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- Thirsty Generative AI
☕️ Greetings, and welcome to my daily dive into the Generative AI landscape.
I’m evolving this newsletter to ensure you, as a valued reader, eagerly anticipate opening it daily for valuable insights that keep you updated on Generative AI. My goal is to streamline its content, making it a concise, under-five-minute read. 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.com. For more of my articles, visit ReROAR.com.
Today’s issue covers the following:
Thirsty Generative AI.
What is AI Automation?
Needed AI skills facing unknown regulations and advancements.
Building generative AI applications for your startup.
Ad more.
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♨️ TOP STORY
Generative AI's rise spotlights Big Tech's escalating water use, with significant environmental implications. Studies highlight AI's substantial water consumption, fueling concerns over worsening global water shortages.
Key Points:
Environmental Concern: The global push for advanced generative AI, including models like OpenAI's ChatGPT, is raising awareness of Big Tech's increasing water consumption.
Research Findings: A study by Shaolei Ren at the University of California, Riverside, reveals that ChatGPT uses approximately 500 ml of water per 10 to 50 prompts.
Water Crisis Impact: The rising water footprint of AI technologies could exacerbate existing water scarcity issues, prompting public concern and protests.
Tech Giants' Consumption: Microsoft and Google have reported significant increases in water usage, largely attributed to their AI development efforts.
Data Centers' Role: Big Tech's data centers, crucial for AI operations, are major contributors to water and energy consumption.
Sustainability Goals: Both Microsoft and Google aim to become “water positive” by the end of the decade, replenishing more water than they consume.
Efficiency vs. Resource Use: Advances in AI efficiency are countered by increased energy and resource demands, particularly in water usage for cooling heavy computation servers.
Tech Firms' Responses: Microsoft and Google are exploring methods to make large AI systems more energy and water-efficient.
Google's Carbon-Free Goal: Google aims to power its data centers with carbon-free energy by 2030.
Future Challenges: The launch of new AI tools like Microsoft’s Bing Chat and Google Bard could further increase water consumption, underscoring the need for sustainable solutions in AI development.
📈 TRENDS
Here are some significant trends and developments resulting from Generative AI as of December 2023:
Data Management Revolution: Generative AI is transforming data management in various ways:
Natural Language as the New Query Language: The Natural Language to SQL (NL2SQL) technology is enabling individuals without SQL programming knowledge to query databases using plain language. This democratization of data access is expected to enhance decision-making across diverse industries.
Vector Search for Structured Data: Vector embeddings are revolutionizing data storage and searching, enabling more sophisticated query mechanisms and pattern recognition in both structured and unstructured data.
Expansion of GPUs in Data Management: The role of GPUs is expanding beyond just powering AI algorithms to being integral in GPU database architectures, facilitating faster and more efficient data management.
Large Language Models: The development and application of large language models like GPT-4 are expected to continue growing. These models are evolving to handle more than just language, integrating capabilities such as image or video recognition with text. This advancement is paving the way for more general-purpose and powerful AI tools.
Regulatory Changes: 2023 is witnessing significant shifts in the regulatory landscape for AI. New laws and stricter regulations are being introduced globally, with a focus on protecting human rights, privacy, and fairness in AI applications. These changes are expected to shape how technology companies build, use, and sell AI technologies.
Shift in AI Research Dynamics: The dominance of Big Tech in AI research is being challenged by an open-source revolution and the rise of AI startups. This shift is resulting in more diversified and innovative AI developments, potentially leading to a more open and collaborative AI research environment.
Impact on the Pharmaceutical Industry: AI is significantly impacting biotech, with developments like AI-produced protein banks and AI-designed drugs. This trend is altering the landscape of drug discovery and development, with AI playing a crucial role in creating new drugs and understanding diseases
🗞️ IN THE NEWS
How generative AI could help foreign adversaries influence US elections - Poynter This article explores how foreign adversaries might use generative AI to interfere in U.S. elections, continuing a trend of utilizing technology for political manipulation. Poynter Article
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Generative AI Report – 12/6/2023 - insideBIGDATA A round-up feature focusing on new applications and integrations tied to generative AI technologies. (Note: I couldn't access the specific details due to website restrictions.) insideBIGDATA Article
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Businesses gain upper hand with GenAI integration - Help Net Security The article discusses how companies leveraging generative AI are achieving significant growth and productivity, outperforming their competitors. Help Net Security Article
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The Bulleit Group Unveils Open-Source Generative AI - GlobeNewswire The Bulleit Group has announced the open sourcing of its generative AI policy, a move that could have significant implications for the tech communications sector. GlobeNewswire Article
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Generative AI a stumbling block in EU legislation talks -sources - Reuters This article covers the challenges that generative AI is posing in the discussions around new EU legislation. Reuters Article
📦 USE CASES
As of December 2023, Generative AI has found significant applications in IT operations, offering a range of use cases that enhance efficiency, automate processes, and drive innovation. Here's a list of notable use cases:
Code Generation, Review, Debugging, and Style Check: Generative AI can assist in various stages of software development, from generating code to debugging and ensuring code adheres to specified styles and standards.
Process Automation: AI systems can automate repetitive business tasks such as compliance assurance and data integrity, potentially remediating issues with minimal human input.
Risk Assessment and Management: Analyzing vast volumes of data from IT infrastructure, generative AI identifies risks like security vulnerabilities and system failures, suggesting and implementing fixes.
Infrastructure Optimization: Observing properly running infrastructure, AI can identify improvements in system and network configurations, troubleshoot issues, and implement changes automatically.
Reporting and Interfacing: Synthesizing text based on data, generative AI enhances IT reporting platforms. IT administrators can use natural language queries to interact with these systems for tasks like changing system configurations.
Test Generation: Particularly adept at synthesizing data and text, generative AI is used for creating data and test cases in software testing, executing tests, reporting results, and offering suggestions for code optimization.
Bug Remediation: Generative AI models can analyze code for bugs and propose fixes, improving software quality and ensuring adherence to coding standards.
Automated Deployment: Post-testing, generative AI can be involved in automated deployment, optimizing workload placement and connecting instrumentation for monitoring and data gathering.
♾️ AUTOMATION
AI automation has types, each suited for specific applications and industries. Understanding these types helps in appreciating the breadth and depth of AI automation's impact.
Rule-Based Automation
Definition: The simplest form of AI automation, it operates on predefined rules and procedures.
Applications: Widely used in data entry, form processing, and routine administrative tasks.
Advantages: Highly reliable for tasks with fixed patterns and minimal variation.
Intelligent Process Automation (IPA)
Definition: Combines traditional robotic process automation (RPA) with AI components like machine learning.
Applications: Streamlines complex business processes, including data analysis, customer service, and decision-making tasks.
Advantages: Enhances efficiency and accuracy in tasks that require data interpretation and decision-making.
Cognitive Automation
Definition: Employs advanced AI technologies, including machine learning, NLP, and computer vision, for complex problem-solving.
Applications: Used in industries like healthcare for diagnostic automation, finance for predictive analysis, and customer support for intelligent interactions.
Advantages: Capable of handling high-level cognitive tasks, offering solutions that mimic human reasoning and learning.
Each type of AI automation brings a unique set of capabilities to the table. Rule-based automation provides steadfast reliability for routine tasks, IPA adds a layer of intelligence to process automation, and cognitive automation takes on tasks that require sophisticated understanding and decision-making. These types allow AI automation to permeate various facets of business and everyday life, showcasing its versatility and transformative potential.
Reference: What is AI Automation?
👩💻👨💻 SKILLS & CAREERS
Generative AI, while promising, faces challenges in skill adaptation and regulatory oversight. The demand for skilled personnel in AI is high, yet there's a lack of capabilities to meet technological demands, especially in post-pandemic times. Traditional education may not fully equip individuals for specific AI applications. As AI transforms industries, there's a need for skills that blend human involvement, industry knowledge, and technological advancement. The challenge extends to balancing AI implementation with data privacy, security, and workforce concerns. The future of AI requires a new framework for skill development, integrating domain-specific needs and overcoming traditional hiring and training limitations.
📚LEARNING
This two-part article discusses the development process of generative AI applications for startups. It highlights the effects of transformer neural networks and machine learning, underscoring an inflection point in technology. The discussion includes practical aspects like building, scaling, and innovating with generative AI, emphasizing Amazon CodeWhisperer as an exemplary tool. The piece further explores various aspects of generative AI, from foundational models and customization techniques to the role of AWS services in this evolving landscape.
Key Takeaways:
Generative AI Applications: These are AI-driven tools capable of creating new content across various mediums, powered by machine learning models and foundation models.
Amazon CodeWhisperer: A prime example of generative AI, assisting in code development through AI suggestions.
Rapid Development in Generative AI: Startups are witnessing a surge in tools simplifying generative AI adoption, including frameworks like LangChain.
Foundation Models (FMs): These are central to generative AI applications, pre-trained on extensive data for diverse tasks.
Customization Techniques: Fine-tuning, domain adaptation, and retrieval-augmented generation are key methods for tailoring foundation models.
AWS and Generative AI: AWS services play a pivotal role, offering various components and infrastructure for building generative AI applications.
Practical Implementation: The article illustrates the use of AWS services in different approaches to building generative AI applications, showcasing how AWS can be leveraged for efficient and effective generative AI solutions.
Conclusion: The cle offers a comprehensive overview for startups looking to harness generative AI, highlighting AWS’s instrumental role in this technology's evolution and practical application.
💡 MUSING
What is Magic?
“When we experience something impossible for the first time, we call it magic. When we see something evolve into the impossible, we call it innovation. There is no such thing as magic”
AI was once beyond magic.
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