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From the transformative potential of Multimodal AI, which goes beyond traditional single-mode data processing to encompass multiple input types like text, images, and sound, to the emergence of Quantum AI, the trends shaping the future of AI are both exciting and daunting.
“The interfaces of the world are multimodal,” says Mark Chen from OpenAI
As AI steadily integrates into our everyday lives, the ten AI trends to watch in 2024 encapsulate a spectrum of possibilities, challenges, and the need for a nuanced understanding of AI’s impact on society, the economy, and the global regulatory landscape.
Here are the ten trends to look out for in this exciting year at the dawn of AI:
Multimodal AI
Multimodal AI is set to redefine user interaction with technology by integrating multiple types of data inputs, such as text, images, and sound. This advancement is exemplified by applications like Be My Eyes, which leverages multimodal AI to assist blind and low-vision individuals by interpreting their environment through the camera feed and providing real-time feedback. The technology enables more natural interactions with AI, such as suggesting recipes based on ingredients visible in the refrigerator.
Small Language Models (SLMs)
The trend towards SLMs focuses on creating models that are both efficient and accessible without sacrificing performance. Innovations in SLMs, exemplified by Mistral’s Mixtral model, have shown that it’s possible to achieve or surpass the performance of larger models like GPT-3.5 with substantially fewer parameters. Mixtral, a mixture of expert models, integrates eight neural networks with 7 billion parameters each, achieving faster inference speeds and matching or outperforming larger models on standard benchmarks.
GPU Shortages and Cloud Computing Costs
Practical necessities, such as the increasing costs of cloud computing and a notable shortage of essential hardware like GPUs, significantly influence the push towards smaller models in artificial intelligence.
James Landay from Stanford HAI highlights the growing demand for AI capabilities within big companies, which has led to a scramble for GPUs. This demand is expected to put considerable pressure on both the production of GPUs and the innovation of cheaper, more accessible hardware solutions.
The late 2023 O’Reilly report underscores AI adopters’ reliance on cloud providers due to the challenges and expenses associated with setting up on-premise servers, compounded by hardware shortages. This reliance will likely elevate the hurdles and increase cloud computing costs as providers strive to meet the demand from generative AI applications by upgrading and optimizing their infrastructure.
Local Models
The shift towards local models in AI reflects a growing emphasis on privacy, data security, and the need for bespoke solutions tailored to specific industry requirements. This trend is particularly relevant in legal, healthcare, and finance sectors, where specialized vocabulary and concepts are prevalent. These industries benefit from local models that run on modest hardware, enabling AI training, inference, and retrieval-augmented generation (RAG) to remain in-house.
This approach mitigates the risk of sensitive data being exposed to third parties. It addresses the practical constraints of large, cloud-based models, such as prohibitive costs and infrastructure requirements. By leveraging local models and custom data pipelines, enterprises can develop AI solutions that are finely tuned to their unique operational contexts without significant investments in infrastructure.
Agentic AI
Agentic AI represents the transition from systems that passively respond to user commands to proactive agents capable of independent action and decision-making. This evolution allows AI agents to understand their environment, set objectives, and autonomously work towards achieving them. The applications of agentic AI encompass fields such as environmental monitoring, where AI can predict and act against natural disasters like forest fires, and financial management, where it can adaptively oversee investment portfolios in real time.
Associated with Stanford’s Human-Centered AI Institute, Peter Norvig anticipates that 2024 will usher in AI agents capable of performing tasks such as making reservations, planning trips, and interfacing with other services on behalf of users.
AI Ethics and Security Risks Concerns
As AI technologies become more embedded in various aspects of life and business, concerns about ethics and security have moved to the forefront of the conversation. The proliferation of deepfakes, sophisticated AI-generated content, and enhanced phishing attacks highlight the dual-use nature of AI, which is capable of significant benefits but also poses risks such as misinformation, manipulation, identity theft, and fraud.
Efforts to mitigate these risks include developing technologies to detect AI-generated content and ensuring AI systems are transparent and fair, emphasizing the importance of carefully vetting training data and algorithms for bias. The evolving landscape of AI regulation, notably with the EU’s pioneering AI Act, underscores a global move towards establishing legal and ethical frameworks to govern AI development and deployment. This act aims to ban certain AI uses, impose obligations on developers of high-risk AI systems, and enhance transparency from companies using AI.
Customized Enterprise Generative AI Models
Customized enterprise generative AI models mark a departure from the one-size-fits-all approach, catering to the specific needs of businesses. Unlike the broad applications of general-purpose tools like Midjourney and ChatGPT, these specialized models offer a targeted solution to niche market requirements. The process typically involves modifying existing AI models through architectural adjustments or fine-tuning with domain-specific datasets. It is a cost-effective alternative to developing a new model from scratch or the high expenses associated with API calls to LLMs. This customization allows for highly relevant applications to sectors with specialized terminologies and practices, such as healthcare, finance, and legal.
Open Source AI
Open-source AI has significantly democratized access to advanced AI technologies, enabling startups, amateurs, and smaller players to harness sophisticated AI capabilities. This trend is facilitated by the parallel advancements in open-source models, particularly in the space of 3 to 70 billion parameters, which are computationally lightweight yet powerful. Enterprises increasingly leverage open-source AI models and tools to develop bespoke solutions tailored to real-world scenarios, ranging from providing customer support to supply chain management and complex document analysis.
The open-source approach promotes collaboration, allowing organizations and researchers to build upon each other’s work, thus reducing costs and barriers to entry. Projects like Stable Diffusion and AutoGPT have drawn thousands of contributors, showcasing the vibrant community engagement and potential for innovation within the open-source AI ecosystem.
Shadow AI
Shadow AI emerges when AI technologies are utilized within an organization without explicit approval or oversight, a trend growing as AI tools become more accessible. Employees, driven by the desire for quick solutions or to explore new technology, increasingly bypass official channels to use AI tools independently. This trend, fueled by user-friendly and readily accessible AI chatbots and tools, poses various risks, from security breaches to compliance issues, as sensitive information may unwittingly be exposed.
While this entrepreneurial spirit among employees showcases an innovative mindset, it underlines the importance of establishing governance frameworks. Organizations must balance fostering innovation with safeguarding against potential risks, necessitating clear policies around the responsible use of AI and measures to manage shadow AI effectively. This approach will help mitigate the risks associated with unsanctioned AI usage while leveraging its benefits for organizational advancement.
Evolving AI Regulation
2024 is pivotal for AI regulation, reflecting growing concerns over ethics and security. With laws, policies, and industry frameworks evolving rapidly worldwide, organizations must stay informed and adaptable.
The EU’s AI Act, potentially the world’s first comprehensive AI law, could ban certain AI uses, enforce obligations for high-risk AI systems developers, and require transparency from AI-using companies. This development, alongside the GDPR, may position the EU as a global regulator, influencing international AI standards.
In the US, while comprehensive federal legislation akin to the EU’s AI Act is lacking, initiatives like President Biden’s executive order and guidance from various federal agencies hint at the direction of stateside regulation. The dual-use nature of AI drives the push for AI regulation, which is capable of significant benefits but also poses risks such as misinformation, manipulation, and privacy breaches. This evolving regulatory landscape underscores the need for AI developers and users to navigate carefully, balancing innovation with ethical considerations and legal compliance.
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“}]] [[{“value”:”From the transformative potential of Multimodal AI, which goes beyond traditional single-mode data processing to encompass multiple input types like text, images, and sound, to the emergence of Quantum AI, the trends shaping the future of AI are both exciting and daunting. “The interfaces of the world are multimodal,” says Mark Chen from OpenAI As
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