Deductive reasoning: like a detective, start with clues and end up with a conclusion.
Inductive reasoning: use a specific conclusion to draw broader conclusions. It is sunny outside, therefore I don’t need an umbrella. It is sunny ouside, therefore I don’t need a sweatshirt. It is sunny, so I need sunglasses.
- Deductive equivalent: I don’t need an umbrella, I don’t need a sweatshirt, I do need my sunglasses - it must be sunny.
Deductive deals with certainty, inductive with probability, abductive with guesswork
AI vs ML
AI is where a machine seems “human like and can imitate human behavior”
Define “artificial”: made by humans, not arising from natural causes
AI is an machine developed by humans that has the capacity to think based on data. It is “intellegent” compared to traditional programming because of its ability to have dynamic logic statements
ML is a type of AI that is dynamic based on data. Over time as data grows and changes, ML programs change how they behave.
“ML can go far beyond human capabilites” yes, in very specific specialized ways, like a calculator can. Computers can store an analyze data in quantities that most human brains do not take pleasure in doing.
Imperative vs Declarative programming
- Imperative focuses on how to do something, declarative focuses on what to do
- In Java, using a for loop to filter manually is imperative. Using a stream to filter functionally is declarative.
Qualitative vs Quantitative
- Quantitative is measurable, based on objective data, can be counted, measured, expressed using numbers
- Logical and consistent regardless of circumstance - if data is the same, result is the same
- Qualitative is descriptive and conceptual. Qualitative data can be categorized based on traits and characteristics
- Good breakdown: qualitative vs quantitative
- Example: A bookcase
- Qualitative data:
- Dark brown
- Golden knobs
- Smooth finish
- Quantitative data:
- 3ft long
- Weighs 100lbs
- costs $1500
- “made of oak” site lists as Qualitative, but I would argue this is quantitative. Oak is a testable, measurable thing. Determining Qual vs quan is dependent on the definition of Truth. If it is true that an oak is a specific, unchanging, repeatedly verifiable object in the world - it can be quantified.
- “Built in italy” - same, that is unchanging, where it is built, assuming that statement is true.
- “made of wood” - same arguments as above
- has 3 shelves and 2 cabinets * <- qual depending on definition of cabinet/shelf, what if I consider the top a shelf, or the bottom row a shelf
- Qualitative is typically unstructured data
- Ex: a photo. Can make it “structured” by defining metadata about it, the metadata will be qualitative since it’s based on perception
Empirical & Theoretical
- Empirical is verifiable by observation or experience, not based on idea or pure logic
- Theoretical, based on thought, logic, hypothesis
Reasoning in general, whether it be inductive or deductive, is dependent on the set of input observed and the foundation of truth.
- Use trees as an example. “Trees are tall”.
- I have looked at 20 trees, all of them are tall. Therefore trees are tall.
- I have looked at 20 trees, some of them are tall. Therefore trees are sometimes tall.
- I have looked at 20 trees, all of them are short. Therefore trees are never tall.
- Trees are tall. I have looked at 20 trees, all of them are tall. Theory is correct.
- Trees are tall. I have looked at 20 trees, some of them are tall. Theory is somewhat correct.
- Trees are tall. I have looked at 20 trees, all of them are short. Theory is false.
- There is a minimum amount of required truth that must be agreed upon in order for this research to even be possible. The definition of: tree, tall, short.
- Tree, tall, short are qualitative variables. All, some, 20 are quantitative variables.
Using the statement, “Trees are tall”, build a qualitative analysis computer program.
- First define the qualitatve and quantitative variables in the statement
- Qualitative: tall
- Quantitative: tree
it is critical to define moral and creation-based standards for what is quantitative
- What God has created and defined cannot be changed. There are things on earth that have bounds and strict definitions. Nature, biology, Truth. To allow these to become qualitative enables
- In tree example above: It is important to draw the line with qualitative and quantitative. To argue that “tree” is opinon based is dangerous and incorrect. There are bounds to what defines a tree.
Reducing possibilities into reasonable conclusions
Abductive Reasoning API
- Use human knowledge (knowledge based system) to aid AI decisions, to enable “making the best guess”.
- Build an API the tracks relationships between entities, then enable customer to provide written statements for how to make decisions when certain entities are searched.
- Example: build a graph of campgrounds and all their related attributes.
- Customer: selects tags to build a relationship, “Texas, Hill Country, short stay” tie the relationship to an action -> “suggest, hide, etc” -> action points to the right side of relationship: “Texas, botique, 30mile radius of Dallas”
- Customer can build tags for relationship. Example, “30 mile radius of Dallas” would be a distance-based rule centered in dallas. “Short stay” would be a stay length rule for X number of days.
- API can use tags as input data for a request, or they can use entire JSON blobs.
- ex, to market this to Campspot - to plug into the API, just send over the JSON of search request or result. ARPI will scan for known keywords (based on category/tag configuration database) and fetch relations
HAD - Human Aided Design
- To achieve successful abductive AI, must lean on human wisdom to make educated guesses
- Throws the idea of runaway AIs away, the idea that AIs are more than machines that are slave to humans, totally dependent.