Harnessing the power of Quantum Computing to ChatGPT could be one marriage that holds immense promise for the future of Generative AI
ChatGPT is transforming the ways-of-working in every profession. It is a conversational software-as-a-service AI chatbot that understands the intent of complex, specific questions and can come up with relevant human-quality responses based on information available online in the public domain. It uses OpenAI’s large language models (LLMs), language processing techniques that enable computers to understand and generate text. These models are trained to pick up context and meaning by sifting through billions of pages of material available in the public domain.
Essentially a machine-learning solution, ChatGPT keeps getting more refined all the while as more and more people use it across the world.Within its short life of just a few months, it is already making significant waves across industries and is posed to bring about a paradigm shift by replacing all sorts of human roles at the workplace.
ChatGPT is constantly being refined yet, and developers are looking for options that would enhance both its speed and accuracy.And the most exciting improvement that is being considered is to harness the power of Quantum Computing technology.
Speed and computing power
The unprecedented power of quantum computers makes them useful in many scenarios where classical computers fall far behind. Compared to traditional computer systems, the USP of quantum computing is twofold – the speed of computing and the level of complexity it can handle. Taken together, these can transform the world of computing altogether, and achieve solutions that are as yet unthinkable. And combining the unique capabilities of traditional and quantum computing over the Cloud to derive the best of both worlds together – could offer limitless possibilities. However, the acid test is going to be channelising the progress made so far into creating commercial applications.
With quantum computing’s promise of faster data analysis and more extensiveoptimisationalgorithms, the combination of these two nascent technologies could create magic. This is one marriage that holds immense promise for the future of Generative AI.
How Quantum helps
ChatGPT is a generative artificial intelligence (AI) solution, which describes algorithms that can be used to create new content from pre-existing data. Part of the process is natural language processing (NLP), which combines linguistics, computer science and artificial intelligence to understand and mimic how humans use language. This involves extensive training based on existing data – in the case of ChatGPT this data is sourced from the Internet – as well as user feedback.
There are three basic areas where Quantum Computing can contribute to the entire process:
- Quantum computing can be dramatically more efficient in training, needing much less training data to achieve the current level of proficiency. Speeding up the training and inference processes of machine learning algorithms will make ChatGPT faster and more accurate with its responses.
- Quantum computing can help improve natural language processing tasks, which could enhance ChatGPT’s ability to understand and generate human-like language.
- Quantum computing can provide new ways of solving complex optimisation problems, enabling ChatGPT to improve its decision-making and recommendation capabilities.
But first, QML algorithms
For a true amalgamation of ChatGPT with Quantum Computing, creating software with Quantum Machine Learning (QML) algorithms capabilities, should be the first action point. Such algorithms potentially bridge the gap between the abilities of AI and Quantum Computing, as they offer greater benefits over and above traditional ML algorithms.
Compared to classical generative AI, using QML algorithms would drastically reduce the volume of training data needed to achieve the same level of inferencing capability. Large foundational models like ChatGPT are enormous, costly to train, requires months to train (months), and consume huge amounts of energy. QML would change all these.
Enterprises interested in using ChatGPT for business purposes are wary of the fact that OpenAItrainsthe model on Internet-based data. Organisations would want to use their own internal data for training, and yet get the same level of inferencing output.This is one use case where a capable QML algorithm could clinch the deal.
Extensive training, faster reinforcement learning
A Quantum algorithmwould be able to explore a wider search space than classical machine learning. This makes QML a better performer when it comes to translating idiomatic language, or translating between two languages that are structured very differently at a core grammatical level. This has immense implications –starting from politics and international translation devices to the reconstruction of extinct languages.
Another area where Quantum algorithm scores is reinforcement learning. ChatGPT is equipped with reinforcement learning from human feedback (RLHF). This requires the ML model to go through extensive trial-and-error processes before it can identify the intended or “rewarded”behaviour. This traditionallyinvolves ample time, cost, and effort.However, in experiments involving a traditional-quantum hybrid system, reinforcement learning worked 60% faster than purely traditional algorithms.
Hybrid computing as a first step
Yes, hybrid computing may be the first practical step on the road to Quantum salvation. Quantum systems are still work-in-progress, are quite unstable yet, and unreliable in terms of performance. It is not yet clear when a pure Quantum-based system would be ready to take on ChatGPT.
However, most expertsbelieve that the natural next step is to combine classical computing AI models with Quantum computing in a hybrid platform. Platforms like NVIDIA QODA and others have already moved some extent towards developing practicable hybrid quantum-classical systems. These can well be leveraged to create the next-level Generative AI models.
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