AI to Help the Workforce - Not Control it

🗞️ The Tech Issue | December 14, 2023

Author

Qamar Zia
December 14, 2023

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

I want to ensure this newsletter delivers valuable insights that keep you updated on Generative AI. My goal is to streamline its content, making it a concise, under-five-minute read containing 1500 words or less. Today’s newsletter is around 1600 words long. For those interested in deeper dives, I always provide references for extended reading. Most of my content springs from my ongoing research and development projects at INVENEW.

Today’s issue covers the following:

  • 9 Problems with Generative AI, in One Chart

  • Generative AI in 2024 IT Management Trends

  • Google unveils MedLM, a family of healthcare-focused generative AI models

  • Generative Agents: Interactive Simulacra of Human Behavior

  • And more

🔔 Please forward this newsletter to your friends and team members and invite them to join. This will help me grow my reach. Thanks, Qamar.

♨️ HEADLINE

MIT Says AI Development Should Help the Workplace, Not Control It

Historically, technological advancements like 18th-century textile machinery, 19th-century harvesters, and sewing machines have led to job creation, not reduction. This trend continues with 21st-century innovations like machine learning and AI, expected to grow the generative AI industry to $1.3 trillion by 2032, enhancing productivity and optimizing processes. Despite fears of job loss, AI is seen as augmenting human capabilities. MIT's recommendations for pro-worker AI policies include tax equalization, updated safety rules, and AI research funding, aiming to ensure AI complements rather than replaces human labor.

Reference: PYMNTS

♨️ OPINION

9 Problems with Generative AI: Generative AI, while showcasing immense potential, faces significant challenges across quality control, ethical considerations, and technical complexities. Issues range from inherent biases in data, lack of transparency, high operational costs, and the replication of human values, to environmental impacts, information accuracy, copyright infringements, and keeping information up-to-date.

Key Points:

  • Bias In, Bias Out: Generative AI often magnifies biases present in training data, affecting accuracy and ethics.

  • The Black Box Problem: Lack of transparency in AI decision-making processes, posing ethical and legal challenges.

  • High Cost to Train and Maintain: Exorbitant expenses involved in training models like ChatGPT-4.

  • Mindless Parroting: AI's limitations in replicating the breadth of human knowledge and scenarios.

  • Alignment with Human Values: AI's inability to align actions with human values, risking ethical breaches.

  • Power Hungry: Significant environmental impact due to high energy consumption of AI models.

  • Hallucinations: AI's tendency to fabricate information, questioning reliability.

  • Copyright & IP Infringement: Ethical concerns over the use of copyrighted material in AI training without consent.

  • Static Information: The challenge of keeping AI models updated with the latest information.

📈 TRENDS

GenAI’s World-Changing Power Is Putting Knowledge To Work: Generative AI will not just disrupt — it will shatter markets, industries, and economies in waves over the next 10 to 15 years. We sense this, but why and how will this happen? We think that generative AI drives the cost of knowledge activation to zero and creates a virtual knowledge loop that increases what is known in the world by more people. We are launching deep research into this topic and invite you to join us.
Read more at www.forrester.com

🗞️ IN THE NEWS

Gartner Survey on Legal and Compliance Concerns: A survey conducted by Gartner, Inc. in September 2023 highlighted that 70% of the 179 legal, compliance, and privacy leaders surveyed ranked rapid Generative AI (GenAI) adoption as a top concern for the next two years​​. Read more at Gartner.

Venture Capital Firms and Human Rights Concerns: Amnesty International USA (AIUSA) has raised concerns that the 10 largest venture capital funds and two largest start-up accelerators investing in Generative AI companies are not taking adequate steps to safeguard human rights in the context of new Generative AI technologies​​. Read more at amnesty.org

Generative AI in Design: A seminar on the benefits of generative AI in design highlighted its potential impact on the Architecture, Engineering, and Construction (AEC) industry. Generative design, a blend of AI and machine learning, is poised to improve building design options, speed up the design process, and reduce costs. Read more at ConstructConnect.

Generative AI in 2024 IT Management Trends: As 2023 comes to a close, IT organizations are gearing up for 2024 with a sense of enthusiasm and cautious optimism, largely fueled by the arrival and impact of generative AI services. Generative AI has become a significant productivity booster in knowledge work, comparable to the influence of word processing and spreadsheet software​​. Read more at BlockSandFiles.

News-Medical.net: An article discussing the ability of the AI chatbot GPT-4 to perform probabilistic reasoning in diagnosis, comparing its performance against a large survey of human clinicians. This highlights the evolving capabilities of LLMs in complex decision-making contexts​​. Read more at News-Medical.

🔦 INDUSTRY SPOTLIGHT

The Year Ahead: How Gen AI Is Reshaping Fashion’s Creativity: McKinsey's analysis reveals that up to a quarter of generative AI's value in fashion could come from design and product development. While 73% of fashion executives see generative AI as crucial in 2024, only 28% have used it in creative processes. 2023 saw a surge in equity funding for generative AI startups, reaching $14.1 billion. GenAI's role in enhancing creativity in fashion is evolving, with the potential to transform the industry significantly, though its full integration and impact remain to be seen. Read more at TheBusinessOfFashion.

🧰 TOOLS & APPS

Google unveils MedLM, a family of healthcare-focused generative AI models: Google introduces MedLM, AI models tailored for healthcare, enhancing medical tasks for healthcare workers. MedLM, built on Google's Med-PaLM 2, offers two versions: a larger model for complex tasks and a smaller, adaptable one. Piloted by organizations like HCA Healthcare and BenchSci, these models assist in drafting patient notes and analyzing biomarkers. Amidst a competitive healthcare AI market, concerns persist over AI's accuracy and potential risks, prompting caution in its adoption and deployment in healthcare. Read more at TechCrunch.com.

🏢 LLMs

Retrieval Augmented Generation for LLMs: Generative AI (GenAI), powered by advanced neural network architectures and large language models (LLMs), has the remarkable ability to generate coherent and contextually relevant content — including text, images and even music — with minimal human intervention. Read more at thenewstack.io

👩‍💻👨‍💻 SKILLS & CAREERS

The biggest worry is the jobs for people who won’t be using generative AI: At Fortune's Brainstorm AI conference, Accenture CTO Paul Daugherty stressed the importance of investing more in employees than technology as AI reshapes the workplace. Emphasizing the need for continuous workforce development, he highlighted critical thinking and problem-solving as essential skills in an AI-driven future. Daugherty also noted that while AI will streamline tasks, leading to job consolidation, the challenge lies in preparing those not directly using generative AI. Read more at Fourtune.com.

📚LEARNING

7 Best Practices for Developers Getting Started with GenAI: With the advent of accessible generative AI into the mainstream, and the resulting ability to transform all of human knowledge. Read more at thenewstack.io

📚SUSTAINABILITY

Why Microsoft’s Orca-2 AI Model Marks a Significant Stride in Sustainable AI: Despite the notable advancements made by artificial intelligence in the last decade, which include defeating human champions in strategic games like Chess and GO and predicting the 3D structure of proteins, the widespread adoption of large language models (LLMs) signifies a paradigm shift. These models, poised to transform human-computer interactions, have become indispensable across various sectors. Read more at www.unite.ai

💡 IDEAS

The Unspoken Mismatch of Web3 and Generative AI: The fusion of generative AI and Web3 is a key digital asset trend, posing significant integration challenges due to differing data and computational demands. While AI wasn't initially a core part of Web3, its integration is crucial for Web3's evolution. Autonomous agents, a generative AI trend, offer potential for blockchain capabilities, addressing challenges like computational intensity and decentralized coordination. This integration could revolutionize both fields, bridging technological gaps and enhancing applications like DeFi protocols and semi-autonomous agents with blockchain's transparency and security features. Read more at CoinDesk.com.

🔬 RESEARCH

The paper titled "Generative Agents: Interactive Simulacra of Human Behavior" discusses the development of generative agents, which are computational software agents designed to simulate realistic human behavior. These agents are capable of performing daily activities, interacting with their environment, and engaging in social behaviors. The key components of their architecture include a memory stream for recording experiences, reflection for generating higher-level inferences, and planning for coherent long-term behavior. The agents are evaluated in a sandbox environment called Smallville, which illustrates their ability to form relationships, spread information, and coordinate activities autonomously. This work contributes to the field of human-AI interaction by creating believable agents that can be used in various applications such as role-play, social prototyping, and virtual worlds. Read the paper at arxiv.org.

Key Points:

  1. Introduction of generative agents as believable proxies for human behavior.

  2. Description of the generative agent architecture, including memory and retrieval, reflection, and planning.

  3. Implementation in a sandbox environment (Smallville) to demonstrate agent interactions.

  4. Evaluations showing the agents' capability for believable individual and social behaviors.

  5. Discussion of applications, future work, limitations, and ethical considerations of generative agents.

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