AI vs Machine Learning

IBM Technology
10 Apr 202305:49
EducationalLearning
32 Likes 10 Comments

TLDRThe video explains the difference between artificial intelligence (AI), machine learning (ML), and deep learning (DL). AI tries to match human intelligence and capabilities like discovering new information, inferring, and reasoning. ML involves predictions and decisions from data, learning without explicit programming. DL uses neural networks to model the brain. ML is a subset of AI, DL is a subset of ML. Rather than competing concepts, AI encompasses ML, DL, and other capabilities like vision, language, speech, and robotics that allow machines to perform human-like tasks.

Takeaways
  • 😀 AI is about matching or exceeding human intelligence and capabilities like discovering new information, inferring, and reasoning
  • 🤖 Machine learning involves making predictions and decisions based on data - it learns from data rather than being explicitly programmed
  • 🧠 Deep learning uses neural networks to model the human brain and find insights from data - but the reasoning behind those insights isn't always transparent
  • 🔭 Machine learning and deep learning are subsets of artificial intelligence
  • 👁️ Natural language processing, computer vision, speech recognition are also parts of AI - along with robotics
  • 🗣️ AI encompasses the ability for computers to see, hear, speak, move and more in an intelligent way
  • ❌ AI vs ML and AI = ML are not the right ways to frame the relationship
  • ⚖️ Rather, ML is a subset of AI - when doing ML, you are doing AI, but not all of AI is ML
  • 📊 Other capabilities like natural language processing and robotics also fall under the broader umbrella of AI
  • 📝 In summary, AI is the overarching concept of machines matching human intelligence in different capabilities - with ML, DL and more being approaches under AI
Q & A

  • What are the key capabilities that define AI according to the speaker?

    -The key capabilities that define AI according to the speaker are: the ability to discover new information, infer information that has not been explicitly stated, reason to figure things out, and match or exceed human intelligence and capabilities in general.

  • What does machine learning involve?

    -Machine learning involves making predictions or decisions based on data. It is a sophisticated form of statistical analysis that learns from data rather than needing to be explicitly programmed.

  • What is the difference between supervised and unsupervised machine learning?

    -In supervised machine learning, there is more human oversight and labeling of the training data. In unsupervised learning, the system is able to run more independently and find patterns that were not explicitly labeled.

  • What is deep learning and how does it relate to machine learning?

    -Deep learning is a subset of machine learning that involves neural networks to model human mind capabilities. It is called 'deep' because it uses multiple layers of neural networks.

  • Why might deep learning provide insights that are not fully explainable?

    -Because deep learning systems derive their outputs through multiple layers of neural networks, we don't always have full visibility into how the system arrived at a particular insight or decision.

  • What capabilities beyond machine learning and deep learning are part of AI?

    -Other capabilities that are part of AI include natural language processing, computer vision, speech recognition and synthesis, robotics, and more - basically any capability that attempts to match human intelligence.

  • How should we visualize the relationship between AI, machine learning, and deep learning?

    -We should visualize it as a Venn diagram with machine learning and deep learning as subsets fully within artificial intelligence. AI is the superset that encompasses all attempts to match human intelligence and capability.

  • Can machine learning exist independently from AI?

    -No, machine learning cannot exist independently from AI. Since machine learning aims to match human analytical capability, it is considered a subset of overall artificial intelligence.

  • What is an example of something that would be AI but not machine learning?

    -An example would be natural language processing to understand human speech. This exemplifies AI's goal to match human capability but does not directly involve machine learning algorithms.

  • What is the best way to summarize the relationship between AI, machine learning, and deep learning?

    -The best way to summarize their relationship is: Machine learning and deep learning are specific subsets and techniques within the broader scope of artificial intelligence. AI is the overarching concept for any technology that attempts to match human intelligence and capability.

Outlines
00:00
😃 Defining AI and capabilities it involves

This paragraph defines AI as matching human intelligence and capabilities. It states AI involves discovering new information, making inferences, reasoning to figure things out, etc. The goal is to match what the human mind can do.

05:00
😊 Explaining machine learning and its types

This paragraph defines machine learning as making predictions or decisions based on data. It explains the difference between supervised and unsupervised ML. It also introduces deep learning as a subset of ML involving neural networks to model the human mind.

🤔 Relating AI, ML, and DL in a Venn diagram

This paragraph concludes by showing the relationship between AI, ML, and DL in a Venn diagram. ML is a subset of AI. DL is a subset of ML. AI includes robotics, NLP, computer vision etc. - things matching human capabilities.

Mindmap
Keywords
💡Artificial Intelligence (AI)
Artificial Intelligence (AI) refers to the capability of a machine to match or exceed human intelligence and abilities. In the video, AI is defined as exceeding or matching human capabilities across areas like discovering new information, making inferences, reasoning, etc. AI encompasses various approaches like machine learning as well as application areas like computer vision and robotics.
💡Machine Learning (ML)
Machine Learning (ML) involves making predictions or decisions based on data using sophisticated statistical analysis. As the system processes more data, the accuracy of predictions and decisions improves over time through 'learning'. Unlike explicit programming, machine learning models and algorithms can adapt and evolve on their own.
💡Deep Learning
Deep Learning is a subset of machine learning based on neural networks modeled after the human brain. It involves using multi-layered neural networks that can uncover insights from big data that may not be intuitively obvious. A limitation is that the internal workings of the model may not be fully explainable.
💡Supervised Learning
In supervised machine learning, models are trained on labeled data with the right answers provided upfront. This allows for more direct human monitoring and tuning of the training process.
💡Unsupervised Learning
In unsupervised learning, models are let loose on raw, unlabeled data to find hidden patterns and intrinsic structures without pre-defined labels or supervision.
💡Natural Language Processing
Natural language processing (NLP) is an area of AI focused on enabling computers to understand, interpret, and manipulate human languages like English.
💡Computer Vision
Computer vision is a field of AI that enables computers and systems to derive meaningful information from digital images, videos and other visual inputs - much like human vision and biological vision systems.
💡Robotics
Robotics is an interdisciplinary field involving AI and engineering focused on building machines capable of assisting humans in complex tasks requiring perception, mobility, grasping objects, as well as decision making.
💡Relation between AI and ML
The video establishes that machine learning, including deep learning, is an important subset and approach within the broader scope of AI. While all machine learning qualifies as AI, not all AI involves machine learning.
💡Capabilities of Humans
A common thread in the video is defining AI as the ability of machines to match various capabilities of human intelligence like language, vision, hearing, inferencing, reasoning, etc. Human abilities provide a benchmark for AI aspirations.
Highlights

AI is basically exceeding or matching the capabilities of a human

AI involves discovering new information, inferring unstated information, and reasoning to figure things out

Machine learning involves making predictions or decisions based on data, like a sophisticated form of statistics

Machine learning systems learn from data rather than having to be explicitly programmed

Supervised machine learning uses labeled data and human oversight during training

Unsupervised machine learning finds patterns without explicit supervision

Deep learning involves neural networks modeled after the human brain

Deep learning derives interesting insights, but the reasoning is not always explainable

Artificial intelligence encompasses machine learning, deep learning, and other capabilities

AI involves natural language processing, computer vision, robotics, and more

Machine learning is a subset of artificial intelligence

When doing machine learning, you are doing a type of AI

No single capability constitutes all of AI - it encompasses many disciplines

AI equals the combination of machine learning, deep learning, and more

The key is that machine learning and deep learning are parts of the broader concept of AI

Transcripts

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