CommenceAI

In this interview I did with CBS Amarillo, TX a few months ago, I touched on neural networks. A neural network is a computational model inspired by the human brain, composed of layers of interconnected nodes, or "neurons," that process and transmit information. These layers are structured in a way that allows the network to learn patterns by adjusting the connections between neurons based on input data. Through training on large datasets, neural networks can recognize complex relationships and make predictions or decisions, adapting over time to improve accuracy. This ability to learn from experience makes neural networks particularly useful for tasks involving pattern recognition, such as image and speech recognition, natural language processing, and predictive analytics. Large Language Models (LLMs) use neural networks to process and generate human language by analyzing vast amounts of text data to understand context, grammar, and meaning. By applying this understanding, LLMs can assist people across various fields, improving productivity by automating routine tasks and generating insights quickly. In law, LLMs can draft documents or analyze legal language, while in engineering, they can generate code snippets or provide technical explanations. Marketing and human resources also benefit, as LLMs create personalized content or streamline recruiting processes, enabling professionals to focus on higher-level, strategic work. https://lnkd.in/gf8CKPnX

https://lnkd.in/gA2VHEne Here's an interview I did with NBC Albuquerque in the Spring of 2023. In the interview, I being up the fact that AI will have a pronounced impact on the legal industry. Here's why: Efficiency in Document Review: AI can rapidly analyze vast amounts of legal documents, contracts, and case files, reducing the time required for review and minimizing human error, which streamlines case preparation and contract analysis. Enhanced Legal Research: AI-powered tools can swiftly identify relevant case law, statutes, and legal precedents, enabling attorneys to find critical information faster and with more precision. Predictive Analytics for Case Outcomes: AI can assess past case data to predict the likely outcomes of litigation, helping law firms make more informed decisions on case strategies and manage client expectations. Automated Routine Tasks: AI can handle repetitive tasks like drafting standard contracts, filling forms, and other administrative duties, freeing up attorneys to focus on more complex legal work and providing better service to clients.

https://lnkd.in/gaUqy7P4 Here's an interview I did last year with Fox San Antonio. In the interview, I stress the need for our education systems to avoid banning ChatGPT because students will be able to use ChatGPT in the workforce. Why do I think this? The whole point of school is to prepare students for the workforce. I recently came out saying that Prompt engineering needs to be taught starting freshman year of high school. In case you don't know what Prompt engineering is, here is the definition below: Prompt engineering is the process of designing and refining inputs (prompts) to guide AI models, like ChatGPT, toward generating accurate, relevant, and high-quality outputs. It involves crafting specific questions, commands, or instructions, sometimes with constraints or examples, to achieve desired responses. Effective prompt engineering helps optimize the model’s performance, ensuring the output aligns with the user’s goals across various applications such as chatbots, creative writing, coding, or customer service.

https://lnkd.in/gpidYqYP In this interview with Fox Oklahoma City, the Governor of Oklahoma, Kevin Stitt, speaks to the fact that he will utilize artificial intelligence to lower the operating costs of government operations in Oklahoma. When reporter Ashley Moss asked me if this was a good idea, I said, “There aren’t a lot of tech jobs in Oklahoma. So kudos to Governor Stitt for doing this and helping advance Oklahoma’s economy...the best run states are going to utilize artificial intelligence.” Earlier in the interview, she asked me if AI would lead to job losses. Here's what I said: “Well, it could potentially happen, and it’s already happening. It’s going to be tough for artificial intelligence to do things that involve creativity, emotional intelligence, and practical intelligence. But convergent tasks can be automated."

Here's an interview I did with CBS Waco, TX in June of 2023. In the interview, I quote Einstein who once said "the highest form of intelligence is not knowledge, but imagination." Einstein knew how important divergent thinking was. There are three reasons why AI is able to think in a very divergent manner now. Here they are below: 1. Access to Large, Diverse Datasets for Training AI models like GPT are trained on enormous datasets that span a variety of fields (e.g., science, literature, pop culture). This broad exposure allows them to combine knowledge from multiple disciplines in ways that mimic creative, divergent thought. Example: AI can suggest novel business ideas by blending concepts from different industries, like blockchain-based healthcare or AI-powered education. 2. Generative Models and Reinforcement Learning Generative models (like GANs and LLMs) are designed to create new outputs, from artwork to product ideas, by exploring a wide range of possibilities. Additionally, reinforcement learning allows AI to experiment with different strategies, optimizing its outcomes through trial and error. This mimics human brainstorming processes, where multiple ideas are tried, evaluated, and refined over time. 3. Advanced Neural Architectures and Transfer Learning Modern AI benefits from transformer architectures that can model complex relationships between ideas and concepts. Transfer learning allows AI to apply knowledge from one task to another, leading to creative applications in new areas. Example: AI trained to compose music can transfer its pattern recognition abilities to write poetry or design novel algorithms. https://lnkd.in/gJaFtHq6

https://lnkd.in/g9N9DhmW Here's an interview I did a year ago with ABC Cleveland, OH on how AI will affect the workforce in the next few years. Below are a few points that I highlight: 1. While Raw IQ and the ability to think convergently was extremely important in the past, it will not as important in the future because those tasks are started to be automated by AI whether that be through machine learning or LLMs (Large Language Models). Convergent thinking is like doing a physics problem, a calculus problem, leaning a foreign language or doing a close reading to a novel. It's where there is one solution to a problem. 2. What will be important is to have strong EQ or emotional intelligence, being able to think divergently, being able to have a high degree of integrative complexity, and being able to have a high degree of practical intelligence. a. EQ- similar to the personality trait agreeableness, this means having empathy and being likable b. divergent thinking - the best way to describe divergent thinking is answering the question how many uses can you have for a tin can? It involves thinking creatively. c. integrative complexity - being able to look at a situation from multiple points of view d. practical intelligence - Malcom Gladwell speaks the importance of practical intelligence in his book "Outliers." He does so by quoting my mentor Dr. Robert Sternberg who defines practical intelligence as "knowing what to say to whom, when to say it, and how to say it with maximum effect." In other words, "street smarts." Be on the lookout for an interview tomorrow that I did a year ago on how AI will affect the workforce on CBS Waco, TX

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