No ChatGPT without LLM

#LLM stands for "Large Language Model." LLM are machine learning (ML) algorithms that can recognize, summarize, translate, predict and generate human language based on a VERY LARGE Text-Based datasets. This is a specific class of Artificial Intelligence (AI) model designed for Natural Language Processing (NLP) and Natural Language Understanding (NLU).?To dummy-it-down, LLM using NLP is a type of AI than can mimic human spoken language to communicate.?This place a significant role in advancing AI capabilities related to language understanding and generation.

Generative AI is?frequently referred with LLM – since LLM is a type of generative AI that was specifically architected to generate text-based content.?Like when you download those Apple Apps (for example search OpenAI or ChatGPT in the App Store.) But remember, Generative AI expands to various types of contents beyond text - like image (for example art generation from text), audio or video generation.?Can you imagine with the SAG-AFTRA strike ongoing – how the future can replace actors with Generative AI (imagine if this becomes a reality – Jen AI?)?

Sidebar: Voice Biometrics, along with a variety of other methods that are widely installed in most banks and healthcare companies, are used to authenticate customer calls. Even with Generative Voice AI – most companies still provide additional MFA (multi-factor authentication) to validate the caller is you. ?For Generative Video AI – we have companies like Synthesia who’s doing a fine job of creating your own AI Avatars. The most practical use of this, that I can see applies today, is used in Training. All you do is type in all the training material and let your AI Avatar do the training.?

Large Language Models are built using deep learning techniques, particularly those based on Transformer architectures, which allow them to process and understand human language at a sophisticated level. They are trained on massive amounts of text data, enabling them to learn patterns, grammar, semantics, and context from the language they have been exposed to during training.

Having worked with Voice Analytics and Conversational AI for many years, LLM was extensively used by these applications.?Conversational AI for example, aims to create virtual agent/assistant or chatbots than can interact in a human-like manner, providing relevant information, answering questions and asserting various tasks. Here are some examples:

• NLU Enhancement. LLM adds comprehension and user input interpretation – whether this is in the form of Voice (speech) or Text (chat). It recognizes intent, extract entities, and understand context to allow for example chatbot to grasp what you’re asking.
• Dialog Generation and Grammatically Correct response. Everyone is using text messaging to add emoticons but cannot build a grammatically correct sentence.?LLM can generate human-like “intelligent” responses based on input requested. They can understand the context and provide relevant and comprehensive statements that mimic natural human language. Imagine, no more misspelled words and you’re able to put sentences together “correctly.”
• Language Translation. I watched Succession, without doing a spoiler, a language translator was used to determine the finale. LLM can facilitate real-time language translation during conversations – how cool is that.
• Chatbot Enhancement. We are using LLM to train how Chatbot replies. Yes, there’s a data scientist out there that calibrates vast amounts of data they acquired, and fine-tune the specific datasets so replies can be specific to a particular industry or domain.?Your social media chatbot replies – those are the results of post-trained datasets.
• Virtual Assistants. LLM based conversation AI help users with various tasks such as setting their reminders, scheduling appointments, and even performing online transaction. In time, LLM is trained to personalized your dataset model. It keeps your user preferences and provide better / relevant responses. This also has expanded to do sentiments analysis – recognizing your emotions through speech pattern, which enables the assistant to respond empathetically. Like having your BFF right next to you. ?

Large Language Models have revolutionized AI's ability to comprehend and generate natural language, leading to significant advancements in various applications across industries, such as customer support (contact center) automation, content generation, language tutoring, and even medical research.

It's worth noting that the AI field is continuously evolving, and newer advancements and models are emerging. However, the fundamental role of Large Language Models in advancing AI's capabilities in language processing remains relevant.

As a Technology leader, you’ll have a unique challenge to overcome since there isn’t a one-size fits all approach when it comes to LLM. The question you must answer “How will I load my corporate data into LLM to support my strategy?” “What use cases are used to teach my model to respond based on my organization’s goals?” “How long will it take to calibrate and fine-tune?”

The way I see it, products will be more data driven in the next year. LLM is an incredible tool, but a strong data foundation must be the framework. Open-Source Models will grow exponentially and thousands of contributors will participate in providing use cases, summarization and classifications. Companies who have a tight grip on not allowing open-source will eventually have their hands full in catching up.