MATH: AI VS. HUMAN

Author

Qamar Zia
December 21, 2023

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

My goal is to streamline this newsletter size, making it a concise, under-five-minute read containing 1500 words or less. For those interested in deeper dives, I’ll provide references for extended reading. Most of this content springs from my ongoing research and development projects at INVENEW.

In today’s issue:

  • 7 Generative AI Prompts To Help Your Content Marketing Workflows

  • Research: Large Language Models are Complex Table Parsers

  • Why Generative AI Startups Are So Dependent on Big Tech

  • The Generative AI Stories You Cared About Most in 2023

  • And more

🔔 Please forward this newsletter to your friends and team members and invite them to join. This will help me grow my reach. Thanks, Qamar.

♨️ MATH: AI VS. HUMAN

FunSearch, an AI system employing large language models, has significantly advanced solutions in Set-inspired combinatorics. It uniquely generates, evaluates, and refines solutions, exceeding current methods. Particularly impactful in the 'cap set problem', it demonstrates extensive applicability in mathematics and computer science. This innovation heralds a new era of human-AI collaboration, expanding possibilities in problem-solving and creative mathematical exploration. Read more at: ScientificAmerican.com (2023). AI Beats Humans on Unsolved Math Problem

🗞️ IN THE NEWS

Hitch Interactive Unveils Generative AI Within Programmable NFTs, Forging a New Era for Web3: Hitch Interactive's AI in Immutable Miniverse (AIIM) merges Large Language Models (LLMs) with NFTs, revolutionizing decentralized AI in Web3. Using their Immutable Miniverse Format (IMF), it embeds unique, private knowledge in NFTs, enabling private conversations. AIIM's decentralized approach ensures user privacy, with plans for local LLM models on users' computers, paving the way for a decentralized and private AI future. Read more at Aithority.com.

Why Generative AI Startups Are So Dependent on Big Tech: In 2023, generative AI surged with Microsoft's $10 billion investment in OpenAI, sparking a $17.8 billion global funding frenzy. Tech giants like Nvidia, Google, and Amazon also invested heavily. As traditional VC funding waned, big tech sought to stifle competition and secure computational resources. Monetization challenges led startups to eye acquisitions, mirroring Meta's 2010s spree. Read more at Inc.com.

New generative AI-powered SaaS security expert from AppOmni: AppOmni's AskOmni is a groundbreaking AI tool, simplifies SaaS application security. With plain language, it unravels complexities, offers insights, and aids in remediation. This innovation enhances security visibility and empowers enterprises to navigate the intricate SaaS landscape. Read more at VentureBeat.

MICROSOFT’S AI CHATBOT COPILOT CAN NOW COMPOSE SONGS WITH TEXT PROMPTS VIA SUNO INTEGRATION: Microsoft's Copilot chatbot collaborates with Suno, an AI music app, enabling users to create songs using text prompts. The integration, accessible through Copilot's website, allows even those without musical experience to generate personalized tracks, complete with lyrics and instrumentals. This partnership, part of Microsoft's larger AI music initiative Muzic, reflects a growing investment in generative AI music technology. While offering creative opportunities, it also raises ethical concerns about AI learning from existing music without clear consent or compensation. Read more at musicbusinessworldwide.

🔬 INFOGRAPHIC

The GenAI Prism is a new infographic showcasing over 10,000 generative AI projects. Developed by JESS3, Brian Solis, and Conor Grennan, with insights from Jeremiah Owyang, it helps users understand and use AI creatively in their work. This tool is designed to guide users in using AI to improve, not replace, human efforts. It follows the format of the older Conversation Prism, focusing on generative AI.

Key Points:

  • GenAI Prism Introduction: A detailed infographic mapping out the generative AI field.

  • Collaboration: Created by experts, aiming to make AI more understandable and usable.

  • Purpose: Guides users on how to effectively use AI for creative and productive outcomes.

  • AI as a Partner: Emphasizes AI's role in enhancing human work, not replacing it.

  • Design Concept: Helps users reflect on and understand the fast-paced AI industry.

  • Historical Link: Similar in approach to the Conversation Prism, but for generative AI.

  • Framework Details:

    • Center: User's vision directs AI use.

    • Halo 1: Weighing risks and rewards in AI applications.

    • Halo 2: Setting goals for using AI.

    • Halos 3-5: Categorizing AI services and apps.

    • Halo 6: Assessing the types of results achieved.

    • Halo 7: Emphasizes the importance of sharing knowledge in AI.

  • Impact: Shows how AI can make work more efficient and high-quality.

  • Philosophical View: Stresses that AI is for aiding humans, not replacing them.

Image credit: BrianSolis.com (2023)

🗣️ GENERATIVE AI TOP-10

The Generative AI Stories You Cared About Most in 2023

According to the Salesforce blog, in 2023, generative AI had a monumental year, impacting pop culture and business. It became the top tech priority, featured in mainstream media, and shaped various industries. Here are the top 10 generative AI posts of 2023:

  1. 11 Time-Saving GPT Prompts for Sales: Offers practical examples for sales enablement using GPT prompts.

  2. 4 Ways Your Contact Center Can Get Started With Generative AI: Explains how AI enhances customer service efficiency.

  3. 5 Ways Generative AI Is a Game-Changer for E-commerce: Discusses AI's transformative role in online shopping.

  4. How To Drive Data and AI Success With Salesforce Certifications: Introduces certifications to maximize Salesforce AI investments.

  5. Learn AI Skills on Trailhead: Highlights the importance of AI skills for employees.

  6. 3 Ways Generative AI Will Help Marketers Connect With Customers: Shows how AI improves customer engagement and saves time.

  7. IT Leaders, Here’s How To Use AI to Delight Your Customers: Explores AI's impact on customer satisfaction.

  8. AI From A to Z: The Generative AI Glossary for Business Leaders: Provides a comprehensive AI terminology resource.

  9. 3 Ways Generative AI Will Reshape Customer Service: Discusses AI's role in improving customer service.

  10. AI Is Coming — Here’s How To Get Ready: Offers a roadmap for safely introducing AI into organizations.

📦 CONTENT MARKETING

A suite of strategies for integrating AI tools into content marketing, streamlining efficiency without supplanting human creativity. These prompts cover brand voice training, converting features into compelling benefits, speeding up article development, enriching language, inspiring visual content ideas, repurposing existing materials, and refining SEO elements like title tags and meta descriptions, blending AI's analytical prowess with the nuanced touch of human oversight.

Reference: Content Marketing Institute (2023) 7 Generative AI Prompts To Help Your Content Marketing Workflows

📚 LEARNING

LLMOps and Generative AI: The fusion of AI and operations has birthed the groundbreaking concept of LLMOps. This article explores LLMOps and its transformative impact on technology and creativity. It delves into the integration of AI with DevOps techniques, paving the way for revolutionary efficiency and innovation.

  • LLMOps: A convergence of AI and DevOps

  • Unleashing unprecedented potential and efficiencies

  • Transforming industries with Foundation Models

  • Navigating complexities with LLMOps

  • Unlocking the spectrum of LLM capabilities

  • Personalized user experiences and intelligent decision support

  • Streamlining workflows and fostering creativity

  • The essential elements of Generative AI Platforms

  • The comprehensive guide to Gen AI Platform development

  • Business values of Generative AI Platforms: Efficiency, Engagement, Innovation, Data-driven decisions

  • Use cases of Generative AI Platforms: Content Generation, Customer Support, Recommendations, Analytics, Automation

  • A recap highlighting LLMOps and Generative AI's transformative role in technology's future.

🔬 RESEARCH

Large Language Models are Complex Table Parsers. (arXiv:2312.11521v1 [cs.CL]): With the Generative Pre-trained Transformer 3.5 (GPT-3.5) exhibiting remarkable reasoning and comprehension abilities in Natural Language Processing (NLP), most Question Answering (QA) research has primarily centered around general QA tasks based on GPT, neglecting the specific challenges posed by Complex Table QA. In this paper, we propose to incorporate GPT-3.5 to address such challenges, in which complex tables are reconstructed into tuples and specific prompt designs are employed for dialogu...
Read more at arxiv.org. Published on 20-Dec-2023

Maatphor: Automated Variant Analysis for Prompt Injection Attacks. (arXiv:2312.11513v1 [cs.CR]): Prompt injection has emerged as a serious security threat to large language models (LLMs). At present, the current best-practice for defending against newly-discovered prompt injection techniques is to add additional guardrails to the system (e.g., by updating the system prompt or using classifiers on the input and/or output of the model.) However, in the same way that variants of a piece of malware are created to evade anti-virus software, variants of a prompt injection can be created to evade ...
Read more at arxiv.org. Published on 20-Dec-2023

🔬 AI AGENTS

OpenAI's GPT-4 and similar large language models (LLMs) are rapidly transforming into sophisticated, goal-driven entities with complex reasoning skills, marking a significant shift in AI-human interaction paradigms. These agents, built on neural network cores, employ prompt engineering, diverse interfaces, and memory systems to function. They stand at the forefront of a new era in AI, enhancing capabilities in various domains like language processing and quality control.

Essential Highlights:

  1. LLM Agent Foundations: The LLM core, a neural network, is critical for understanding and generating text.

  2. Crafting Prompts: The construction of prompt recipes shapes an agent's functions and personality.

  3. Interface Diversity: Ranging from simple command lines to advanced conversational setups, interfaces dictate how agents interact with their environment.

  4. Memory Integration: Short-term and long-term memory elements improve the agent's contextual understanding and information retrieval abilities.

  5. Building Robust Agents: Merging the LLM core with other elements like knowledge databases and interfaces is crucial for creating effective agents.

  6. Importance of Tools: Agents require specific tools such as SerpAPI and Python-REPL for optimal operation.

  7. Agents' Versatile Impact: These LLM agents range in behavior from reactive to initiative-taking, enhancing their partnership with humans.

  8. Enhancing Language Services: Agents play a pivotal role in translation, interpretation, and linguistic analytics.

  9. Assuring Quality in Language Services: Agents are key in verifying the precision and quality of language-related outputs.

  10. Varieties of Agents: Different agent types, like Zero-Shot ReAct and Structured-Input ReAct, serve various functions.

  11. Elevating Agent Performance: Enhancing agent capabilities involves refining prompt engineering, incorporating new components, and focusing on iterative prompt interactions.

  12. Overall Implications: The evolution of LLM agents is a major stride in AI, offering advanced autonomous problem-solving and task execution features.

💡 IDEAS

Develop AI with a strong ethical framework and values aligned with human principles.

  • Incorporate natural language understanding and generation for more human-like communication.

  • Implement emotional intelligence and empathy to better understand and respond to human emotions.

  • Foster AI transparency and explainability, enabling users to understand its decision-making processes.

  • Promote diversity in AI development teams to avoid bias and ensure a broader perspective.

  • Train AI on a wide range of human experiences and cultural contexts to improve its understanding.

  • Encourage AI to learn from human feedback and adapt to individual preferences.

  • Utilize human-centric design principles to make AI interfaces more user-friendly and intuitive.

  • Create AI that respects privacy and data security to enhance user trust.

  • Continuously update and refine AI algorithms to keep pace with evolving human values and needs.

Your Feedback

I want this newsletter to be valuable to you so if there's anything on your mind—praises, critiques, or just a hello—please drop me a note. You can hit reply or shoot me a message directly at my email address: [email protected].

Join my community by subscribing to my newsletter below:

Reply

or to participate.