What's the difference between AI and ML?

Artificial Intelligence (AI) and Machine Learning (ML) are powerful technologies that often get lumped together as AI/ML. AI is often dependent on ML, but they don’t always go hand in hand. Each requires different technologies and expertise to use effectively. So it’s important to know the difference between AI and ML to approach their implementation systematically and leverage both to their full capacity. 

The distinction between AI and ML is nuanced. While all ML is AI (since it falls under the umbrella of creating intelligent machines), not all AI is ML (since there are AI techniques that don't involve learning from data).

While AI encompasses a broad range of algorithms and approaches designed to mimic human-like tasks, ML specifically focuses on using data-driven algorithms that improve over time. ML techniques are a key component of many AI systems, enabling them to learn from data and improve performance.

Here’s a breakdown of the key differences between the two and their capabilities. 

What is AI?

Artificial Intelligence (AI) is a branch of computer science that aims to create machines capable of performing tasks that require human intelligence. These tasks include, but are not limited to: problem-solving, pattern recognition, understanding natural language, and decision-making. 

The goal of AI is not just to program machines to perform tasks, but to enable them to do so with an adaptability and autonomy resembling human cognition.

In a business context, AI can optimize workflows, enhance customer experiences, provide insights, and drive revenue growth by automating various tasks and providing valuable insights.

What is ML?

Machine Learning (ML) is a subset of artificial intelligence that focuses on developing algorithms and statistical models that enable machines to perform a task without using explicit instructions. ML always involves machines analyzing large volumes of data.

Essentially, ML allows computers to learn from and make decisions based on data, improving their performance on tasks as they are exposed to more data over time. It’s a field of study and application that gives machines the ability to learn without being programmed. 

Don’t they both rely on data?

While both AI and ML require some kind of data (e.g. the rules for an AI system in rules-based techniques are a kind of data), ML's dependence on data is more pronounced. ML models are trained on labeled or unlabeled data to learn patterns and generalize from examples. The quality, quantity, and relevance of data are crucial for ML models. 

AI, on the other hand, may utilize data for many tasks, but its focus is broader and may involve other techniques beyond data-driven learning. For example, a rule-based character in a video game is a type of AI, but these non-player characters (NPCs) don’t necessarily have to learn how to update their own rules based on ML data models.

But characters or systems that don’t learn and evolve aren’t nearly as useful. Ideally, AI is supported by ML so that intelligent behaviors are learning, evolving, and updating their own rules and parameters as they go. 

Let’s take a look at the different business capabilities AI and ML are both typically responsible for. 

Common AI business capabilities

Rule-based systems: Decision-making systems that operate based on predefined rules rather than learning from data.
Natural Language Processing (NLP): Enable human-like interaction with computers and analyze unstructured text data.
Computer vision: Allow systems to interpret and make decisions based on visual data.
Intelligent automation: Combine robotic process automation with AI for advanced task automation.
Knowledge representation: Store, retrieve, and manage information intelligently.
Expert systems: Mimic human experts to make decisions in specific areas.
Emotion recognition: Analyze human emotions for enhanced customer interactions.
Chatbots and virtual assistants: Automated and enhanced customer support.

Common ML business capabilities

Supervised learning: Training algorithms with labeled data – e.g., image classification or spam detection.
Unsupervised learning: Algorithms that identify patterns in unlabeled data – e.g., clustering or dimensionality reduction.
Reinforcement learning: Algorithms that optimize actions based on reward feedback – e.g., game-playing bots or certain ad-bidding strategies.
Deep learning: Complex neural networks that can process vast amounts of data, often used in advanced image and speech recognition.
Predictive analytics: Make forecasts about future events based on historical data.
Anomaly detection: Identify unusual patterns that do not conform to expected behavior.
Algorithmic trading: Utilize algorithms to make automated, timely trading decisions.
Customer segmentation: Categorize customers based on various factors to tailor marketing and sales strategies.
Fraud detection: Detect fraudulent activities by analyzing patterns and anomalies.

Combined AI and ML Capabilities

Chatbots: Modern chatbots use a mix of rule-based logic and machine learning models to understand and generate human language, adapt to user interactions, and provide contextually relevant responses.
Advanced Natural Language Processing: Such as sentiment analysis, machine translation, and chatbots which use ML models.
Computer vision: While there are traditional AI methods, modern applications like facial recognition or object detection often use ML-driven approaches.
Predictive analytics: Using both rule-based logic (AI) and data-driven predictions (ML) to forecast future outcomes.
Recommendation systems: Leveraging both AI logic and ML models to suggest products, services, or content.
Voice recognition: Translating voice into text or commands often relies on deep learning (a subset of ML) but can also incorporate other AI logic.
Autonomous systems: Systems like self-driving cars that use a combination of rule-based logic and data-driven learning to operate.

Technology and infrastructure needed

A wide variety of technology powers AI and ML systems – from the Nvidia GPUs that everyone has on backorder to new AI-specific programming languages like Modular Mojo. 

Here’s a breakdown of the technology you’ll need to implement something like an AI-powered content personalization system at your business. 

Software and platforms

AI-specific

• Rule-based systems platforms: Tools like Drools or CLIPS for building expert systems.
• Knowledge graphs & databases: Platforms like Neo4j for knowledge representation.

ML-specific

• ML frameworks & libraries: TensorFlow, PyTorch, Scikit-learn, Keras, and XGBoost are popular ones.
• Deep learning platforms: For neural networks and deep learning, consider tools like Caffe, TensorFlow, and DL4J.
• AutoML platforms: Tools like Google's AutoML or H2O.ai which automate parts of the machine learning process.

Both AI and ML

• Cloud AI/ML platforms: Providers like AWS (SageMaker, Comprehend), Google Cloud (AI Platform, Vision AI), and Microsoft Azure (Azure Machine Learning) offer comprehensive platforms covering a broad range of AI and ML capabilities.

Hardware and infrastructure

AI-specific

• Traditional servers and workstations: These are often sufficient for rule-based AI applications. Combined AI/ML technologies that demand high compute resources require more robust hardware like rows of powerful GPUs. 

ML-specific

• GPUs: Essential for deep learning tasks due to their parallel processing capabilities. Nvidia's Tesla and Titan are popular choices.
• TPUs (Tensor Processing Units): Developed by Google for neural network machine learning.
• FPGAs (Field-Programmable Gate Arrays): For specific custom-tailored ML computations.

Both AI and ML

• High-performance computing clusters: For large-scale simulations or data processing tasks.
• Data storage solutions: Robust storage systems (like Hadoop or cloud storage options) for handling large datasets.
• Edge devices: If deploying AI/ML solutions on edge devices (like IoT devices), specific hardware and edge services support the AI/ML inference.

Data management and processing

Both AI and ML

• Big data platforms: Tools like Hadoop, Spark, or Flink for processing and analyzing large datasets.
• Data warehouses: Systems like Amazon Redshift, Snowflake, or Google BigQuery.
• Data lakes: Solutions such as AWS Lake Formation or Azure Data Lake.
• ETL tools: Platforms like Apache NiFi, Talend, or Informatica for extracting, transforming, and loading data.

Deployment and integration

Both AI and ML

• Containerization: Docker and Kubernetes for encapsulating and deploying models.
• API development: Tools and platforms to turn models into accessible web services (e.g., Flask, FastAPI).
• Integration middleware: Platforms like MuleSoft or Apache Camel to integrate AI/ML solutions into existing IT ecosystem

Monitoring and maintenance

Both AI and ML

• Model monitoring tools: Platforms like ModelDB or Fiddler to monitor the performance of deployed models.
• MLOps tools: Platforms like MLflow or TFX for managing the end-to-end ML lifecycle.

All the technologies you need to implement AI and ML capabilities are available. 

Roles and responsibilities needed to implement AI and ML

This is one of the most important areas in AI and ML right now. You could have all the funding and technology to implement new capabilities, but you need expertise at every level of your organization to make them work. 

Hiring the right AI and ML experts is one of the hardest problems in the AI field (besides securing enough physical GPUs).

Here’s who you’ll need to hire to implement AI and ML at your business. 

1. Executive leadership (CEO, CTO, CIO)

AI responsibilities:

• Define and oversee the AI strategy and integration within the business.
• Ensure ethical and responsible use of AI technologies.

ML responsibilities:

• Oversee ML initiatives and their alignment with business objectives.
• Approve resources and budget allocation for ML projects.

2. AI researcher

AI responsibilities:

• Conduct research to advance the use of artificial intelligence.
• Develop new algorithms and models for complex tasks and problems.
• Stay abreast of AI trends and breakthroughs – ensuring your business strategy remains at the forefront of technological advancements.

3. AI/ML product and project managers

AI and ML responsibilities:

• Design AI/ML products and guide development.
• Coordinate and manage AI/ML projects.
• Work with different teams to ensure project goals and timelines are met.

4. Data scientists

AI and ML responsibilities:

• Design and develop AI/ML models.
• Analyze data and derive insights to inform AI/ML model development.

5. Machine learning engineers

AI and ML responsibilities:

• Implement and deploy machine learning models.
• Ensure the technical efficiency and scalability of ML systems.

6. Data engineers

AI and ML responsibilities:

• Design and maintain robust data pipelines and infrastructure.
• Clean and organize data for AI/ML projects.

7. Senior software engineers

AI and ML responsibilities:

• Develop software integrating AI/ML functionalities.
• Work closely with data scientists and ML engineers to ensure seamless integration.

7. AI ethics and compliance manager

AI responsibilities:

• Monitor AI systems for adherence to ethical standards and regulations.
• Address AI-related ethical and compliance issues.

ML responsibilities:

• Ensure ML models comply with relevant standards and regulations.

8. Business analysts

AI and ML responsibilities:

• Communicate business requirements and goals for AI/ML projects.
• Evaluate and report on the business impact of AI/ML initiatives.

9. User experience (UX) designers

AI and ML responsibilities:

• Design user-friendly interfaces for AI/ML-powered applications.
• Ensure clarity and usability in AI/ML system interactions.

10. Support and maintenance team

AI and ML responsibilities:

• Monitor, maintain, and update AI/ML systems.
• Handle issues and ensure the evolution of AI/ML systems with business needs.

11. Cybersecurity experts

AI and ML responsibilities:

• Implement security protocols for AI/ML systems and data.
• Address security and privacy concerns related to AI/ML technologies.
  

Senior software engineers, AI researchers, data scientists, ML specialists, and other highly technical people are in extremely high demand. They’re also expensive and hard to find. Building out a team to design, develop, and deploy your AI and ML initiatives just might be the biggest challenge to making progress on your roadmap. 

Luckily, there are external AI and ML partners you can engage to build out your roadmap faster than you can hire for.

How do I hire a senior team for AI and ML?

AI and ML exports are in such high demand that even companies like Meta have a hard time finding and keeping AI talent. Netflix currently has a $900,000 a year AI product manager position and most companies will never have that kind of budget for a single expert. Instead of waiting 6-18 months to recruit for expensive AI and ML teams, you could start with Codingscape in 4-6 weeks. 

We’re already building AI and ML solutions for our partners in 2023 and helping them plan their investments for 2024. We can assemble a senior AI or ML development team for you in 4-6 weeks. It’ll be faster to get started, more cost-efficient than internal hiring, and we’ll deliver high-quality results quickly. 

Zappos, Twilio, and Veho are just a few companies that trust us to build software with a remote-first approach. We know AI and ML at enterprise scale and build high-quality solutions you can stake your reputation and career on.

You can schedule a time to talk with us here. No hassle, no expectations, just answers.