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☕️ Greetings, and welcome to my daily dive into the Generative AI landscape.
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In today’s issue:
Cognizant Impact Study Predicts Generative AI Could Inject $1 Trillion Into U.S. Economy Over 10 Years
ChatGPT Will Not Make Your Cars Smart
OpenAI Launches GPT Store to Expand Revenue and User Base
What Temperature Means in Natural Language Processing and AI
Amazon and Generative AI - What's Going On?
And more
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🗞️ Rabbit Sells 10,000 R1 Units in One Day
The Rabbit R1, launched at CES 2024, is a charming, AI-powered pocket device likened to an "iPhone of AI". It sold 10,000 units on its first day for $199 each, surpassing the company's expectations. The R1 offers natural language responses and controls smartphone apps, featuring a camera, touchscreen, storage, and connectivity options. It's set to transform personal AI assistance. The R1 is powered by a Large Action Model (LAM), understands and executes human intentions, and interacts with various interfaces. It features real-time interaction, quick responses, comprehensive task handling, and an integrated real-time translator.
Watch this Youtube video by Matthew Berman titled Rabbit R1: The First Personal AI AGENT Device NO ONE Saw Coming (Look Out, Apple)
🗞️ TRENDS
NVIDIA's State of AI in Financial Services Report for 2024 highlights a broad adoption of AI in the industry, with 91% of firms either evaluating or implementing AI solutions. Key applications include portfolio optimization, fraud detection, risk management, and the emerging use of generative AI and large language models. The report underscores AI's impact on operational efficiency, customer experience, and a shift towards addressing data-related challenges and increasing AI investments.
Key Points:
Widespread Adoption of AI: 91% of financial services companies are exploring or using AI in production, focusing on innovation, operational efficiency, and enhanced customer experiences.
Emerging Trend of Generative AI and LLMs: 55% of respondents are actively integrating generative AI and large language models, notably in marketing, sales, and report generation.
AI Across Departments: AI implementations are proving beneficial in operations, risk management, compliance, and marketing, with 43% reporting improved operational efficiency and 42% gaining competitive advantages.
Data Challenges and Regulation: The primary concern has shifted to data-related issues, including privacy, sovereignty, and regulatory compliance, alongside the ongoing challenge of recruiting AI talent.
Positive Financial Impact and Investment Plans: A significant majority (86%) report positive revenue impacts, 82% note cost reductions, and 97% of companies plan to increase AI investments, focusing on new use cases and infrastructure.
Future Focus and Enterprise AI Platform Needs: There's a strong focus on identifying new AI applications, optimizing workflows, and enhancing AI infrastructure to support collaborative efforts among data scientists and developers, aiming for productive and customer-centric outcomes.
🗞️ IMPACT (Economy, Workforce, Culture, Life)
Cognizant and Oxford Economics' study, "New Work, New World," forecasts significant disruption from generative AI, affecting 90% of jobs. It highlights potential productivity gains, economic growth, and job market shifts, urging leaders to harness AI's potential. The study explores U.S. economic impacts, projecting substantial changes in knowledge work and C-suite roles, and underscores the importance of workforce reskilling in the advancing AI landscape.
Generative AI Impact: Predicted to disrupt 90% of jobs, influencing work and productivity.
Economic Growth: Potential boost to U.S. productivity and GDP growth.
Job Market Disruption: Significant changes in job tasks; potential displacement of 9% of U.S. workforce.
Knowledge Work Affected: Higher disruption in areas like programming, web development, and graphic design.
C-Suite Impact: Even CEOs face over 25% potential automation of tasks.
Adoption Rates: AI adoption could leap from 13% to 31% in 4-8 years, continuing to grow for 15+ years.
Reskilling Imperative: Emphasis on reskilling for AI management and strategy.
Synapse Initiative: Cognizant's program for tech education and workforce development.
Methodology: Oxford's Global Economic Model (GEM) used for forecasting.
🗞️ OPINION (Opinion, Analysis, Reviews, Ideas)
Stephen Schmidt, Amazon's Chief Security Officer, champions generative AI as essential for cybersecurity. Integrating these tools since his shift to the role in 2022, he's utilizing internally developed AI, like Code Whisperer, for detecting software vulnerabilities. Schmidt prioritizes data protection, ensuring Amazon's corporate data remains confidential. While highlighting generative AI's role in enhancing security teams, he maintains it's not a human replacement. These tools aid in incident response automation, behavior monitoring, and secure coding practices, amplifying human capabilities and addressing the cybersecurity talent gap. Schmidt views AI as an efficiency booster, not a replacement for security engineering.
Reference: Amazon and Generative AI - What's Going On?
🗞️ LEARNING (Tools, Frameworks, Skills, Guides, Research)
OpenAI Launches GPT Store to Expand Revenue and User Base
Two months post-launch, over 3 million custom ChatGPT versions have emerged. A new GPT Store for ChatGPT Plus, Team, and Enterprise users offers a platform to discover and utilize these. It features a range from partners and community, including diverse categories like DALL·E, writing, and education. Featured GPTs include AllTrails, Consensus, Khan Academy's Code Tutor, Canva, and more. Building and sharing GPTs is straightforward, with a new revenue program for builders launching soon. Enhanced controls for Team and Enterprise customers ensure privacy and customization. Read more about the OpenAI GPT Store.
Image source: OpenAI
Watch this YouTube video by Skill Leap AI titled GPT Store Just Launched - Everything You Need to Know.
🗞️ BUSINESS (Use Cases, Industry spotlight, Business Models)
Retailers are rapidly adopting generative AI, with 98% planning investments in the next 18 months, according to a NVIDIA survey. They're using AI for personalized shopping, adaptive advertising, and efficiency improvements, shifting from generic models like GPT-4 to custom ones for brand consistency. NVIDIA's solutions like AI Foundations and NeMo are aiding in this transition, enabling large-scale, tailored deployments. Retailers apply generative AI in various ways, from internal operations like code generation and marketing to customer-facing applications such as AI shopping advisors and multilingual chatbots.
🗞️ IN THE NEWS
What Temperature Means in Natural Language Processing and AI: ChatGPT's occasional inclusion of incorrect information, known as 'hallucinations', is linked to the concept of 'temperature' in natural language processing. Temperature affects response creativity and variety. A higher temperature enhances creativity but risks accuracy, leading to unpredictable and sometimes incorrect responses. This reflects the challenge of balancing diverse content with factual accuracy in AI models. Read more at Thenewstack.io.
ChatGPT Will Not Make Your Cars Smart: Volkswagen announces ChatGPT integration in vehicles, including electric models, starting in Europe and possibly expanding to the US. Mercedes-Benz previously added ChatGPT to its MBUX system, receiving criticism for its limited usefulness. General Motors and French automaker DS also plan to incorporate ChatGPT, with DS starting a pilot run. However, privacy concerns arise due to data retention and usage by car manufacturers. The real-world utility of ChatGPT in cars remains questionable, with existing privacy issues in the automotive industry adding to the skepticism. Read more at Analyticsindiamag.com.
Using ChatGPT to be More Productive: The article discusses the use of Large Language Models (LLMs) like ChatGPT to enhance productivity and creativity in routine tasks. LLMs can translate languages and reformat text. The author suggests using an Integrated Development Environment (IDE) for more efficient LLM interactions and adjusting the 'temperature' setting to control response creativity, with higher temperatures yielding more creative outputs. Examples are given of party invitations generated at different temperatures. The article also explores using LLMs for automated customer email responses based on sentiment analysis and proposes potential AI-driven product ideas for Gmail and Shopify. Read. more at Hackernoon.com.
OpenAI pleads that it can't make money without using copyrighted materials for free: OpenAI has urged the British Parliament to permit the use of copyrighted material for training its AI models, stating it's crucial for developing effective large language models (LLMs). The company argues that limiting data to public domain content is insufficient for creating AI systems that meet current needs. This stance faces opposition, notably from the New York Times and the Authors Guild, who have sued OpenAI for copyright infringement. OpenAI insists on compliance with copyright laws but faces challenges in forming publisher partnerships and addressing concerns of individual content creators reliant on copyright for livelihood. Read more at Futurism.com.
How to use LLMs to create custom embedding models: A Microsoft study suggests using generative language models (LLMs) like GPT-4 to simplify the training of custom embedding models. The technique involves generating synthetic data for diverse tasks using LLMs, bypassing complex traditional methods. This approach could significantly lower the cost and complexity of developing tailored embeddings for specific applications, potentially expanding LLM applications and enabling more customized solutions. Read more at Bdtechtalks.com.
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