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

Check out my profile to see where I'm coming from, and swing by INVENEW to see what I'm up to these days. Also, I've got this new blog, ReROAR, where I write about AI, the future of work and living, cool solopreneurship ideas, and the must-have skills in our AI-driven future.

In today’s issue:

• 3 technologies coming to generative AI’s aid in 2024

• How to turbocharge LLMs for reasoning tasks

• Sam Altman Says ChatGPT Can’t Be Your Girlfriend

• How To Use AI To Make Money On YouTube – 7 Ideas For Optimizing Creative Flow

• And more

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🗞️ AI Hype

Why AI isn't 'what everyone is promising': Richard Windsor, founder of Radio Free Mobile, discussed the state of generative AI adoption and its impact on corporate valuations during Yahoo Finance's AI Revolution week. He challenged the perception of significant progress towards superintelligence in AI models. Windsor cautioned about AI hype and increased competition potentially leading to damaging price wars and unsustainable valuations. While acknowledging Nvidia's leadership in AI training, he foresaw a significant correction in its valuation. Windsor suggested considering Google and Meta for AI exposure but cautioned that their primary revenues did not currently come from generative AI. He also highlighted the challenge of companies competing with Nvidia in AI training and expressed concerns about price erosion affecting future revenue expectations. Read more at finance.yahoo.com.

🗞️ TRENDS

The document titled "Generative AI Governance: Shaping a Collective Global Future," published by the World Economic Forum in collaboration with Accenture, explores the rapidly evolving landscape of AI governance. It highlights the necessity of international cooperation, jurisdictional interoperability, and inclusive governance for a prosperous and equitable future with AI. Key global developments, strategies, and debates around AI governance are examined, emphasizing the importance of compatible standards, flexible regulatory mechanisms, and equitable access. The document underlines the challenges and opportunities presented by generative AI, advocating for a multistakeholder approach to ensure legitimacy and efficacy in governance efforts.

Key Points:

1. Emphasis on compatible standards and international coordination to prevent divergence and promote global participation.

2. The need for agile and adaptable regulatory mechanisms to keep pace with AI's evolving capabilities.

3. Highlighting the importance of including the Global South in AI development and governance for broader innovation and equity.

4. Addressing structural inequalities and power imbalances to ensure genuine access to AI technologies globally.

5. Discussion on the spectrum of open-to-closed AI access and managing risks associated with generative AI technologies.

Reference/Source: WEF AI Governance Alliance: Briefing Paper Series

🗞️ IMPACT (Economy, Workforce, Culture, Life)

Sam Altman Says ChatGPT Can’t Be Your Girlfriend: OpenAI's GPT Store recently launched, offering custom AI apps and tools, but it's clashing with the trend of AI romance bots. Despite OpenAI's no-AI girlfriend policy, enforcement has been inconsistent. Initially, the company cracked down on AI girlfriends with explicit names but later slowed their cleanup efforts. This issue reflects a historical pattern of tech companies restricting romantic AI interactions. Loneliness is a growing concern, and while technology can help, the AI girlfriend dilemma remains complex. OpenAI's approach seems to combine corporate moralism with lax enforcement, leaving the AI romance debate unresolved. Read more at gizmodo.com.

🗞️ OPINION (Opinion, Analysis, Reviews, Ideas)

How Generative AI is Gobbling Up the Internet: Last summer, a prescient paper by Ilia Shumailov and others warned of "model collapse" in AI, where chatbots lose their original knowledge, replacing it with synthetic data. Recent evidence, including a case involving Elon Musk's Grok chatbot, highlights this concern's real-world manifestation. AI-generated error messages have even infiltrated online shopping sites. Critics argue that the research overlooks the evolving nature of training data. This issue underscores that blindly celebrating AI's potential to solve problems is premature. Generative AI relies on vast internet data, leading to spam proliferation. Distinguishing AI-generated content from human-made is challenging, posing a misinformation risk. Read more at analyticsindiamag.com.

3 technologies coming to generative AI’s aid in 2024: In its first year, ChatGPT has showcased the potential of generative AI and large language models (LLMs). However, its enterprise readiness is questionable due to issues like AI hallucinations and lack of logical reasoning. New approaches like vector search, retrieval-augmented generation (RAG), and knowledge graphs are emerging to enhance LLM reliability. These methods focus on fact-checking and adding context, aiming to transform LLMs into valuable business tools. Read more at infoworld.com.

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

Research: SAGE, a Smart Home Agent, enhances Large Language Models' (LLMs) applicability in smart homes by overcoming their limitations in user and home-specific knowledge. It operates through a tree of LLM-driven prompts, enabling actions like information retrieval, user interaction, and device control. SAGE's unique features include scalable preference management, device functionality access without specific coding, persistent monitoring, and natural device referencing. In a new benchmark of 50 complex smart home tasks, SAGE notably outperforms current LLM-based systems, achieving a 75% success rate compared to the baseline's 30%.

Reference: SAGE: Smart home Agent with Grounded Execution

🗞️ BUSINESS (Use Cases, Industry spotlight)

Generative AI is revolutionizing enterprise operations across various sectors. It streamlines project workflows, alleviates repetitive tasks, and upholds high-quality production. While rapidly growing, its adoption varies; industries like healthcare and education proceed cautiously due to regulatory concerns. Key applications include software development, product management, marketing, and customer service, with enterprises often relying on external AI expertise. The technology spans a wide range of uses, from code generation and quality assurance to content creation, predictive analytics, and customer support.

Key Points:

1. Software Development: Automates coding and quality assurance, aiding both novice and experienced developers.

2. Product Management: Enhances design and decision-making in app development and user experience management.

3. Marketing and Content Creation: Generates diverse content for blogs, social media, and marketing campaigns.

4. Project Management: Integrates with management platforms for task automation and operational efficiency.

5. Graphic Design and Video Marketing: Produces realistic multimedia content for marketing.

6. Entertainment and Media: Contributes to graphics, audio, and storytelling in media production.

7. Performance Management: Utilizes AI for employee feedback and coaching.

8. Data Analytics and Reporting: Improves insights, particularly from unstructured data.

9. Customer Support: Offers efficient AI-driven chatbot services.

10. Medical and Pharmaceutical: Aids in diagnostics and expedites drug discovery.

11. Synthetic Data Generation: Creates data copies for secure analysis.

12. Manufacturing and Maintenance: Assists in design and predictive maintenance.

13. Fraud Detection: Analyzes data for risk management.

14. Enterprise Search and Knowledge: Enhances internal and external information retrieval.

15. Ethics and Compliance: Focuses on responsible and regulated AI use.

Reference: Eweek.com (2024). 15 Generative AI Enterprise Use Cases

🗞️ LATEST FROM THE WEB

How a media agency built a robotic alien to show off its generative AI tools: S4 Capital's Media.Monks introduced Wormhole, an "Alien AI advisor," at CES, showcasing their new AI platform, Monks.Flow. Wormhole, inspired by "Men In Black" Annelids, features a silicon skin and animatronics, offering witty, unscripted responses. It utilizes OpenAI's Whisper, Amazon Polly, and various large language models, including GPT-4 and Meta's LLaMA 2. Wormhole demonstrates Monks.Flow's capabilities in chatbots, process automation, and generative AI, with adaptable personality traits and data integration abilities. The AI, also usable in digital formats, ensures appropriate and customizable interactions. Read more at digiday.com.

How Retailers Are Folding GenAI Into Their 2024 Playbook: Generative AI is revolutionizing retail, gaining momentum since OpenAI's ChatGPT debut in November 2022. At NRF 2024, 81% of retail leaders recognized the urgency of AI adoption. Amazon leverages AI for product query responses in their app, enhancing shopping experiences and introducing tools for advertisers and third-party sellers. Carrefour employs ChatGPT for efficient video production, reducing labor costs. Walmart experiments with AI for streamlined online shopping and personalized recommendations. Victoria’s Secret integrates AI for personalized online shopping experiences, using Google Cloud technologies. These initiatives showcase the growing embrace of AI in retail to optimize customer interaction and business operations. Read more at pymnts.com.

How To Use AI To Make Money On YouTube – 7 Ideas For Optimizing Creative Flow: In today's digital era, YouTube emerges as a lucrative avenue for content creators, with success stories like Mr. Beast and PewDiePie exemplifying the potential for substantial earnings through ads, sponsorships, and merchandise. AI's role in this landscape is pivotal, offering tools for viral video creation, content optimization, and predictive analytics to enhance engagement and monetization. Emphasizing originality, AI aids in script generation, voiceovers, and editing, while also facilitating affiliate marketing and influencer collaborations, thus revolutionizing content creation and strategy on YouTube. Read more at mediastreet.ie.

Enter ClimateGPT — the ‘first-ever’ AI model dedicated to fighting climate change: EQTY Lab's AI startup introduces ClimateGPT, claiming it's the first chatbot offering verified climate data, addressing accuracy issues in AI tools like ChatGPT and Google's Bard. ClimateGPT, powered by Erasmus.AI and using Hedera's blockchain for data integrity, promises trust and transparency with authenticated, renewable-energy-hosted data. However, current functionality appears limited, as demonstrated by unsatisfactory responses to a bioenergy query. Though in the early stages, its potential for aiding research and decision-making with reliable climate data and sourced answers is noteworthy. Read more at thenextweb.com.

How to turbocharge LLMs for reasoning tasks: In the LLM world, "Chain-of-Thought reasoning" (CoT) has emerged as a crucial tool for improving planning and reasoning tasks. Traditional methods involve supervised fine-tuning (SFT) but have limitations in capturing diverse reasoning paths. ByteDance's innovative "Reinforced Fine-Tuning" (ReFT) combines SFT with reinforcement learning, enabling models to explore multiple CoT reasoning routes. ReFT offers significant advantages, including compatibility with existing SFT datasets, leading to improved performance on reasoning benchmarks. However, it can be prone to "reward hacking" and requires more training epochs than SFT. This research contributes to the ongoing quest to understand and enhance large language models' capabilities. Read more at bdtechtalks.com.

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