9 Workplace Trends For HR in 2024

🗞️ The Tech Issue | January 3, 2024

Author

Qamar Zia
January 03, 2024

☕️ Happy New Year and welcome to my daily dive into the Generative AI landscape.

I aim to streamline this newsletter size, making it a concise, under-five-minute read containing 1500 words or less. Today’s newsletter is 1917 words long which I think is still too long. 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.

In today’s issue:

  • Hybrid AI and apps will be in focus in 2024, says Goldman Sachs CIO

  • ChatGPT Meets Its Match: The Rise of Anthropic Claude Language Mode

  • Generative AI Terminology

  • How SuperDuperDB delivers an easy entry to AI apps

  • 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.

🗞️ THE LATEST

9 Workplace Trends For HR in 2024

Gartner Inc. presents nine critical workplace trends for HR leaders in 2024, emphasizing the evolving dynamics of technology and workforce management. Key predictions include addressing the rising cost of work, harnessing generative AI for expanding workforce opportunities, normalizing four-day workweeks, enhancing conflict resolution skills in management, and navigating the challenges of integrating GenAI. Additionally, the trends highlight a shift towards skills-based hiring over degrees, offering climate change protection as an employee benefit, integrating DEI into everyday work culture, and adapting to non-traditional career paths. These insights guide HR strategies in attracting and retaining talent, driving business outcomes, and reshaping the workplace.

Reference/Source: Gartner (2023) How Employees Are Using Generative AI?

📰 Latest From The Web

ChatGPT Meets Its Match: The Rise of Anthropic Claude Language Model: In the generative AI arena, Anthropic's Claude emerges as a formidable competitor to OpenAI's ChatGPT, offering advanced features like enhanced context understanding, honesty, and ethical AI design. Funded significantly and founded by former OpenAI researchers, Claude stands out with its focus on safety and accuracy, challenging ChatGPT's early dominance in the field. Read more at unite.ai.

How SuperDuperDB delivers an easy entry to AI apps: The integration of generative AI, such as GPT-4, into corporate applications is expected to accelerate in 2024, beginning with basic infrastructure tools like SuperDuperDB. This Python library interfaces between databases and AI models, enabling more intuitive and efficient data queries and management, marking a significant advancement in enterprise AI applications. Read more at zdnet.com.

Intel spins out Articul8 AI to offer GenAI stack to enterprises: Intel and Digital Bridge Group have launched Articul8 AI, led by former Intel executive Arun Subramaniyan, to provide secure generative AI software for enterprises, not intended for public trading. The platform, compatible with various infrastructures and already adopted by firms like Boston Consulting Group, supports Intel's broader AI strategy and has attracted significant investment, indicating its potential impact on enhancing enterprise GenAI integration. Read more at fierceelectronics.com.

Microsoft Copilot is now available on iOS and Android: During the holiday season, Microsoft introduced its Copilot app on various platforms including Android, iOS, and iPadOS. This app, an evolution of Bing Chat, incorporates OpenAI's GPT-4 and DALL·E 3 technologies, offering users a broad spectrum of AI-powered functionalities. These include crafting emails, creating content, and generating artistic designs. Since its launch, Copilot has seen over 1.5 million downloads, marking a significant step in Microsoft's strategy to enhance and broaden the accessibility of AI technologies. Read more at techcrunch.com.

OpenAI shifts EU data processing to its Dublin office: OpenAI has assigned its Dublin office as the data controller for European Economic Area and Swiss customers, complying with EU data privacy laws. This change, following regulatory challenges in Europe, places the company under the scrutiny of Ireland's Data Protection Commission, known for stringent GDPR enforcement. Meanwhile, UK users' data continues to be managed by the San Francisco head office. Read more at siliconrepublic.com.

🗞️ USES

📦 Use Cases

Wedbush Securities analyst Dan Ives marks 2024 as "The Year of AI," predicting a significant surge in AI integration within big companies, with generative AI claiming up to 10% of IT budgets. His research, involving a survey of 672 companies, indicates over 80% of them recognize multiple use cases for generative AI in improving business operations. Concurrently, Microsoft is emerging as a frontrunner in the AI domain, with tech giants like Google and others poised to invest heavily in this arena, reshaping the landscape of IT and business strategies.

🗞️ IMPACT

💼 Workforce

In 2023, the tech industry witnessed a surge in developer productivity focus amidst widespread layoffs and growing complexity in cloud tooling. This led to the emergence of platform engineering, a sociotechnical approach aiming to enhance developer work life and efficiency, recognizing that contented workers are more productive. The discipline emphasizes removing barriers in software delivery, and with the continued evolution into 2024, it's expected to further streamline the software development process, making teams more efficient in delivering high-quality code. This shift is a strategic response to increasing workload and cognitive pressures, marking a significant trend in tackling the challenges of modern software development.

📚 LEARNING

📣 Generative AI Terminology

The following list covers the important GenAI terminology for GenAI enthusiasts:

  • Agents & AGI: Agents are autonomous software robots, while AGI represents a hypothetical AI with human-like learning abilities across multiple domains.

  • Alignment & Attention: Alignment focuses on aligning AI goals with human values. Attention mechanisms in neural networks prioritize important data.

  • Autoencoders & Back Propagation: Autoencoders compress and reconstruct data, and backpropagation is an algorithm for AI learning and correction.

  • Bias & BigGAN: Bias refers to unintentional assumptions in AI models, while BigGAN is known for generating high-resolution images.

  • Capsule Networks & Chain of Thought: Capsule Networks understand spatial relationships, and Chain of Thought explains AI reasoning processes.

  • Chatbot & ChatGPT: Chatbots simulate human conversations, and ChatGPT is known for generating human-quality text and conversations.

  • CLIP & CNN: CLIP connects text and images, while CNNs are specialized in processing grid-based data like images.

  • Conditional GAN & CycleGAN: Conditional GAN generates data based on specific info, and CycleGAN translates images across styles without paired examples.

  • Data Augmentation & DeepSpeed: Data augmentation improves AI robustness, and DeepSpeed optimizes the efficiency of training large language models.

  • Diffusion Models & Double Descent: Diffusion Models generate data by adding/reversing noise, and Double Descent describes a performance trend in AI complexity.

  • Emergence & Expert Systems: Emergence is the complex behavior from simple AI rules, and Expert Systems have deep domain-specific knowledge.

  • Few-Shot Learning & Fine-tuning: Few-Shot Learning involves training on minimal data, and fine-tuning adapts pre-trained AI to specific tasks.

  • Foundation Model & GAN: The Foundation Model is a base for specialized AI applications, and GAN involves competing AI models for realistic outputs.

  • Generative AI & GPT: Generative AI autonomously creates new content, and GPT is known for its advanced text generation capabilities.

  • GPU & Gradient Descent: GPU is specialized for AI tasks, and Gradient Descent is an optimization algorithm for AI models.

  • Hidden Layer & Hyperparameter Tuning: Hidden Layer performs complex data transformations, and Hyperparameter Tuning adjusts model settings for optimal performance.

  • Instruction Tuning & Large Language Model: Instruction Tuning adapts AI models with specific guidelines, and Large Language Models generate human-quality text.

  • Latent Space & Latent Diffusion: Latent Space is a low-dimensional data representation, and Latent Diffusion generates data by denoising.

  • Mixture of Experts & Multimodal AI: Mixture of Experts combines specialized submodels, and Multimodal AI processes and generates diverse data types.

  • NeRF & Objective Function: NeRF creates 3D scenes from 2D images, and the Objective Function is central to AI model training.

  • One-Shot Learning & PEFT: One-Shot Learning uses minimal examples, and PEFT improves large language models with prompt engineering.

  • Regularization & Reinforcement Learning: Regularization prevents overfitting in AI models, and Reinforcement Learning is about maximizing reward through interactions.

  • Self-Supervised Learning & Sequence-to-Sequence Models: Self-Supervised Learning uses unlabeled data, and Seq2Seq transforms element sequences.

  • StyleGAN & Singularity: StyleGAN generates realistic faces, and Singularity is a hypothetical point of surpassing human AI control.

  • Text-to-Speech & TPU: Text-to-Speech converts text to voice, and TPU is optimized for AI workloads.

  • Transfer Learning & Transformer: Transfer Learning applies knowledge from one task to another, and Transformer excels in sequential data processing.

  • Variational Autoencoders & Vector Databases: VAEs encode and reconstruct data, and Vector Databases efficiently store and query high-dimensional vectors.

  • XAI & Zero-shot Learning: XAI aims for understandable AI, and Zero-shot Learning handles tasks without explicit training.

🔬 Research

Social-LLM: Modeling User Behavior at Scale using Language Models and Social Network Data. (arXiv:2401.00893v1 [cs.SI]): The proliferation of social network data has unlocked unprecedented opportunities for extensive, data-driven exploration of human behavior. The structural intricacies of social networks offer insights into various computational social science issues, particularly concerning social influence and information diffusion. However, modeling large-scale social network data comes with computational challenges. Though large language models make it easier than ever to model textual content, any advanced n...
Read more at arxiv.org. Published on 03-Jan-2024

📚 TOOLING

🧰 Tools

In 2024, AI is poised to evolve with "hybrid" models combining foundational and specialized AIs, predicts Goldman Sachs CIO Marco Argenti. While large firms will develop complex systems, others will focus on smaller, custom neural networks. The year will also see a surge in third-party apps built on these models, amidst growing concerns over the security and ethical management of AI.

🧬 Data Engineering

In 2024, data engineering will significantly evolve with GenAI becoming commonplace in applications and a shift in data governance practices to manage growing volumes. Apache Flink will gain popularity, especially with its 2.0 update, making stream processing more accessible. Additionally, the concept of 'data as a product' will become mainstream, allowing more efficient and innovative use of data across various applications. These trends underscore the increasing importance and transformative impact of data engineering in the tech landscape.

🚠 Infrastructure

In 2024, enterprises are expected to increasingly integrate large language models and generative AI into their operations, focusing on automating routine tasks and improving customer support and decision-making. Security concerns will rise with the swift adoption of AI, mirroring past challenges with cloud services. A shift towards private clouds for cost and efficiency, along with a movement towards passwordless authentication and better management of machine identities, is anticipated. Additionally, the As-a-Service model for networking is predicted to gain popularity for its efficiency and cost-effectiveness.

Neuchips to showcase Gen AI Inferencing Accelerators at CES 2024: At CES 2024, Neuchips will debut its Raptor AI chip and Evo PCIe card, offering affordable, energy-efficient large language model (LLM) deployment. These innovations promise significant cost reductions and enhanced performance in natural language processing, making advanced AI applications more accessible to enterprises. Read more at ai-techpark.com.

Your Feedback

I want this newsletter to be valuable to you so if there's anything on your mind—praises, critiques, or just a hello—please drop me a note. You can hit reply or shoot me a message directly at my email address: [email protected].

Join my community by subscribing to my newsletter below:

Reply

or to participate.