OpenAI chatbot : OpenAI chatbot, chatGPT, has gained immense popularity within a week of its launch, with over a million users, growing to 100 million users in its first month.
The chatbot is designed to offer human-like responses in various areas such as long-form content creation, in-depth conversations, document search, analysis, and more, powered by 45 terabytes of text data.
According to Uljan Sharka, CEO of iGenius, generative AI has the potential to revolutionize the business world by democratizing data. In the past, data and analytics have been designed for data-skilled individuals, leaving business users facing barriers to accessing information required for data-driven decisions.
By shifting the user interface towards language interfaces and humanizing data, generative AI can make data people-centric.
However, the interface is just a small part of a complex system that requires data science, machine learning, and conversational AI to create a single system.
Composite AI brings together these technologies to provide a safe, equal, and integrated experience. Sharka compares this to the iPhone of the category, which offers an integrated experience for impact in the enterprise.
Business users have been left behind as the gap between B2C and B2B apps has grown. Generative AI has the potential to connect every data product in the world, index it in an organization’s “private brain,” and improve data quality with natural language processing, algorithms, and user-created metadata. Gartner has named this “conversational analytics.”
Generative AI can virtualize complexity and clean, manipulate, and serve data for every use case. It can also scale the integration between systems and create an AI brain, composed of private sources of information, using natural language to create a no-code interface that democratizes data science before business users even consume the information.
It is an innovation accelerator that can dramatically reduce costs and time to identify and develop use cases.OpenAI’s chatbot, chatGPT, has gained immense popularity within a week of its launch, with over a million users, growing to 100 million users in its first month.
The chatbot is designed to offer human-like responses in various areas such as long-form content creation, in-depth conversations, document search, analysis, and more, powered by 45 terabytes of text data.
According to Uljan Sharka, CEO of iGenius, generative AI has the potential to revolutionize the business world by democratizing data. In the past, data and analytics have been designed for data-skilled individuals, leaving business users facing barriers to accessing information required for data-driven decisions.
By shifting the user interface towards language interfaces and humanizing data, generative AI can make data people-centric.
However, the interface is just a small part of a complex system that requires data science, machine learning, and conversational AI to create a single system.
Composite AI brings together these technologies to provide a safe, equal, and integrated experience. Sharka compares this to the iPhone of the category, which offers an integrated experience for impact in the enterprise.
Business users have been left behind as the gap between B2C and B2B apps has grown. Generative AI has the potential to connect every data product in the world, index it in an organization’s “private brain,” and improve data quality with natural language processing, algorithms, and user-created metadata. Gartner has named this “conversational analytics.”
Generative AI can virtualize complexity and clean, manipulate, and serve data for every use case. It can also scale the integration between systems and create an AI brain, composed of private sources of information, using natural language to create a no-code interface that democratizes data science before business users even consume the information.
It is an innovation accelerator that can dramatically reduce costs and time to identify and develop use cases.
On the front end, business users are literally having a conversation with data and getting business answers in plain natural language. Making the front-end user experience even more consumerized is the next step.
Instead of a reactive and single task-based platform, asking text questions and getting text answers, it can become multi-modal, offering charts and creative graphs to optimize the way people understand the data.
It can become a Netflix or Spotify-like experience, as the AI learns from how you consume that information to proactively serve up the knowledge a user needs.
Generative AI and iGenius in action
From an architectural perspective, this natural language layer is added to the applications and databases that already exists, becoming a virtual AI brain. Connecting across departments unlocks new opportunities.
“This is not about using data more — this is about using data at the right time of delivery,” Sharka says. “If I can use data before or while I make a decision, whether I’m in marketing or sales or supply chain, HR, finance, operations — this is how we’re going to make an impact.”
For instance, connecting marketing data and sales data means not only monitoring campaigns in real time, but correlating results with transactions, conversions and sales cycles to offer clear performance KPIs and see the direct impact of the campaign in real time.
A user can even ask the AI to adapt campaigns in real time. At the same time, the interface surfaces further questions and areas of inquiry that the user might want to pursue next, to deepen their understanding of a situation.
At Enel, Italy’s leading energy company now focused on sustainability, engineers consume real-time IOT information, mixing finance data with data coming from the production plants, having conversations with that data in real time.
Whenever their teams need to perform preventative maintenance or plan activities in the plant, or need to measure how actual results compare to budgets, asking the interface for the synthesized information needed unlocks powerful operational analytics that can be reacted on immediately.
The future of generative OpenAI chatbot
ChatGPT has sparked a massive interest in generative AI, but iGenius and OpenAI (which both launched in 2015) long ago realized they were headed in different directions, Sharka says. OpenAI built the GPT for text, while iGenius has built the GPT for numbers, a product called Crystal.
Its private AI brain connects proprietary information into its machine learning model, allowing users to start training it from scratch. It uses more sustainable small and wide language models, instead of large language models to give organizations control over their IP.
It also enables large-scale collaboration, in which companies can leverage expertise and knowledge workers to certify the data used to train models and the information generated to reduce bias at scale, and provide more localized and hyper-personalized experiences.
It also means you don’t need to be a prompt engineer to safely work with or apply the data these algorithms provide to produce high-quality actionable information.
“I’ve always believed that this is going to be a human-machine collaboration,” Sharka says. “If we can leverage the knowledge that we already have in people or in traditional IT systems, where you have lots of semantic layers and certified use cases, then you can reduce bias exponentially, because you’re narrowing it down to quality.
With generative AI, and a system that’s certified on an ongoing basis, we can achieve large-scale automation and The natural language layer added to applications and databases creates a virtual AI brain that enables business users to have conversations with data and receive answers in plain language.
The next step is to make the front-end user experience more consumerized, offering charts and creative graphs to optimize the way people understand the data, creating a multi-modal experience similar to Netflix or Spotify.
The AI learns from how users consume the information to proactively serve up the knowledge they need.
By connecting data across departments, new opportunities can be unlocked, and users can make more informed decisions. For example, connecting marketing and sales data can monitor campaigns in real-time and offer clear performance KPIs, allowing users to adapt campaigns in real-time.
The interface also surfaces further questions and areas of inquiry for users to deepen their understanding of a situation.
iGenius has built the GPT for numbers called Crystal, which connects proprietary information into its machine learning model, allowing users to train it from scratch.
It uses more sustainable small and wide language models, instead of large language models, to give organizations control over their IP. This enables large-scale collaboration, reducing bias and providing more localized and hyper-personalized experiences.
The goal is to achieve large-scale automation and reduce bias, making it safe, equal, and pushing the idea of virtual copilots in the world.
What are the Notable projects and releases of OpenAI Chatbot
| 1. GPT-3 | This powerful language model serves as the basis for other OpenAI products. It analyzes human-generated text to learn to generate similar text on its own. |
| 2. DALL-E and DALL-E 2. | These generative AI platforms can analyze text-based descriptions of images that users want them to produce and then generating those images exactly as described. |
| 3. CLIP | CLIP is a neural network that synthesizes visuals and text pertaining to them to predict the best possible captions that most accurately describe those visuals. Because of its ability to learn from more than one type of data (both images and text), it can be categorized as multimodal AI. |
| 4. ChatGPT | ChatGPT is currently the most advanced AI chatbot designed for generating humanlike text and producing answers to users’ questions. Having been trained on large data sets, it can generate answers and responses the way a human would. |
| 5. Codex | Codex was trained on billions of lines of code in various programming languages to help software developers simplify coding processes. It’s founded on GPT-3 technology, but instead of generating text, it generates code. |
| 6. Whisper | Whisper is labeled as an automatic speech recognition (ASR) tool. It has been trained on a multitude of audio data in order to recognize, transcribe and translate speech in about 100 different languages, including technical language and different accents. |
What do you use OpenAI Chatbot for ?
The OpenAI Playground lets you ask an AI bot to write nearly anything for you. You can ask the AI questions, start a conversation with it, use it to write short stories, and more.
What is the concept of OpenAI ?
OpenAI chatbot is a nonprofit research company that aims to develop and direct artificial intelligence (AI) in ways that benefit humanity as a whole. The company was founded by Elon Musk and Sam Altman in 2015 and is headquartered in San Francisco, California.
Can OpenAI write code ?
OpenAI chatbot Codex is an artificial intelligence model developed by OpenAI. It parses natural language and generates code in response. It is used to power GitHub Copilot, a programming autocompletion tool developed for select IDEs, like Visual Studio Code and Neovim.
What model does OpenAI use ?
GPT-3, or the third-generation Generative Pre-trained Transformer, is a neural network machine learning model trained using internet data to generate any type of text. Developed by OpenAI, it requires a small amount of input text to generate large volumes of relevant and sophisticated machine-generated text.
How does OpenAI generate images ?
DALL-E (stylized as DALL. E) and DALL-E 2 are deep learning models developed by OpenAI to generate digital images from natural language descriptions, called “prompts”. DALL-E was revealed by OpenAI in a blog post in January 2021, and uses a version of GPT-3 modified to generate images.
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