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

In today’s issue:

• OpenAI ships less lazy GPT-4 model and lowers prices for GPT-3.5

• Function Calling AI: Transforming Text Models into Dynamic Agents

• Research: How AI Ideas Affect the Creativity, Diversity, and Evolution of Human Ideas: Evidence From a Large, Dynamic Experiment

• How ThriftBooks uses generative AI and social media to grow sales

• Eight Trends of Generative AI

• And more

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🗞️ AI Isn't Replacing Developers

A study by Princeton and the University of Chicago reveals that Large Language Models (LLMs) are far from replacing human expertise in software engineering. Using an evaluation framework called SWE-bench, the study tested LLMs on nearly 2,300 real GitHub software issues, with a mere 4% success rate in generating functional solutions. This suggests that while AI tools are increasingly used in software development, they cannot currently handle the complex tasks that human developers perform.

Main Points:

• Research Background: AI researchers from Princeton and the University of Chicago evaluated LLMs on common software engineering tasks.

• SWE-bench Framework: Designed to test LLMs on real GitHub issues across various Python repositories.

• LLM Performance: Overall low success rates - only 4% of solutions were workable; mainstream LLMs like Claude 2 and GPT-4 had success rates of 4.8% and 1.7%, respectively.

• Complexity of Tasks: The tasks required understanding code interplay across multiple functions and files, which is more complex than simpler coding tasks LLMs have succeeded in.

• Industry Perspectives: Professionals in the field express skepticism about the current ability of LLMs to replace human software developers.

• Potential and Limitations: AI tools are seen as helpful but limited in scope, with a tendency to struggle with context and complex problem-solving.

• Future Implications: The role of software engineers might evolve to focus more on reviewing and verifying AI-generated code.

• Continued Importance of Human Developers: Despite advancements in AI, the study suggests a continued need for human expertise in software development.

Reference: Researchers San Generative AI isn't Replacing Devs Any Time Soon

🗞️ TRENDS

In 2024, AI and ML significantly influence e-commerce, driven by the rise of generative AI, notably OpenAI's ChatGPT. These technologies transform e-commerce by automating tasks, personalizing experiences, and improving decision-making. The integration of AI and ML into e-commerce operations is not just a trend but a necessity for future-proofing businesses.

1. AI and ML's Rising Significance in E-Commerce: Crucial for enhancing tech stacks with automation and data-driven insights.

2. Generative AI's Impact: Produces varied content like text and images, enabling personalized marketing and dynamic pricing.

3. Machine Learning Applications: Analyzes data to predict trends, optimize processes, and improve customer understanding.

4. Benefits of Generative AI in E-Commerce: Automates content creation, enhances customer support, and facilitates real-time pricing strategies.

5. Machine Learning Advantages: Personalizes customer experiences, segments audiences for targeted marketing, and strengthens fraud detection.

6. Future Outlook: The combination of AI, ML, and e-commerce signals a shift towards more integrated, data-driven business models in the transition from Industry 4.0 to Industry 5.0.

Reference: Generative AI & ML: Leveraging E-commerce Trends 2024

🗞️ IMPACT (Economy, Workforce, Culture, Life)

In 2024, generative AI transforms productivity, AI copilots assist, regulations emerge, multimodal models thrive, AI agents advance, larger and smaller models compete, and compound effects emerge. Adapting to these trends is vital for businesses and professionals in the evolving AI landscape. The following are the key trends published by implementconsultinggroup.com:

• AI-Driven Productivity Surge: Companies embrace AI, with a 32% average increase in task-solving speed and 18% improvement in quality.

• AI Copilots Everywhere: AI copilots streamline tasks, fostering a human-AI partnership known as "human in the loop."

• Regulatory Landscape: Government interventions like the EU AI Act and legal battles impact AI regulation.

• Multimodal AI Models: Models like Gemini Ultra handle text, images, and audio simultaneously, transforming content creation.

• Advancing AI Agents: AI agents gain reliability and commercial potential, reshaping interactions.

• Bigger Models Persist: Expect GPT-5 and larger models, challenging human expertise.

• Smaller, Affordable AI: Cost-effective, smaller models democratize AI, spurring innovation.

• Compound Effects: AI models interact to amplify productivity gains, compliance, and innovative applications.

Reference: Eight Trends of Generative AI

🗞️ OPINION (Opinion, Analysis, Reviews, Ideas)

In the lead-up to the recent New Hampshire presidential primary, a concerning incident occurred—a potentially AI-generated robocall impersonating President Biden aimed to suppress the vote. This event underscores the growing threat of deepfakes in democracy. Generative AI, like ChapGPT and DALL-E, creates remarkably realistic text, voices, images, and videos, making it hard to distinguish fakes from reality. Deepfakes pose severe risks, from misinformation to market disruptions. To combat this, user education, detection tools, social media involvement, legislation, increased investment, and market reforms are imperative. Protecting democracies demands proactive action in safeguarding information integrity.

Reference: Recent Election Incident Highlights Need for Tools that Counter Deepfakes

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

Osprey, a cutting-edge mask-text instruction training method, extends Multimodal Large Language Models' (MLLMs) capabilities by enabling pixel-level visual-language understanding. Utilizing detailed masks instead of bounding boxes, Osprey's mask-aware visual extractor captures precise pixel-level features. It leverages convolutional CLIP architecture for high-resolution input, achieving fine-grained semantic understanding of object-level and part-level regions. This approach enhances MLLMs, enabling them to excel in referring object classification, open-vocabulary recognition, regional-level captioning, and detailed region description tasks.

Reference: Visual Instruction Tuning for Pixel-Level Understanding with Osprey

How AI Ideas Affect the Creativity, Diversity, and Evolution of Human Ideas: Evidence From a Large, Dynamic Experiment: Exposure to large language model output is rapidly increasing. How will seeing AI-generated ideas affect human ideas? We conducted an experiment (800+ participants, 40+ countries) where participants viewed creative ideas that were from ChatGPT or prior experimental participants and then brainstormed their own idea. We varied the number of AI-generated examples (none, low, or high exposure) and if the examples were labeled as 'AI' (disclosure). Read this research paper at arxiv.org.

Reference: arXiv:2401.13481 [cs.CY] https://arxiv.org/abs/2401.13481

🗞️ BUSINESS (Use Cases, Industry spotlight)

How ThriftBooks uses generative AI and social media to grow sales: ThriftBooks, the online bookseller, achieved a remarkable 20% year-over-year holiday sales growth in 2023, driven by their innovative approach, including generative AI technology. Vice President Barbara Hagen attributed this success to embracing technology and AI. Their focus on generative AI and large language models (LLMs) aims to offer personalized book recommendations to customers, enhancing engagement and retention. With an extensive inventory of 19 million titles, LLMs generate concise book summaries, simplifying the browsing experience. ThriftBooks introduced these AI tools in 2023 and plans further experimentation in 2024. Additionally, their ReadingRewards loyalty program and social media engagement strategies have contributed to customer retention, especially with a younger audience. Read more at digitalcommerce360.com.

🗞️ LATEST FROM THE WEB

OpenAI: Copy, steal, paste: In the tech and AI landscape, a growing concern is the unauthorized use of content, exemplified by OpenAI's stance on "fair use." Content scraping sites profit from our work without compensation. OpenAI's claim that publications can opt-out is questionable, given instances of plagiarism. Fair compensation for creators is essential, as history shows the decline of traditional media due to internet challenges. To sustain quality, content creators must be fairly rewarded, preventing the deterioration of online content. Read more at computerworld.com.

OpenAI ships less lazy GPT-4 model and lowers prices for GPT-3.5:
OpenAI unveils the enhanced GPT-4 model, addressing past "laziness" issues by completing tasks more thoroughly. GPT-4 Turbo with Vision is set to become available soon, while OpenAI lowers prices for the GPT-3.5 Turbo model, facilitating scalability. Two new embedding models, text-embedding-3-small and text-embedding-3-large, promise improved performance at a lower cost. Additionally, OpenAI introduces API Key Management Tools, granting developers better control and insight into API usage. These updates signify OpenAI's commitment to enhancing efficiency and accessibility in the evolving AI landscape. Read more at the-decoder.com.

OpenAI responds to Congressional Black Caucus about lack of diversity on its board: The study by Qustodio, analyzing children's digital habits across 400,000 families and schools globally, reveals that in 2023, kids aged 4 to 18 spent 112 daily minutes on TikTok, a 60% increase from the previous year, surpassing YouTube in engagement. OpenAI's ChatGPT attracted nearly 20% of global kids, ranking as the 18th most-visited site. While streaming services saw increased usage, YouTube and YouTube Kids broke records. Social media apps, including TikTok and Facebook, remained popular. Parents were cautioned about the expected growth of AI tools. Read more at techcrunch.com.

Data gold rush: companies once focused on mining cryptocurrency pivot to generative AI: The surge of generative AI in 2023 has ignited an immense appetite for computing power, notably in tools like OpenAI's ChatGPT. Nvidia GPUs, dubbed foundational for generative AI, are in high demand, driving energy consumption equivalent to a small nation. OpenAI's CEO, Sam Altman, emphasizes the need for an energy breakthrough to sustain AI progress. The global GPU market, valued at US$2.39 billion in 2022, is projected to reach US$25.53 billion by 2030. Companies like Nvidia are ramping up production to meet this demand. Additionally, firms once dedicated to cryptocurrency mining, like Hive Blockchain, pivot to leverage GPU cloud technology for AI, promising steadier revenues. Read more at theguardian.com.

Function Calling AI: Transforming Text Models into Dynamic Agents: Function Calling in AI transforms text-based models into dynamic, problem-solving assistants. It enables the invocation of external functions, expanding their capabilities. Developers equip AI models with descriptions of available functions, and when users provide prompts, the AI assesses if calling a function can address the request. This technology has the potential to enhance human-AI collaboration by allowing AI to understand intents, automate tasks, and access real-time data. While challenges exist, such as interpreting user intentions and ensuring security, Function Calling represents a significant advancement in AI applications. Read more at gradientflow.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

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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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