Deep Learning
In machine learning, the system is built for a problem where writing all the necessary rules would be extremely difficult, so machine learning provides another approach.
The Automation Question
A useful way to begin studying artificial intelligence is not to ask whether a computer thinks exactly like a person. Instead, ask what kind of human intellectual work the computer is being designed to perform. Artificial intelligence is the effort to automate intellectual tasks normally performed by humans. This definition describes a broad goal, not one particular technique.
Artificial intelligence is broader than machine learning. AI includes machine learning and deep learning, but it also includes approaches that do not involve learning. Therefore, describing deep learning as if it were the whole of AI reverses the relationship between the concepts.
Two Routes to Intelligent Results
There are two useful routes for imagining how a system can produce an intelligent-looking result. In symbolic AI, programmers describe knowledge and decisions directly through explicit rules. In machine learning, the system is built for a problem where writing all the necessary rules would be extremely difficult, so machine learning provides another approach.
| Approach | Main idea | Problem fit described in the source |
|---|---|---|
| Symbolic AI | Programmers handcraft explicit rules | Problems whose logic can be described clearly |
| Machine learning | Used as an approach when writing all the rules is extremely difficult | Complex, fuzzy problems such as image classification, speech recognition, and language translation |
The distinction is about how the system is developed and the type of problem being addressed.
When Rules Stop Scaling
Explicit rules work well when the logic of a problem can be described clearly. The difficulty changes when a problem is complex and fuzzy. For image classification, speech recognition, and language translation, it proved intractable to work out all the explicit rules needed to describe the solution. Machine learning arose as an approach for these kinds of problems.
Choosing an Approach
A learner must classify a problem as either one whose logic can be described clearly or one for which all the explicit rules would be extremely difficult to write.
- 1Step 1: Inspect the problem
If the problem has clearly describable logic, explicit rules are a suitable fit according to the source.
- 2Step 2: Check for complexity and fuzziness
If the problem is like image classification, speech recognition, or language translation, writing all the rules may be intractable.
- 3Step 3: Identify the approach
For the second kind of problem, machine learning arose as an alternative approach.
The problem type helps distinguish where symbolic AI is suitable from where machine learning is motivated.
The Five-Part Rise
Deep learning's recent surge is presented as the result of several contributing factors rather than one isolated cause. The complete list named in the source is hardware, data, algorithms, a new wave of investment, and democratization of deep learning.
- Hardware
- Data
- Algorithms
- A new wave of investment
- Democratization of deep learning
From Conditions to Growth
The five factors should be read together as a broad explanation of why deep learning has recently grown. Hardware, data, algorithms, investment, and democratization are all part of the account. The source does not present them as a ranked sequence, so the safest interpretation is that they are several contributing conditions rather than five steps that must occur in a fixed order.
Evaluating a Short Explanation
Evaluate this explanation: Deep learning recently grew because of better hardware.
- 1Step 1: Compare the explanation with the source
Hardware is one of the factors named by the source.
- 2Step 2: Check whether the list is complete
The explanation omits data, algorithms, a new wave of investment, and democratization.
- 3Step 3: Classify the explanation
It identifies one contributing factor, but it is not a complete explanation of the list presented in the source.
- 4Step 4: Improve it
A stronger source-grounded explanation names hardware, data, algorithms, a new wave of investment, and democratization together.
The explanation is partially relevant but incomplete.
Common Misreadings
Common mistake Treating artificial intelligence and deep learning as synonyms.
What it looks likeDeep learning is the entire field of artificial intelligence.
Why it failsAI is the broader field. It includes machine learning and deep learning, as well as approaches that do not involve learning.
Do this insteadDescribe deep learning as an approach within machine learning and machine learning as one approach within AI.
Common mistake Assuming symbolic AI and machine learning are identical.
What it looks likeBoth approaches require programmers to write every decision as an explicit rule.
Why it failsSymbolic AI depends on explicit rules handcrafted by programmers. Machine learning arose as an approach for problems where writing all the rules is extremely difficult.
Do this insteadContrast handcrafted rules with the machine-learning approach used for complex, fuzzy problems.
Common mistake Claiming that explicit rules are always inferior.
What it looks likeRule-based systems cannot solve useful problems.
Why it failsThe source says symbolic AI is suitable for problems whose logic can be described clearly.
Do this insteadMatch explicit rules to clearly described logic, and recognize why complex, fuzzy problems create a different challenge.
Common mistake Explaining the recent rise of deep learning with only one factor.
What it looks likeDeep learning grew recently because of hardware.
Why it failsHardware is named, but the source presents a list of five contributing factors.
Do this insteadInclude hardware, data, algorithms, a new wave of investment, and democratization.
Common mistake Inventing detailed mechanisms for the five factors.
What it looks likeThe source proves that one listed factor was more important than all the others.
Why it failsThe source does not rank the factors or provide detailed mechanisms for each one in this section.
Do this insteadName the complete list and avoid claiming a ranking or mechanism not supplied by the source.
Practice the Classification
A short answer says: Artificial intelligence is machine learning, and deep learning became popular because of algorithms. Identify two problems with this answer and rewrite it as a source-grounded explanation.
Hints
- Check the relationship between AI, machine learning, and deep learning.
- Check whether the explanation lists all five factors named for the recent surge.
- Do not add detailed mechanisms or rankings that are not provided in the source.
- 1Artificial intelligence is the effort to automate intellectual tasks normally performed by humans. It is a broad field that includes machine learning, deep learning, and approaches that do not involve learning. Symbolic AI uses explicit rules and suits clearly described logic. Machine learning arose as an approach for complex, fuzzy problems such as image classification, speech recognition, and language translation, where writing all the rules proved intractable. The recent surge of deep learning is attributed to hardware, data, algorithms, a new wave of investment, and democratization. A complete explanation names all five factors without inventing unsupported rankings or mechanisms.
Key Takeaways
- Artificial intelligence is a broad effort to automate intellectual tasks normally performed by humans.
- Machine learning and deep learning are approaches within AI, not synonyms for the entire field.
- Symbolic AI uses explicit handcrafted rules and fits problems whose logic can be described clearly.
- Machine learning arose for complex, fuzzy problems where writing all the necessary rules is extremely difficult.
- The named factors in deep learning's recent rise are hardware, data, algorithms, investment, and democratization.