The most important AI trends in 2024

IBM Technology
6 Mar 202409:35

TLDRThe video script outlines nine emerging AI trends for 2024, emphasizing the shift towards realistic expectations and integrated AI applications. It highlights the rise of multimodal AI, the need for smaller, less resource-intensive models, and the importance of model optimization techniques. Additionally, it discusses custom local models, virtual agents, increasing regulations, and the concept of 'shadow AI', urging viewers to consider the potential overlooked trend that could shape the AI landscape.

Takeaways

  • ๐Ÿ“‰ **Realistic Expectations**: AI industry is moving towards more grounded expectations, focusing on integrating AI tools as enhancements rather than replacements.
  • ๐Ÿค– **Multimodal AI Advancements**: Multimodal AI models are expanding capabilities by processing diverse data inputs, such as combining natural language with computer vision tasks.
  • ๐Ÿ‹๏ธโ€โ™‚๏ธ **Smaller Models on the Rise**: There's a shift towards smaller, less resource-intensive AI models that can still deliver high performance, reducing energy consumption and costs.
  • ๐Ÿ’ฐ **Cost-Effective AI**: Innovations in AI are aimed at reducing the need for extensive computational resources, making AI more accessible and affordable for various applications.
  • ๐Ÿ”ง **Model Optimization Techniques**: Techniques such as quantization and Low-Rank Adaptation (LoRA) are being adopted to optimize and fine-tune AI models more efficiently.
  • ๐Ÿ”’ **Custom Local Models**: Organizations are developing custom AI models trained on proprietary data to maintain control over sensitive information and reduce reliance on third-party services.
  • ๐ŸŒ **Virtual Agents for Task Automation**: Virtual agents are evolving beyond simple chatbots to automate tasks and interact with other services, enhancing user experience and productivity.
  • ๐Ÿ“œ **Increasing AI Regulation**: Regulatory efforts, such as the EU's Artificial Intelligence Act, are underway to address the legal and ethical implications of AI technologies.
  • ๐Ÿ•ต๏ธโ€โ™‚๏ธ **Shadow AI Phenomenon**: The unofficial use of AI by employees without corporate oversight, known as shadow AI, raises concerns about security, privacy, and compliance.
  • ๐Ÿš€ **Untapped AI Trend**: The script invites viewers to consider and identify an additional AI trend for 2024 that has not been discussed, highlighting the dynamic nature of the field.

Q & A

  • What is the overarching theme for AI trends in 2024?

    -The overarching theme for AI trends in 2024 is the shift towards more realistic expectations, with a focus on integrating AI tools into existing workflows and the development of smaller, more efficient models.

  • How has the initial excitement around generative AI evolved?

    -The initial excitement around generative AI has evolved into a more refined understanding of its capabilities, with a focus on using AI to enhance and complement existing tools rather than replace them.

  • What is multimodal AI and how does it extend the capabilities of generative AI?

    -Multimodal AI refers to models that can process multiple types of data inputs, such as text, images, and video. This allows for a more comprehensive understanding and interaction with users, providing richer and more contextually relevant responses.

  • What challenges do massive AI models pose in terms of resource consumption?

    -Massive AI models require significant amounts of electricity for both training and inference, which can be costly and contribute to high energy consumption, as exemplified by the yearly electricity consumption of over 1000 households for training a single GPT-3 size model.

  • How are smaller models addressing the resource intensity of AI?

    -Smaller models, with fewer parameters, are less resource-intensive and can be run at lower costs on many devices, including personal laptops, making AI more accessible and reducing the reliance on cloud infrastructure.

  • What is the significance of model optimization techniques like quantization and LoRA?

    -Model optimization techniques like quantization and LoRA help reduce memory usage and speed up inference by lowering the precision of model data points and introducing trainable layers into pre-trained models, respectively. This makes AI models more efficient and cost-effective.

  • Why is the development of custom local models beneficial for organizations?

    -Custom local models allow organizations to train AI on their proprietary data and fine-tune it for specific needs without the risk of exposing sensitive information to third parties. This also helps in reducing model size and ensuring compliance with data privacy regulations.

  • What role do virtual agents play in enhancing task automation?

    -Virtual agents go beyond traditional chatbots by automating tasks such as making reservations, completing checklists, and connecting to other services, thereby improving efficiency and user experience.

  • How is the European Union addressing AI regulation?

    -The European Union has reached a provisional agreement on the Artificial Intelligence Act, which aims to regulate the use of AI within its member states, addressing issues such as ethical standards, transparency, and accountability.

  • What is shadow AI and why does it pose a risk to organizations?

    -Shadow AI refers to the unofficial, personal use of AI in the workplace by employees without IT approval or oversight. This can lead to security, privacy, and compliance issues, such as the unintentional sharing of trade secrets or the use of copyrighted material.

  • What is the missing 10th trend for AI in 2024?

    -The missing 10th trend is not specified in the script, but it invites viewers to contribute their ideas on additional AI trends that might emerge in 2024, encouraging engagement and discussion on the topic.

Outlines

00:00

๐Ÿš€ AI Trends in 2024: Reality Check and Multimodal Advancements

This paragraph discusses the anticipated AI trends for 2024, emphasizing a reality check in terms of expectations and the rise of multimodal AI capabilities. It highlights the shift from generative AI tools as standalone applications to integrated elements that enhance existing tools, such as Copilot features in Microsoft Office or generative fill in Adobe Photoshop. The paragraph also delves into the potential of multimodal AI models, which can process various data inputs like natural language and images, and how they expand the information available for training and inference. The discussion includes examples of current models like OpenAI's GPT-4v and Google Gemini, and the integration of video data for more holistic learning.

05:05

๐Ÿ“ˆ Focus on Efficiency: Smaller Models and Model Optimization

The second paragraph focuses on the trend towards smaller AI models due to their lower resource intensity and the need for optimization in model training and inference. It points out the high energy consumption of large models like GPT-3 and the efforts to yield greater output from fewer parameters. The paragraph introduces techniques like quantization and Low-Rank Adaptation (LoRA) to reduce memory usage and speed up fine-tuning while decreasing the number of parameters that need updating. It also touches on the implications of these trends for GPU and cloud costs, as well as the potential for custom local models that avoid the risks associated with proprietary data.

Mindmap

Keywords

๐Ÿ’กAI trends

AI trends refer to the emerging patterns and developments in the field of artificial intelligence that are expected to gain prominence in the coming year. In the context of the video, these trends are pivotal in shaping the future of technology and its applications across various industries. The script outlines nine specific trends that are anticipated to influence the trajectory of AI in 2024, such as the shift towards more realistic expectations, the rise of multimodal AI, and the importance of model optimization.

๐Ÿ’กReality check

The term 'reality check' in the video refers to the adjustment in expectations and understanding of AI capabilities after the initial excitement and hype surrounding generative AI. It emphasizes a more grounded and practical approach to integrating AI into existing systems and workflows, recognizing the limitations as well as the potential of AI technologies. This concept is central to the video's theme, as it sets the stage for the other trends discussed.

๐Ÿ’กMultimodal AI

Multimodal AI refers to AI models that can process and understand multiple types of data inputs, such as text, images, and video. These models are capable of performing tasks that involve understanding and generating content across different modalities, such as providing natural language responses to queries about images or offering step-by-step instructions with visual aids. The integration of multimodal capabilities in AI is a significant trend for 2024, as it expands the scope of AI applications and makes them more versatile and contextually aware.

๐Ÿ’กSmaller models

Smaller models in the context of AI refer to models with fewer parameters, which require less computational resources and energy to train and run. The focus on smaller models is driven by the need for more sustainable and cost-effective AI solutions, as large models like GPT-3 consume significant amounts of electricity. Smaller models can still deliver high performance and can be run on local devices, reducing the reliance on cloud infrastructure and associated costs.

๐Ÿ’กModel optimization

Model optimization in AI involves techniques to improve the efficiency and performance of AI models while reducing their computational requirements. This includes methods like quantization, which lowers the precision of model data points to save memory and speed up inference, and Low-Rank Adaptation (LoRA), which injects trainable layers into pre-trained models without updating all parameters. The goal of model optimization is to make AI models more practical for widespread use by minimizing their resource needs and enhancing their speed and accuracy.

๐Ÿ’กCustom local models

Custom local models refer to AI models that are tailored to specific organizational needs, trained on proprietary data, and run within the organization's own infrastructure. This approach allows for greater control over data security and privacy, as sensitive information does not leave the organization's local environment. It also enables faster and more secure processing of data, as there is no need to send it to a cloud service.

๐Ÿ’กVirtual agents

Virtual agents are AI-driven software programs designed to automate tasks and interact with users in a way that mimics human agents. They go beyond simple chatbots by performing complex tasks, such as making reservations, managing checklists, or connecting to other services. The advancement of virtual agents is a key trend for 2024, as they become more integrated into business processes and daily tasks, improving efficiency and user experience.

๐Ÿ’กRegulation

In the context of AI, regulation refers to the establishment of laws, rules, and guidelines that govern the development, deployment, and use of AI technologies. As AI continues to advance and permeate various aspects of society, the need for regulation becomes more critical to ensure ethical use, protect privacy, and maintain security. The video highlights the ongoing discussions and agreements, such as the European Union's Artificial Intelligence Act, which aim to create a legal framework for AI.

๐Ÿ’กShadow AI

Shadow AI refers to the unauthorized or unofficial use of AI technologies within an organization by employees, without the knowledge or approval of the IT department. This can lead to potential security risks, privacy breaches, and compliance issues, as employees might inadvertently share sensitive information or use copyrighted materials in ways that expose the company to legal risks. The video emphasizes the importance of corporate AI policies and oversight to mitigate these risks.

๐Ÿ’กGenerative AI

Generative AI is a subset of AI that focuses on creating new content, such as text, images, or audio, based on learned patterns and data. It includes technologies like GPT and Dall-E, which have gained widespread attention for their ability to generate human-like text and images. The video discusses the evolution of generative AI, highlighting the shift from standalone applications to integrated tools and the development of more sophisticated, multimodal capabilities.

Highlights

The pace of AI in 2024 is not slowing down, with 9 emerging trends expected throughout the year.

Trend #1: 2024 is the year of the reality check with more realistic expectations for AI capabilities.

Generative AI tools are being integrated into existing tools like Microsoft Office and Adobe Photoshop, rather than replacing them.

Multimodal AI is extending capabilities by processing diverse data inputs like images and text, providing richer interactions.

Smaller AI models are gaining attention due to lower resource requirements compared to massive models like GPT-3.

Innovation in LLMs focuses on greater output from fewer parameters, with success seen in models with 3 to 17 billion parameters.

Mistral's Mixtral model demonstrates that smaller models can match or outperform larger models in benchmark tests and inference speeds.

The trend towards smaller models is driven by the high costs of GPU and cloud resources for training and inference of larger models.

Model optimization techniques like quantization and LoRA are becoming more prevalent to reduce computational needs and costs.

Custom local models allow organizations to train AI on proprietary data without risking exposure to third parties.

Virtual agents are evolving beyond chatbots to automate tasks and interact with other services.

The European Union is working on the Artificial Intelligence Act, indicating a growing focus on AI regulation.

Shadow AI refers to the unofficial use of AI in the workplace, which can lead to security and compliance issues without proper policies.

The dangers of generative AI increase with its capabilities, emphasizing the need for responsibility in its application.

The transcript challenges viewers to identify the missing 10th AI trend for 2024.