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Open-source LLM reduced enterprise cost & provide customization with rapid innovation VS. close-source LLM provide ease of use, dedicated support with high security measures
Why is AI important in the banking sector? | The shift from traditional in-person banking to online and mobile platforms has increased customer demand for instant, personalized service. |
AI Virtual Assistants in Focus: | Banks are investing in AI-driven virtual assistants to create hyper-personalised, real-time solutions that improve customer experiences. |
What is the top challenge of using AI in banking? | Inefficiencies like higher Average Handling Time (AHT), lack of real-time data, and limited personalization hinder existing customer service strategies. |
Limits of Traditional Automation: | Automated systems need more nuanced queries, making them less effective for high-value customers with complex needs. |
What are the benefits of AI chatbots in Banking? | AI virtual assistants enhance efficiency, reduce operational costs, and empower CSRs by handling repetitive tasks and offering personalized interactions. |
Future Outlook of AI-enabled Virtual Assistants: | AI will transform the role of CSRs into more strategic, relationship-focused positions while continuing to elevate the customer experience in banking. |
Early Days:
LLMs have been around for decades, but their capabilities were limited due to computational constraints and smaller datasets. Early models focused on statistical language processing techniques.
Their capabilities and popularity have surged in recent years
Around 2017: Advancements in deep learning architectures, particularly transformers, and the availability of massive datasets like Google Books and Common Crawl fueled significant progress in LLMs.
By 2018: OpenAI's Generative Pre-trained Transformer (GPT-2) demonstrated impressive capabilities in text generation, attracting widespread attention & is often considered a landmark due to its public release and capabilities.
GPT-2 was not fully open-source. OpenAI opted for a controlled release due to concerns about potential misuse. This sparked the debate about open vs. closed-source approaches in LLM development.
Universities have a long history of sharing research and code, fostering open collaboration. This philosophy naturally extended to the fields of AI and LLMs. The success of open-source software movements like Linux demonstrated the power of collaboration and community-driven development. This inspired researchers and developers to explore open-source approaches for LLMs.
Numerous research groups and independent developers are actively contributing to the open-source LLM landscape. This collaborative effort is constantly expanding the range of available models (OpenAI GPT-J, Meta AI Llama, EleutherAI Jurassic-1 Jumbo, Hugging Face Transformers,) and fostering innovation. A growing community of independent developers and companies are actively contributing to improve the open-source LLM landscape.
It's constantly evolving, with new models being developed and released frequently. The Hugging Face Transformers Library alone offers access to over 100 pre-trained models, and there are numerous independent projects launching new open-source LLMs all the time.
Companies like Google, OpenAI, Microsoft, Amazon, and Baidu are at the forefront of closed-source LLM development. These models are often shrouded in secrecy regarding their code and training data.
Closed-source models are probably more numerous. Big companies with vast resources often prioritize closed-source development for commercial gain and control over intellectual property.
Let's take a look at some limitations of closed source llm
Understanding these differences is crucial for users to choose the right LLM for their needs. Lets understand characteristic of close source llm vs. open source llm.
The LLM landscape is likely to see a blend of both approaches. Closed-source models will continue to push the boundaries of performance, while open-source models will democratize access and foster innovation. Collaboration between these two sides could lead to even more powerful and responsible AI advancements.
Keep pace with the dynamic advancements in LLM landscape by engaging with Fluid AI. Reach out to us to adopt & deploy the latest LLM model for your organisation with utmost security, privacy & added capabilities to make it Enterprise-ready, user-friendly & support throughout. We work with every LLM model avaliable & allow organisations to also easily switch to any new model launched swiftly to be ahead of the technological curve. We help organisation to deploy the advance model according to your usecases & organisational need. Fluid AI offers the flexibility of Private Deployment option or explore the flexibility of public along with hybrid hosting options.
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