On this episode of Everyday AI, Jordan is joined by Piotr, a computer science master with a concentration in AI, to discuss the impact of AI on the world. They explore various topics, including the use of language models like ChatGPT to assist in coding, the role of hard rules in avoiding biases in AI, and the future of AI integration into various operating systems. Time Stamps:[00:00:50] AI Leaders Warn of Extinction Risk[00:04:03] Deepfakes at an All-Time High[00:05:47] Amex Turns to AI for Customer Service[00:06:45] Chatbots in Customer Service: Finding Limits[00:07:51] Piotr's Role as a Programmer Analyst[00:10:03] Large Language Models Revolutionizing Code Efficiency[00:15:20] The Fascinating Growth of Large Language Models[00:17:14] ChatGPT Speeds Up Coding Troubleshooting Process[00:21:22] Managing AI Bias: Tackling the Black Box[00:24:27] Is AI Making Us Smarter or Dumber?Topics Covered in This Episode:The efficiency of using language models like ChatGPT in coding:- ChatGPT's ability to help with solving problems in different ways- The potential for technology like ChatGPT to save hours of struggling through logic- The desire to use proprietary code safely while taking advantage of new technologies- ChatGPT's effectiveness in writing essays on any topic in secondsNeural networks, biases, and AI:- Explanation of neural networks and coding AI- Construction of set hard rules and questions to avoid biases in AI- The impact of data feeding on the AI- The view that robots taking over is not possible unless allowedPiotr's experience with natural language processing and chatbots for troubleshooting:- Piotr's experience with a natural language processing course- The creation of ChatGPT and its usefulness in coding- The effectiveness of AI bots as customer support for general issues like credit card problems- The need for human support in technical issues like codingThe impact of AI on intelligence:- The comparison of the impact of AI on intelligence to that of the internet- The concern that people may rely on AI to obtain answers, rather than learning and understanding information- The agreement that learning and understanding the information is necessary before relying on technology to solve problems Concern on the inclusivity and bias in AI systems:- The concern on ensuring AI systems remain unbiased and inclusive- The difficulty in ensuring AI systems do not reflect human biasesIntegration of AI into operating systems:- The potential for AI integration into operating systems beyond individual applications- Microsoft's unveiling of Copilot systemUse of proprietary code in financial institutions and the impact of deep fakes:- The use of proprietary code in financial institutions- The potential impact of deep fakes, even poorly made onesAmerican Express using generative AI in customer service:- The unusual decision of Amex to use generative AI in customer service- The discussion of Piotr's background in fintech and his thoughts on Amex's decisionKeywords:Bard, ChatGPT, language models, coding, perspective, efficiency, proprietary code, technology, Neural Networks, AI, biases, hard rules, data, intentions, natural language processing, robots, chatbots, troubleshooting, customer support, intelligence, Internet, learning, understanding, errors, Google, Copilot, Microsoft, game changer, inclusive, unbiased, financial institutions, fintech industry, deep fakes, market, American Express, generative AI, extinction, global priority, pandemics, nuclear war. Get more out of ChatGPT by learning our PPP method in this live, interactive and free training! Sign up now: https://youreverydayai.com/ppp-registration/