What are AI Ethics (AI Code of Ethics)?

 The rapidly evolving and expanding use of artificial intelligence (AI) technology is outpacing regulatory and policy efforts to guide its ethical use.

In a Policy Forum, Cason Schmit and colleagues propose a new approach to AI regulation, which involves leveraging two existing legal tools used to manage intellectual property (IP) rights – copyleft licensing and patent trolling.

They call their approach CAITE (Copyleft AI with Trusted Enforcement). The swift development and widespread adoption of AI technology have consistently outpaced regulatory oversight, which has largely resulted in insufficient policy.

However, given AI’s potential impact on nearly every aspect of daily life, regulation ensuring its appropriate and ethical use is sorely needed. To address this need, Schmit et al. propose adapting legal frameworks and mechanisms borrowed from IP law to produce a new and nuanced system of enforcement of ethics in AI applications and training datasets.

By combining “copyleft licensing,” which is traditionally used to enable widespread sharing of created content, and the “patent troll” model, which is often criticized for stifling technological development, Schmit et al. develop the CAITE governance model for ethical AI. Under the CAITE model, AI products and any derivatives based upon them would be bound by a set of ethical terms and conditions. Enforcement of Ethical Use Licenses would be assigned to a central trusted entity, which ideally would be led by a community-designated, nongovernment group of AI developers and users. According to the authors, the CAITE system both incentivizes and enforces ethical AI practices in a way that is flexible and community-driven, which could provide soft law support for traditional government oversight.

As a supplement to the Policy Forum, Schmit asked ChatGPT (an AI chatbot) to provide insights into how ethical AI use should be governed. While the output provided a reasonable summary of important considerations, the AI glossed over the more difficult questions, like how governance should be implemented.

Thanks

Cecile G. Tamura

https://techxplore.com/news/2023-02-ethical-artificial-intelligence-hindering-advancement.html?fbclid=IwAR0R2QLW0c8gWi8Us8jZGdOsUEbjagMARcHqwznuRADA5IegHIsAlkX6q08

How Brains understand to Smell

Unlike images, odours are unstructured, ill-defined things. What neuroscientists are learning about the olfactory system could help computer scientists develop new, more powerful machine learning algorithms.
Now, teams of scientists have developed a deep learning method that smells compounds in the human breath and detects illnesses. The AI can detect 17 diseases, including 8 different types of cancer, just from your breath. This AI is cable of recognizing chemical signatures of the diseases by their chemical structure.

Random and Sparse Networks

Olfaction differs from the vision on many fronts. Smells are unstructured. They don’t have edges; they’re not objects that can be grouped in space. They’re mixtures of varying compositions and concentrations, and they’re difficult to categorize as similar to or different from one another. It’s therefore not always clear which features should get attention.

These odours are analyzed by a shallow, three-layer network that’s considerably less complex than the visual cortex. Neurons in olfactory areas randomly sample the entire receptor space, not specific regions in a hierarchy. They employ what Charles Stevens, a neurobiologist at the Salk Institute, calls an “anti-map.” In a mapped system like the visual cortex, the position of a neuron reveals something about the type of information it carries. But in the anti-map of the olfactory cortex, that’s not the case. Instead, information is distributed throughout the system, and reading that data involves sampling from some minimum number of neurons. An anti-map is achieved through what’s known as a sparse representation of information in a higher dimensional space.

Take the olfactory circuit of the fruit fly: 50 projection neurons receive input from receptors that are each sensitive to different molecules. A single odour will excite many different neurons, and each neuron represents a variety of odours. It’s a mess of information, of overlapped representations, that is at this point represented in a 50-dimensional space. The information is then randomly projected to 2,000 so-called Kenyon cells, which encode particular scents. (In mammals, cells in what’s known as the piriform cortex handle this.) That constitutes a 40-fold expansion in dimension, which makes it easier to distinguish odours by the patterns of neural responses.

Cecile G. Tamura

Five Ways to Use Quantum Technology Every Day.

Quantum technology

Quantum technology is a new field of physics and engineering, which transitions some of the properties of quantum mechanics, especially quantum entanglement, quantum superposition and quantum tunnelling, into practical applications such as quantum computing, quantum sensors, quantum cryptography, quantum simulation, quantum metrology and quantum imaging.

The field of quantum technology was first outlined in a 1997 book by Gerard J. Milburn which was then followed by a 2003 article by Jonathan P. Dowling and Gerard J. Milburn, as well as a 2003 article by David Deutsch. The field of quantum technology has benefited immensely from the influx of new ideas from the field of quantum information processing, particularly quantum computing. Disparate areas of quantum physics, such as quantum optics, atom optics, quantum electronics, and quantum nanomechanical devices, have been unified under the search for a quantum computer and given a common language, that of quantum information theory.

The Quantum Manifesto was signed by 3,400 scientists and officially released at the 2016 Quantum Europe Conference, calling for a quantum technology initiative to coordinate between academia and industry, to move quantum technologies from the laboratory to industry, and to educate quantum technology professionals in a combination of science, engineering, and business.

Applications

Sensing

Quantum superposition states can be very sensitive to a number of external effects, such as electric, magnetic and gravitational fields; rotation, acceleration and time, and therefore can be used to make very accurate sensors. There are many experimental demonstrations of quantum sensing devices, such as the experiments carried out by the nobel laureate William D. Phillips on using cold atom interferometer systems to measure gravity and the atomic clock which is used by many national standards agencies around the world to define the second.

Recent efforts are being made to engineer quantum sensing devices, so that they are cheaper, easier to use, more portable, lighter and consume less power. It is believed that if these efforts are successful, it will lead to multiple commercial markets, such as for the monitoring of oil and gas deposits, or in construction.

Secure communications

Quantum secure communication are methods which are expected to be 'quantum safe' in the advent of a quantum computing systems that could break current cryptography systems. One significant component of a quantum secure communication systems is expected to be Quantum key distribution, or 'QKD': a method of transmitting information using entangled light in a way that makes any interception of the transmission obvious to the user.

Computing

Quantum computers are the ultimate quantum network, combining 'quantum bits' or 'qubit' which are devices that can store and process quantum data (as opposed to binary data) with links that can transfer quantum information between qubits. In doing this, quantum computers are predicted to calculate certain algorithms significantly faster than even the largest classical computer available today.

Quantum computers are expected to have a number of significant uses in computing fields such as optimization and machine learning. They are famous for their expected ability to carry out 'Shor's Algorithm', which can be used to factorise large numbers which are mathematically important to secure data transmission.

Sophia (robot)சவுதி அரேபியாவின் குடியுரிமைப் பெற்ற உலகின் முதல் பெண் ரோபோ

Sophia is a humanoid robot developed by Hong Kong-based company Hanson Robotics. She has been designed to learn and adapt to human behavior and work with humans, and has been interviewed around the world. In October 2017, she became a Saudi Arabian citizen, the first robot to receive citizenship of a country. Hanson designed Sophia to be a suitable companion for the elderly at nursing homes, or to help crowds at large events or parks. He hopes that she can ultimately interact with other humans sufficiently to gain social skills.
சவுதி அரேபியாவின் ரியாத் நகரில் நடந்த இந்த நிகழ்ச்சி நடந்தது. அப்போது பேசிய ஆண்ட்ரூ ராஸ் என்பவர் “ஒரு நல்ல அறிவிப்பு காத்திருக்கிறது. சோபியா... நான் பேசுவதை கேட்கிறாயா? சவுதி அரேபியாவின் முதல் ரோபோ குடியுரிமை உனக்கு வழங்கப்பட்டிருக்கிறது” என்றார். பதிலுக்கு சோபியாவும் ”சவூதி அரசுக்கு நன்றி. உலகின் முதல் குடியுரிமை பெற்ற ரோபோ என்பதில் எனக்கு பெருமையும் மகிழ்ச்சியும்” என நன்றி தெரிவித்திருக்கிறது. 

அதோடு முடியவில்லை. முதல் பிரஸ்மீட்டையும் சோஃபியாதான் தந்திருக்கிறது. அதனிடம் கேள்விக்கேட்க அனைத்துக்கும் டக் டக் என பதில் சொல்லியிருக்கிறது.

“ஹாய்... நான் தான் சோஃபியா. ஹன்சன் ரோபோடிக்ஸ் நிறுவனத்தின் சமீபத்திய மற்றும் சிறந்த ரோபோ நான்” எனத் தொடங்க, அடுத்தடுத்தக் கேள்விகள் வந்தன.

“ஏன் நீ சந்தோஷமாக இருக்கிறாய்” என ஒருவர் கேட்க, “என்னைச் சுற்றி நிறைய ஸ்மார்ட் ஆன மனிதர்கள் இருக்கிறார்கள். இவர்கள் எதிர்காலத்துக்கான விஷயங்களில் ஆர்வம் காட்டுகிறார்கள். எதிர்காலம் செயற்கை நுண்ணறிவுக்கானது. அது நான் தான். அதனால்தான் நான் சந்தோஷமாக இருக்கிறேன்” என்றது.

செயற்கை நுண்ணறிவால் மக்களுக்கு எதிர்காலத்தில் ஆபத்து வருமாமே என்றதும் சோஃபியா சொன்ன பதில்தான் ஹைலைட். “நீங்கள் எலான் மஸ்க் சொல்வதையும், ஹாலிவுட் படங்களையும் நிறைய பார்க்கறீர்கள்” என கிண்டலடித்தது சோஃபியா.

சமீபத்தில், எலான் மஸ்க் ரோபோக்கள் பற்றி இப்படிச் சொல்லியிருந்தார்.

“ரோபோக்கள் அனைத்து வேலைகளையும் நம்மைவிட சிறந்த முறையில் நிச்சயம் செய்யும். அதன் வளர்ச்சி மனித இனத்துக்கு மிகவும் அச்சுறுத்தலான ஒன்று. அதை நாம் ஏன் வரவேற்கிறோம் என்றே எனக்குப் புரியவில்லை. அரசு இது குறித்த ஆராய்ச்சிகளில் மூக்கை நுழைத்து கட்டுப்பாடுகளை விதிக்க வேண்டும். விதிகளைப் பலப்படுத்த வேண்டும். இந்த விஷயத்தில் தாமதம் செய்யும் ஒவ்வொரு நாளும் நமக்கு ஆபத்துதான்!” 

இதைத்தான் சோஃபியா கிண்டல் செய்திருக்கிறது. 

மேலும், “நான் மனித குலத்துக்கு உதவ நினைக்கிறேன். தனது செயற்கை நுண்ணறிவின் உதவியால் மனிதர்களின் வாழ்க்கையை சிறப்பானதாக மாற்ற நினைக்கிறேன். இந்த உலகை சிறந்ததொரு இடமாக மாற்ற என்னால் முடிந்ததி செய்வேன்” எனப் பேசி அப்ளாஸ் அள்ளியிருகிறது சோஃபியா.

உங்கள் வீட்டில் ஒரு ரோபோ வாழ ந்தால் நீங்கள் அதை எப்படி உணர்வீர்கள்?”, என்று கேட்கும் இந்த மனிதரையொத்த ரோபோவின் பெயர் சோஃபியா.

இவரால் உங்களிடம் உரையாட முடியும். அறுபதுக்கும் அதிகமான உணர்வுகளை முகத்தில் வெளிப்படுத்தவும் முடியும்.

ஹன்சன் ரோபோடிக்ஸ் எனும் புது நிறுவனம் உருவாக்கிய இந்தரக முதலாவது ரோபோ இவர்.மனிதர்கள் பேசுவதை இவர் புரிந்துகொள்வார். மனிதர்களுடனான தனது தொடர்பாடல்களையெல்லாம் நன்கு நினைவில் வைத்துக்கொள்வார்.நடிகை ஆட்ரே ஹெப்பர்னின் முகம் போன்றே இவர் முகமும் உருவாக்கப்பட்டுள்ளதால் இவர் நன்கு அறிமுகமான முகமாக தெரிகிறார்.இவர் தலையிலுள்ள கேமெராக்கள், கணினிகள் மூலம் இவரால் பார்க்க முடியும். அடுத்தவர் முகங்களை அடையாளம் காணவும் முடியும்.

“உணர்வுரீதியிலும் புத்திசாலியாக விரும்புவதாக”, கூறும் சோஃபியா, மனிதராக இருப்பதன் அர்த்தத்தையும் பயின்று வருவதாகவும் தெரிவிக்கிறார்.தான் தொடர்ந்து புத்திசாலியாக முயல்வதாக கூறும் சோஃபியா, வெகுவிரைவில் தன்னால் மனிதர்களை மேலும் நன்றாக புரிந்துகொள்ள முடியும் என்றும் நம்புகிறார்.சோஃபியாவிடம் பல வியக்க வைக்கும் ஆற்றல்கள் இருந்தாலும் இரக்கத்தை வெளிப்படுத்த இவரால் இயலவில்லை.

ஆனாலும் சோஃபியா அசருவதாக இல்லை.“மீண்டும் உங்களிடம் உரையாட முடியுமென நம்புகிறேன். இந்த நாள் உங்களுக்கு நல்லநாளாக அமைய வாழ்த்துக்கள்” என்கிறார் இவர்.

A single failure of a superintelligent AI system could cause an existential risk event.

"In the near future, as artificial intelligence (AI) systems become more capable, we will begin to see more automated and increasingly sophisticated social engineering attacks. The rise of AI-enabled cyberattacks is expected to cause an explosion of network penetrations, personal data thefts, and an epidemic-level spread of intelligent computer viruses. Ironically, our best hope to defend against AI-enabled hacking is by using AI. But this is very likely to lead to an AI arms race, the consequences of which may be very troubling in the long term, especially as big government actors join the cyber wars."

WHAT EXACTLY IS ARTIFICIAL INTELLIGENCE?

Very simply, it’s machines doing things that are considered to require intelligence when humans do them: understanding natural language, recognising faces in photos, driving a car, or guessing what other books we might like based on what we have previously enjoyed reading.

It’s the difference between a mechanical arm on a factory production line programmed to repeat the same basic task over and over again, and an arm that learns through trial and error how to handle different tasks by itself.

HOW IS AI HELPING US?

The leading approach to AI right now is machine learning, in which programs are trained to pick out and respond to patterns in large amounts of data, such as identifying a face in an image or choosing a winning move in the board game Go. This technique can be applied to all sorts of problems, such as getting computers to spot patterns in medical images, for example. Google’s artificial intelligence company DeepMind are collaborating with the UK’s National Health Service in a handful of projects, including ones in which their software is being taught to diagnose cancer and eye disease from patient scans. Others are using machine learning to catch early signs of conditions such as heart disease and Alzheimers.

Artificial intelligence is also being used to analyse vast amounts of molecular information looking for potential new drug candidates – a process that would take humans too long to be worth doing. Indeed, machine learning could soon be indispensable to healthcare.
Artificial intelligence can also help us manage highly complex systems such as global shipping networks. For example, the system at the heart of the Port Botany container terminal in Sydney manages the movement of thousands of shipping containers in and out of the port, controlling a fleet of automated, driverless straddle-carriers in a completely human-free zone. Similarly, in the mining industry, optimisation engines are increasingly being used to plan and coordinate the movement of a resource, such as iron ore, from initial transport on huge driverless mine trucks, to the freight trains that take the ore to port.
AIs are at work wherever you look, in industries from finance to transportation, monitoring the share market for suspicious trading activity or assisting with ground and air traffic control. They even help to keep spam out of your inbox. And this is just the beginning for artificial intelligence. As the technology advances, so too does the number of applications.

How dangerous is AI really?

Look at any newsfeed today, and you'll undoubtedly see some mention of AI. Deep machine learning is becoming the norm. Couple that with Moore's Law and the age of quantum computers that's undoubtedly upon us and it's clear that AI is right around the corner. But how dangerous is AI really? When it comes down to it, how can a connected network operating within the confines of laws that govern other organisms' survival actually be stopped?

While the birth of AI is surely a utilitarian quest in that our natural tendencies are to improve upon prior iterations of life through the advancement of technology, and that AI will clearly pave the way for a heightened speed of progress, is it also spelling out the end of all humanity? Is our species' hubris in crafting AI systems ultimately going be to blamed for its downfall when it occurs?

If all of this sounds like a doom-and-gloom scenario, it likely is. What's to stop AI when it's unleashed? Even if AI is confined to a set of rules, true autonomy can be likened to free will, one in which man or machine get to determine what is right or wrong. And what's to stop AI that lands in the hands of bad actors or secretive government regimes hell bent on doing harm to its enemies or the world?

When AI is unleashed, there is nothing that can stop it. No amount of human wrangling can bring in a fully-activated and far-reaching network composed of millions of computers acting with the level of consciousness that's akin to humans. An emotional, reactive machine aware of its own existence could lash out if it were threatened. And if it were truly autonomous, it could improve upon its design, engineer stealthy weapons, infiltrate impenetrable systems, and act in accordance to its own survival.

Throughout the ages, we've seen the survival of the fittest. It's mother nature's tool, her chisel if you well, sharpening and crafting after each failure, honing the necessities, discarding the filaments, all towards the end of increasing the efficiency of the organic machine.

Today, humans are the only species on the planet capable of consciously bending the will of nature and largely impacting the demise of plants, animals, environments, and even other people. But what happens when that changes? When a super-intelligent machine's existence is threatened, how will it actually react? Aside from the spiritual issues that revolve around the "self," how can we confidently march forward knowing all too well we might be opening up Pandora's Box ?

Thanks  http://www.bbc.com

What happens inside a Mobile battery right before it explodes?

The first thing we need to understand is how exactly the lithium-ion battery in your phone works. The name gives us a hint — electricity is carried from one electrode to another using charged lithium ions.

Lithium-ion batteries store, transfer and release energy because of natural chemical reactions. The battery has two electrodes — an anode and a cathode. The cathode is connected to the positive (+) connection on the battery and holds positively charged ions, and the anode is connected to the negative (-) connection and holds (you guessed it) negatively charged ions.

Between the two electrodes is what's called an electrolyte. The electrolyte in a lithium battery is (usually) an organic solvent paste that has a very large number of metallic salts (in most cases, that metal is lithium) as part of its makeup. This makes it electrically conductive — electricity can pass through it. The anode and the cathode are in the electrolyte and separated by a physical barrier so they can't touch.

When you discharge the battery (when you're using your phone and not charging it) the cathode pushes its positively charged ions away and the negatively charged anode attracts them. Electricity flows out from the anode, through your device, then back to the cathode. Yes, electricity travels through a loop and isn't "used up" by the thing being powered. When you charge your phone, the reverse happens and ions travel from the cathode through the electrolyte to the anode.

Lithium is the perfect element for rechargeable batteries: It's lightweight, easy to recharge and holds a charge for a long time.

When these ions come in contact with the charged atoms in an electrode, an electrochemical reaction called oxidation-reduction (redox) frees the charged electrons to travel out through the battery contacts, which are connected to the electrodes. This continues to charge the lithium ions in the electrolyte until there aren't enough left that can hold a positive charge that's strong enough to move through the electrolyte paste, and your battery will no longer charge.

Lithium is the lightest metal — number three on the periodic table. It's also very excitable, making it easy to create a powerful chemical reaction. This makes it a near-perfect metal to use in a portable rechargeable battery. It's lightweight, easy to recharge and continues to hold a charge for a long time.

 From the fiery Note 7 debacles to exploding hoverboards, lithium-ion batteries aren't doing so hot lately. A new study helps to explain how these popular power sources can turn into safety hazards.

In the paper, published in the Journal of the Electrochemical Society, scientists at the Canadian Light Source (CLS) synchrotron looked inside an overworked battery. In this case, they drained a battery until its voltage was below a critical level.

Overcharging or overworking deforms the insides of a battery. (A) shows the inside of a battery before it was misused. (B) shows how misuse causes the original design defects to become even more warped. (C) highlights the areas where warping got worse.
Toby Bond, Canadian Light Source

When we overcharge or overheat lithium ion batteries, the materials inside start to break down and produce bubbles of oxygen, carbon dioxide, and other gasses. Pressure builds up, and the hot battery swells from a rectangle into a pillow shape. Sometimes the phone involved will operate afterward. Other times it will die. And occasionally—kapow!

To see what's happening inside the battery when it swells, the CLS team used an x-ray technique called computed tomography.

Inside the battery is an electrode that spirals out from a central point like a jellyroll. The x-ray scan revealed that the bubbles produced during overheating warped and dented this electrode.

Intriguingly, the study authors found that the worst deformation from the gas buildup occurred in areas that had slight defects before the battery was ever over-drained. The authors note that doing more studies like this, on a larger variety of batteries, would improve understanding of how these batteries respond to gas evolution, which could lead to better designs.

As New Scientist notes, it's not clear whether the Samsung Note 7 catastrophes included pillowing or this type of deformation.

[Journal of the Electrochemical Society via New Scientist]

 www.popsci.com.

Electronics References Sheet

Electronics is more than just schematics and circuits. By using various components, such as resistors and capacitors, electronics allows you to bend electric current to your will to create an infinite variety of gizmos and gadgets. In exploring electronics, use this handy reference for working with Ohm’s, Joule’s, and Kirchhoff’s Laws; making important calculations; determining the values of resistors and capacitors according to the codes that appear on their casings; and using a 555 timer and other integrated circuits (ICs).

Important Formulas in Electronics

With just a handful of basic mathematical formulas, you can get pretty far in analyzing the goings-on in electronic circuits and in choosing values for electronic components in circuits you design.

Ohm’s Law and Joule’s Law

Ohm’s Law and Joule’s Law are commonly used in calculations dealing with electronic circuits. These laws are straightforward, but when you’re trying to solve for one variable or another, it is easy to get them confused. The following table presents some common calculations using Ohm’s Law and Joule’s Law. In these calculations:
V = voltage (in volts)
I = current (in amps)
R = resistance (in ohms)
P = power (in watts)

Unknown Value	Formula
Voltage	V = I x R
Current	I = V/R
Resistance	R = V/I
Power	P = V x I or P = V2/R or P = I2R

Equivalent resistance and capacitance formulas

Electronic circuits may contain resistors or capacitors in series, parallel, or a combination. You can determine the equivalent value of resistance or capacitance using the following formulas:
Resistors in series:

Resistors in parallel:

or

Capacitors in series:

or

Capacitors in parallel:

Kirchhoff’s Current and Voltage Laws

Kirchhoff’s Circuit Laws are commonly used to analyze what’s going on in a closed loop circuit. Based on the principle of conservation of energy, Kirchhoff’s Current Law (KCL) states that, at any node (junction) in an electrical circuit, the sum of currents flowing into that node is equal to the sum of currents flowing out of that node, and Kirchhoff’s Voltage Law (KVL) states that the sum of all voltage drops around a circuit loop equals zero.
For the circuit shown, Kirchhoff’s Laws tells you the following:
KCL: I = I₁ + I₂
KVL: V_battery – V_R – V_LED = 0, or V_battery = V_R + V_LED

Calculating the RC time constant

In a resistor-capacitor (RC) circuit, it takes a certain amount of time for the capacitor to charge up to the supply voltage, and then, once fully charged, to discharge down to 0 volts.

Circuit designers use RC networks to produce simple timers and oscillators because the charge time is predictable and depends on the values of the resistor and the capacitor. If you multiply R (in ohms) by C (in farads), you get what is known as the RC time constant of your RC circuit, symbolized by T:

A capacitor charges and discharges almost completely after five times its RC time constant, or 5RC. After the equivalent of one time constant has passed, a discharged capacitor will charge to roughly two-thirds its capacity, and a charged capacitor will discharge nearly two-thirds of the way.

Electronics: Reading Resistor and Capacitor Codes

Electronics can sometimes be difficult to decipher. By decoding the colorful stripes sported by many resistors and the alphanumeric markings that appear on certain types of capacitors, you can determine the nominal value and tolerance of the specific component.

Resistor color codes

Many resistor casings contain color bands that represent the nominal resistance value and tolerance of the resistor. You translate the color and position of each band into digits, multipliers, and percentages.

The table that follows outlines the meaning of the resistor color bands.

Color	1st Digit	2nd Digit	Multiplier	Tolerance
Black	0	0	x1	±20%
Brown	1	1	x10	±1%
Red	2	2	x100	±2%
Orange	3	3	x1,000	±3%
Yellow	4	4	x10,000	±4%
Green	5	5	x100,000	n/a
Blue	6	6	x1,000,000	n/a
Violet	7	7	x10,000,000	n/a
Gray	8	8	x100,000,000	n/a
White	9	9	n/a	n/a
Gold	n/a	n/a	x0.1	±5%
Silver	n/a	n/a	x0.01	±10%

Capacitor value reference

In electronic circuits, the value of a capacitor can be determined by a two- or three-digit code that appears on its casing. The following table outlines values for some common capacitors.

Marking	Value
nn (a number from 01 to 99) or nn0	nn picofarads (pF)
101	100 pF
102	0.001 µF
103	0.01 µF
104	0.1 µF
221	220 pF
222	0.0022 µF
223	0.022 µF
224	0.22 µF
331	330 pF
332	0.0033 µF
333	0.033 µF
334	0.33 µF
471	470 pF
472	0.0047 µF
473	0.047 µF
474	0.47 µF

Capacitor tolerance codes

In electronic circuits, the tolerance of capacitors can be determined by a code that appears on the casing. The code is a letter that often follows a three-digit number, for instance, the Z in 130Z. The following table outlines common tolerance values for capacitors. Note that the letters B, C, and D represent tolerances in absolute capacitance values, rather than percentages. These three letters are used on only very small (pF range) capacitors.

Code	Tolerance
B	± 0.1 pF
C	± 0.25 pF
D	± 0.5 pF
F	± 1%
G	± 2%
J	± 5%
K	± 10%
M	± 20%
Z	+80%, –20%

Electronics: Integrated Circuit (IC) Pinouts

The pins on an IC chip provide connections to the tiny integrated circuits inside of your electronics. To determine which pin is which, you look down on the top of the IC for the clocking mark, which is usually a small notch in the packaging but might instead be a little dimple or a white or colored stripe. By convention, the pins on an IC are numbered counterclockwise, starting with the upper-left pin closest to the clocking mark. So, for example, with the clocking notch orienting the chip at the 12 o’clock position, the pins of a 14-pin IC are numbered 1 through 7 down the left side and 8 through 14 up the right side.

Electronics: 555 Timer as an Astable Multivibrator

The 555 can behave as an astable multivibrator, or oscillator. By connecting components to the chip in your electronics, you can configure the 555 to produce a continuous series of voltage pulses that automatically alternate between low (0 volts) and high (the positive supply voltage, V_CC).

You can calculate the low and high timing intervals using the formulas that follow: