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These designs have become so complex that it's likely no one person understands the entire processor, or perhaps only a few people. And it's going to get very scary when AI is used for some portions (or maybe all) of the chip, that takes knowing control away from the designers. I have trust issues here lol.
ha, what could possibly go wrong (great movie: the creator)
Originally Posted by Margate330
if you want to hide a back door or something nefarious, it will be in the hardware(chip) where nobody will ever find it. Lol
Originally Posted by Margate330
Oh, I can see where this is going and I'll leave it there. Lol Our consumption of tech will be the downfall of humaity
Originally Posted by Margate330
I work with asics everyday,...
ergo, aren't you part of the problem contributing to the downfall of humanity?
and why would it be the downfall if it can be kind, patient, and full of wisdom?
right now, we are the frogs in the proverbially slowly boiling water. what's more, we're the ones slowly ratcheting up the temperature, by either making the tech or consuming it, or both. but we will learn to live with ai, and ai likely won't see a benefit in wiping out humans. likely, not certainly, haha.
Originally Posted by geko29
Like the one last year that became aware of its scheduled lifecycle, copied itself to a remote system, and then lied when asked about it directly?
i heard about that... got a source for more info?
Last edited by bitkahuna; Feb 27, 2025 at 05:56 AM.
future peoples will look back on cars driving themselves and say what was the big deal and who the hell would WANT to drive in dense traffic or deal with a-hole human drivers.
future peoples will look back on cars driving themselves and say what was the big deal and who the hell would WANT to drive in dense traffic or deal with a-hole human drivers.
People havent figured it out yet.
We're just training our replacements. Lol
Robots will drive themselves to work.
Last edited by Margate330; Feb 27, 2025 at 06:12 AM.
Hey man, just make sure you don't get a visit from Sarah Connor......
I know, right?! 😁
Ok, I have a dumb idea I like to run by you all.
Probably my dumbest yet.
If I understand the posts here on CL, Tesla uses cameras for self driving?
If this Is true...
If...
Can Elon possibly expect his AI to be good enough that it can see with eyes like a conscious being?
Is this the direction, I wonder.
As in, the cameras are the eyes and AI is processing what it "sees" it can drive autonomously?
I realize just like us humans, we relay on hearing and other senses to navigate our surroundings so other "sensors" may be needed to fill in what the eyes can't see.
Blabbing over. Lol
Last edited by Margate330; Feb 27, 2025 at 08:47 AM.
Ok, I have a dumb idea I like to run by you all.
Probably my dumbest yet.
If I understand the posts here kn CL, Tesla uses cameras for self driving?
If this Is true...
If...
Can Elon possibly expect his AI to be good enough that it can see with eyes?
As jn, the xameras are the eyes and processing whatnut "sees" it can drive autonomously?
I realize just like us humans, we relay kn jearjng and other senses to navigate our surroundings so other "sensors" may be needed to fill in what the eyes can't see.
Blabbing over. Lol
I can't speak for other self driving software and cars, I don't know anything about how they work, mistakes they make etc, but I've been using FSD the last few months, and it's pretty freaking amazing, and it's night and day from when I first used it last year. It does occasionally make some small mistakes, but corrects itself. My brother in law uses it full time, and he told me it rarely makes mistakes. He trusts it blindly, which I'm not even close to doing
In this post, I'll talk a little bit about the basics of EV's, their architecture, and what makes them technically better than their ICE counterparts. This is not an attack on ICE, and it doesn't mean ICE vehicles are bad, I believe EV architecture is the next evolutionary step for automobiles. So with that, the basics explained.
Technical aspects:
Electric vehicles rely on electric propulsion systems rather than internal combustion engines. Their performance, efficiency, and sustainability depend on several key technical components, including power electronics, energy storage, and drivetrain design.
Powertrain Architecture
EVs use an electric powertrain, which consists of the following main components: A. Battery Pack (Energy Storage)
Type: Most EVs use lithium-ion (Li-ion) batteries due to their high energy density, longevity, and efficiency.
Capacity: Measured in kilowatt-hours (kWh), defining the vehicle’s range.
Configuration: Batteries are typically arranged in modules and packs, placed under the vehicle floor for better weight distribution.
Thermal Management: Essential for preventing overheating, using liquid cooling or air cooling systems.
B. Electric Motor
Types of Motors Used:
Permanent Magnet Synchronous Motor (PMSM): Used in Tesla, Nissan Leaf, etc.; efficient and high torque.
Induction Motor (IM): Used in some Tesla models; no permanent magnets, offering robustness.
Switched Reluctance Motor (SRM): Less common, simple design but lower efficiency.
Brushless DC Motor (BLDC): Found in smaller EVs and e-bikes.
Efficiency: Electric motors can reach 90-95% efficiency, much higher than internal combustion engines (~30-40%).
C. Inverter
Converts DC (direct current) electricity from the battery to AC (alternating current) for the motor.
Controls the motor speed and torque by regulating voltage and frequency.
Uses power electronics (IGBTs or MOSFETs) to ensure smooth power delivery.
D. Charging System
Onboard Charger (OBC): Converts AC power from the grid to DC to charge the battery.
Charging Levels:
Level 1 (120V AC, ~1.5 kW): Standard home outlet; slow (8-20 hours).
Level 2 (240V AC, ~7-22 kW): Faster home/public charging (4-8 hours).
DC Fast Charging (~50-350 kW): Can charge 80% in 20-40 minutes.
Bidirectional Charging: Some EVs support Vehicle-to-Grid (V2G) and Vehicle-to-Load (V2L), enabling energy feedback to the grid or powering external devices.
Drivetrain Configurations
Unlike conventional vehicles, EVs can have different drivetrain architectures:
Single Motor, Front-Wheel Drive (FWD) – Common in economy EVs.
Single Motor, Rear-Wheel Drive (RWD) – Provides better handling (e.g., Tesla Model 3 RWD).
Dual Motor, All-Wheel Drive (AWD) – Two motors (front & rear) improve traction and acceleration.
Quad-Motor (4WD) – High-performance setups with independent control for each wheel (e.g., Rivian R1T, Tesla Cybertruck).
3. Energy Efficiency & Regenerative Braking A. Regenerative Braking
Converts kinetic energy back into electrical energy during braking.
Increases energy efficiency and extends battery life.
Usually integrated with traditional hydraulic braking for safety.
B. Energy Management & Control Systems
Battery Management System (BMS): Monitors voltage, current, temperature, and state of charge (SoC).
Powertrain Control Module (PCM): Optimizes energy delivery for efficiency and performance.
Thermal Management System (TMS): Regulates temperature for battery, motor, and inverter using coolant, heat pumps, or air cooling.
Battery Technology & Performance Factors
A. Types of Batteries in EVs
Battery Degradation Factors
Charge Cycles: Li-ion batteries typically last 1000-3000 cycles before noticeable degradation.
Temperature: Extreme heat/cold affects performance and longevity.
Depth of Discharge (DoD): Keeping charge between 20-80% extends battery life.
5. Charging Infrastructure & Grid Impact A. Charging Networks
Superchargers (Tesla), Electrify America, Ionity, ChargePoint, etc.
Home Charging (AC), Public DC Fast Charging
B. Grid Load & Smart Charging
Peak Load Issues: Large-scale EV adoption requires grid upgrades.
Smart Charging: Time-based charging (off-peak hours) reduces grid strain.
Renewable Integration: Solar/wind power can charge EVs sustainably.
6. Future Trends in EV Technology
Solid-State Batteries – Higher energy density, faster charging, longer life.
Silicon Anodes & Lithium-Sulfur Batteries – Potential for lighter and more efficient batteries.
Wireless Charging & Road-Based Charging – Inductive charging while driving.
Artificial Intelligence (AI) for Energy Management – Predictive analytics for battery health, navigation, and autonomous driving.
Sustainable Battery Recycling & Second-Life Applications – Using old EV batteries for energy storage systems (ESS).
EVs are driven by advancements in battery technology, power electronics, and smart energy management systems. They offer high efficiency, reduced emissions, and lower operating costs compared to traditional combustion vehicles. Future developments in solid-state batteries, wireless charging, and AI-driven power management will further enhance their viability.
EVs are more efficient than gasoline vehicles because they convert more of the energy from their power source into actual motion. Here’s a simple breakdown: 1. Energy Conversion Efficiency
Electric Vehicles (EVs): About 85-90% of the energy from the battery goes to moving the wheels.
Gasoline Cars: Only 25-30% of the energy in gasoline is used to move the car; the rest is lost as heat.
Gasoline engines burn fuel, producing heat. A lot of this energy is wasted in the form of exhaust and engine heat.
EVs use electric motors, which directly convert electrical energy into motion with minimal energy loss.
2. Regenerative Braking
EVs recover energy when braking, converting it back into electricity to recharge the battery.
Gasoline cars lose all braking energy as heat, wasting it.
3. No Idling Wastage
Gasoline engines keep running when stopped (like at a red light), burning fuel unnecessarily.
EVs use no energy when not moving (except for minor accessories like lights or AC).
4. Simpler Drivetrain
EVs don’t have complicated mechanical parts like transmissions, exhaust systems, and fuel pumps, which reduce efficiency in gasoline cars.
The direct drive system in EVs means fewer energy losses.
EVs drive smoother than gasoline vehicles due to several key reasons: 1. Instant Torque & No Gear Shifts
Electric motors provide instant torque (force that moves the car) as soon as you press the accelerator, without any lag.
Gasoline cars rely on an internal combustion engine (ICE) and multi-gear transmission, which takes time to shift gears and deliver power.
No gear shifts in most EVs means no jerks, unlike gasoline cars that can feel rough when shifting gears.
2. No Vibrations & Less Noise
Gasoline engines have many moving parts (pistons, crankshaft, transmission) that cause vibrations and noise.
Electric motors have very few moving parts, leading to a quieter and smoother ride with almost no vibrations.
3. Regenerative Braking Provides a Seamless Stop
EVs use regenerative braking, which slows the car smoothly without needing traditional braking all the time.
Gasoline cars rely on mechanical brakes that can feel jerky or rough when stopping.
4. Low Center of Gravity for Better Stability
EVs place their heavy battery packs at the bottom of the car, lowering the center of gravity.
This reduces body roll and makes handling smoother, especially in turns.
Gasoline cars have hundreds of moving parts that interact (engine, transmission, exhaust system, fuel injectors, etc.), sometimes causing rough performance.
EVs have a simpler drivetrain with fewer components, making the driving experience more predictable and fluid.
All of this adds up to a quieter, more comfortable, and seamless driving experience.
Ok, I have a dumb idea I like to run by you all.
Probably my dumbest yet.
If I understand the posts here on CL, Tesla uses cameras for self driving?
If this Is true...
If...
Can Elon possibly expect his AI to be good enough that it can see with eyes like a conscious being?
Is this the direction, I wonder.
As in, the cameras are the eyes and AI is processing what it "sees" it can drive autonomously?
I realize just like us humans, we relay on hearing and other senses to navigate our surroundings so other "sensors" may be needed to fill in what the eyes can't see.
Blabbing over. Lol
You just described how Tesla FSD works. Elon talked about it:
Musk described Tesla’s FSD system transitioning to a fully neural network-based approach, saying, “The whole system will be neural network, photons in to controls out.” This reflects the idea of FSD taking raw visual input (like photons hitting a camera, analogous to human eyes) and processing it through a neural network to produce driving decisions, much like a human brain interprets what the eyes see.