Now that the advantages of AI-enabled natural language solutions are becoming more tangible, especially with the recognition of the potential of large language models (LLMs), organizations are seeking insights that will help them address the full range of their business requirements.
At expert.ai, we have delivered hundreds of natural language solutions, helping organizations transform even the most complex language-intensive processes. As we pave the way for a future where language complexities are no longer barriers but data assets you can leverage, this selection of our most-read posts can provide pragmatic guidance for the year ahead.
Natural Language Processing: Where Are We in the Hype Cycle
As companies work to understand the AI solutions and capabilities that have tangible value, distinguishing hype from reality will continue to be an area of focus into 2024. Hype cycles help us understand what we’re experiencing with generative AI today and can help companies prepare for the ups and downs as technologies and overall adoptions evolve. Natural language processing (NLP) is a key technology for AI and large language models (LLMs) in general and the increased adoption of AI has generated more widespread awareness about what NLP can deliver, especially when it’s based on a hybrid AI approach.
LLM Hype and Concern: Benefits Versus Harm
As the use of AI grows, organizations are looking for solutions that can keep them competitive while also ensuring business benefits, regulatory compliance and internal accountability. Amid the risks being raised around generative AI and LLMs, it’s also a time to reflect on the proven AI solutions and capabilities already at work. Transparency and explainability should be built into any AI solution, the data you use to train any AI model matters, and a human-centered approach is critical. Read on for the considerations you should keep in mind when using any AI to solve your real-world business problems.
Enterprise LLMs of the Future: Bigger is not Better
As the hype surrounding large language models (LLMs) subsides, the reality of how they will play in the enterprise space is beginning to come into focus. If you consider ChatGPT the coming out party for the widespread, public use of LLMs, it is no surprise that, while its capabilities were initially met with amazement, this soon turned to caution due to challenges for toxicity, privacy and governance. At expert.ai, we have been exploring different techniques to leverage LLMs for our clients for several years now. The goals have been to push the envelope and determine the best way to leverage specific LLMs to save time, reduce costs and increase accuracy for our clients. As a result, the integration of multiple approaches via hybrid AI, as acknowledged by the analyst community, will help mitigate the risks and other issues that enterprises currently face in driving the value that LLMs can provide.
Understanding NLP vs NLU vs NLG
Thanks to ChatGPT, there is a greater focus on language AI and therefore, technologies like NLP. Together, NLP and natural language understanding (NLU) are a powerful combination that can be used to transform language data, otherwise known as unstructured data, into information that can be leveraged for insight, intelligence, efficiency and automation for several real-world applications and use cases. This explainer post looks at the difference between NLP and NLU and where generative AI models (or natural language generation, NLG) fit in.
What Are Taxonomies and How Should You Use Them
Taxonomies provide a formal hierarchical structure for data within an organization or domain so that it can be easily retrieved and analyzed. In fact, data is among an organization’s most valuable assets but is different from one organization to another as each organization has its own nomenclatures, terminology and business domains. Combined with AI and NLP, taxonomies make it easier for machines, and ultimately organizations, to find any asset in the form of language. Keep reading to learn more.
