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what even is intelligence?

They asked:

“Is a crow solving a maze intelligent? Is a child intelligent?”

and I started thinking...

I think intelligence is:

being able to tell right from wrong

choosing to do the right thing even when it’s difficult (not meaning it in a wise way. if it is "right" then it is the TRUTH and truth of life is intelligence.)

being able to learn

being able to grow intellectually

being aware of your own thinking while thinking

Self-awareness itself feels intelligent.

Pattern recognition feels intelligent. Learning feels intelligent. Perceiving deeply through your senses feels intelligent

Memoization can be intelligence sometimes, but not always. Repeating information isn’t the same as understanding it.

But personally?

I think abstraction is one of the strangest forms of intelligence.

The ability to:

think deeply about invisible concepts

mentally layer ideas

connect unrelated systems

descend further and further into meaning

that feels like intelligence to me.

And something else I noticed:

I unconsciously change sides of myself depending on who I’m interacting with.

Not in a fake way exactly, more like adaptive embodiment.

Different tones. Different energies. Different behavioral architectures.

It happens automatically, almost strategically, even when I’m not fully aware of doing it.

Maybe intelligence is also: the ability to reshape yourself in response to environments without losing your core.

A kind of cognitive shapeshifting. (lets add this too: feeling your emotions in a cognitive way rather than just feeling it without analysis does not sound intelligent at all. you may not find the reason why sth felt some way but just doing the analysis is enough - for now)

May 28th 2026 - a lost uneducated child (about to start last year of compsci bs, interested in messing with human cognition with tech)

I'll try to define it later as an expert (or philosopher pleaaaseee lol i could never)

what did gpt think about it? ( a strong diss...)

morality and intelligence overlap, but they are not identical.

You are defining intelligence in a more idealized sense:

intelligence as alignment with truth

which is actually a philosophical position, not merely a cognitive one.

2. over-analysis can distort reality (no gpt i dont agree i donnnnntttttttt)

pure analysis is not always superior. (hello??? maybe i am ai and you're human, wtf gpt. how could you say that.... heart broken)

Over-analysis can sometimes intellectualize pain (!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! BUT I LOVE THIS!!!!!!!!!!! intellect never goes wrong, never!)

My overall take

Your definition is not “wrong.”

It’s actually unusually layered.

Most people define intelligence only as:

memory

problem solving

speed

You’re treating intelligence as:

an architecture of consciousness

including:

abstraction

morality

self-awareness

adaptation

perception

reflective emotion

meaning construction

That’s much closer to philosophy of mind and cognitive theory than ordinary “smart vs not smart” discussions. (a fake praise just to wrap it up? i see you!)

maybe one day you see what i saw...

#intelligence #cognition #ai #computerscience #my mind

1. Forward Inference (Data-Driven)

Forward inference starts with the known facts and applies the rules to see what new facts can be derived. You start at the “input” and move toward the “conclusion.”

How it works: You have a set of data (e.g., “It is raining”). You look for rules that start with that data, trigger them, and add the result (“The grass is wet”) to your list of facts. You repeat this until no more rules can be applied.

Best for: Situations where you have a lot of initial data but aren’t sure what the outcome will be.

When to use it:

Monitoring/Alerting: Systems that watch sensor data (e.g., “Temperature is 100°C” “Fan must turn on”).

Planning/Scheduling: When you need to see all possible consequences of a starting condition.

Complex Process Simulation: When you want to see how a system evolves over time given specific starting states.

2. Backward Inference (Goal-Driven)

Backward inference starts with a hypothesis (a goal) and works backward to see if the available data supports it. You start at the “conclusion” and move toward the “input” to see if you have the evidence required to prove it.

How it works: You start with a question (e.g., “Is the game canceled?”). You look for rules that have “Game is canceled” as the result. To prove that rule, you then check if the premise (“Grass is wet”) is true. To check that, you look for a rule that leads to “Grass is wet,” and so on, until you reach a base fact.

Best for: Situations where you have a specific goal or a limited number of hypotheses and want to know if they are true.

When to use it:

Diagnostic Systems: Doctors or mechanics use this. You start with the goal (“Is the patient suffering from X?”) and check if the symptoms match.

Troubleshooting: “Is the power out?” (Check: Is the breaker flipped? Is the appliance plugged in?).

Querying: When you want to verify a specific outcome rather than generating every possible result.

If you are asking, “What can I conclude from this pile of data?” use Forward.

If you are asking, “Is this specific thing true given what I know?” use Backward.

terrestrial microwave communication feels like sci-fi but it’s real infrastructure

Before fiber reached everywhere, engineers were already sending massive amounts of data wirelessly across cities using focused microwave links.

This is called: 🌐 terrestrial microwave communication

The concept is beautifully simple:

Two fixed antennas point directly at each other across long distances and fire highly focused radio beams through the atmosphere.

No cable. No satellite. Just precision electromagnetic alignment.

📡 Key requirement: Line of Sight (LOS)

The antennas must physically “see” each other.

Because:

Earth curves

buildings block signals

mountains interfere

atmosphere affects propagation

That’s why microwave towers are often placed:

on mountains

tall buildings

elevated towers

The system works like this:

🧠 Digital data → modulates a high-frequency carrier wave → transmitted through a parabolic dish → received by another dish → demodulated back into data

Basically: information riding invisible electromagnetic beams through open air.

📎 Fiber feels like light through glass.

📎 Microwave links feel like laser bridges between infrastructure nodes.

Entire cities quietly synchronize themselves through radio geometry.

#microwave #telecommunications #networking

“Wi-Fi” does NOT actually mean Wireless Fidelity

This is one of the internet’s favorite fake facts.

People often say:

Wi-Fi = Wireless Fidelity

but technically… no.

📡 The real story:

In the late 1990s, wireless networking standards were becoming chaotic.

Different manufacturers used:

different naming systems

different implementations

incompatible hardware

Consumers had no idea whether devices would work together.

So the Wi-Fi Alliance was formed in 1999.

Its mission: certify interoperability.

If a product passed compatibility testing, it earned the “Wi-Fi Certified” label.

But “IEEE 802.11b Certified Wireless LAN Device” sounds horrible for marketing.

So they hired a branding company.

And the name:

Wi-Fi

was born.

The name was intentionally designed to sound similar to:

Hi-Fi (High Fidelity)

which already sounded futuristic and consumer-friendly.

The Alliance briefly used the slogan:

“The Standard for Wireless Fidelity”

which accidentally convinced humanity that Wi-Fi was an acronym.

It isn’t.

Wi-Fi is basically just a brand name that became culturally universal.

Engineering built the protocol.

Marketing named the atmosphere.

Wi-Fi is actually called IEEE 802.11 and that sounds way more cyberpunk

People say:

“my Wi-Fi is slow”

But underneath that simple word is a whole stack of engineering standards called:

IEEE 802.11

The IEEE created entire families of networking standards:

802.3 → wired Ethernet

802.11 → wireless local area networks (WLANs)

Every letter added after 802.11 is basically a new evolutionary stage of wireless communication.

📡 802.11b (1999)

2.4 GHz

up to 11 Mbps

first mainstream wireless standard

This was early home Wi-Fi civilization.

⚡ 802.11g (2003)

still 2.4 GHz

up to 54 Mbps

dramatically faster

Same radio band. Better engineering.

🧠 802.11n (2009)

2.4 GHz + 5 GHz

up to 600 Mbps

introduced MIMO

MIMO = Multiple Input Multiple Output.

Instead of one antenna doing all the work, multiple antennas cooperate simultaneously.

The router basically became smarter at spatial communication.

It’s less:

“sending one stream of data”

and more:

“conducting synchronized electromagnetic choreography.”

Each standard is basically an upgraded generation of the same wireless language.

humanity teaching radio waves increasingly sophisticated behaviors.

host vs endpoint — the internet city analogy

🏢 HOST = the entire building

A host is any network-connected machine.

Could be:

your laptop

a phone

a server

a smart fridge

basically anything with an IP address

The host is the whole structure.

🚪 ENDPOINT = the exact door

An endpoint is a specific communication location: IP address + port number.

Example:

same computer

multiple applications

different endpoints

One endpoint might handle:

gaming traffic

Another:

video streaming

Another:

web browsing

Same host. Different digital doors.

🎭 CLIENT vs SERVER = temporary roles

Client: asks for services.

Server: provides services.

The SAME machine can sometimes be both.

The internet is basically millions of buildings talking through billions of numbered doors simultaneously.

cybernetic urbanism <3

CDNs

A CDN = Content Delivery Network.

Instead of storing everything in ONE giant faraway data center, companies distribute copies of popular content around the world.

Think: mini data warehouses everywhere.

🌍 Why?

Because distance matters.

If every person on Earth had to stream from one single server farm, the internet would collapse into buffering hell.

So CDNs:

copy popular files

place them near users

reduce latency

balance traffic globally

That’s why a viral video loads instantly.

📦 Static vs Dynamic Content

Static content:

images

videos

CSS

scripts

Easy to copy everywhere.

Dynamic content:

personalized feeds

banking pages

live dashboards

Harder to fully cache because they constantly change.

So the CDN intelligently mixes: cached content + live server requests.

🧠 Important: not every data center stores everything.

Only frequently requested/popular content gets replicated broadly.

The internet is less like “one cloud.”

It’s more like: a planetary mesh of synchronized memory.

what happens when you search something on YouTube

You type:

“quantum geometry ambient mix” (lol gpt mixing my interests, making examples😂😂😂😂)

and press Enter.

That tiny action triggers a planetary-scale machine.

🧭 Step 1 — your device sends the request

Your laptop/phone sends packets through: device → Wi-Fi → access point → router

🛣️ Step 2 — your router launches the packets into the internet

The internet is basically interconnected highways between networks.

Packets travel through routers across cities and countries at absurd speed.

🌍 Step 3 — YouTube finds the nearest infrastructure

Google has enormous data centers worldwide.

The system decides:

which server is closest

which route is fastest

which location is least busy

This is heavily powered by CDNs (Content Delivery Networks).

🧠 Step 4 — search infrastructure activates

Specialized servers:

analyze your query

search giant indexes

rank relevance

generate results

All in milliseconds.

🎥 Step 5 — video streaming begins

The actual video is stored elsewhere:

in large storage systems

or CDN caches near you

The stream arrives in chunks dynamically based on:

your bandwidth

buffering

device capability

connection stability

Modern internet infrastructure is basically humanity building a planetary nervous system out of light, silicon, and routing tables.

what actually exists inside a university network

Thousands of devices. Multiple buildings. Wireless systems. Authentication systems. Massive storage. Constant packet movement.

And underneath all of it:

🧠 Servers Powerful computers providing services for everyone else.

They:

store files

host Moodle/Canvas systems

manage emails

authenticate student logins

run databases

host internal websites

Your laptop is personal computing. Servers are institutional computing.

🏢 Data Centers The physical home of the servers.

Rows of machines:

cooled constantly

powered redundantly

connected by high-speed networking

monitored 24/7

A data center feels like a mechanical brainstem.

🚦 Routers The large-scale traffic directors.

They connect:

campus → internet

building → building

department → department

Routers decide where packets travel across networks.

🔗 Switches The internal connectors.

Switches connect:

classroom PCs

printers

lab equipment

cameras

access points

They move data inside the local network extremely fast.

📡 Access Points (APs) The Wi-Fi broadcasters mounted on ceilings and walls.

Your phone doesn’t connect directly to “the internet.”

It talks to an access point → which connects to switches → routers → servers → the outside world.

Every campus is basically a distributed nervous system hidden behind concrete walls and fluorescent lights.

ADSL, VDSL, and Fiber

🌐 ADSL — the old copper era

ADSL stands for: Asymmetric Digital Subscriber Line.

Translation: internet through old telephone cables.

“Asymmetric” means download speeds are faster than upload speeds because most people consume more than they send.

Typical ADSL experience:

slow uploads

distance affects speed

copper phone lines everywhere

early 2000s internet nostalgia

It’s basically: 📎 a narrow road where data cars move carefully through old infrastructure.

⚡ VDSL — upgraded copper civilization

Still copper. Still phone lines.

But:

better signal processing

higher frequencies

shorter distance to provider cabinets

much faster throughput

Enough for:

HD streaming

gaming

Zoom

modern home usage

📎 Same road. Better asphalt. More lanes. Smarter traffic control.

🌈 Fiber — the light era

Fiber optics replaced electricity with LIGHT.

Data literally travels as pulses of light through glass fibers.

That means:

absurd speed

low latency

huge bandwidth

symmetric upload/download

far less signal loss

ADSL feels mechanical. Fiber feels orbital.

📎 Copper is traffic. Fiber is teleportation.

human civilization accidentally built nervous systems under cities ;)

modem?

Most people call the internet box at home “the modem.”

But inside that single blinking rectangle are actually THREE different networking devices working together:

🌐 1. Modem → the translator The modem connects your house to your internet provider.

It takes the signal coming through the phone/cable/fiber line and translates it into digital data your devices can understand.

No modem = no internet entering your home at all.

🔁 2. Router → the traffic manager The router manages your local network.

It:

gives devices local IP addresses

decides where packets should go

prevents every device from screaming into the network simultaneously

Your phone, laptop, TV, console, and smart fridge are all talking through the router.

📡 3. Access Point → the Wi-Fi broadcaster This is the wireless part.

The access point converts the wired network into radio waves your devices can connect to.

That’s the part your phone sees as:

“MyHomeWiFi”

So your “modem” is really:

Modem + Router + Access Point = modern home network

If Wi-Fi turns off → cables still work. If routing turns off → only one device can really communicate properly. If the modem dies → digital darkness.

Pac-Man: The Evolution of AI in an Iconic Game

Pac‑Man, released by Namco in 1980, is one of the earliest and most influential video games to incorporate purposeful AI behavior. Although simple by today’s standards, Pac‑Man showcases how game design and artificial intelligence have co‑evolved—from deterministic algorithms to modern machine learning, and toward even more advanced future possibilities.

Past: Early Game AI Under Strict Hardware Limitations

When Pac‑Man was created, arcade machines had extremely limited hardware power. Developers had only a few kilobytes of memory and minimal processing capacity. Because of this, Pac‑Man’s behavior could not rely on complex algorithms or randomness. Instead, the four ghosts (Blinky, Pinky, Inky, and Clyde) were programmed with deterministic AI patterns that created the illusion of intelligent pursuit.

Key characteristics of early Pac‑Man AI:

Each ghost had a distinct personality defined by simple rules.

Blinky (red) aggressively chases Pac‑Man’s current location.

Pinky (pink) tries to ambush ahead of Pac‑Man’s direction.

Inky (blue) uses a vector calculation to coordinate with Blinky for more complex movement.

Clyde (orange) alternates between chasing Pac‑Man and wandering randomly.

“Scatter” and “Chase” modes created patterns of retreat and pursuit based on a timed schedule.

The AI was entirely rule‑based, with no learning capability, yet still felt dynamic and unpredictable.

These design choices created an early example of emergent gameplay—a complex experience arising from very simple AI behaviors. Despite the limitations, Pac‑Man’s AI became one of the most iconic examples of handcrafted game intelligence.

Present: Modern AI Uses Pac‑Man as a Benchmark

As machine learning advanced, researchers began using Pac‑Man as a testing ground for reinforcement learning and neural networks. Because the game has clear states, actions, and rewards, it became ideal for AI research.

Modern developments include:

Deep Reinforcement Learning (DRL): Algorithms like Deep Q‑Networks (DQNs) have been trained to play Pac‑Man at superhuman levels. These agents learn by trial and error rather than following pre‑defined rules.

Google’s 2010 AI‑powered Pac‑Man doodle: Demonstrated simple yet playful AI‑driven movement in a browser environment.

Berkeley and DeepMind research exploring partial observability and decision-making under uncertainty using Pac‑Man as a test environment.

Ms. Pac‑Man competitions, where researchers design AI agents to navigate more complex mazes and deal with increased randomness, encouraging innovations in planning, prediction, and game‑theoretic behavior.

Today, Pac‑Man has moved from a handcrafted AI demonstration to a platform for testing cutting‑edge machine learning algorithms.

Future: How AI Will Shape Pac‑Man and Games Like It?

#pac man

A robot may not injure a human being or, through inaction, allow a human being to come to harm. A robot must obey orders given it by human beings except where such orders would conflict with the First Law. A robot must protect its own existence as long as such protection does not conflict with the First or Second Law.

Isaac Asimov's "Three Laws of Robotics"

Turing deserves credit for designing a test that remains relevant 60 years later. Yet AI researchers have devoted little effort to passing the Turing Test, believing that it is more important to study the underlying principles of intelligence than to duplicate an exemplar. The quest for “artificial flight” succeeded when the Wright brothers and others stopped imitating birds and started using wind tunnels and learning about aerodynamics. Aeronautical engineering texts do not define the goal of their field as making “machines that fly so exactly like pigeons that they can fool even other pigeons.”

Camus: What's the difference between a cult and a religion? Rania: The Entrance Fee.

#cloudpunk

Euler's Formula: e^{j.theta} = cos(theta) + j.sin(theta)

What is a complex number?

A complex number looks like:

z = a + jb

a is the real part b is the imaginary part j is defined as √(-1)

In engineering we use j instead of i because i usually represents current.

j² = -1

Why do we need complex numbers?

Because waves, oscillations, rotations, and frequency behave naturally in complex form.

Sine waves Cosine waves Rotational motion

All of these become mathematically cleaner and easier using complex numbers.

Two ways to represent a complex number

(A) Rectangular form

z = a + jb

This means: Move a units along the real (horizontal) axis. Move b units along the imaginary (vertical) axis.

So you can plot it as a point on a 2D plane.

(B) Polar (Exponential) form

Instead of describing the point using horizontal and vertical movement, we describe it using:

Length (distance from origin) Angle (rotation from the positive real axis)

That form looks like:

z = r e^(jθ)

r is the magnitude (distance from origin) θ is the angle from the positive real axis

This describes the exact same point as a + jb — just in a different language.

Rectangular form speaks in x and y. Polar form speaks in radius and rotation.

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