# An Infinite Set of Attributes?

How many attributes does the string “(31 - 7.5) * (2 + 4)” have? Or, which is equivalent, in how many ways can that string be described? I argue that the number of ways I can describe the string “(31 - 7.5) * (2 + 4)” is infinite. Yes, infinite. Here are some attributes I can use to describe that string:

the number of multiplication (‘*’) operators
the number of odd numbers
the number of integers
the number of operands
the number of operators
the number of parentheses
the number of spaces
the length with spaces
the length without spaces
the number of spaces after an odd number
……

# What exactly do we mean by ‘hybrid’?

What does the binary sequence “010010110100110001001110” represent? Well, it can be a sequence that represents the ASCII codes of the letters ‘K’, ‘L’ and ’N’ (in that order). But it can also be a sequence that has three binary inputs to a module doing binary arithmetic and is expecting two binary values and a code for some binary operation. The input could also be the code for some color, where the three 8-bit sequences correspond to the values of ‘R’, ‘G’ and ‘B’. And so on… the point here is that inputs are received differently by different input channels.

# Reward is NOT Enough, and Neither is (Machine) Learning

This is a short and critical commentary on a recently published paper entitled “Reward is Enough”, the main thesis of which is that most, if not all, intelligent behavior is the result of a generic objective of maximizing reward. We believe the paper’s thesis and the subsequent claims are false, and precisely because they are based on false assumptions. We will argue instead that reward is not enough, by making the stronger claim that learning itself (in all its paradigms) is not enough. …

# Knowing-How vs. Knowing-That

Philosophers have long recognized the difference between two types of knowledge: knowing-how and knowing-that, where (roughly and very informally) the former is typically associated with skills and abilities, and the latter is associated with propositions (truths/established facts). In our everyday discourse we use the word ‘know’ for both types of knowledge, which creates some confusion. So, for example, we say things like:

# The Missing Text Phenomenon, Again: the case of Compound Nominals

(Last updated April 28, 2021)

Note: The literature on ‘compound nominals’ is immense, and you will find the same phenomenon discussed under the label ‘compound nominals’ or ‘nominal compounds’ — so, I will use these terms interchangeably.

# What are Nominal Compounds and why do they Matter?

In simple words, a nominal compound (henceforth, NC) is 0 or more adjectives followed by 1 or more nouns. You can think of it as some subject or topic of discussion (semantically, an entity) that can fill some ‘slot’ in a larger discourse (subject, agent, location, theme, etc.) So while I can discuss a certain ‘system’ I can also discuss a ‘computer system’…

# The Missing Text Phenomenon, Again: the case of Compound Nominals

Note: The literature on ‘compound nominals’ is immense, and you will find the same phenomenon (which we will describe shortly) discussed under the label ‘compound nominals’ or ‘nominal compounds’. To decide which one to use here I consulted Google. The phrase ‘nominal compounds’ resulted in 21,900,000 results while the phrase ‘compound nominals’ resulted in 44,200,000 results (almost exactly double) — so, I will stick with ‘nominal compounds’.

# What are Nominal Compounds and why they Matter?

In simple words, a nominal compound (henceforth, NC) is 0 or more adjectives followed by 1 or more nouns. You can think of it as some subject or topic of discussion (semantically, an…

# A Personal Prologue

About four years ago I joined one of the coolest start-ups in Silicon Valley. For me, ‘cool’ here means that I was around some of the brightest people I ever met — with backgrounds in neuroscience, astrophysics, AI, computational mathematics, cognitive linguistics, … the group of (very) passionate and learned AI’ers was very vocal and eager to discuss, debate, learn and state an opinion, and I just love that environment. …

# Generalization and Concepts

I can still recall the amazement I felt the first time it was fully explained to me how important our ability to abstract and generalize instances into abstract concepts was to our cognitive development. Without this brilliant invention we could not have had the cognitive abilities that, by far, surpass those of all other species.

Imagine that were not the case and that we could only reason at the instance (object) level. It would mean, literally, that every time we felt like eating a banana, we would need to taste it a bit to see if we would like how…

# Of course, every thing “isa” thing

The image above might, at first read, sound silly. You might be saying: of course “everything is a thing” — so what? All I’m saying is that “every x is an x” which is vacuously true, because it is an empty statement with no information content to speak of.

Well, maybe — so far. But the statements in the image above do say, ontologically, something that is not trivial. If relations (friendship), events (war), properties (darkness), activities (dancing), states (death), etc. are objects like like birds and dogs, then, like any other object, relations can in turn participate in relations…

# What Newell, Knuth, and Turing can tell John Searle about Semantics: It’s all about Levels of Abstraction

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(last revised: March 20, 2021)

Not that the Chinese Room Argument (CRA) that the philosopher John Searle launched in 1980 as an attack on Computationalism needs another debate, but all the (in my opinion successful) rebukes of CRA concentrated on the ‘mechanics’ of the experiment missing in my view the central problem in Searle’s argument.

# The Most Common Reply to CRA

Luminaries such as Dan Dennet, Jerry Fodor, etc. mostly used the Systems Argument: “while [John Searle] understands only English, when he is combined with the program, scratch paper, pencils and file cabinets, they form a system that can understand Chinese.” The technical details of…

## Walid Saba, PhD

Principal AI Scientist, ONTOLOGIK.AI

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