As we bid farewell to 2024, the ai space reflects a year of transformative advances reshaping industries, workflows and human-technology interaction. Andrew Ng, one of the key figures in the ai community, summarizes the essence of this year with key reflections and takeaways. Let's take a look at Andrew Ng's 2024 ai summary.
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Upstream Agents: The Growth of Agent Workflows
One of the biggest trends of 2024 has been the rise of ai agents and agent workflows. These autonomous systems, capable of performing complex tasks with minimal human intervention, have become integral to industries ranging from healthcare to finance. Frames like Agent Qwen, automatic chainand TaskGPT have demonstrated the potential of ai agents to optimize operations, improve productivity and enable more efficient decision making.
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Prices fall: LLM token prices fall rapidly
The cost of using large language models (LLMs) has seen a dramatic decline in 2024, which will make ai more accessible than ever. The drop in LLM token prices has been driven by advances in model efficiency, increased competition among ai providers, and economies of scale. For example, DeepSeek V3's token price has been notably lower than other models with similar capabilities, allowing startups and researchers to experiment with advanced ai at a fraction of traditional costs.
Generative video takes off
Generative ai has expanded beyond text and images to revolutionize the video production industry. In 2024, generative video models such as Sora from OpenAI, Google's I see 2and AWS Nova Reel have taken center stage, allowing creators to produce high-quality video content with minimal effort. These models can generate realistic scenes, edit images, and even create completely synthetic videos based on textual cues.
Small is beautiful: small language models
While large language models (LLM) have dominated the ai landscape in recent years, 2024 has seen a growing emphasis on small language models (SLM). Compact and efficient, these models suit resource-constrained environments such as edge computing and on-device applications. Releases like Fi 4, Flame 3.2 3Band Qwen 2.5 demonstrate advances in performance and scalability, leveraging specialized domains such as healthcare and legal technology. SLMs highlight a shift towards pragmatic ai, combining practicality with innovation.
Also Read: Top 13 Small Language Models (SLM)
Alternatives to acquisitions
In 2024, the ai industry will witness a shift in the way companies approach growth and innovation. Instead of relying solely on acquisitions, organizations are increasingly exploring alternative strategies, such as partnerships, open source collaborations, and ecosystem development. This trend reflects a recognition of the value of community-driven innovation and the need to foster a more collaborative ai landscape.
For example, Meta's Llama 3.2 was integrated into Snapdragon platforms through a partnership with Qualcomm, enabling advanced ai experiences on the device.
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Microsoft deepened its ties with Hugging Face, focusing on integrating open source models seamlessly into cloud platforms.
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Google's strategic partnership with Hugging Face enabled models to be trained and deployed on platforms such as Vertex ai and Google Kubernetes Engine.
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Final note
Looking ahead to 2025, these trends are likely to continue shaping the ai landscape, creating new opportunities and challenges. The current evolution of ai promises to transform industries, enhance human capabilities, and unlock unprecedented possibilities for innovation. By staying alert to these developments, you can position yourself to thrive in the ai-driven future. What do you think about these trends? Share your ideas in the comments below!
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