What is CodeOps?

☕️ 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:

• What is CodeOps?

• From Search to Chat: Key Trends and 2024 Predictions

• Can GPT-4 and GPT-4V perform abstract reasoning like humans

• AI-driven news channel

• And more

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♨️ CodeOps

CodeOps marks a novel paradigm in software engineering, focusing on the systematic recycling of existing code components. This approach integrates advanced AI tools and modular programming techniques, substantially quickening the software development process. CodeOps is not just a technical shift but a strategic one, prioritizing the repurposing of design elements, architectural frameworks, and actual code, leading to a more efficient, adaptable, and cooperative software production environment.

Key Points:

• Introduction of CodeOps: A new wave in software development, prioritizing efficient code reuse over traditional development methodologies.

• Strategic Code Repurposing: Establishing a repository for universally accessible, approved code modules, enhancing cross-team development efficiency.

• Incorporation of AI in Development: Leveraging AI for assembling modules, creating new components, and tailoring unique code segments.

• Revolutionizing the Software Cycle: CodeOps significantly trims down the time required for various stages like analysis, design, coding, testing, and deployment.

• Focus on Module Utility and Scope: Prioritizing the reusability of modules over their overall application coverage in the development process.

• Advantages for Businesses: Increased adaptability, uniform product quality, expedited learning curves, minimized risks, and a stronger emphasis on innovation.

• Maximizing AI's Return on Investment: Refined understanding and application of AI in enhancing development efficiency.

• Innovative Development Approach: CodeOps offers a doubled speed in delivery, nurturing an environment rich in innovation and teamwork.

Reference: Infoworld (2023). CodeOps: Using LLMs and modular coding to accelerate development

💡 AI DRIVEN NEWS

Experience the future of news with Channel 1

Channel 1's AI-enabled news service, launching in 2024, will feature virtual human newscasters and AI-written scripts, aiming to offer personalized, multilingual news experiences. It plans to blend trusted source material with user-tailored content, sparking discussions on personalized news versus traditional curation. The service raises questions about content originality and the ethical use of AI in news production.

About The Channel 1

• AI Newscasters and Scripts: Virtual anchors and LLM-generated scripts.

• Personalized, Multilingual Content: User-specific news in various languages.

• Trusted Sources: Emphasis on fact-checking and reliability.

• Mobile App for Customization: App learning user preferences for tailored news.

• Ethical and Authenticity Concerns: Debates over AI-generated visual content.

• Impact on News Industry: Potential shift in news consumption and production dynamics.

• Competition and Challenges: Facing established media and digital platforms.

🗞️ IN THE NEWS

Generative AI filled us with wonder in 2023 - but all magic comes with a price: Chatbots spun our prompts into gold this year - or something like it. Figuring out which is which and what else we need to watch for is next year's problem. Read more at www.zdnet.com.

Big Tech's race to control generative AI in healthcare raises ethical concerns: Researchers argue that Big Tech should not control generative AI in healthcare. The article Big Tech's Race to control generative AI in healthcare raises ethical concerns appeared first on THE DECODER. Read more at the-decoder.com.

2023 in Review: Recapping the Post-ChatGPT Era and What to Expect for 2024: How the LLMOps landscape has evolved and why we haven’t seen many Generative AI applications in the wild yet — but maybe in 2024. Read more at towardsdatascience.com.

These scientists aren’t using ChatGPT — here’s why: Since its release a year ago, it has been impossible to escape the ChatGPT craze. The chatbot, which generates incredibly realistic human-like text and was released by OpenAI in November 2022, seems to have permeated every industry, including science. Read more at www.nature.com.

ChatGPT Cheat Sheet: Complete Guide for 2023: Get up and running with ChatGPT with this comprehensive cheat sheet. Learn everything from how to sign up for free to enterprise use cases, and start using ChatGPT quickly and effectively. Read more at www.techrepublic.com.

📈 TRENDS

From Search to Chat: Key Trends and 2024 Predictions

By 2024, AI-driven chat platforms are set to transform how we access information. Following are some key trends:

1. Growing Preference for Chat Interfaces:

   • Users increasingly choosing chat for its interactive nature.

   • Rise in voice search reflecting a move towards conversational information access.

2. Advancements in AI and NLP:

   • Significant improvements in chatbot intelligence and context understanding.

   • More investments in AI to boost chatbot performance.

3. Chatbots in Diverse Sectors:

   • Wide adoption in e-commerce, healthcare, and customer service.

   • Chatbots becoming essential for personalized support and recommendations.

4. Evolving SEO for Chat Queries:

   • Businesses adapting SEO for chat-based searches.

   • Focus on conversational keywords and detailed queries.

2024 Predictions:

1. AI Chat Platforms to Lead:

   • AI-driven chats expected to surpass traditional search engines in popularity and reliability.

2. Personalization through Machine Learning:

   • Chat platforms offering more personalized experiences.

   • Improved accuracy in user preference analysis.

3. Increased Use in Education and Work:

   • Widespread use in academic and professional settings for research and learning.

4. Growth of Voice-Activated Chatbots:

   • Surge in sophisticated, voice-responsive chatbots.

5. Rise of Chatbot Marketplaces:

   • Development of specialized chatbot marketplaces for various needs.

🗣️ CHATGPT

On November 30, 2023, ChatGPT's anniversary was highlighted by Similarweb's infographic. Initially, the chatbot saw swift growth, peaking at 1.8 billion monthly visits by May 2023, then experiencing a dip in June. A rebound occurred from September 2023, possibly influenced by students resuming academic activities post-summer break.

📚QUESTIONS

The ongoing debate about large language models (LLMs) like GPT-4 centers on their ability to mimic human logic and reasoning, especially in abstract thinking. Recent studies, including one by the Santa Fe Institute, reveal that despite advances, LLMs like GPT-4 and its multimodal version, GPT-4V, still fall short in abstract reasoning compared to human capabilities. The research, employing tests like the Abstraction and Reasoning Corpus (ARC) and ConceptARC, demonstrates that these models struggle with tasks requiring the induction of general rules from limited examples, a fundamental aspect of human intelligence.

Key Points:

• LLMs are speculated to have emerging capabilities in abstract reasoning, but their internal mechanisms remain largely unexplained.

• Abstract reasoning involves discerning patterns or rules from sparse data, crucial for human intelligence.

• The Abstraction and Reasoning Corpus (ARC) by Francois Chollet is a key framework for testing abstract reasoning in AI and humans.

• Studies show that human performance on ARC is significantly higher (around 84%) compared to AI systems, with LLMs scoring only 10-12%.

• Experiments with GPT-4 on ConceptARC, a variant of ARC, showed an average performance of about 33%, still lagging behind human performance (91%).

• Tests on GPT-4V, the multimodal version of GPT-4, suggest that multimodality does not necessarily enhance abstract reasoning capabilities.

• The findings highlight a significant gap in abstract reasoning between humans and advanced AI systems, underscoring the need for cautious integration of these models in decision-making processes and emphasizing the importance of human oversight.

Reference: BDTechTalks (2023). Can GPT-4 and GPT-4V perform abstract reasoning like humans?

🔬 RESEARCH

Personalized Decision Supports based on Theory of Mind Modeling and Explainable Reinforcement Learning. (arXiv:2312.08397v1 [cs.LG]): In this paper, we propose a novel personalized decision support system that combines Theory of Mind (ToM) modeling and explainable Reinforcement Learning (XRL) to provide effective and interpretable interventions. Our method leverages DRL to provide expert action recommendations while incorporating ToM modeling to understand users' mental states and predict their future actions, enabling appropriate timing for intervention. To explain interventions, we use counterfactual explanations based on RL...
Read more at arxiv.org. Published on 17-Dec-2023

LDM$^2$: A Large Decision Model Imitating Human Cognition with Dynamic Memory Enhancement. (arXiv:2312.08402v1 [cs.LG]): With the rapid development of large language models (LLMs), it is highly demanded that LLMs can be adopted to make decisions to enable the artificial general intelligence. Most approaches leverage manually crafted examples to prompt the LLMs to imitate the decision process of human. However, designing optimal prompts is difficult and the patterned prompts can hardly be generalized to more complex environments. In this paper, we propose a novel model named Large Decision Model with Memory (LDM$^2...
Read more at arxiv.org. Published on 17-Dec-2023

🔬 AI RISK

The OpenAI Preparedness Framework (Beta) is a comprehensive document outlining the organization's approach to monitoring, evaluating, and mitigating the catastrophic risks posed by increasingly powerful AI models. It emphasizes proactive, science-based strategies for safe development and deployment, ensuring AI advancements align with safety and security standards.

Key Points:

1. Risk Tracking and Evaluation: The framework involves constant monitoring and assessment of various risk categories, including cyber threats, CBRN (Chemical, Biological, Radiological, Nuclear) dangers, persuasion, model autonomy, and unknown risks, using a detailed scorecard system.

2. Safety Baselines and Governance: Strict safety baselines are set for model development and deployment, dependent on their post-mitigation risk score. A Safety Advisory Group oversees risk assessment and emergency scenario handling, ensuring comprehensive governance.

3. Proactive Measures and Accountability: The framework includes proactive identification of new risks, regular safety drills, third-party audits, and enabling external research and government access to model releases. This ensures a well-rounded and accountable approach to AI safety.

Reference: OpenAI Preparedness Framework - December 18, 2023

💡 GEN AI ON WALL STREET

Generative AI is poised to revolutionize wealth management on Wall Street, offering significant productivity and revenue growth. Firms like BlackRock and Morgan Stanley are integrating AI tools, enhancing client interactions and operational efficiency. McKinsey predicts generative AI could contribute $2.6 to $4.4 trillion annually across various sectors, with wealth management potentially gaining $45 billion. This AI evolution, blending with existing technologies, is essential for firms to stay competitive and cater to the growing digital-native investor base. Smaller companies are also leveraging AI for innovative financial solutions. The technology is seen not as a replacement for humans but as a co-worker, enhancing client services and operational efficiency.

Reference: CNBC (2023) Generative AI has landed on Wall Street. Here’s how it can help propel ‘massive’ revenue growth

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