IBM has recently introduced a new “Lightweight Engine” for its WatsonX.ai service. Although it is predominantly designed for “enterprise,” it could be used as an on-ramp to secure, in-house generative AI deployment for smaller businesses seeking to scale or mid-sized companies in burgeoning industries, such as fintech. The revenue growth of the technology sector in the first half of 2024 is unquestionably driven by the generative AI market. Many could not have anticipated the immense size and scope of a sector that was primarily propelled by the explosive popularity of large language models, such as Anthropic’s Claude and OpenAI’s ChatGPT, just a decade ago.
Financial services utilizing generative AI
Experts in the AI and finance communities broadly observed that large language models, such as GPT-3, were simply not reliable or accurate enough for use in the financial sector or any other environment where there is no margin for error prior to the launch of ChatGPT. The aphorism that AI models trained for general use on public data are as unpredictable as the information they are trained on remains true, despite the fact that the field has made significant progress since ChatGPT’s 2023. It is necessary for generative AI models to be specialized in order to transcend the status of a mere chatbot that can execute certain coding functions.
For instance, JPMorgan Chase recently acquired enterprise access to OpenAI’s ChatGPT for its entire workforce of 60,000 employees. This access includes custom guardrails and fine-tuning of internal data. It is evident that the financial services sector is also embracing generative AI. ChatGPT and other prominent public-facing AI services provide enterprise-level capabilities; however, they are exclusively cloud-based. Cloud-based AI solutions may not satisfy security requirements in industries where regulatory and fiduciary obligations necessitate the isolation of specific categories of data from the potential for external manipulation, such as the fintech and financial services sectors.
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The Lightweight Engine addition enables models to be performed and deployed on-site with a reduced footprint, and IBM’s WatsonX.ai is compatible with both cloud-based and on-premises solutions. IBM’s vice president of ecosystem engineering and developer advocacy, Savio Rodrigues, responded to Cointelegraph’s inquiry regarding the service’s applications: The flexibility of a cloud-based and on-premises capable solution could be the deciding factor between developing and deploying models internally or subscribing to another firm’s solution in fintech and other burgeoning industries, such as mining, blockchain, and crypto-lending, where off-site AI solutions may not meet all of a company’s security needs.
Nevertheless, there are numerous competitor services, including enterprises that specialize in the development of custom AI solutions that offer comparable services, as well as Microsoft, Google, and Amazon. Although this article does not permit a direct comparison of services, IBM’s Lightweight Engine appears to fulfill its appellation. The reduced footprint and increased efficacy of the product are offset by the loss of certain features that are exclusively available in the full-weight version.
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