☕️ Happy Friday and welcome to the Friday Edition.

The Friday edition recaps the top stories of the week and summarizes key points.

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🗞️ LOW CODE AND GENERATIVE AI

The technology landscape in 2024 is significantly influenced by the integration of low-code platforms and generative AI, streamlining development and aligning technology with human-centric needs. Key developments include widespread adoption in enterprises, enhanced security, and compliance features in low-code platforms, and growing emphasis on domain-specific assets. Predictions for 2024 focus on deeper AI integration, expansion in IoT and edge computing, and increased use in regulated industries.

Key Points:

1. Widespread Adoption of Low-Code Platforms: Foreseen to be essential for 75% of large enterprises by 2024 for digital transformation.

2. Enhancements in Low-Code Platforms: Notable improvements in integration capabilities, security measures, and regulatory compliance.

3. Domain-Specific Growth: Increased development of tailored assets for sectors like finance, healthcare, and supply chain.

4. Generative AI Integration: Anticipated to automate development tasks and enhance user experience.

5. Expansion in IoT and Edge Computing: Enhanced use of low-code platforms for real-time data processing and insights.

6. Increased Use in Regulated Industries: Predicted surge in adoption across sectors with stringent regulatory demands.

Reference: Shekhar Dipanshu (2023). Navigating the Future: Predictions for Low Code and Generative AI Redefining Technology in 2024

🗞️ THIS WEEK’S PICKS

AI and Automation Predictions for 2024: IDC's FutureScape Report: In 2024, artificial intelligence (AI) and automation are on the verge of significant transformative changes, as highlighted by IDC's top 10 predictions. These forecasts emphasize the growing impact and risks of Generative AI (GenAI), the evolving regulatory landscape, the shift towards conversational user interfaces, and a focus on outcome-centric approaches in automation. Additionally, advancements in GenAI are set to revolutionize software testing and application modernization, enhance AI-powered knowledge discovery, and open new avenues for monetization. The anticipated arrival of Artificial General Intelligence (AGI) by 2028 marks a transformative era in various sectors while changing chip priorities indicate a technological shift in AI workloads. These developments underscore the critical need for businesses to stay informed and adaptable in the rapidly evolving AI landscape. Read more at cryptopolitan.com.

AI weather and climate prediction face real-world tests: AI is transforming weather and climate forecasting, supplementing traditional methods with faster, data-driven models like Google DeepMind's GraphCast. These models face skepticism due to their novelty and potential limitations in predicting unprecedented events. This shift marks a pivotal moment in meteorology, balancing established practices with innovative AI approaches. Read more at axios.com.

From Apache Flink to GenAI: 5 Data Engineering Predictions: 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. Read more at thenewstack.io.

Hybrid AI and apps will be in focus in 2024, says Goldman Sachs CIO: 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. Read more at zdnet.com.

More integration of LLMs, a switch to private clouds, and more emphasis on machine identity -- enterprise IT predictions for 2024: 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. Read more at betanews.com.

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.

🗞️ OPINION

Generative AI has many issues: Generative AI has introduced complexities to the future of human productivity, evoking a spectrum of reactions from optimism to alarm. While celebrated as a potential savior of creativity, it also faces skepticism over its ability to replace human originality and adapt to legal, financial, and cultural frameworks. Challenges include copyright limitations, inconsistent reliability, data restrictions, and the inherent human-centric nature of value judgment, underscoring a future where AI complements rather than replaces human intellect and creativity.

The Inevitable Copyright Conundrum – Generative AI Forces a Reckoning: David Karpf's analysis draws parallels between this scenario and the Napster controversy, highlighting the challenges and opportunities faced in adapting copyright laws to new technologies. While some propose a middle ground through licensing schemes or grey markets, the situation demands a reevaluation of both legal frameworks and societal attitudes towards copyright, questioning how generative AI can innovate without infringing on intellectual property rights. Read more at the source: coinmarketcap.com.

🗞️ 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.

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