AscentCore AI trends in 2024 blog cover

AI Trends in 2024

Embarking on exploring AI trends in 2024, the past year marked a pivotal moment as generative AI gained efficiency and quality across diverse workflows. As we step into the new year, let’s closely examine unfolding trends that promise to redefine the AI landscape from the multifaceted capabilities of Multimodal Generative AI to the evolving regulatory landscape and cost efficiencies of models like GPT-3.5-turbo. Join us as our CTO, Cornel Stefanache, unveils key AI trends anticipated in 2024.

Multimodal Generative AI

In 2024, generative AI is set to dominate the AI landscape, revolutionizing numerous workflows with its efficiency and enhanced quality. These advanced AI systems will be integrated into diverse tasks, streamlining processes and fostering innovation. The key feature propelling this surge is their multimodality, enabling these models to aggregate and interpret information from a variety of sources, including text, images, and sound. Integrating generative AI in workflows signifies a significant technological leap, accordingly offering unprecedented levels of productivity and creative possibilities.

AI Regulations in 2024

In 2023, the European Union advanced the European Union Artificial Intelligence Act, a pioneering legal framework globally for AI. This act classifies AI systems into categories of risk (unacceptable, high, and limited) and sets corresponding regulations. The EU AI Act covers a broad spectrum of AI uses, from high-risk areas like welfare and education to lower-risk ones like chatbots. Furthermore, it bans AI uses deemed to pose unacceptable risks, such as workplace emotion recognition and social scoring based on behavior or personal traits.

As we progress into 2024, legislation surrounding Artificial Intelligence is anticipated to emerge more prominently. This shift is driven by the increasing integration of AI in various sectors, necessitating the establishment of legal frameworks to ensure responsible and ethical use. Key areas of focus for this legislation will likely include:

Data Privacy and Security. With AI systems often relying on large datasets, there will be a push for laws that protect personal and sensitive data. This may include regulations on collecting, storing, and using data, ensuring transparency and consent from individuals.

Accountability and Liability. The legislation will aim to clearly define who is responsible for the actions and decisions made by AI systems. This includes addressing the complexities surrounding machine learning algorithms that evolve independently, making pinpointing liability in malfunctions or harm challenging.

Ethical Standards and Bias Mitigation. New laws are expected to set ethical standards for AI development and deployment. This includes measures to prevent and mitigate biases in AI systems, ensuring they do not perpetuate or amplify social inequalities.

Transparency and Explainability. Regulations may mandate that AI systems be transparent in their operations and decision-making processes. This is crucial for critical applications like healthcare, law enforcement, and finance, where AI decisions can have significant impacts.

Cheaper Inference Technologies

The reduced cost of large AI models, including OpenAI’s GPT-3.5-turbo, is primarily attributed to technological advancements and increased competition. The GPT-3.5-turbo model, powering ChatGPT, now costs only $0.002 per 1000 tokens (around 750 words). That marks a 90% decrease from the original GPT-3.5 usage costs. Moreover, this “turbo” version signifies an enhanced GPT-3.5 with quicker response times.

OpenAI’s cost reduction stems from a range of optimizations across different levels. At the model architecture level, this involves methods like pruning, quantization, and fine-tuning, which make the model smaller and faster and improve accuracy and performance while cutting down on computational requirements and inference costs.

Efficient algorithms and GPU parallel computing have been instrumental in accelerating calculations and boosting overall computing efficiency. Business-level optimization enhances system-wide performance and efficiency through caching and predictive techniques, minimizing latency and redundant operations. Model-level optimization streamlines the network structure, while quantization reduces computational and parameter costs using lower-precision calculations. Finally, compiler-level optimization leverages advanced compilers for more efficient code execution and computing efficiency.

AI specialized chips are increasingly in demand due to their innovative architectures, significantly enhancing performance and reducing inference costs. Unlike general-purpose hardware specializing in serial in-memory calculations, these specialized AI chips are designed to cater to the unique needs of neural networks. Neural networks require a multitude of parallel and simple arithmetic operations. Standard powerful chips struggle to support simultaneously due to their design for more complex, sequential processing. In contrast, AI-optimized hardware incorporates numerous less powerful chips, allowing for extensive parallel processing. This approach enables these specialized chips to efficiently handle the high volume of simple, concurrent operations essential for neural network tasks. This meets the growing demand for more effective and cost-efficient AI computing solutions.

Open Source AI Models

In 2024, the popularity of open-source pre-trained AI models has surged, offering businesses a powerful tool to boost their growth. Companies achieve heightened productivity and cost savings by integrating these models with their private or real-time data. A notable example of this trend is IBM’s active involvement in contributing to open-source AI models, as demonstrated by its collaborative efforts with NASA.

Supporters of open-source AI view it as a way to democratize AI technology and spur innovation. Platforms like Huggingface champion that open collaboration in AI research fosters broader progress and application. Open access to source code and models offers transparency, allowing developers globally to benefit from shared advancements and refine their solutions.

Conversely, critics of open-source AI deem it potentially hazardous. They advocate for limiting access to large language models to a select group of meticulous researchers, suggesting tighter control over these technologies.

In wrapping up our piece on the exciting AI trends in 2024, we’ve explored transformative facets, including the dynamic prowess of Multimodal Generative AI, evolving regulations, and strides in cost-efficient models. Stay on the forefront of innovation, exploring the unfolding possibilities in Artificial Intelligence for the year ahead.


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