Generative AI Predictions For 2024

🗞️ The Tech Issue | February 5, 2024

Author

Qamar Zia
February 05, 2024

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

In today’s issue:

  • Uncovering the Financial Costs Behind the Generative AI Revolution

  • Does ChatGPT Have The Potential To Become A New Chess Super Grandmaster?

  • ChatGPT users can now invoke GPTs directly in chats

  • Why LLMs Used Alone Can’t Address Your Company’s Predictive Needs

  • 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.

🗞️ Generative AI Predictions For 2024

As we venture into 2024, the trajectory of GenAI is set to profoundly influence various facets of IT and business, steering a course towards pervasive AI integration, strategic deployments, and innovative advancements. Industry leaders and experts predict a year where AI will not only be a competitive differentiator but a necessity for survival in the rapidly evolving market.

Predictions for 2024:

  • Broad AI Integration: AI becomes a staple in technology offerings, essential for competitiveness and market entry ease (Lior Koriat).

  • Existential Role of AI: AI's strategic importance in business survival and meeting evolving expectations becomes paramount (Gaurav Dhillon).

  • AI as a Standard: Embedding AI in products transitions from luxury to necessity, with a majority of businesses integrating AI features (David DeSanto).

  • Mainstreaming of Generative AI: Essential AI functionalities become expected in all products, driving innovation and integration urgency (Hubert Palan).

  • Strategic AI Shift: From ad-hoc AI experiments to strategic, business-aligned AI deployments, reducing reliance on repetitive data roles (Mathias Golombek).

  • Advancement in AI Autonomy: Shift towards semi-autonomous AI agents and fully autonomous AI optimizations, boosting productivity (Esko Hannula, Vipul Vyas).

  • ROI-Driven AI Adoption: Emphasis on strategic AI investments with clear ROI to avoid unnecessary expenditures and focus on efficiency (Steven Salaets).

  • Commoditization of AI Models: Success hinges on leveraging commoditized AI models for developing valuable, immediate-use applications (Unnamed source). Read more at apmdigest.com.

Reference: ApmDigest.com (2024). 2024 AI Predictions - Part 1

🗞️ TRENDS

This Generative AI report by insideBIGDATA spotlights the latest applications and integrations of generative AI technologies, particularly focusing on the innovative use of large language models (LLMs) tailored to proprietary data. By launching this dedicated channel, the report aims to keep its global audience informed about the rapid advancements in AI, highlighting strategic partnerships, cutting-edge deployments, and the burgeoning impact of generative AI across diverse industries, from cloud computing to data protection and pricing platforms.

  • Google Cloud and Hugging Face form a strategic partnership to leverage Google Cloud for enhancing AI and ML development, promoting AI democratization.

  • HYCU, Inc. collaborates with Anthropic to incorporate generative AI into its R-Cloud data protection platform, setting a new standard in data security.

  • Pricefx integrates conversational AI features into its pricing platform, simplifying interactions and improving pricing decision processes.

  • Typeface announces the availability of its Multimodal Content Hub and acquires TensorTour, expanding its enterprise content creation capabilities.

  • Swimm introduces an AI-powered coding assistant, offering developers instant access to accurate code knowledge and boosting productivity.

  • SnapLogic unveils GenAI Builder, a no-code tool that revolutionizes digital experiences for various stakeholders through generative AI.

  • Deci partners with Qualcomm to enhance AI accessibility and efficiency for a broader range of applications by optimizing AI models for Qualcomm’s hardware.

  • Typeform launches Formless, an AI-driven form builder that enhances data collection through conversational interactions.

  • Airbyte and Vectara partnership facilitates the development of scalable GenAI applications by improving data access and efficiency.

  • Pecan AI introduces Predictive GenAI, combining predictive analytics with generative AI to accelerate predictive modeling and enterprise AI adoption.

  • DataStax releases a comprehensive Data API, optimizing the development of GenAI applications with high relevancy and low latency.

  • Databricks launches a Data Intelligence Platform for the telecommunications sector, enabling CSPs to gain holistic insights without compromising data privacy.

  • Stratio BD demonstrates that its generative AI tools achieve up to 99% accuracy, significantly enhancing enterprise decision-making capabilities.

  • Pinecone announces a breakthrough in vector database technology, offering a serverless solution that drastically reduces costs and development complexities.

  • Algolia focuses on advancing Generative AI for e-commerce, aiming to improve search and discovery experiences for online shoppers.

  • Relativity expands its suite of generative AI solutions, Relativity aiR, to address legal industry challenges, enhancing litigation and investigation processes.

  • p0 emerges from stealth with funding to prevent software failures using generative AI, promising greater reliability and security.

  • Avetta introduces AskAva, a generative AI-powered risk assistant, to advance contractor compliance and promote safety and sustainability in supply chains.

🗞️ IMPACT (Economy, Workforce, Culture, Life)

This research delves into the evolving landscape of disinformation, heightened by the rapid advancement of generative AI. While some predict a worsening disinformation crisis due to AI's ability to create convincing media, others urge caution in forecasting its impact. Generative AI poses risks like facilitating fake content creation, but its actual influence on political disinformation remains limited, as demonstrated by ineffective deepfakes. The belief in disinformation often stems from factors beyond realism, such as narrative appeal and authority. Countermeasures like digital content verification are emerging, yet their success hinges on public trust. Generative AI's future role remains uncertain, with potential both as a disinformation tool and a countermeasure. Policymakers are encouraged to embrace this complexity and use evidence-based strategies to address disinformation challenges.

🗞️ OPINION (Opinion, Analysis, Reviews, Ideas)

LLMs, like ChatGPT, excel in language-based tasks but are less effective with predictive modeling involving numerical data, better suited to traditional machine learning models. LLMs, built on a foundation of words, are great for text generation and brainstorming but struggle with numerical, tabular data common in business. Traditional models are more efficient, interpretable, and regulatory compliant. Pecan's Predictive GenAI innovatively combines LLMs with machine learning for business applications, leveraging LLMs to translate business problems into models and streamline predictive modeling, making AI more accessible to businesses.

🗞️ LEARNING (Tools, Frameworks, Skills, Guides, Research)

The article presents a comprehensive taxonomy to demystify Generative AI terminology, aimed at newcomers and those with a basic understanding of machine learning. The taxonomy categorizes key terms into 12 groups, covering various model types, language model (LLM) specifics, architecture, and operational aspects of Generative AI. It serves as an evolving guide, with a focus on Large Language Models, their lifecycle, architecture, and applications. The categories range from types of models, like foundation models and large multimodal models, to operational considerations like cost, efficiency, and security. Each category provides insights into specific concepts and terms, making the complex field of Generative AI more accessible.

Key Points:

  1. Types of Models:

    • Foundation Models: Large, general-purpose AI models.

    • Large Language Models (LLMs): Specialized in Natural Language Processing.

    • Small Language Models (SLMs): Less complex than LLMs.

    • Large Multimodal Models (LMMs): Handle various data types.

    • Vision Language Models (VLMs): Focus on image and video modalities.

    • Generative Image Models: Produce images from text prompts.

    • Text-to-Speech (TTS) and Speech-to-Text (STT) models.

  2. Common LLM Terms:

    • Prompt, Completion, Inference, Tokens, Parameters, Context Window, Temperature, Top N/P Sampling, Hallucinations, Bias and Toxicity.

  3. LLM Lifecycle Stages:

    • Pre-training, Prompt Engineering, Supervised Fine Tuning, Catastrophic Forgetting, Reinforcement Learning from Human Feedback (RLHF).

  4. LLM Evaluations:

    • Perplexity, BLEU, ROUGE, BIG-bench, ARC, HellaSwag, MMLU, TruthfulQA, GLUE, SuperGLUE, HELM.

  5. LLM Architecture:

    • Tokenization, Recurrent Neural Network (RNN), Transformer, Encoder, Decoder, Attention, Self-Attention, Multi-Headed Self-Attention, Encoder Only Models, Decoder Only Models, Masked Language Modeling, Sequence-to-Sequence Models, Embeddings.

  6. Retrieval Augmented Generation (RAG):

    • Components: Vector Databases, Retrievers.

    • Types: Naive RAG, Advanced RAG, Modular RAG.

  7. LLM Agents:

    • Concepts: Agent, Memory, Planning, Tools, ReAct, Chain-of-thought, Tree-of-thought, Task-Question Decomposition, Reflection.

  8. LMM Architecture:

    • Generative Adversarial Network (GAN), Variational Auto-encoder (VAE), Modalities, Multimodal Embedding Space, Contrastive Language-Image Pretraining (CLIP), Vision Encoder.

  9. Cost & Efficiency:

    • GPU, Parameter Efficient Fine Tuning (PEFT), Quantisation, Low Rank Adaptation (LoRA), Soft Prompting, Fully Sharded Data Parallel (FSDP), Distributed Data Parallel (DDP).

  10. LLM Security:

    • Prompt Injection, Data Leakage, Training Data Poisoning.

  11. Deployment & Inference Optimization:

    • Concepts: Latency, Throughput, Pruning, Distillation, Flash Attention, KV Cache, Positional Encoding, Speculative Decoding.

  12. LLMOps (Providers):

    • Model Access, Training and FineTuning (OpenAI, HuggingFace, Google Vertex AI, Anthropic, AWS Bedrock, AWS Sagemaker Jumpstart), Data Loading, Vector DB and Indexing, Application Framework, Prompt Engineering, Evaluation, Deployment Frameworks, Monitoring, Proprietary and Open Source LLM/VLMs.

🗞️ BUSINESS (Use Cases, Industry spotlight)

ChatGPT users can now invoke GPTs directly in chats: OpenAI has enhanced ChatGPT's functionality, allowing paid users to integrate various GPTs into chats using an "@" command. This feature provides context awareness for these GPTs, enabling them to contribute effectively to ongoing conversations. This innovation follows the launch of the GPT Store, a platform for easy-to-create, customizable GPT applications. Despite the potential for diverse applications, like trail recommendations or coding tutorials, custom GPTs currently account for a minimal portion of ChatGPT's web traffic. Challenges include moderating content, as some developers initially created inappropriate or politically charged chatbots, prompting OpenAI to enforce its guidelines more stringently. Read more at techcrunch.com.

🗞️ IN THE NEWS

Does ChatGPT Have The Potential To Become A New Chess Super Grandmaster?: An NLP data scientist and chess enthusiast tests ChatGPT's chess-playing capabilities, concluding its performance is suboptimal, with an ELO below 1500. Despite strong openings, ChatGPT falters in complex game stages, indicating limitations in implicit learning and handling chess intricacies. The article suggests combining LLMs with specialized AI for applications like chess training, while urging caution in AI hype and emphasizing the need for accurate, responsible communication about AI capabilities. Read more at kdnuggets.com.

Salesforce Launches AI Cloud to Bring Generative AI to the Enterprise: Salesforce has launched AI Cloud, a comprehensive suite featuring AI tools and services, including its acclaimed Einstein AI model, at its inaugural AI Day in New York. This platform integrates various technologies like Tableau and MuleSoft to facilitate generative AI experiences in enterprise workflows. Key to AI Cloud is the Einstein GPT Trust Layer, ensuring data security and compliance by preventing large language models from storing sensitive customer data. It supports models from AWS and other providers and accommodates externally trained domain-specific models. AI Cloud aims to revolutionize business practices, from personalized sales emails to tailored commerce insights, reinforcing Salesforce's ambition to lead as an AI-first. Read more at aibusiness.com.

Uncovering the Financial Costs Behind the Generative AI Revolution: A survey of over 2,800 IT professionals by O’Reilly shows that nearly two-thirds use generative AI in business, with a rapid adoption rate. Large companies prioritize adopting generative AI, triggering an 'AI arms race' utilizing cloud data platforms like Databricks and Snowflake. However, the financial burden is significant, with McKinsey citing costs between $2 million to $200 million for different AI model types. Escalating cloud data costs challenge companies, necessitating a focus on ROI and cost visibility. AI workloads often suffer from inefficiency, leading to unnecessary expenses. Addressing these inefficiencies in infrastructure and code, often hidden, can optimize resource allocation and reduce costs, a crucial aspect in leveraging generative AI. Read more at expresscomputer.in.

IBM says generative AI can help automate business actions: IBM's recent research introduces SNAP, a software framework utilizing generative AI and large language models (LLMs) to predict next actions in business processes, aiming to enhance automation and efficiency in enterprises. By analyzing past events, SNAP generates suggestions for future steps, improving prediction performance across various datasets. This approach diverges from traditional time series data analysis, instead focusing on sequential event outcomes. SNAP, which stands for Semantic Stories for Next Activity Prediction, combines the narrative capability of LLMs like GPT-3 with business process data to create coherent stories, aiding in more accurate next-action predictions. The research demonstrates SNAP's superior predictive performance over established benchmarks, highlighting the potential of generative AI in refining business process management and automation. Read more at zdnet.com.

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