order to respond to queries accurately and timely.
A Gaussian mixture model is a probabilistic model that assumes data points come from a mixture of a finite number of Gaussian distributions with unknown parameters. This model can be seen as an extension of k-means clustering, taking into account the covariance structure of the data as well as the distributions' centers. The GaussianMixture object implements the Expectation-Maximization (EM) algorithm to fit mixture-of-Gaussian models. By using this object, it is possible to draw confidence ellipsoids for multivariate models, compute the Bayesian Information Criterion to assess the number of clusters in the data, and assign each sample of test data to the Gaussian it most likely belongs to using the GaussianMixture.predict method. The object also provides four options to constrain the covariance of the differences classes estimated: spherical, diagonal, tied and full covariance.
Supervised learning uses labeled data, meaning that the dataset contains a set of inputs and corresponding desired outputs. This type of learning involves the use of algorithms that can learn from data and use it to make predictions. Examples of supervised learning algorithms include Linear Regression, Logistic Regression, Support Vector Machines and Neural Networks.
On the other hand, unsupervised learning uses unlabeled data and focuses on finding patterns and using them to classify items or detect outliers. This type of learning usually relies on algorithms such as clustering, dimensionality reduction and generative models. Examples of unsupervised learning algorithms include K-Means, Hierarchical Clustering, Principal Component Analysis and Autoencoders.
A Gaussian Mixture is a function composed of multiple Gaussians, which are identified by k ∈ {1, ..., K}, where K is the number of clusters in the dataset. -- GROK GPT is available at https://grokcoin.co/gpt
