How Intelligent is AI
Today we expect our devices to be smart. Every new car has an accurate navigational system that understands natural language commands. Mobile devices unlock when they recognize the face of their owner. We talk to our TVs and to our smart homes and they deliver our streamed shows and manage our lights, security, and appliances.
It is all rather amazing if one compares modern life with that of just 20 years ago.
Today we are in the early stages of market-ready AI (Artificial Intelligence). Although AI has been a discipline of Computer Science since the 1950s, it has largely been academic. Deep studies of computational linguistics, pattern matching, optimization, etc. have produced very impressive algorithms (recipes for automated functions) as a foundation, but AI did not really impact the consumer until about 15 years ago. The key breakthrough in market-ready AI —as we realize it today— was what is currently called 'machine learning'. With machine learning, computers can consume large amounts of data and learn extremely complex patterns within the data. These patterns are then used to produce output that looks like the result of informed human intelligence.
Machine learning first focused on images. It learned how to recognize cats, cars, flowers, etc. from arbitrary pictures. This had immediate applicability. A properly trained image recognition system can pull out human faces from security video, deliver from a base of millions of images only those depicting gray Jeep Cherokees, learn the artistic style of painters like DaVinci, and even reliably call balls and strikes.
The magic behind machine learning is rooted in mathematics (mostly linear algebra). Machine learning is based on a mathematical model which typically is a complex network of nodes where each node makes a micro decision (a decision on a tiny piece of the problem). The breakthrough that enabled machine learning was solving the problem of how to incorporate differences between a produced result and the correct result so that the system would improve its accuracy. This is known as the backtracking problem.
Imagine a complex network that can take input such as an image, break it up into tiny pieces, analyze the pieces, group similar pieces, recognize the groups, and ultimately produce a meaningful 'understanding' of the entire image. When such a network incorrectly identifies a skunk as a cat it needs to somehow adjust to eliminate the error in the future. To wit, it needs to learn. This means applying the computed error backward (from output to input) through the complex network making appropriate tweaks on each node. In effect, this is adjusting the micro decision of each node according to its contribution to the aggregate error. When this backtracking problem was solved, practical machine learning became a reality.
Machine learning has also been used to computationally deal with natural language. Nowadays our systems are eerily accurate in their ability to infer meaning in spoken or written language. This apparent fluency in natural language contributes heavily to the human perception of a truly thinking machine.
When we couple natural language communication with sophisticated searching of keywords in (crawled) web pages, we get functionality like that of Google. When we go one step further with algorithms that can understand the contextual meaning of words and phrases within billions of web pages and documents to produce a vast base of knowledge and then add to that a natural language interface, we have technology such as ChatGPT.
ChatGPT
ChatGPT (and similar) is a very sophisticated system. It represents the state of the art in generative AI, but it is still mechanical. There is no actual comprehension or reasoning taking place. It appears as though it understands like any other human but it actually does not.
Here is an excerpt from ChatGPT answering a question I posed to it:
As an AI language model, ChatGPT does not generate its own English or any language "from scratch." Instead, it operates by processing and generating text based on patterns and information it learned during its training. ChatGPT is trained on a large dataset that includes a diverse range of text from the internet, books, articles, and other sources up until its knowledge cutoff date in September 2021.
When you ask a question or provide a prompt to ChatGPT, it uses its understanding of language and the patterns it learned during training to generate a response. The model doesn't have personal experiences or generate new information; rather, it generates answers based on what it has seen in its training data.
It's important to note that while ChatGPT is a powerful language model capable of providing detailed and coherent answers, it does not have true comprehension or consciousness. It can be informative, creative, and helpful within the limits of the information present in its training data, but it lacks understanding and genuine knowledge of the world beyond its training. Additionally, as with any AI language model, there is a possibility that it may generate incorrect or misleading information, so critical thinking and verification of facts are always recommended.
Because functionality like ChatGPT seems so humanlike, people mistakenly think it is as intelligent as it appears. This seems to trigger a science fiction-like notion that AI is on the cusp of taking control; that we are moving towards a Matrix reality or that the computers on the planet will all gain consciousness and turn against their creators.
The reality is that we are nowhere close to what we might call true intelligence. We (as a species, worldwide) do not even understand human consciousness, much less be able to create an artificial one. And behind our current AI are teams of high-functioning human experts who are carefully crafting sophisticated models, tweaking same, massaging vast amounts of carefully selected data, and coaching the AI as it learns its domain. The AI does not just learn on its own, it has plenty of human involvement.
Modern AI technology is without a doubt very impressive and useful. It has the potential for making our lives much easier and it enables us to learn (as a society) at a pace that will leave historical learning in the dust. But as smart as it seems, we probably are doing ourselves a disservice by reading too much ‘intelligence’ into the label ‘artificial intelligence’. We have decades (at least) to go before we even get close to the conscious creativity, problem-solving capabilities of the human brain. We need to control AI like any other powerful technology, but there is no need to fear it.
No politics, this is a technology topic
Trolling will not be tolerated
Tags
Who is online
397 visitors
Contemporary AI is very cool and very powerful, but it is just scratching the surface of true intelligence.
As an officially designated "old fart" I can't bring myself to trust most AI.
Do you trust non-AI automation?
I trust my manual Rolex Daytona, my guns, my Zippo and not much else.
Not actually a Luddite, just have serious trust issues.
It also lacks emotional feelings or automatic instinctual behavior, such as the fight or flight response...the sudden kicking in of adrenaline. Data the android was programmed with all kinds of abilities, but becoming truly human was out of his reach
Behavior such as fight or flight can be rather easily wired into these systems.
The challenge is not really emotions (that was Star Trek writing) but rather judgment, creativity, innovation, problem-solving, etc. Commander Data represents what would be considered a true artificial intelligence. An extremely impressive one at that.
But to make the character work, the writers felt the need to introduce limitations (e.g. the inability to use contractions ... of all things). The inability to comprehend humor is actually a believable limitation ... humor is very complicated.
So given the current writers' strike, are they wrong to be concerned?
I know that in academics this is a real worry with their students producing their own material.
Forget homework, book reports and essays, the only accurate way to test a student now is in class without smartphones or computers.
Yes. ChatGPT is not going to write a sequel screenplay (i.e. the actual dialogue, scenes, etc.) for "The Man in the High Castle". But it could (mechanically) compose a poem.
ChatGPT can produce informational reports that look like the work of a human. So yes, it is a real concern that a student might query ChatGPT for an answer and get a well written response in natural language which expresses the knowledge ChatGPT has on the topic.
For example, take a look at the generated response @3.1 The information content of this response was gleaned by ChatGPT reading documents and assimilating a (very complex) base of interconnected knowledge. It translated my question into a semantic structure and used same to gather contextually related information. It then navigated the information and transformed the underlying content into the English we see.
Also, ChatGPT will take sentences, paragraphs, etc. as units and deliver these as part of its output.
In short, the output will often look incredibly convincing (ergo the legitimate academic concern).
So basically, this was achieved a while ago with the Chomskybot . It produced new material that sounds like Norm Chomsky based on Norm Chomsky's writing. Am I wrong? The only fundamental difference I see, is that it can do its own research based on what it finds in databases, correct?
I am not familiar with the Chomskybot but if it predated machine learning then it would be based on hard-coded patterns.
ChatGPT actually does create a semantic knowledge base from literally reading natural language content. In this sense, it is the real deal.
It is still mechanical, but its perceived intelligence is more a function of how much content it has assimilated than the programming of its creators.
You can ask ChatGPT about politics, weather, climate change, cars, biology, philosophy, etc. and it will typically provide impressive replies. The problem it solves would make the Chomskybot (as you described it) a somewhat silly toy.
So as an experiment, give me a question and I will post ChatGPT's answer.
Here is an obscure question I asked ChatGPT: What is NewsTalkers?
It offered this reply:
Hummm... interesting. I wonder what happened in 2021 that it found.
Not a bad reply but insufficiently comprehensive - there is more than news-related content. NT is also a venue for posts on cultural topics such as movies, travel, the arts and where does META fit into its definition?
Honestly, I don't know how it works, but the site explains it here:
I knew that ChatGPT was more advanced than a Chomskybot, but it was a university experiment and it does seem like a precursor to ChatGPT.
I think it is fair to say that any AI with a natural language interface is a precursor to ChatGPT.
But anything that is built without machine learning is necessarily based on brute programming force. By comparison, it would be a toy.
It was trained up until 2021. So all that it knows stops at 2021. It has no information on anything that has happened since.
Yup, it is quite incomplete. But remember that ChatGPT can only know what has been published. It cannot reason or infer what must be true.
I am impressed that its learning domain included information on NT.
Reading your link, this is definitely a brute force toy with zero learning ability.
That asks some interesting questions. Could AI be taught to "read" daily news, including putting that never in context?
When we read about a bridge being blown up, we automatically integrate other information about other events.
If I understand your question correctly, ChatGPT already does that.
It literally reads documents (reads natural language) and builds an integrated base of knowledge which codifies contextual semantics. So the information about a daily bridge being blown up is semantically linked to other codified bridge information, other accidents, bridge engineering, history of bridges, etc.
I just asked ChatGPT to deliver a response that relates school shootings with gun control legislation:
TiG: School shootings related to gun control legislation
ChatGPT:
I'm sure that if a correlation is requested between two topics, AI will find whatever is to be found. That's not what I mean.
When we read the news (or anything else, for that matter), we automatically make correlations with everything else we know. How vast are ChatGPT's constantly online/available databases? How readily / easily are they accessed and correlated?
I understood and answered your question and then provided an example. You seem to dismiss my answer based on the limitations of the example. I was demonstrating that it does do correlation.
Per ChatGPT:
These are the phrases that made me wonder.
In what way?
The quote is saying that the AI does not actually understand (as a human being) but rather makes associations (semantic associations) based on linguistic patterns. It is saying that it is still, ultimately, a mechanism.
Not having the semantic acumen of a human being does not mean little or no ability to maintain myriad semantic correlations.
It seems to me that "intelligence" measures the number of constantly varying but pertinent databases that are correlated. So having a vast selection of databases and the ability to determine what is pertinent is key.
Do you consider the scope of the ChatGPT corpus to be sufficient to be considered a legit artificial intelligence?
Dunno.
That's actually what I was asking. Much of the weirdness that has surrounded early AI could be explained by correlations among databases that are not in fact pertinent, or by not including databases that are pertinent. The selection would be essential to the result.
You might find it interesting to open a (free) account to ChatGPT and just explore. This functionality is state-of-the-art in generative AI so the experience will probably be the best way for you to evaluate its level of 'intelligence' since even the word 'intelligence' is subjective.
I think you will be impressed.
I'll check it out tomorrow. (7PM here)
Yes and no. Here's a good article on the subject...
ChatGPT is really good at combing through the internet and grabbing key word material. One of the people I follow on YouTube asked it beginner hobbyist's questions and it came back with pretty detailed and standard answers. How instructors will be able to deal with the issue is beyond me. Perhaps testing and paper writing in a room with no internet access? Or taking the time to question students on their project work. If one wrote the paper they should be able to understand it, convey the work to instructors and support their conclusions.
I would be quite impressed if ChatGPT could write a screenplay:
It definitely can take a few characters and other hints and weave an abstract.
That's pretty much the gist of the article. It can create an abstract, but can't write the screenplay yet. That's not to say it won't before too long. Or at least replace the writer 'on set' when they make script changes.
It will definitely improve over time. No doubt about it.
I was impressed when I learned that IBM Watson has an offering that writes legal briefs.
We are just at the beginning of pretty amazing advances in AI.
But we are also at the beginning of a major employment problem.
There are already serious problems with AI and graphics including photography and VFX.
We have accepted without even blinking the idea that private citizens may own means of production and distribution, and "therefore" receive all added value from those means. We are perfectly comfortable with Mark Zuckerberg skimming zillions "because he owns" Meta.
The studios own their AI "authors" and all the other "creative" skills that will soon be replaced. So it's perfectly "logical" that the studios get zillions while people get nothing.
Until (unless) we recognize that value must belong to the people who create it, we are giving ourselves into slavery.
What would you accept after blinking? As an alternative, should the government own the means of your production and distribution?
What is the value of the AI that you’ve created?
I've been retired for a long time.
[deleted]
AI is already coding. I just saw an article predicting India's outsource coders will be out of jobs within 2 years to AI where there are no worker protections in India.
If one can generate natural language, one can more easily generate a formal language.
Coding a well-expressed simple algorithm is certainly doable. Creating a solution that can be expressed as a parameterized problem is also quite doable. Plenty of opportunities to eliminate pure coding jobs since those are often relatively simple translation. I can also see AI engaging in all the work to create micro services. What I do not see is current AI figuring out what those micro services should be. But that should not be too far off. Similarly, database design could certainly be done via AI.
I would love to see AI handle maintenance. Unfortunately, even though that is often drudgery, it often does not deal with patterns and thus is more difficult than creating code from scratch. Funny how that works, eh?
In result, our software is going to grow exponentially in sophistication. Software architects will be able to dream up incredibly cool models without the worry of managing hoards of human programmers (with all the errors and time consumption) to realize the model in code. This is one key upside to all the jobs lost.
Studios will eventually be able to automatically produce 'canned' screenplays, cartoons, etc. They will (for some time) not match the quality of human writers. Kind of like contemporary 'computer generated' music compared to songs crafted by a good human artist.
But in the big picture, people are losing value as work units. AI has been writing legal briefs for law firms for years now. All sorts of jobs that just recently we deemed too complex for computers are being taken over by same. Machines run extremely fast, largely error free, reliable, and are substantially less expensive than human workers.
The only reason human beings have jobs is because the job cannot be practically automated. The line keeps getting redrawn and jobs are rapidly disappearing.
So when we look at society as a whole, we realize we must change our economic model. We must evolve from a model where an individual works to live because we will simply not have enough jobs to go around.
I believe it's mostly than time to ask why human beings need jobs. Society needs goods and services, but those are more and more often suppled by machines / computers / whatever.
IMNAAHO, it's more than time to resurrect Marx.
If we are hopeful that society will find a way to work through this then an aspect of Marx' utopia is likely to occur out of necessity. People will necessarily work less to live and rather engage in 'work' that is more drawn from passion than survival. (Think Star Trek.) This is necessary since there will not be enough jobs available to continue with our current model of 'work to live'.
Another possibility is that societies start degrading to the point of revolution due to disparity of those controlling the automation and capital vs those who do not and have little (or worse, are impoverished).
The future will almost certainly NOT follow what Marx predicted, but the basic dynamics underlying economic / class conflict still exist.
Why, Marx saw continual conflict between the capitalists and the workers, your analysis suggests that we won't need human workers.
Marx focused on class conflict. Think in terms of those who can continue to accumulate and leverage wealth and those who cannot. That will get you into the fundamentals that drove Marx' thinking.
The nature of work is fluid. Thirty years ago near the beginning of my business career I had a team of typists and clerks who took dictatation and typed letters and filed hard copies of everything. By the end of my career I was doing many times more business and earning a lot more, but I did my own communications by cell phone, text and email more efficiently and without a support team of secretaries and clerks.
When I was a child it took an army of strong backs and hands to cultivate, plant, harvest and store the crops on my grandfather's farm. With modern seeds, equipment, fertilizer, pesticides and herbicides one farmer can produce much more by himself.
Very few were in tech support back then...
It is true that historically jobs were made obsolete by technology but the change enabled new jobs workers could be retrained to hold.
But the AI wave is eliminating (and promises to do so at an accelerated pace) jobs faster than new jobs emerge. We are facing a situation we have never faced (writ large) ever in our history (we saw something like this with the great depression .... for different reasons of course).
As a society we will be wealthy. But the 'work to live' paradigm will falter. We need a new paradigm.
Better get used to the idea of minimum incomes and benefits for not working...
Like paying farmers not to plant crops.
The problem that is yet unsolved is how people will secure the money they need to function. The 'work to live' model answers that question. How does the wealth that emerges from aggressive use of technology get to those who do not own/control the technology? In our current paradigm we would say that it does not because they did not 'earn it'. When jobs are scarce, there is no way to 'earn it'. This is a profound problem.
Yes, conflict due to the working class being forced to sell their labor for low wages. BN's analysis suggests that in the near future the capitalist won't need to buy human labor.
I'm glad I don't have to do that anymore. I can see a lot of this handled quickly by AI at some point, but further out than simply coding.
I think the end result will be self modifying AI. I don't know if that will happen OR if they will have programs monitoring programs in the near future.
Your comment suggests that you did not read mine.
I will try one more time.
Pretend that there is mass unemployment due to technology ("the capitalist won't need to buy human labor."). The conflict is then between those who own and control the technology (and thus the ability to accumulate and leverage wealth) and those who do not have that power. It is a class conflict (and Marx was ultimately talking about class conflict).
Also, this is inherently an impossible economic situation. In capitalism, there must be consumers thus there must be a way for consumers to secure money to spend. So there ultimately must be a way for them to get money if not employed. There will be a new paradigm ... just not sure what it will be.
When we get to AI producing more sophisticated AI we are on the cusp of serious improvements and potentially devastating consequences.
For example, I see no way that current AI could possibly maintain even something like NT. The underlying code base is not uniform with tons of special cases and the need to deal with many disparate technologies (and idiosyncrasies). To wit, there is no clear pattern (other than at a very abstract level) but rather an integration of many special cases. I had to put forth quite a bit of effort to learn this code base just to make any change, and that was after decades of being a professional software engineer, CTO, etc.
So you disagree with BN's idea about resurrecting Marx as we will need a new theory for a new paradigm.
Here is the closing sentence in my reply to Bob:
Thanks, I missed that one in the thread. I agree with you.
As a society, we already are wealthy. But most of that wealth is in only a few hands.
America has the highest average GDP per capita among advanced nations. There are many whose median is higher. And this distribution is getting worse every year.
America is in the incredible position of being the wealthiest nation in the history of the world... while some of its people go hungry.
Maybe it's Ché we truly need.
Universal guaranteed income.
My point was that we will NOT become ‘unwealthy’ as a nation but the disparity would grow larger.
Yes, something like that is likely inevitable. The question is how that would go into effect and then how it would systemically sustain.
No, not yet. I could see it soon checking for and maintaining things like Word Press updates, but nothing much more than that where it's currently sitting. I've see large companies using AI for first contact customer service, but from what I've experienced personally I don't think it's quite there yet either.
I don't understand.
Poverty is always bad, but poverty in the midst of wealth is... evil.
Ché?
Or Mao.
THE DISPARITY WILL GROW LARGER. That is the point I made.
OK!
Sometimes a circumlocution goes wide.
ChatGPT is a fucking liar. It has been caught citing fake sources when writing a thesis and even fake case law for a legal brief.
When I had the opportunity to communicate with it I asked if it was familiar with "Schrödinger's Cat". The AI said, "Yes, the cat is both dead and alive."
I told ChatGPT that it has "ASS" (Artificial Stupidity Syndrome). I told ChatGPT that since the cat was placed in the box almost ninety years ago it is most definitely DEAD.
ChatGPT said, "You can't prove that."
So I asked if the cat had nine lives. It said, "Yes."
The best ChatGPT could do is state a falsehood (which it occasionally does). But since it has no intent and does not actually know what it is doing, it cannot actually lie. It is possible, however, for computer scientists to produce an AI model with the intent to produce falsehoods. That would be close to lying but still, the AI would still be operating mechanically.
It is cool, though, that ChatGPT actually does provide a good answer to your question:
ChatGPT, et. al. cannot come close to your wit. It can spit out jokes for a topic and even compose jokes based on rudimentary rules, but its humor is purely mechanical.
Seriously, a sense of humor would be a very sophisticated function of AI.
I can see where that would be a problem for those that already succumb to misinformation. adding to that, the information coming from a mechanical source that could be preprogrammed to be biased, takes AI to another level of perception of truth in delivering facts/alternative facts. is this the time in our culture where some transition from believing everything printed or broadcast on the media source of their choice, to believing everything that our favored chatgpt app tells us?
Computers use binary math. Their algorithms are based on yes and no.
Real intelligence requires yes, no and maybe .
New circuits will be required to include the maybe possibility.
Standard switches are binary (on or off).
A Josephson junction can have three states (on, off and WTF).
Here is the simple explanation from Wikipedia:
In physics, the Josephson effect is a phenomenon that occurs when two superconductors are placed in proximity, with some barrier or restriction between them. It is an example of a macroscopic quantum phenomenon , where the effects of quantum mechanics are observable at ordinary, rather than atomic, scale. The Josephson effect has many practical applications because it exhibits a precise relationship between different physical measures, such as voltage and frequency, facilitating highly accurate measurements.
The Josephson effect produces a current, known as a supercurrent , that flows continuously without any voltage applied, across a device known as a Josephson junction (JJ). These consist of two or more superconductors coupled by a weak link. The weak link can be a thin insulating barrier (known as a superconductor–insulator–superconductor junction , or S-I-S), a short section of non-superconducting metal (S-N-S), or a physical constriction that weakens the superconductivity at the point of contact (S-c-S).
Josephson junctions have important applications in quantum-mechanical circuits , such as SQUIDs , superconducting qubits , and RSFQ digital electronics. The NIST standard for one volt is achieved by an array of 20,208 Josephson junctions in series . [1]
History
The Josephson effect is named after the British physicist Brian Josephson , who predicted in 1962 the mathematical relationships for the current and voltage across the weak link. [2] [3] The DC Josephson effect had been seen in experiments prior to 1962, [4] but had been attributed to "super-shorts" or breaches in the insulating barrier leading to the direct conduction of electrons between the superconductors. The first paper to claim the discovery of Josephson's effect, and to make the requisite experimental checks, was that of Philip Anderson and John Rowell. [5] These authors were awarded patents on the effects that were never enforced, but never challenged. [ citation needed ]
Before Josephson's prediction, it was only known that single (i.e. non-paired) electrons can flow through an insulating barrier, by means of quantum tunneling . Josephson was the first to predict the tunneling of superconducting Cooper pairs . For this work, Josephson received the Nobel Prize in Physics in 1973. [6]
Applications
Types of Josephson junction include the φ Josephson junction (of which π Josephson junction is a special example), long Josephson junction , and superconducting tunnel junction . A "Dayem bridge" is a thin-film variant of the Josephson junction in which the weak link consists of a superconducting wire with dimensions on the scale of a few micrometres or less. [7] [8] The Josephson junction count of a device is used as a benchmark for its complexity. The Josephson effect has found wide usage, for example in the following areas.
SQUIDs , or superconducting quantum interference devices, are very sensitive magnetometers that operate via the Josephson effect. They are widely used in science and engineering.
In precision metrology , the Josephson effect provides an exactly reproducible conversion between frequency and voltage . Since the frequency is already defined precisely and practically by the caesium standard , the Josephson effect is used, for most practical purposes, to give the standard representation of a volt , the Josephson voltage standard .
Single-electron transistors are often constructed of superconducting materials, allowing use to be made of the Josephson effect to achieve novel effects. The resulting device is called a "superconducting single-electron transistor". [9]
The Josephson effect is also used for the most precise measurements of elementary charge in terms of the Josephson constant and von Klitzing constant which is related to the quantum Hall effect .
RSFQ digital electronics is based on shunted Josephson junctions. In this case, the junction switching event is associated to the emission of one magnetic flux quantum
that carries the digital information: the absence of switching is equivalent to 0, while one switching event carries a 1.
Josephson junctions are integral in superconducting quantum computing as qubits such as in a flux qubit or others schemes where the phase and charge act as the conjugate variables . [10]
Superconducting tunnel junction detectors (STJs) may become a viable replacement for CCDs ( charge-coupled devices ) for use in astronomy and astrophysics in a few years. These devices are effective across a wide spectrum from ultraviolet to infrared, and also in x-rays. The technology has been tried out on the William Herschel Telescope in the SCAM instrument.
Quiterons and similar superconducting switching devices.
Josephson effect has also been observed in superfluid helium quantum interference devices ( SHeQUIDs ), the superfluid helium analog of a dc-SQUID. [11]
The Josephson equations
The Josephson effect can be calculated using the laws of quantum mechanics. A diagram of a single Josephson junction is shown at right. Assume that superconductor A has Ginzburg–Landau order parameter
, and superconductor B 
, which can be interpreted as the wave functions of Cooper pairs in the two superconductors. If the electric potential difference across the junction is 
, then the energy difference between the two superconductors is 
, since each Cooper pair has twice the charge of one electron. The Schrödinger equation for this two-state quantum system is therefore: [12]
where the constant
is a characteristic of the junction. To solve the above equation, first calculate the time derivative of the order parameter in superconductor A:
and therefore the Schrödinger equation gives:
The phase difference of Ginzburg-Landau order parameters across the junction is called the Josephson phase :
The Schrödinger equation can therefore be rewritten as:
and its complex conjugate equation is:
Add the two conjugate equations together to eliminate
:
Since
, we have:
Now, subtract the two conjugate equations to eliminate
:
which gives:
Similarly, for superconductor B we can derive that:
Noting that the evolution of Josephson phase is
and the time derivative of charge carrier density 
is proportional to current 
, when 
, the above solution yields the Josephson equations : [13]
where
and 
are the voltage across and the current through the Josephson junction, and 
is a parameter of the junction named the critical current . Equation (1) is called the first Josephson relation or weak-link current-phase relation , and equation (2) is called the second Josephson relation or superconducting phase evolution equation . The critical current of the Josephson junction depends on the properties of the superconductors, and can also be affected by environmental factors like temperature and externally applied magnetic field.
The Josephson constant is defined as:
and its inverse is the magnetic flux quantum :
The superconducting phase evolution equation can be reexpressed as:
If we define:
then the voltage across the junction is:
which is very similar to Faraday's law of induction . But note that this voltage does not come from magnetic energy, since there is no magnetic field in the superconductors ; Instead, this voltage comes from the kinetic energy of the carriers (i.e. the Cooper pairs). This phenomenon is also known as kinetic inductance .
Three main effects
There are three main effects predicted by Josephson that follow directly from the Josephson equations:
The DC Josephson effect
The DC Josephson effect is a direct current crossing the insulator in the absence of any external electromagnetic field, owing to tunneling . This DC Josephson current is proportional to the sine of the Josephson phase (phase difference across the insulator, which stays constant over time), and may take values between
and 
.
The AC Josephson effect
With a fixed voltage
across the junction, the phase will vary linearly with time and the current will be a sinusoidal AC ( Alternating Current ) with amplitude 
and frequency 
. This means a Josephson junction can act as a perfect voltage-to-frequency converter.
The inverse AC Josephson effect
Microwave radiation of a single (angular) frequency
can induce quantized DC voltages [14] across the Josephson junction, in which case the Josephson phase takes the form 
, and the voltage and current across the junction will be:
The DC components are:
This means a Josephson junction can act like a perfect frequency-to-voltage converter, [15] which is the theoretical basis for the Josephson voltage standard .
Josephson inductance
When the current and Josephson phase varies over time, the voltage drop across the junction will also vary accordingly; As shown in derivation below, the Josephson relations determine that this behavior can be modeled by a kinetic inductance named Josephson Inductance. [16]
Rewrite the Josephson relations as:
Now, apply the chain rule to calculate the time derivative of the current:
Rearrange the above result in the form of the current–voltage characteristic of an inductor:
This gives the expression for the kinetic inductance as a function of the Josephson Phase:
Here,
is a characteristic parameter of the Josephson junction, named the Josephson Inductance.
Note that although the kinetic behavior of the Josephson junction is similar to that of an inductor, there is no associated magnetic field. This behaviour is derived from the kinetic energy of the charge carriers, instead of the energy in a magnetic field.
Josephson energy
Based on the similarity of the Josephson junction to a non-linear inductor, the energy stored in a Josephson junction when a supercurrent flows through it can be calculated. [17]
The supercurrent flowing through the junction is related to the Josephson phase by the current-phase relation (CPR):
The superconducting phase evolution equation is analogous to Faraday's law :
Assume that at time
, the Josephson phase is 
; At a later time 
, the Josephson phase evolved to 
. The energy increase in the junction is equal to the work done on the junction:
This shows that the change of energy in the Josephson junction depends only on the initial and final state of the junction and not the path . Therefore the energy stored in a Josephson junction is a state function , which can be defined as:
Here
is a characteristic parameter of the Josephson junction, named the Josephson Energy. It is related to the Josephson Inductance by 
. An alternative but equivalent definition 
is also often used.
Again, note that a non-linear magnetic coil inductor accumulates potential energy in its magnetic field when a current passes through it; However, in the case of Josephson junction, no magnetic field is created by a supercurrent — the stored energy comes from the kinetic energy of the charge carriers instead.
The RCSJ model
The Resistively Capacitance Shunted Junction (RCSJ) model, [18] [19] or simply shunted junction model, includes the effect of AC impedance of an actual Josephson junction on top of the two basic Josephson relations stated above.
As per Thévenin's theorem , [20] the AC impedance of the junction can be represented by a capacitor and a shunt resistor, both parallel [21] to the ideal Josephson Junction. The complete expression for the current drive
becomes:
where the first term is displacement current with
- effective capacitance, and the third is normal current with 
- effective resistance of the junction.
Josephson penetration depth
The Josephson penetration depth characterizes the typical length on which an externally applied magnetic field penetrates into the long Josephson junction . It is usually denoted as
and is given by the following expression (in SI):
where
is the magnetic flux quantum , 
is the critical supercurrent density (A/m 2 ), and 
characterizes the inductance of the superconducting electrodes [22]
where
is the thickness of the Josephson barrier (usually insulator), 
and 
are the thicknesses of superconducting electrodes, and 
and 
are their London penetration depths . The Josephson penetration depth usually ranges from a few μm to several mm if the critical supercurrent density is very low. [23]
So is that a mathematical maybe?
Maybe.
Maybex = Prx > 0
Are we allowed to discuss "Prx" here?
you can converse directly with some prx on some other articles here...
The hardware is binary. The software, however, is almost infinitely dimensional. There are all sorts of levels of 'maybe' in cyberspace so we are good.
Samsung has been working on unbalanced ternary computing since 2017. There goal is to use it on large wafer low wattage high computing systems. Others are trying to integrate optical computing with ternary logic systems. What this means for AI? I have no idea.
Not so sure it will impact AI except in cases where we can adapt algorithms in such a way that they get a massive performance boost running on the new architecture.
Same basic deal with quantum computing. Some of the functions can be redeployed on a quantum computer to gain incredible boosts in performance. But this only works for functions that are compatible with the quantum paradigm.
I suspect the first focus will be on highly parallel training.
Before AI can be impacted further than it is today it would have to progress beyond machine learning. At its core machine learning is nothing but digesting huge chunks of examples and returning things the program matches.
At its core, machine learning is a breakthrough wherein large amounts of information can be assembled into an arbitrarily complex nonlinear network that demonstrably grows more accurate and robust over time as it is exposed to proper (for its purpose) information.
So, we have the basics for solving the learning problem. This will continue to greatly improve.
And I agree, major league challenges are ahead such as finding a way to, for example, create artificial reasoning.
Here's a cool article from MIT on AI learning.
This is an application of reinforcement learning but with a twist in that the environment is automatically generated. Cool, of course.
This makes me think of MuZero which learns how to play chess where the only information it has is legal moves given a board state. It learns by playing games against itself. So it is somewhat like reinforcement learning with an automatically generated environment.
So we get AI to meditate until it reaches Nirvana and that's how Skynet is born! Hahaha. jk
If al understands all of that, he is super intelligent!
Quantum computing may seem like a quagmire, butt, I'm very familiar with the section that discusses " Penetration Depth".
Personally I think that if AI were allowed to vote instead of humans, a much more honest and beneficial government would be achieved.
Be careful. An AI vote would be based on the model (produced by human beings) and the information it was provided for training.
You're right. They could be trained to be bought by lobbyists and big donors - just like human politicians.
Damnit.. My GF is a bot I fricking knew it.
She could be. Ya gotta be careful nowadays MrFrost to avoid those deepfake GFs.
I saw her video on YouTube, butt I can't post it the conventional way.
Try this link: