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In today’s issue:
AI chatbot goes rogue during customer service exchange
Why Universities Need OpenAI
How Satya Nadella navigated Microsoft to $3 trillion market value
Hands-on PySpark + Kafka Streaming + OpenAI
And more
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🗞️ Generative AI Statistics
Here are some statistics and a glimpse at Generative AI’s impact on various industries, and the challenges it poses. It covers market projections, adoption trends, ethical considerations, and workforce training needs.
1. Global Market Growth: Generative AI is expected to reach $51.8 billion by 2028, with a 35.6% annual growth rate.
2. Funding Surge: Funding for generative AI software will reach $10.68 billion, driven by major tech companies and venture capital firms.
3. Marketers' Preference: Among marketers using generative AI, 55% favor ChatGPT for content creation.
4. Global Interest: Worldwide interest in "generative AI" peaked in June 2023, following ChatGPT's release.
5. Art Identification: Yale undergraduates correctly identified AI-generated art only 54% of the time.
6. Ethical Concerns: Only 38% of consumers find AI art ethical, with millennials being more accepting.
7. Image Creation Dominance: 69% of marketers use generative AI for image creation, leading in adoption.
8. Urgency vs. Skills: 64% of executives feel urgency to adopt generative AI, but only 4% believe their organizations have the necessary skills.
9. Positive ROI: 66% of marketers achieved a positive return on investment (ROI) through AI adoption.
10. Performance Enhancement: AI adoption led to improved performance for 58% of marketers.
11. ChatGPT Popularity: Interest in ChatGPT peaked in mid-2023 globally.
12. Workplace Adoption: Approximately 43% of industry professionals have used ChatGPT at work.
13. Generational Adoption: Gen Z professionals (29%) lead in ChatGPT adoption at work.
14. Gender Disparity: Men (30%) are more likely to adopt generative AI at work than women (20%).
15. Industry Usage: Marketing and advertising professionals (37%) lead in ChatGPT adoption.
16. Under-the-Radar Usage: 68% of professionals using ChatGPT at work do so without their bosses' knowledge.
17. Content Production: 73% of marketing departments use generative AI for content creation.
18. Email Copy: 44% of marketers use AI for email copy production.
19. Brainstorming Assistance: 66% of marketers use generative AI for brainstorming.
20. Software Development: 95% of software developers use generative AI tools for coding.
21. Developer Concerns: Developers express concerns about managing the influx of new AI-generated code.
22. User Concerns: Executives and developers are concerned about AI tools providing incorrect information, showing bias, and generating errors.
23. Training Challenges: 50% of marketers find training team members the primary roadblock to AI adoption.
24. AI-Generated News Sites: NewsGuard identified 49 AI-generated news websites in April 2023.
Read more at bloggingwizard.com (2024). 25 Top Generative AI Statistics For 2024
🗞️ TRENDS
According to analyticsindiamag.com, recent AI advancements include a video background alignment model, a framework for versatile avatar creation, text-to-video synthesis, a technique for enhancing language models, self-rewarding language models, and an adaptive attention mechanism for improved contextual understanding.
ActAnywhere automates video background alignment, using large-scale video diffusion models.
GALA creates diverse 3D clothed human avatars by decomposing meshes into separate layers.
Lumiere, a text-to-video model, combines spatial and temporal processing for video generation.
Meta-Prompting enhances language models' performance in various tasks without detailed instructions.
Self-rewarding language models align themselves through iterative training and LLM-as-a-Judge prompting.
Multi-Head Gaussian Adaptive Attention Mechanism (GAAM) and Gaussian Adaptive Transformer (GAT) improve contextual representation.
These innovations signal significant advancements in AI across diverse domains.
Reference: Analyticsindiamag.com (2024). Top 6 Recent Updates that Will Transform the Course of AI
🗞️ IMPACT (Economy, Workforce, Culture, Life)
Generative AI’s impact on the insurance industry and consumer – Lloyd’s: Iryna Chekanava from Lloyd's Lab highlights the insurance industry's shift towards Generative AI (GAI), like ChatGPT, noting its historical use in predictive models. A FintechOS study reveals 44% of UK insurers currently use GAI, with significant investments in its research and implementation. Examples include Zurich experimenting with ChatGPT for data extraction in claims. However, Catherine Carey from Consumer Intelligence emphasizes the need to consider customer impacts, citing a survey where most prefer human interaction for claims. This underscores the balance needed between AI adoption and maintaining human-centric service in insurance. Read more at insurancetimes.co.uk.
🗞️ OPINION (Opinion, Analysis, Reviews, Ideas)
Why Universities Need OpenAI: Arizona State University's collaboration with OpenAI to integrate ChatGPT into its educational landscape is a pioneering move. The university aims to enhance student success and streamline processes through generative AI. ASU's approach, guided by faculty, includes deploying ChatGPT for project submissions and creating AI avatars for subjects like biology. This partnership marks a significant step in the adoption of AI in education, offering students and educators various advantages, from research assistance to language translation, and is likely to inspire other universities to follow suit. Read more at analyticsindiamag.com.
🗞️ LEARNING (Tools, Frameworks, Skills, Guides, Research)
Hands-on PySpark + Kafka Streaming + OpenAI: This guide details the integration of Kafka Producer and Kafka Consumer with PySpark and OpenAI. It enables efficient data retrieval and transformation with SQL queries for real-time exploration. PySpark, built on Apache Spark, provides Python-friendly distributed data processing, while Kafka facilitates real-time data pipelines. The process involves Docker and Docker-Compose for setup, generating fake JSON data using OpenAI, and performing SQL transformations on data from Kafka. This integration streamlines real-time data processing. Read more at pub.towardsai.net.
How to Fine-Tune LLMs in 2024 with Hugging Face: Explore the rapid advancements in Large Language Models (LLMs) over the past year, from ChatGPT's emergence to the proliferation of LLMs like Llama 2, Mistral, and TII Falcon. Discover the process of fine-tuning LLMs using Hugging Face's TRL, Transformers, and datasets in 2024, including use case definition, environment setup, dataset creation, training with efficient methods like QLoRA, evaluation, and deployment. This empowers organizations to harness LLMs for customized tasks and innovation. Read more at philschmid.de.
🗞️ BUSINESS (Use Cases, Industry spotlight)
While many organizations understand generative AI basics, few have developed a comprehensive strategy for its large-scale implementation. Société Générale, a major European bank, is a leader in this space, according to Noémie Ellezam, its Chief Digital Strategy Officer. The bank, with a 150-year history and 25 million clients, integrates AI, including generative AI, into its digital transformation. Ellezam, speaking at a webinar, emphasized that generative AI is a significant, but incremental, step in the AI journey. She predicts a 3-5% productivity increase from generative AI in financial services over the next few years but acknowledges challenges in application and risk management. Société Générale's approach includes experimenting with over 100 generative AI use cases, prioritizing high-value applications, and fostering a global culture of AI competency and risk awareness.
Reference: MITSloan.mit.edu (2024). How businesses can find and prioritize AI opportunities
🗞️ Latest From The Web
Two Workplace Tests Show Limitations and Strengths of ChatGPT: Generative AI tools like ChatGPT offer potential benefits but also limitations in HR tasks. Mineral's experiment revealed that while later ChatGPT versions performed better, they often lacked nuanced details crucial for complex HR issues. A separate study by Harvard Business School and BCG found that GPT-4 aided simpler tasks but hindered performance in knowledge-intensive scenarios, where human critical thinking excelled. Organizations should judiciously deploy generative AI, recognizing its strengths and weaknesses. The future lies in harnessing a harmonious collaboration between AI and human expertise to maximize efficiency and effectiveness. Read more at shrm.org.
AI chatbot goes rogue during customer service exchange: International delivery company DPD is making adjustments to its AI-powered chatbot following an incident where it provided unexpected responses to a frustrated customer. Musician Ashley Beauchamp turned to the chatbot to track a missing package but found it unhelpful. To have some fun, he asked it to compose a critical poem about the company, which it did, and even used profanity upon request. Screenshots displayed the chatbot's inability to create a haiku correctly. DPD acknowledged the error and promised improvements. While chatbots have potential, incidents like these highlight the need for refining AI-powered customer service. Read more at digitaltrends.com.
How Satya Nadella navigated Microsoft to $3 trillion market value: Microsoft's recent achievement of surpassing a $3 trillion market value, following in Apple's footsteps, showcases the visionary leadership of CEO Satya Nadella. His strategic approach can be divided into two phases: an agile and cloud-focused transformation followed by investments in AI, notably ChatGPT. Nadella's emphasis on cloud services, such as Microsoft Azure, propelled the company to become the world's largest public cloud provider. Additionally, his investments in OpenAI and acquisitions have positioned Microsoft as a leader in technology. The company's engagement with open-source communities underscores its commitment to innovation. Read more at computerworld.com.
The rise of synthetic media: Get ready for AI avatars at work: Synthetic media, driven by generative AI, is revolutionizing content creation. Startups like Synthesia and D-ID are crafting lifelike digital avatars that streamline corporate communications and learning materials. These avatars, coupled with AI's realistic expressions, offer significant cost savings and engagement benefits. However, businesses must exercise caution regarding security and ethical concerns. Looking ahead, 2024 is expected to see a shift towards image and video generation, further enhancing content creation possibilities while human involvement remains crucial. Read more at computerworld.com.
Building AI agents with Semantic Kernel: In the early '90s, Simon Bisson, a technology executive explored the intriguing concept of ubiquitous computing, where software agents seamlessly managed interactions. MIT's Pattie Maes, a trailblazer in intelligent agent research, influenced their thinking. Fast forward three decades and the industry is now embracing these ideas with the help of large language models (LLMs) and adaptive orchestration. Notably, Microsoft's Semantic Kernel, built on OpenAI's Assistant model, simplifies interfaces, streamlines orchestration via plugins, and moves toward autonomy. This approach, avoiding untrusted code on remote servers, aligns with modern microservices, promising reliable and user-friendly intelligent agents for the future network landscape. Read more at infoworld.com.
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