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▶️ This article provides an interesting look at the current level of artificial intelligence, namely large language models or LLMs, and their impact on our daily jobs. The author says that, while artificial intelligence has grown rapidly, with applications such as ChatGPT having 200 million weekly active users, 🚀 it has yet to live up to its transformative potential. ▶️ My take: While AI has not yet significantly transformed our lives, it is already offering valuable support in a variety of aspects of our professional and personal life. Who knows. As technology continues to evolve and we continue to improve how we use AI, we may see even more imaginative applications that go beyond the "button problem". 🤓 #AI #LLMs #technology

Looks like the robots are learning from us; if only they could pick up our coffee habits! Summary: - Prompt engineering is vital for optimizing AI interactions and usefulness. - Success in prompt engineering requires understanding AI capabilities and iterating on input phrasing. - Stakeholders must stay informed on best practices to maximize AI benefits. #Prompt_Engineering #AI_Applications #Stakeholder_Engagement #AI_Optimization #TPMC https://lnkd.in/gv9MfyGx Author: Deven Panchal, AT&T Labs

Google Gemini 1.5 Flash: The Future of AI is Here AI has been the focus of the technology industry for several years now and Google isn’t leaving the race to anybody as it introduces Gemini 1. 5 Flash, a revolution in the AI capabilities and prospects, of colossal proportions. Such highly advanced model of the Gemini family is in Google, which changes the possibilities of artificial intelligence. Gemini 1.5 is fast, it bursts with context expansiveness and its ability to navigate between multiple modalities. 1.5 Flash is ready to disrupt industries and liberate people. https://lnkd.in/g5TvQtqA

AI is the future of development, but not as I imagined. A developer at Moze shares how using AI technologies has changed his perspective on the limitations of LLMs—and on opportunities he hadn’t anticipated. #AIRevolution #FutureOfSoftwareDevelopment #LLMTech #ArtificialIntelligence #DeveloperInsights #SoftwareEngineering #TechInnovation #AIAdoption #CodingWithAI #IntelligentDevelopmentTools https://lnkd.in/gaJGZwsE

Biases in training data can haunt AI #codeassistants like ghosts in the machine. But fear not! Here's how we can guide them towards the light: ✅ Build #Diversity: Just like a balanced diet, diverse training data ensures fairer outcomes. Including code from all genders, ethnicities, and backgrounds reduces bias in the end product ✅ Human Oversight: AI needs a guiding hand. Human review ensures fairness, just like a code editor tidies up errors. Together, humans and AI create ethically sound solutions ✅ Debiasing Algorithms: Science leads the way! Debiasing #algorithms offer neutral ground for #AI to learn, paving the path for fairer, more inclusive technology Let's harness AI's potential for progress, leaving bias behind. Join the journey: https://bit.ly/3JOctTO

Excited to present my latest blog article, 'A Developer’s Guide to Effective Prompt Engineering: Harnessing AI Power.' I encourage you to read and share your thoughts.

Today's RAIZOR Report: GitHub Enhances Copilot, Apple's AI Expansion, xAI's Valuation Surge, New AI Graphic Design Model PLUS the latest AI tools, and more. Read the newsletter here and subscribe to stay in the loop.

The process of writing, refining, and optimizing inputs—or “prompts”—to encourage generative AI systems to create specific, high-quality outputs is called prompt engineering. It helps generative AI models organize better responses to a wide range of queries—from the simple to the highly technical. The basic rule is that good prompts equal good results. Prompt engineering is […] https://lnkd.in/d9PyBm4A www.Cyprus-CEO.com #CEO #business #management #marketing #tech #AI #legal #money

I've been using AI for my daily work tasks for at least the past 12 months and have been experimenting with GPT-2 since its closed beta. The current AI capabilities are incredibly exciting. From summarizing all my interactions to building scripts, coding, and data manipulation with my guidance, it's been astonishing how rapidly things have changed in the last 18 months. A year and a half ago, AI was hallucinating so badly that the code it produced was barely worth troubleshooting. Now, if you break down problems into smaller components, it can provide approximate solutions that fit into your architecture. It can even perform self-debugging on its own code if you help structure it to log everything for the AI to parse. In some ways, this reminds me of the early days of search engines. They began as simple text scrapers, and learning to use them for troubleshooting was an art in itself, with certain engines excelling in specific areas. Now we're seeing divergence and specificity in AI models. Rather than relying on general-purpose models for all problems, people need to apply the right model to their specific problem space. Of course, we're still in the early stages, similar to the early search engine era. There are weekly advancements in the field, and Anthropic's latest model appears to be outperforming others for general-purpose tasks and coding. It'll be interesting to see if this remains the case. How we build and maintain code in the future will undoubtedly change. Developers should focus on mastering AI just as they did with early search engines. This technology isn't going anywhere, and proficiency in its use will become increasingly valuable.