Different AI Models Explained: From Machine Learning to Generative Intelligence

Machine Learning Models: The Foundation of Modern AI

Machine learning (ML) models form the foundation of modern AI, allowing systems to learn from data and use the knowledge to make predictions. Below are explanations of different learning styles:

• Supervised learning: Relies on datasets or outcomes to train models.
• Unsupervised learning: Looks for patterns within data to inform future outcomes.
• Semi-supervised learning: Combines a small amount of labeled data with large pools of unlabeled data to improve model training accuracy. 

ML algorithms use statistical techniques to uncover relationships within historical data. From there, the models generate predictions or modify classifications based on new information. Use cases for using AI in enterprises include:

• Detecting anomalies in network or performance data
• Forecasting trends in storage capacity needs or potential system failures
• Automating routine decisions, such as routing IT support tickets to the correct area

Learning from data gathered from previous incidents helps teams locate problems early and make more informed decisions. One limitation of traditional ML models is that they often lack the compute power of more complex AI models. While they work well within structured datasets and small data volumes, you still need human intervention to define relevant features. 

Deep Learning and Neural Network Models

Deep learning builds on ML, using layered neural networks to learn new information from raw data. There’s no reliance on predefined features by humans. Instead, neural networks use multiple layers of artificial neurons — similar to those in the human brain — to progressively extract higher-level patterns. 

This allows deep learning models to recognize intricate data structures. For example, you can use neural network models to detect objects in images or to capture speech nuances that are harder to capture with manual techniques. 

All of this is possible thanks to advances in computer hardware. The computationally intensive nature of training large neural networks makes GPUs and other specialized accelerators essential to deep learning workloads. Models that might have required weeks of training in the past can now handle complex tasks in hours. 

Enterprises typically use GPI-equipped servers or cloud instances for model training. Some even invest in deploying dedicated AI supercomputers that cluster numerous accelerators for demanding projects. Deep learning thrives on unstructured data like images, free-form text, and audio. Some use cases include:

• Analyzing video feeds or medical images for critical information
• Using speech recognition and natural language processing with voice assistants or document analysis
• Applying advanced pattern recognition to detect credit card fraud or security network intrusions

Training deep models requires extensive datasets and substantial processing time. That means higher costs for hardware, like GPUs and distributed servers. Once deployed in production, it can deliver benefits such as higher accuracy. However, this can yield diminishing returns beyond a certain point and higher infrastructure costs. 

Companies can mitigate these issues using techniques such as model compression or batching. However, businesses still need robust infrastructure to deliver models to potentially thousands of users. 

Reinforcement Learning and Decision-Based AI Models

Reinforcement learning (RL) models, called agents, learn by interacting with environments and receiving feedback in the form of rewards or penalties. Agents receive programming with a specific goal and are reinforced to achieve it. They receive positive rewards for desired outcomes and negative feedback for mistakes. 

Agents learn to maximize their cumulative rewards, thereby learning an optimal strategy for achieving goals through trial and error. You often see reinforcement learning used in automation and control scenarios where agents must make a sequence of decisions. A robot in a factory can learn to adjust movements to improve precision when loading and boxing items by receiving rewards for correct actions. 

RL is also used in cybersecurity for adaptive defense against attacks. Agents are trained to detect and respond to breach attempts by simulating cyber threats and evaluating the effectiveness of various responses.

Some of the challenges of employing RL include:

• The need for multiple trial-and-error cycles, which can mean millions of training steps
• Agent behavior may be random and unpredictable at the start
• Reliance on substantial computing resources to develop reliable RL models

Be careful about designing reward criteria for RL models. Every agent should undergo thorough testing, with fallback controls in place for safety, including keeping human personnel informed of outcomes. RL offers a robust, feedback-driven AI approach when used with appropriate oversight. 

Generative AI Models and Large Language Models

Generative AI models generate new content instead of analyzing existing information. Models look for patterns in training data to create original outputs, including images, text, and even synthetic data. Tools like OpenAI’s GPT-4 or Meta’s LLaMA use massive amounts of information gathered from available online resources to produce everything from coherent human text to usable programming code. 

Enterprises have found ways to incorporate generative AI into IT and business operations, including:

• IT support: AI assistants provide answers to employee tech support questions. They also generate knowledge-based articles and generate simple code fixes. 
• Security analysis: Cyberteams use generative AI to analyze and summarize threat intelligence. AI can quickly scan pages of security logs, generate an anomaly report, and recommend actions. 
• Predictive insights: Generative models can turn raw analytics data into insights. That means analysts can use AI to draft reports on trends, risks, and forecasts versus generating them manually.

Models may produce incorrect outputs with high confidence, a phenomenon known as AI hallucination. Generative AI can also reflect biases within training data, leading to unfair outputs if not checked by independent human analysis. There can also be privacy concerns when AI is trained on confidential information. That means organizations must implement safeguards to ensure accuracy, fairness, and appropriate security.  

Applying and Selecting AI Models Across Enterprise Infrastructure

Mapping AI models to workloads requires understanding the problem to solve, the available data, and all performance and cost requirements. Questions to ask include:

• How large and complex is the data set?
• Will the model use structured or unstructured data?
• Will an application using the AI model require real-time responses?
• What are the accuracy requirements?

Companies can use simpler ML algorithms for well-defined problems using structured data running on standard servers. If you need to parse millions of log files to detect potential security issues, you might want to use more advanced deep learning or LLMs, if you can afford the supporting hardware. 

Dell Technologies and its partners provide a range of infrastructure and tools to support a variety of AI models across enterprises. They offer everything in AI-optimized hardware, such as PowerEdge systems equipped with high-performance NVIDIA GPUs. Solutions like secure enterprise broadband and AI-optimized endpoints can improve security and connectivity for AI-driven workforces.

The Elevate User community can provide support as you embrace the power of AI's possibilities in the workplace. Contact us to learn more about how Elevate can help you.