This content originally appeared on DEV Community and was authored by Samuel Ekirigwe
Our world is changing rapidly with so many exciting possibilities and artificial intelligence (AI) is at the heart of this transformation. AI is changing industries and reshaping how we live, work, and interact with technology from self-driving vehicles to healthcare diagnostics, and robot waiters to advanced virtual assistants.
According to Cisco’s AI readiness index, 84% of companies agree that AI will significantly impact their business in the near future. McKinsey estimates that AI could deliver up to $4.4 trillion annually to the global economy by 2030. This explosion is exciting but presents challenges for existing network infrastructure.
AI systems require large amounts of data and computational power to function efficiently, and with AI's increasing adoption, networks are under more pressure than ever before. For example, Gartner predicts that by 2027, more than 40% of current data centers deploying AI workloads will be constrained by electrical power. Businesses are quickly adopting AI, but this adoption is only as strong as the networks supporting these intelligent systems.
In this article, let's look at how network infrastructure is evolving to meet the growing demands of AI workloads, from the need for reduced latency and increased bandwidth to enhanced security features and how AI is in turn helping networks teams reach unchartered territories in network management and fault detection.
How AI Models Work Today
To get a better understanding of why AI is extremely resource intensive, we need to look at how AI models work at their core
- Data collection and Preprocessing: Data serves as the foundation for training models to recognize patterns and make predictions. Preprocessing involves cleaning, formatting and handling inconsistent information to ensure usefulness of the data. Streams of images, texts, audio files are all types of data used in training AI models.
- Model Training: Models are trained using many iterations of them trying to find correlations and relationships between inputs and corresponding outputs.
- Inference: Once the model is trained, it enters the inference phase, where it can make predictions on new, unseen data. The model uses the learned patterns to process real-time input and generate outputs like classifications, translations, or decisions.
- Continuous Learning: The models are continuously updated with new data to adapt to changing trends and improve performance over time.
The development of AI models is extremely data intensive. OpenAI’s GTP-3 released in 2020, was reportedly trained on over 175 billion parameters of around 45TB of text and code. GPT-4 released in 2023 promising to be ten times more advanced was trained with a staggering data set of over 1.76 trillion parameters.
AI Usage and Applications Today:
After the models are developed, where are they used? The job doesn’t stop at just developing the models, Enter the businesses and end users. Let’s look at a few applications of AI today;
- AI in Business: AI-powered Business tools helping businesses collect, analyze, and visualize data more efficiently and effectively. Providing improved decision-making, increased productivity, and reduced costs.
- AI in Healthcare AI-powered tools helping doctors and health care professionals diagnose diseases, develop new treatments, and provide personalized care to patients.
- AI in Education Personalized learning, improved student engagement, and automating administrative tasks for schools and other organizations.
- AI in Finance AI helping financial services institutions in: personalizing services and products for specific customers, managing risks and fraud through predictive analysis, enforcing transparency and compliance, and automate operations and reduce costs.
- AI in Manufacturing In manufacturing, AI is Improving efficiency by automating tasks, such as assembly and inspection, increasing productivity by optimizing production processes, improving quality by detecting defects and improving quality control and assurance.
The Growing Demands on Networks
With all these data moving around in AI development and AI usage, we can completely agree that AI is data intensive. Cisco data prediction,
This content originally appeared on DEV Community and was authored by Samuel Ekirigwe
Samuel Ekirigwe | Sciencx (2024-10-11T23:54:36+00:00) Keeping Pace with AI: Optimizing Network Infrastructure for Increasing Workloads. Retrieved from https://www.scien.cx/2024/10/11/keeping-pace-with-ai-optimizing-network-infrastructure-for-increasing-workloads/
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