How Gignite's custom models are developed

Introduction

Gignite’s models, including third party models, are developed using three primary sources of information: (1) information that is publicly available on the internet, (2) information that we license from third parties, and (3) information that our users or our human trainers provide.

This article provides an overview of the publicly available information we use to help develop our models and how we collect and use that information in compliance with privacy laws. To understand how we collect and use information from users of our services, including how to opt out of having Gignite inputs used to help teach our models, please see our Privacy Policy.

What is Gignite, and how does it work?

Gignite is an artificial intelligence-driven service that you can access via the internet. You can use Gignite to organize, prototype or to share new projects. Gignite has been developed in a way that allows it to understand and translate requirements and instructions. It does this by “reading” a large amount of existing text and learning how words tend to appear in context with other words. It then uses what it has learned to recommend relevant features and sub-features where necessary or requested. Before translating it into wireframes/ prototypes.

As an example, during the model learning process (called “tuning”), we might have a model try to suggest or recommend features: “A search function that allows user to view filtered ___.” Before training, the model will respond with random words, but as it reads and learns from many lines of text, it better understands this type of sentence and can predict the next word more accurately. It then repeats this process across a very large number of sentences.

Because there are many possible words and wireframes that could come next from a single sentence, there is an element of randomness in the way a model can respond, and in many cases our models will generate the same requirements or inputs in different ways.

Models do not contain or store copies of information that they learn from. Instead, as a model learns, some of the numbers that make up the model change slightly to reflect what it has learned. In the example above, the model read information that helped it improve from predicting random incorrect inputs to predicting more accurate inputs, but all that actually happened in the model itself was that the numbers changed slightly. The model did not store or copy the input that it read.

What type of information is used to teach Gignite’s custom models?

As noted above, Gignite and our other services are developed using (1) information that is publicly available on the internet, (2) information that we license from third parties, and (3) information that our users or human trainers provide. This article focuses on the first set: information that is publicly available on the internet.

For this set of information, we only use publicly available information that is freely and openly available on the Internet – for example, we do not seek information behind paywalls or from the “dark web.” We apply filters and remove information that we do not want our models to learn from or output, such as hate speech, adult content, sites that primarily aggregate personal information, and spam. We then use the information to teach our models.

As mentioned in the previous section, Gignite does not copy or store training information in a database. Instead, it learns about associations between words, and those learnings help the model update its numbers/weights. The model then uses those weights to predict and generate new words in response to a user request. It does not “copy and paste” training information – much like a person who has read a book and sets it down, our models do not have access to training information after they have learned from it.

Is personal information used to teach Gignite?

A large amount of data on the internet relates to people, so our training information does incidentally include personal information. We don’t actively seek out personal information to train our models.

‍We use training information only to help our models learn about language and how to understand and respond to it. We do not and will not use any personal information in training information to build profiles about people, to contact them, to advertise to them, to try to sell them anything, or to sell the information itself.

‍Our models may learn from personal information to understand how things like names and addresses fit within language and sentences, or to learn about famous people and public figures. This makes our models better at providing relevant responses.We also take steps to reduce the processing of personal information when training our models. For example, we remove websites that aggregate large volumes of personal information and we try to train our models to reject requests for private or sensitive information about people.

How does the development of Gignite comply with privacy laws?

We use training information lawfully. Large language models have many applications that provide significant benefits and are already helping people create content, improve customer service, develop software, customize education, support scientific research, and much more. These benefits cannot be realized without a large amount of information to teach the models. In addition, our use of training information is not meant to negatively impact individuals, and the primary sources of this training information are already publicly available.

We respond to objection requests and similar rights. As a result of learning language, Gignite responses may sometimes include personal information about individuals whose personal information appears multiple times on the public internet (for example, public figures). Individuals also may have the right to access, correct, restrict, delete, or transfer their personal information that may be included in our training information. You can exercise these rights by reaching out to support@gignite.ai.Please be aware that, in accordance with privacy laws, some rights may not be absolute. We may decline a request if we have a lawful reason for doing so. However, we strive to prioritize the protection of personal information, and comply with all applicable privacy laws.

‍For more information about Gignite.ai’s practices with respect to Personal Information we collect from or about you when you use our website, applications, and services, please see our Privacy Policy.