Industry experts are discussing the business impact of Meta’s free artificial intelligence (AI) model, Llama 3.1, and weighing its potential against the practical implementation challenges.
Boasting 405 billion parameters, the AI model boasts performance comparable to its own competitors such as GPT-4 and Claude 3.5 Sonnet.As Meta gains adoption, with CEO Mark Zuckerberg predicting it will become the most widely used AI assistant by the end of the year, businesses are weighing the implications of access to powerful, cost-free AI against the challenges of implementation and security.
“These models can be used in customer communications to provide instant 24/7 support for simple questions that don’t require human intervention,” Ilya Baddeev, head of data science at Trevolution Group, told PYMNTS. [large language models]Marketing campaigns and recommendations can be truly personalized to each individual customer.”
Some experts predict a fundamental shift in customer service: “Given that the cost of intelligence in customer relationships will essentially become zero over time, call centers will no longer exist in the future. AI systems will manage massive volumes of customer inquiries in a way that is meaningful and satisfying to the end user,” Mike Conover, CEO of AI company BrightWave, told PYMNTS.
The potential for businesses to customize these models is huge: “By fine-tuning Llama to their specific domain data, businesses can create powerful natural language interfaces that understand customer inquiries, provide intelligent recommendations, and automate tasks like product categorization and content generation,” Hamza Tahir, CTO and co-founder of ZenML, an open-source machine learning operations (MLOps) startup, told PYMNTS.
Opportunities for small businesses?
The availability of powerful open-source AI models could level the playing field for SMEs. “Open-source models like Llama have the potential to democratize AI-powered commerce tools for SMEs and startups,” Tahir said.
“Even small teams can leverage cutting-edge natural language processing capabilities to build intelligent chatbots, product recommenders, and content generators.”
Open-source AI also has an advantage when it comes to regulatory compliance: “By processing data in an in-house model, user data remains private and complies with regulatory laws (such as GDPR),” Badeev noted, referring to the EU’s General Data Protection Regulation. This contrasts with a proprietary model, which may require sending user data to a third-party service.
The introduction of Llama 3.1 has sparked discussion about its potential to disrupt the commercial AI market. Conover said Llama’s 405 billion parameter model is comparable to OpenAI’s GPT-4 in the quality of inference. “This means commercial providers don’t have the secret sauce that leads to vendor lock-in. Business owners can control their own destiny,” he added.
Tahir predicted that the introduction of the new model could herald a shift toward a services-based model in which AI companies differentiate through their domain expertise, data assets, and ability to customize and deploy open-source models for specific use cases.
The economic impact could be substantial: “For business owners such as e-commerce platforms and customer service providers, the competitive pressure that open source technology puts on commerce providers will improve the unit economics of these services,” Conover added.
Despite the opportunity, businesses face challenges in adopting open source AI. [small to mid-sized enterprises] “The benefits are that you can do more and reach a wider audience, but this comes at a cost in both personnel and security,” Harry Toole, chief of staff at the OpenSSF, which promotes open source software, told PYMNTS.
He added that “open source AI models need to be used safely to ensure that the output is not manipulated, otherwise it could destroy small businesses operating in cost-constrained environments.”
Security measures are crucial: “Secure open source AI models must be built from a secure development environment, cryptographically signed, and follow best practices already in place for open source software development. This can be achieved by leveraging existing open source tools such as OpenSSF to secure open source AI models,” says Toole.
The Future of AI in Commerce
Potential supply chain issues are also a risk: “The commercial AI market needs to assess the supply chain of open source AI models. Recent global cyber issues such as XZ Utils and the Microsoft Blue Screen of Death demonstrate how widely used software components can have adverse effects on the industry,” Toole warned.
As businesses consider adopting open source AI, they face complex considerations. The long-term impact on e-commerce, customer service, and marketing strategies is still unclear. Some predict dramatic changes in these areas, while others caution that the technology’s impact depends on factors beyond mere availability.
The open-source model can lead to more effective feedback collection: “User feedback/reactions can be effectively collected from various sources such as reviews, social media mentions, customer support interactions etc. These can be processed in bulk using AI to instantly extract insights and analytics,” Badeev pointed out.
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