Speaking AI

In 1950, When Alan Turing asked: “Can machines think,” we had no idea where AI evolution would lead us by 2024.

Evolution of Language

Which is correct: speaking AI, speaking of AI, or speaking about AI? Prepositions are not easy in any language. Now, there is an AI tool for that task.

Grammarly helps people at every stage of the writing process. Grammarly's algorithms use?natural language processing (NLP)?to analyze text, identify grammar and spelling errors, and suggest clarity, conciseness, and tone. The algorithms also consider the context in which the text is being written, such as the audience, purpose, and genre.

NLP is a subset of artificial intelligence that focuses on the interaction between computers and humans through natural language. The objective of NLP is to read, decipher, understand, and make sense of the human language in a valuable way.

NLP is a multidisciplinary domain involving computer science, AI, and computational linguistics to read, decipher, understand, and make sense of human language, not to create content from scratch. Some practical applications include speech recognition and machine translation.

Large Language Models, or LLMs, are machine learning models we use to understand and generate human-like text. The models can predict the likelihood of a word or sentence based on the words that come before it, generating relevant text (although not flawlessly).

LLMs are an evolution of earlier NLP models. Large amounts of text data, typically from the internet, are fed into LLMs, from which they learn language patterns, grammar, facts, and some reasoning (again, not without errors).

LLMs are the foundation of generative AI (GAI). We have experienced the ChatGPT revolution and the succession of GPT-based tools increasing the interaction between humans and machines.

GPT-based tools are at their best when they help us make sense of information. They can take some of the heavy lifting required in numerous job roles, such as text summaries, translation, writing original content, crunching numbers, data analysis, and automated tasks. More innovation is on the horizon.

Evolution of AI Phone Calls and Agents

GAI has enabled a whole new set of technologies to emerge, including AI phone calls.

Bland.ai enables AI phone calling applications allowing users to build, test, and scale AI Phone Agents (which can be customized using enterprise data).

Bland costs $0.09/minute for connected calls in the US and internationally. The Phone Agent can speak multiple languages. For non-developers, users can integrate Bland using Zapier or Make. Via the developer portal, you can configure answering inbound calls.

The current operations at call centers could be forever changed due to the volume of calls. Bland can dispatch or receive thousands of phone calls at once. This is a case of how emerging tech can benefit some (call center companies), and disadvantage others (displaced employees).

How Are We Preparing for AI-Based Changes

Dell Technologies' Realizing 2030 report found that in 2030 every organization will be a technology organization and 85 percent of the jobs in 2030 have not yet been invented.

Workforce trust could be in jeopardy as these changes occur. Reskilling or upskilling after a job loss may cause a heavy burden on workers-- physically, emotionally, and financially. Furthermore, how will those employees who keep their jobs be engaged in their work?

Today, businesses that take the lead in exploring opportunities created by AI will be the ones influencing the future of work.

I welcome your comments.

#speakingAI #futureAI #AIphonecalling #changemanagement #jobs #trust #workforce #futureofwork

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