We want to build an intelligent interface that can act as a natural translator between humans and the digital world — AI and humans working in collaboration instead of competition.
Model is a digital agent intended to communicate with other programs and apps and serve as an interface between us and the digital world, a natural human-computer interface (HCI).
It can take high-level requests expressed in natural language and perform them— pretty much like Google’s PSC. The tasks can take up several steps across software tools and websites, varying in complexity and can do tasks that involve various tools at different points of the process and can take in user feedback to improve.
Most importantly, the Model can perform actions that we wouldn’t know how to do. This is where the model’s usefulness becomes apparent. model can act as a multitasking meta-learner capable of handling all kinds of software apps. To make it work we’d only have to know how to communicate with the model and the outcome we want. If the model worked perfectly, we wouldn’t have to learn to use Excel, Photoshop, or Salesforce. We’d simply delegate the work to the model and focus on more cognitively challenging problems.
Architecture
Our novel approach is a large multimodal that can accept image and text inputs and emit outputs. It exhibits human-level performance on various professional and academic benchmarks.
Model uses a transformer-style architecture in its neural network. A transformer architecture allows for a better understanding of relationships between words in text. It also uses an attention mechanism that allows the neural network to parse out which pieces of data are more relevant than others.
Large Language Models (LLMs) are a type of machine learning model designed to understand and generate human-like text. These models are trained on vast amounts of textual data, enabling them to learn the structure and nuances of language.
Computer vision is a field of artificial intelligence (AI) that enables computers and systems to derive meaningful information from digital images, videos and other visual inputs
What is Multimodal?
Multimodal technology refers to systems that can process and integrate multiple types of inputs and outputs, such as text, speech, image, video, gesture, etc. Multimodal systems can enable more natural and efficient human-computer interactions.
It consists of three main components: an encoder that transforms image and text inputs into vector representations; a decoder that generates text outputs from vector representations; and an attention mechanism that allows the encoder and decoder to focus on relevant parts of the inputs and outputs.
How Multimodal Works?
language model based on the ‘transformer’ architecture. These models are capable of processing large amounts of text and learning to perform natural language processing tasks very effectively.
the model learns to perform natural language processing tasks and generate coherent, well-written text.
Reinforcement learning, based on human feedback, was used for training
Selenium API and Architecture
Selenium API is a critical part of the Selenium Webdriver Test Automation. Selenium test automation comprises four basic concepts these are Selenium Navigation, Selenium Find Elements, Selenium Actions, and Selenium Wait.
Selenium 4 Architecture
Selenium 4 comes as a suite and it comprises 3 important parts these are:
Selenium IDE: This is a “record and play” tool for debugging your tests and creating some small-size test automation suites.
Selenium Webdriver: It is an automation API of the Selenium project. By using the webdriver object we can automate web applications.
Selenium Grid: If you want to run your tests in parallel with several browser types then Grid is the tool that you need to use.
Conclusion
Once we done training our transformer, we can integrate it with selenium webdriver and can play with it. I hope, it works. Have to research more. Thanks.
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