In today’s fast-paced digital world, artificial intelligence (AI) is becoming an ever-present, omnipotent force. One such AI powerhouse that’s creating a whirlwind of excitement is ChatGPT by OpenAI. In this exclusive article, we’ll unravel the mystery of this cutting-edge technology, taking you on a riveting journey into the heart of ChatGPT. Get ready to discover how it has evolved into the ultimate digital sidekick, revolutionizing communication, productivity, and efficiency in countless aspects of our lives.
From decoding its intricate inner workings to exploring the genius behind its natural language processing prowess, we’ll reveal the secrets that have propelled ChatGPT to the forefront of AI innovation. Dive into the fascinating world of ChatGPT and find out how it’s reshaping the future of AI, one conversation at a time. Don’t miss this opportunity to be one of the first to understand the groundbreaking science that’s changing the game for everyone! #AIUnleashed #ChatGPTRevealed #OpenAIBreakthrough
The secret sauce to for the successful application of smart bots like ChatGPT for connected devices, connected homes, connected factories and connected anything is applying a combination of real time data, deep learning and and cognitive intelligence to provide the right information at the time of now for a bot to make an intelligent decision and adding exponential value in the digital journey of AI and human interactions.
Here is an overview of the explosion of the artificial intelligence, data and machine learning ecosystem of solutions including ChatGPT:
Smart bots become more valuable if they can read real time information in the context when it is relevant to the human interacting with the connected device during conversational interactions with chatbots and audio connected smart devices. This is not currently case with ChatGPT which was trained on data from before 2022.
Chat GPT architecture and infrastructure components:
ChatGPT is a large language model that is built on the Generative Pre-trained Transformer 3.5 (GPT-3.5) architecture. GPT-3.5 is an extension of the GPT-3 architecture that includes improvements to the training process, resulting in a more powerful and accurate language model.
The GPT-3.5 architecture is based on the transformer architecture, which is a type of neural network that was originally proposed for machine translation tasks. The transformer architecture is composed of several layers of attention mechanisms, which allow the model to selectively focus on different parts of the input sequence during processing.
ChatGPT4 is reaching human level intelligence for standardized testing:
The GPT-3.5 architecture includes some additional modifications to the transformer architecture to improve its performance on language modeling tasks. Specifically, it uses a combination of transformer layers with a smaller number of parameters and convolutional layers to enable efficient training on large datasets. It also incorporates a dynamic prompt tuning mechanism, which allows the model to adapt its output based on the input prompts given to it.
The infrastructure that ChatGPT runs on is based on a large-scale distributed computing system, which includes thousands of CPUs and GPUs working in parallel. The system is designed to handle the massive amount of computation required for training and running the model.
OpenAI, the organization behind ChatGPT, uses a custom training infrastructure called GPT-3, which is based on a combination of TensorFlow and PyTorch. The system is designed to be highly scalable and efficient, allowing OpenAI to train models with billions of parameters.
In addition to the training infrastructure, OpenAI also uses a cloud-based deployment infrastructure to run the ChatGPT model in production. This infrastructure is based on Kubernetes, a popular open-source container orchestration system, which allows the model to be deployed and scaled across multiple servers in a highly efficient and reliable manner.
Recently, OpenAI released GPT4:
GPT-4 is capable of handling over 25,000 words of text, allowing for use cases like long form content creation, extended conversations, and document search and analysis.
It has significantly improved over the performance previous version 3.5
It is more creative and collaborative generating, editing and iterating with users on creative and technical writing tasks, such as composing songs, writing screenplays, or learning a user’s writing style.
GPT-4 can also accept images as inputs and generate captions, classifications, and analyses.
OpenAI also spent 6 months to make ChatGPT safer with checks and balances in place.
With today’s technological advances what are different applications of AI smart bots in the commercial marketplaces that are adding value during customer journeys of interacting with smart connected products?
These applications include, but are not limited to:
- Chatbots & intelligent agents interactions: Smart Chatbots like OpenAI’s ChatGTP understand natural language by the means of AI natural language processing algorithms that can interpret the intent of questions and pass instructions to the IoT gateway for processing the data running through deep learning algorithms and improving along the way providing ever better solutions during interactions with humans.
- Crawlers & information bots integrations: these are bots that are running in the background and processing and classifying data as used in google search engine spiders, breaking news alerts and pricing assistants that monitor price changes such as in use in Expedia, priceline etc.
- Transaction, Service & Niche Bots for connected devices: these bots are acting on the behalf of their human counterpart accessing any source that has an API to connect to which for instance is utilized in stock exchange type scenarios and automatically coordinating focused tasks.
- Master bots orchestrating multiple bot and non-bot interactions: these bots manage often seemingly disconnected services that need to be orchestrated as laid out in a McKinsey article for tasks that are focused on in home automation, but that are also applicable to bots in factories following the 5 Cs of cyber physical architecture and smart ecosystems such as connected buildings and connected cars
Example of the connected bot automation ecosystem in connected homes:
Real time closes the Gap between data and action
Components of a reference architecture for smart bot and connected device integration can be derived by applying the principles of the 5 Cs of Cyberphysical systems in conjunction with layered architecture like the worldforums IoT Architecture.
Combining that proven model with the Worldforum layered IoT reference architecture can be a guideline on how smart, self aware and self learning bots and machines need to interact in symphony with the layers of edge computing to data acquisition and human collaboration:
In order for bots to be effective in this highly interactive environment and serving up information in real time that is relevant to the user along the lines of these IoT reference models the Bot has to have these physical architecture model components in place:
- Establish API connectivity with connected devices and other peripherals
When we think of connected devices we don’t necessarily have to look far since connected devices could be mobile phones and smart devices in homes, factories, cars etc. connected to IoT hubs. The connected device could be even a webpage where interactions are processed and enhanced with service bots and chat bots. In order for a multitude of different types of Bots, smart devices and sensors to interact Bot developers need to have access to 3rd party REST APIs to drive machine to machine communications for streaming, structured and unstructured IoT.
Connected devices are often interacting together through voice and service orchestration as this future vision of a connected car illustrates:
2. Authenticate and Authorize the user with the device or service:
Security Considerations for Bot interactions regarding authentication and authorization:
- User identity authentication: verified with secure login credentials, such as a username and password. These credentials are exchanged for a secure authenticated token that is used to continually verify the identity of the user.
- Authentication timeouts: revoked either by the user or automatically by the platform after a given amount of time.
- Two-factor authentication: verify a users identity through two separate channels (e.g., once by email, then again by text message).
- Biometric authentication: verify a users identity using a unique physical marker, such as a fingerprint or Face ID (e.g., Apple’s iPhone)
- End-to-end encryption: only the two parties – the user and the IoT interface involved in the conversation can read the otherwise encrypted data e.g. Facebook messenger recently implemented this capability with a secret message feature
- Self-destructing messages: When potentially sensitive information is transmitted, the message containing this information is destroyed after a given amount of time similar to Snapchat
In the case of ChatGPT data security firm Cyberhaven pointed out the dangers of unregulated use of AI chatbot technology:
- 4.2% of the 1.6 million workers at its client companies tried to submit sensitive business data to large language models (LLMs) such as ChatGPT.
- There were instances were executives cut and pasted theor firm’s 2023 strategy document into ChatGPT and asked it to create a PowerPoint deck for their board.
- More concerning a doctor entered his patient’s name and their medical condition and asked ChatGPT to craft a letter to the patient’s insurance company.
The Intel proof of concept of the enhanced privacy ID is an interesting approach to address IoT security concerns for companies and in this case specifically for TSA airline security, but this approach can be applied to other real world scenarios such as authenticating and tracking IoT connected devices in transit as laid out in the illustration below:
3. Gather, store and process the data gathered from IoT to human interactions
- Leveraging data protocols such as MQTT, CoAP, AMQP, Websocket and Node data is transferred and converted into machine readable information. The Bot often interacts physically through audio interfaces or chat with the human end user serving up solutions sifting through massive amounts of knowledge data bases after analyzing the questions through natural language processing. In order to ingest large amounts of data into a data store, at real-time or in batches and to process the data and feed it back to the user based on AI insights multiple components of an IoT storage and processing platform need to be in place. This includes cloud data ingestion layers and data lake cloud infrastructure to manage massive amounts of streaming data to be ingested even if we only care about certain events that are set in a rules event engine. Ad-hoc cloud blob storage, unconstrained bulk data ingestion and specific events based data ingestion require different tools and architectural approaches all leading to being able to gather, store and process the data.
- The following architecture can be applied irrespective if you are a Microsoft shop or rely on other cloud platforms such as Amazon, Googles or others:
4. Cognition Logic
Making all the data we now have “actionable” is the final critical step, towards increasingly personalized services. Being able to segment users based on the data and scenarios and perform actions automatically when certain criteria are met.
In order to set up an interactive and AI driven Bot service we need to continuosly and in real-time monitor data in the background and respond automatically at the right time and place.
Imagine a world where our homes, cars and wearables are connected to flexible and scalable cloud infrastructure and AI driven predictive intelligence that can provide real time feedback and even start a conversation that adds value to our everyday lives.
The bottom line:
Smart bots interacting with connected devices and systems like ChatGPT4 hold immense potential to create new opportunities and empower individuals and organizations in ways that were previously unimaginable. With its advanced natural language processing capabilities, ChatGPT4 has the ability to understand, analyze and respond to complex human queries and provide solutions in real-time.
By leveraging Artificial Intelligence and AI chatbot capabilities, individuals and organizations can access a vast pool of information and knowledge from across the world in a matter of seconds. This can help them make informed decisions, solve complex problems, and explore new ideas and possibilities.
As we are on the path of general artificial intelligence with AI chatbots ability to understand and generate human-like language can also facilitate communication and collaboration between individuals and teams, regardless of geographical boundaries or language barriers. This can enable new levels of innovation and creativity, as well as foster global partnerships and exchange of ideas.
Overall, Artificial Intelligence and AI chatbots like ChatGPT4 hold great promise to transform the way we work, learn, and interact with each other, and pave the way for a more connected and empowered future.
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