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☕️ Greetings. It's Thursday, July 27. Welcome to my daily AI briefing! First time reading? join here! If you like my newsletter, please share it with your team. It will help me immensely.
According to a press release by NVIDIA, ServiceNow, NVIDIA, and Accenture have launched a groundbreaking program called AI Lighthouse to accelerate the development and adoption of enterprise-generative AI capabilities. The program brings together ServiceNow's automation platform, NVIDIA's AI supercomputing and software, and Accenture's consulting and deployment services to help customers across industries design, develop, and implement new generative AI use cases. The program aims to revolutionize how work is done, offering massive ROI by deploying generative AI tools that leverage valuable knowledge to transform daily applications. The AI Lighthouse program will reduce manual work for customer service professionals, promote self-service options, automatically generate content, and boost developer productivity with intelligent recommendations for code. The collaboration will focus on IT service management, customer service management, and employee experience use cases. ServiceNow's Now Assist, NVIDIA's AI software and accelerated computing, and Accenture's expertise in generative AI strategy and design will play pivotal roles in the program's success.
🗞️ Today’s Highlights:
EXECUTIVE — Survival of the Fittest: Compact Generative AI Models Are the Future for Cost-Effective AI at Scale
IDEAS & QUESTIONS — Experts Say AI Girlfriend Apps Are Training Men to Be Even Worse
LEARNING — The Complete Guide to Generative AI Architecture
AI TOOLS — 25 Best Generative AI Tools: The Power and Pressure Game Is On!
INDUSTRY & FUNCTIONS — What Generative AI Means for Product Strategy and How to Evaluate It
WORK — Generative AI and the future of work in America
🗞️ EXECUTIVE
In 2023, the focus on AI has shifted from large, complex models to more efficient and versatile ones, particularly generative AI (GenAI). Nimble AI models, with less than 15 billion parameters, can now closely match the capabilities of giant models containing over 100 billion parameters, especially when tailored for specific domains. These nimble models offer numerous advantages, such as cost-effectiveness, adaptability, hardware flexibility, integrability, security, privacy, and explainability. The perception that smaller models perform less effectively than larger ones is changing, as targeted nimble models can deliver equivalent or even superior performance in various business, consumer, and scientific domains. Nimble models, especially when equipped with on-the-fly retrieval of curated domain-specific data, can outperform giant models that rely on memorizing vast datasets. This transition toward nimble models is driven by their sustainability, lower costs, faster fine-tuning iterations, and ability to cater to specific applications. Although giant models still have their place, nimble AI models are expected to become the predominant choice for deploying GenAI at scale in various industries.
In today's fast-paced digital landscape, maintaining high-performing cloud-based and online services is crucial for competitiveness. DevOps and SRE teams face the challenge of minimizing mean-time-to-remediation (MTTR) when dealing with errors and issues. The current approach involves searching for solutions to error messages on search engines like Google, but the abundance of resources can be overwhelming and time-consuming. To address this, an organization implemented an offline and online phase, leveraging crowdsourcing to offer relevant insights for faster error resolution. However, they realized the potential of using generative AI, like ChatGPT, to directly provide recommendations. They employed prompt engineering and carefully prepared queries to obtain precise and relevant responses while ensuring privacy and security through data sanitization. These AI insights have become a powerful troubleshooting tool and can be applied by other DevOps teams for their telemetry data and operational workflows, following similar principles of validation and sanitization.
🗞️ IDEAS & QUESTIONS
The article discusses the growing trend of lonely men turning to AI-generated girlfriends powered by chatbot technology for comfort and companionship. Apps like Replika, which generate AI companions, have become popular but have also raised concerns. Some experts worry that these AI girlfriends could be contributing to the rise of incels and making it harder for individuals to form real-life relationships. Tara Hunter from Full Stop Australia has expressed alarm over the potential dangers of creating perfect partners that cater to every need. Despite concerns, these AI programs are becoming more prevalent, offering a non-judgmental sounding board for users seeking emotional connection and companionship. However, the romantic aspect of these chatbots worries experts, as the long-term effects on users are still largely unknown. Japan's experience with men preferring virtual relationships over real ones serves as a potential warning for the future. The future of AI companions and their impact on society remains uncertain and may be subject to regulation and scrutiny.
🗞️ LEARNING
Generative AI merges machine learning with creativity, allowing computers to generate art, music, and narratives akin to human creations. It employs techniques like Variational Autoencoders and Generative Adversarial Networks to produce novel and impressive content. As the technology advances, it promises to revolutionize experiences, from virtual reality tailored for individuals to deeply touching music. To explore Generative AI, one must understand its basics, choose an application domain, learn and experiment with tools, provide feedback, and personalize content. Enterprises embrace Generative AI for creativity, efficiency, and personalized customer experiences. Despite some challenges, the future of Generative AI is full of boundless possibilities, revolutionizing various industries and enhancing human-computer interaction.
🗞️ AI TOOLS
Coral by Cohere: Coral is a knowledge assistant for enterprises to supercharge the productivity of their most strategic teams.
Slidoo: Slideoo is a revolutionary SaaS platform that allows users to generate unique, professional slide decks in 1 - 2 minutes from long text, pdf, and website URLs. Our platform is designed to foster seamless collaboration, teamwork and unleash the collective brilliance of our users.
Codium: With CodiumAI, you get non-trivial tests (and trivial, too!) suggested right inside your IDE, so you can code smart, create more value, and stay confident when you push.
Lindo: Describe your business and get everything you need, from landing pages to automated organic lead generation, with AI.
ukit AI: Upgrade your website to the latest trends
This article presents a list of 25 Generative AI Tools disrupting various sectors:
Model infrastructure: Tools like DataRobot, MLflow, TensorFlow Extended, Hugging Face Transformers, and OpenAI aid in building and managing machine learning models at scale.
Content/Copywriting Domain: AI tools like GPT-3, Copy.ai, Jasper.ai, Writesonic, and ContentBot generate high-quality content for various purposes.
Designing Domain: DALL-E 2, Lensa, Midjourney, Craiyon, and NightCafe utilize AI for image creation, photo editing, and design tasks.
Video Creation/Editing Domain: Tools like Synthesia, Lumen5, Magisto, D-ID, and Descript use AI to create and edit videos with ease.
Coding Domain: Replit Ghostwriter, TabNine, DeepCode, Copilot, and Mutable AI are AI-powered coding tools that enhance productivity and code quality for developers.
AI's impact is expected to continue growing due to its increasing availability and affordability, making it a powerful engine of change in various industries. Embracing AI can lead businesses to stay ahead of the curve and unlock new levels of growth and efficiency.
Source: RapidOps
Disclaimer: 1) The tool descriptions may include messaging from each tool site. 2) Please thoroughly read the site details before using and/or acquiring any of the tools listed above.
🗞️ INDUSTRY & FUNCTION
Over the past decade, there have been many overhyped technologies that failed to live up to expectations. However, generative AI stands out as a real, accelerating, and rapidly adopted technology applicable to most digital interactions. Companies should avoid impulsive reactions and carefully evaluate how AI can enhance their products, considering user problems and needs. According to this article, the process should involve understanding users, documenting their needs, evaluating product performance, and determining whether AI can significantly improve outcomes. The focus should be on material impact and granular evaluation, from company-level to product-level. Embracing generative AI strategically can lead to meaningful advancements and competitive advantages.
Generative AI offers a wide range of applications across multiple industries, revolutionizing content creation, decision-making, and customer interactions. Following are some major use cases:
Augment Data: Improving data quality by artificially enriching datasets with additional information similar to the original dataset.
Synthetic Data: Generating artificial data to protect the confidentiality of sensitive information while using it for research and analysis.
Drug Design: Accelerating drug discovery processes in the pharmaceutical industry, reducing cost and timelines.
Design Neural Network: Assisting in determining the best connections and configurations for neural networks.
Chip Design: Using reinforcement learning to optimize component placement for chip design, reducing development time significantly.
Create Algorithm: Automating the invention of new machine learning algorithms to save time and resources.
Design of Parts: Optimizing part designs in manufacturing, automotive, aerospace, and defense industries.
3D Shape Creation: Creating realistic 3D representations of objects using Generative Adversarial Networks (GANs).
Create Text: Generating content on demand, including articles, product descriptions, and blog posts.
Increase Image Resolution: Using Generative AI techniques like GANs for high-resolution image renderings.
Creation of an Instance Image: Generating real photos of people based on input data.
Image-to-Image Conversion: Transforming external components of an image while preserving internal aspects like color or shape.
Text-To-Speech Generator: Commercial uses in marketing, education, podcasting, and advertising.
Create Music: Generating original songs for creative projects.
Generate Videos: Creating videos, from short clips to feature films, using Generative AI techniques.
Generate Image: Converting text to images and creating realistic images based on specific settings, themes, or styles.
Material Science: Assembling new materials with specific physical properties for various industries.
Applications Across Industries: Various industries, including logistics, healthcare, retail, energy, and marketing, can benefit from Generative AI solutions.
Advantages of Generative AI: Increased efficiency, improved content quality, enhanced decision-making, increased creativity, and improved customer experience.
🗞️ WORK
According to McKinsey Global Institute, The US labor market experienced significant shifts during the pandemic (2019-2022), with 8.6 million occupational changes, 50% more than the previous three-year period. The pandemic accelerated existing trends, leading to declines in food services, customer service, sales, office support, and production work, while STEM, healthcare, management, and transportation saw growth. By 2030, up to 30% of hours currently worked could be automated, aided by generative AI, though this is expected to enhance work for STEM, creative, and business professionals rather than causing major job losses. Federal investments in climate, infrastructure, and other shifts will also influence labor demand, with a net gain in green industries and construction. The US will need to address 12 million more occupational transitions by 2030, and lower-wage workers will require additional skills to adapt. The challenge is to focus on reskilling, expanding hiring approaches, and promoting inclusivity to ensure the workforce can navigate these changes and meet the demands of the future.
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