Accuracy of LLMs

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

I’m back after a brief holiday break. Early next year, there will be some changes to the structure to make it more consistent and predictable. I’m also attempting to streamline the newsletter size, making it a concise, under-five-minute read containing 1500 words or less. Today’s newsletter is 1819 words long. 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:

• Increasing the Accuracy of LLMs in the Enterprise with a Knowledge Graph

• Why Amazon Will Come Out Ahead In The Generative AI Race

• Top 10 business applications stories of 2023

• AI Industry Analysis: 50 Most Visited AI Tools and Their 24B+ Traffic Behavior

• AI could make us conversant with critters, unlocking conservation tools – and serious risks

• 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.

♨️ ACCURACY OF LLMs

Large Language Models (LLMs) hold potential for innovative data use in enterprises but are hindered by accuracy issues, notably producing incorrect information. Research led by Juan Sequeda Ph.D., Dean Allemang Ph.D., and Bryon Jacob shows that Knowledge Graphs significantly enhance LLM accuracy, particularly in complex business queries, suggesting a promising solution to these challenges. Read more at data.world.

📈 TRENDS

The fusion of deep learning algorithms with corporate datasets is expected to significantly impact various business sectors, establishing generative AI as an integral component of digital advancement, rather than a fleeting phenomenon. In 2024, it's essential for businesses to keep abreast of the developments in generative AI to preserve their competitive stance and drive innovation.

Highlights:

• AI for Strategic Business Decisions: AI plays a pivotal role in data analysis for strategic planning, risk evaluation, and market insights, facilitating informed decision-making processes.

• Generative AI vs. Predictive AI: Generative AI excels in creating new content, contrasting with Predictive AI's focus on trend forecasting and analysis, each fulfilling distinct roles within the AI sphere.

• AI in Supply Chain Enhancement: Predictive AI is revolutionizing supply chain management by improving accuracy in forecasting, logistics, and inventory management, thereby enhancing efficiency and reducing costs.

• AI in Legal Document Review: AI aids in legal research, contract analysis, and document review, increasing efficiency and accuracy in legal operations.

• AI's Impact on Creative Fields: AI tools are redefining content creation and design, raising important questions about ethics and intellectual property rights, such as those posed by Adobe's Firefly design tools using proprietary data.

• AI in Automated Graphic Design: AI is utilized for generating logos, branding materials, and graphic designs, meeting the demands of digital marketing and visual content creation.

• Personalization in Marketing and Sales via AI: Large Language Models (LLMs) like ChatGPT and Llama are revolutionizing marketing through detailed customer data analysis, enabling highly targeted strategies and improved customer engagement.

🗞️ LATEST FROM THE WEB

Why Amazon Will Come Out Ahead In The Generative AI Race: Amazon's generative AI strategy, emphasizing a wide range of models and accessible ecosystem, sets it apart in the AI race. Leveraging AWS's cloud dominance and efficient AI chips, it focuses on flexibility and choice in AI solutions. This approach positions Amazon uniquely to integrate generative AI across various sectors, potentially transforming its extensive range of products and services. Read more at SeekingAlpha.com.

Top 10 business applications stories of 2023: In 2023, the emergence of Generative AI (GenAI) revolutionized business applications, gaining prominence over global economic and geopolitical issues. Articles highlighted its application in customer experience, enterprise software, and various sectors, with insights from industry leaders. Concurrently, discussions on software's role in sustainability, HR challenges, and digital transformation underscored the dynamic evolution of business technology. Read more at ComputerWeekly.com.

AI could make us conversant with critters, unlocking conservation tools – and serious risks: The Earth Species Project (ESP), inspired by a smart whippet named Roxy, uses AI to decipher animal communication, aiming to aid conservation. Funded by the Paul G. Allen Family Foundation, ESP collaborates globally but faces ethical dilemmas regarding AI's impact on wildlife, balancing groundbreaking insights with potential risks. Read more at GeekWire.com.

From the C-Suite: How Generative AI Will Change Business in 2024: Leading executives from Dell, Lenovo, and Intuit forecast a pivotal shift in generative AI, with scaled enterprise projects and reduced training costs for large language models on the horizon. They predict a rise in specialized, domain-specific AI applications and multimodal models, enhancing various business functions. This evolution underscores the need for robust governance and specialized roles in AI-driven industries. Read more at AIBusiness.com.

🧰 TOOLS

The top 50 AI tools amassed over 24 billion visits from September 2022 to August 2023, demonstrating the sector's booming interest. ChatGPT alone contributed 14 billion visits, showcasing its leading role in the industry. This analysis, powered by SEMrush and covering over 3,000 AI tools, reveals significant trends and patterns in digital behavior within the AI sector. The study focuses on understanding the AI industry's traffic dynamics, user demographics, and behavioral trends, offering a comprehensive view of the current state and potential future directions of AI technologies.

Reference: AI Industry Analysis: 50 Most Visited AI Tools and Their 24B+ Traffic Behavior

🔦 INDUSTRY SPOTLIGHT

Retailer Carrefour is transforming procurement with a process approach to generative AI: Carrefour is leveraging generative AI, specifically ChatGPT, to enhance procurement efficiency, cutting down quote comparison time from 30 to 10 minutes. This innovation, led by Florian Tué, marks a significant productivity boost. Discussed at Celosphere 2023, this initiative is part of Carrefour's broader AI strategy. Key focuses include staying abreast of AI advancements and integrating tools like Celonis' Process Copilots for better process management. These efforts demonstrate Carrefour's commitment to digital transformation in retail. Read more at Carrefour.

🏢 INFRASTRUCTURE

Infrastructure Vendors Were the Market Winners in AI’s First Year: The first year of the generative AI era has been marked by significant developments, with its potential impact on the global economy and market size being immense yet challenging to fully comprehend. Surprisingly, infrastructure vendors, including cloud platform providers like Google, Microsoft, and Amazon, and GPU producers like NVIDIA, have emerged as the primary beneficiaries. This is because AI startups, needing to train their models, heavily invest in these vendors' services. Despite high operational costs, the generative AI industry is expected to expand substantially, and companies that excel in providing AI-based B2B services may ultimately lead the market. This shift towards AI integration in enterprise workflows signals a transformative future for business operations and efficiency. Read more at Pymnts.com.

🗣️ CULTURE

Generative AI to ‘become culturally aware’ in 2024: The future of technology hinges on culturally aware Generative AI, as foreseen by Amazon's CTO, enhancing global accessibility and understanding. Non-Western AI models are emerging, broadening inclusivity. Concurrently, FemTech's rise is reshaping women's healthcare with technology-driven solutions. AI assistants are evolving to significantly boost developer productivity. Lastly, the rapid tech evolution is driving a shift from traditional education to industry-focused, skills-based training, aligning learning with real-world needs. Read more at TradeArabia.com.

🏢 OPENAI

“brace yourselves, agi is coming…” (from Twitter…)

📦 GENERATIVE AI

Gartner's impact radar for generative AI emphasizes immediate-use cases and strategic planning. It categorizes 25 technologies into four themes: model innovations, AI safety, data-related models, and AI applications, guiding competitive GenAI product development. Read more including the infographic at the source.

Reference: Gartner P, Lori (2023). Understand and Exploit GenAI With Gartner’s New Impact Radar.

What are Large Multimodal Models (LMMs)?

1. Evolution to Multimodal AI Systems: Traditional ML models focused on single data modes, but the trend is shifting towards multimodal systems (LMMs) that can process and integrate various data types like text, images, and audio.

2. Significance of Multimodal Integration: Multimodal systems are crucial for more realistic and practical AI applications, emulating human abilities to process multiple types of data simultaneously.

3. Examples of Multimodal Systems: Notable LMMs include DeepMind’s Flamingo, Salesforce’s BLIP, Microsoft’s KOSMOS-1, and OpenAI’s GPT-4V. These systems represent significant advancements in AI capabilities.

4. Variety of Multimodal Systems: While all multimodal systems aren't LMMs, many, like text-to-image models (e.g., Dall-E, Midjourney), play a critical role in AI development.

5. Multimodal Applications in Different Sectors: Multimodality is particularly beneficial in sectors dealing with diverse data types like healthcare, robotics, and retail.

6. Research Focus Areas: Current research in LMMs is directed towards incorporating more data modalities, improving instruction-following capabilities, developing efficient training methods, and generating multimodal outputs.

7. Importance of Diverse Data Modalities: Different data modes like audio, text, and images can often be represented or approximated in other forms, broadening the scope of AI applications.

8. Future of Multimodal AI: The field is rapidly evolving, with ongoing research and development aimed at creating more advanced and versatile multimodal AI systems.

Reference: Chip Huyen (2023). Multimodality and Large Multimodal Models (LMMs)

📚LEARNING

The August 2023 article published by Arize.com discusses real-world applications of Large Language Models (LLMs) in industries like finance and customer support for tasks like data processing and content creation. However, their efficacy is limited by their training data. Enhancing LLMs with proprietary data through search and retrieval methods significantly improves their accuracy and applicability.

Key Points:

1. LLMs assist in financial advice, customer inquiries, and content generation.

2. LLMs are limited to their training data and lack specific, up-to-date information.

3. Integrating LLMs with real-time, proprietary data enhances their functionality.

4. Search and retrieval methods with LLMs lead to more accurate, relevant results.

5. Continuous optimization is essential for effective LLM application in various sectors.

6. Future directions involve the practical implementation of LLMs in custom applications.

Reference: Arize.com (2023) Introduction To Retrieval Augmented Generation

🔬 RESEARCH

This paper presents an effective method to convert Large Language Models (LLMs) into Multi-Modal Large Language Models (MLLMs) using domain adaptation techniques. It highlights that tuning only the LayerNorm in each attention block significantly enhances performance, proving more efficient than full parameter finetuning or LoRA. In tests, this approach improved a 13B model's multi-modal tasks performance by over 20%, while reducing trainable parameters by 41.9% and GPU memory usage by 17.6%. Additionally, fine-tuning with selective conversational data further boosts efficiency. The paper also delves into the role of LayerNorm in enhancing LLMs' adaptation to multi-modal domains and increasing their expressive capabilities.

Reference: arXiv:2312.11420 (2023). Tuning LayerNorm in Attention: Towards Efficient Multi-Modal LLM Finetuning

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:

Join my LinkedIn group communities below:

• Big Data 

• Artificial Intelligence

• Generative AI

• Information Technology

• Technology Leadership

• FinTech 

• Start-ups

• Creator Entrepreneur

• Influencer

• Content Creation