Better ChatGPT Prompts ✅

☕️ Greetings, and welcome to my daily dive into the Generative AI landscape.

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In today’s issue:

• Developer Upskilling and Generative AI

• 4 Keys to Generative AI Success in 2024

• ChatGPT vs ChatGPT Plus: Is it worth the subscription fee?

• Oracle's new offerings bring AI to your data and manage it too

• How Generative AI Can Help You Improve Your Data Visualization Charts

• And more

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🗞️ Better ChatGPT Prompts

This article explores how to effectively use ChatGPT, a prominent AI tool. It emphasizes the art of prompt crafting to elicit accurate responses from the AI's large language model. The piece also sheds light on prompt engineering as a growing and lucrative career field and discusses the opportunity for free AI training offered by IBM.

Key Points:

1. Crafting Effective Prompts: Approach ChatGPT conversationally, use detailed contexts, and engage in interactive prompting.

2. Career in Prompt Engineering: High demand and potential earnings in the field, requiring strong communication and problem-solving skills.

3. Versatility of ChatGPT: Use its ability to assume different roles for diverse responses.

4. Maintaining Accuracy: Keep prompts relevant and on track, and utilize correction techniques.

5. Exploration and Experimentation: Experiment with various prompts to understand ChatGPT’s limits and capabilities.

6. Career Transition to AI: Opportunities for professionals from varied backgrounds to enter AI fields, emphasizing continuous learning.

7. Future of AI: Encourages engagement with AI for both personal and professional development, highlighting its evolving impact across industries.

Reference: How to write better ChatGPT prompts in 5 steps

🗞️ TRENDS

In 2024, Generative AI remains a key focus in technology, with a majority of AI decision-makers in large companies actively engaging with it. Success in this field hinges on four essential elements: empowering domain experts with accessible, data-savvy tools; enabling data scientists to efficiently manage and develop AI models; orchestrating AI growth through diversified Large Language Model (LLM) strategies; and building trust and transparency in AI systems for wider acceptance and regulatory compliance.

Four Essential Elements:

1. Empowering Domain Experts: Maximizing value from Generative AI through accessible tools and user-friendly environments for data-savvy experts.

2. Enabling Data Experts to Move Faster: Streamlining AI model development and management, focusing on effective tools and standardized practices.

3. Orchestrating AI Growth: Avoiding reliance on a single LLM and implementing diversified, cost-effective AI strategies like Dataiku's LLM Mesh.

4. Instilling Trust & Transparency: Overcoming skepticism towards AI with transparent, governed, and auditable systems, ensuring regulatory compliance and broader acceptance.

Reference: dataiku.com (2024). 4 Keys to Generative AI Success in 2024

🗞️ IMPACT (Economy, Workforce, Culture, Life)

Amidst the swift evolution of IT, professionals confront a pivotal choice: embrace genAI or risk professional stagnation. With CEOs predicting workforce reductions due to genAI, employers are offering substantial salary premiums for genAI expertise. To safeguard your career, evaluate your company's GenAI engagement, communicate with your supervisor, explore in-house reskilling, and consider external training. Self-driven learning is an option, demonstrating initiative. Active involvement in genAI projects within your organization can be an entry point to pivotal roles. Seek mentorship to stay competitive and steer clear of potential job insecurity or financial setbacks. Adaptation to the genAI era is paramount.

Reference: Computerworld.com (2024). computerworld.com

🗞️ OPINION (Opinion, Analysis, Reviews, Ideas)

GPT-4, accessible via the ChatGPT Plus subscription, surpasses GPT-3.5 with its remarkable accuracy and adaptability. It handles text, images, and voice inputs, even generating custom AI bots through the GPT Store and offering plugins for diverse applications. With approximately 100 trillion parameters, it provides nuanced, context-aware responses and can access up-to-date information from the web. Despite these advantages, free ChatGPT still caters to many users, offering a wide range of functionalities, including voice interactions through Whisper. For those not requiring advanced features or favoring a cost-free option, the standard ChatGPT remains a robust choice.

Reference: zdnet.com (2024). ChatGPT vs ChatGPT Plus: Is it worth the subscription fee?

🗞️ LEARNING (Tools, Frameworks, Skills, Guides, Research)

This article outlines innovative methods for enhancing data visualization using generative AI. It guides through creating a chart using Python Altair, GitHub Copilot, ChatGPT, and DALL-E, focusing on R&D expenditure in Italy. GitHub Copilot aids in code generation, while ChatGPT suggests titles and annotations. DALL-E adds visual appeal with images. The process includes defining a use case, building the basic chart with GitHub Copilot, enriching it with ChatGPT-generated annotations, and visually enhancing it with DALL-E images. This approach demonstrates how generative AI can streamline and elevate data visualization.

Reference: How Generative AI Can Help You Improve Your Data Visualization Charts

🗞️ BUSINESS (Use Cases, Industry spotlight)

While many organizations understand generative AI basics, few have developed a comprehensive strategy for its large-scale implementation. Société Générale, a major European bank, is a leader in this space, according to Noémie Ellezam, its Chief Digital Strategy Officer. The bank, with a 150-year history and 25 million clients, integrates AI, including generative AI, into its digital transformation. Ellezam, speaking at a webinar, emphasized that generative AI is a significant, but incremental, step in the AI journey. She predicts a 3-5% productivity increase from generative AI in financial services over the next few years but acknowledges challenges in application and risk management. Société Générale's approach includes experimenting with over 100 generative AI use cases, prioritizing high-value applications, and fostering a global culture of AI competency and risk awareness.

Reference: MITSloan.mit.edu (2024). How businesses can find and prioritize AI opportunities

🗞️ LATEST FROM THE WEB

How Apple's AI advances could make or break the iPhone 16: Amid predictions of a lackluster 2024 for Apple's iPhone lineup, the prospect of AI software reshaping the iPhone 16 has garnered attention. Analyst Erik Woodring suggests that 'Edge AI' could rejuvenate the iPhone and potentially exceed growth expectations. Recent research by Apple's Keivan Alizadeh and team focuses on efficiently utilizing device memory for large language models, promising significant speed improvements. Challenges remain, including battery life considerations. Whether an A18 processor or scaled-down LLMs, the shift towards on-device AI seems inevitable, marking a transformative shift for iPhones in the near future. Read more at zdnet.com.

From Data Scientist to AI Developer: Lessons Building a Generative AI Web App in 2023: Venturing into web development as a data science enthusiast can be challenging but rewarding. This guide offers valuable insights, emphasizing tool choices like Next.js and Tailwind CSS for UI development and promoting type annotations and TypeScript for robust coding. Deployment tips include Modal for GPU backends and a Lambda-Vercel combo. Efficiency advice covers avoiding React for landing pages, Firebase with Stripe for authentication and payments, and Sentry for error monitoring. Bonus cautionary advice highlights third-party API challenges. This comprehensive guide reflects deep problem-solving and tech-savvy wisdom, aiding others on their web development journey. Read more at towardsdatascience.com.

Developer Upskilling and Generative AI with Hywel Carver and Suhail Patel: Upskilling in technology evolves from knowledge acquisition to skill development and ultimately, nuanced judgment through diverse applications. Generative AI aids knowledge acquisition and initial skill development, but struggles with complex, open-ended problems. For software engineers, AI advances suggest a future shift from traditional programming to natural language abstractions. However, engineers will still require strong foundational knowledge to tackle complex problems, as AI tools don't replace the need for deep understanding and contextual decision-making. The effective future use of AI in engineering will demand a balance between automated solutions and human insights. Read more at infoq.com.

EU wants to upgrade its supercomputers to support generative AI startups: EU lawmakers are unveiling an "AI innovation package" to support homegrown AI startups and address infrastructure gaps in generative AI. It includes high-performance computing, talent access, and financial backing. The EU plans to create "AI Factories" near supercomputers. Challenges like chip shortages may impact implementation. Nevertheless, this aims to strengthen the EU's generative AI presence and promote responsible AI development. Read more at techcrunch.com.

Oracle's new offerings bring AI to your data, and manage it too: Oracle has unveiled OCI Generative AI Services, promoting the idea that integrating AI applications with existing data stores, like Oracle databases, is more effective and cost-efficient than moving data. Using Oracle Cloud Infrastructure (OCI), this approach eliminates the need for additional infrastructure, ensuring AI leverages unique data. The OCI Generative AI Service offers large language models and partnerships with startups like Cohere. Additionally, OCI Gen AI Agents and OCI Data Science AI Quick Actions aim to enhance AI integration. These services cater to various applications, including HR policy responses and customer support, simplifying user experiences. Read more at zdnet.com.

In partnership with Amazon Web Services

🔔 Architecting continuous Observability from development to production

Save your spot:  February 14, 2024 | 11:00 AM PDT | 2:00 PM EDT

Join this webinar to explore how to establish effective Observability in dynamic, continuously deployed, and decoupled systems. You’ll discover where and what to instrument across your SDLC and how to leverage clear insights in today's evolving tech environment.

You will learn how to:

▶ Choose an optimal data type and service to monitor the progression of your applications throughout the SDLC.
▶ Enhance your ability to store, query, and visualize system telemetry for more effective insights into your systems.
▶ Enable transparency and address issues proactively before they impact operations to systematically enhance value delivery and resiliency.
▶ Use tools from AWS and the broader DevOps tool landscape using AWS Marketplace to establish a comprehensive Observability practice.

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