One camp tells us that safety lies in distribution: put the weights in everyone’s hands, let a million systems check and balance each other, and no single actor, corporate, governmental, or artificial, can dominate the rest. The other camp tells us that safety lies in control: the frontier is too dangerous to hand out, capability must be gated, and the world may soon need tools to deliberately slow the pace of development itself. Both camps are staffed by serious people. Both have arguments I cannot dismiss. And both, I have come to think, are fighting over a question that will not decide the future they are fighting about.

I should say at the outset what kind of essay this is, because it is not the kind we have been getting. I am not here to tell you which camp is wrong. My claim is more uncomfortable than that: nobody is wholly right, everybody is partly wrong, and, this is the part we keep forgetting, that is the normal condition of thinking about anything new. We develop ideas together, usually by arguing, and the arguing works only if each side quietly accepts that it might lose on the merits. The AI debate has been losing that quality. Positions have hardened into identities. Manifestos read like declarations of war. Each camp now explains the other’s motives rather than answering the other’s arguments. Whatever the technology does to us, that habit will hurt us first.

So let me make my case for the wrong axis, and then for the humility.

What the operating system wars actually taught us

There is a version of recent history that both AI camps should sit with, because each remembers only half of it.

Through the 1990s and 2000s, technologists fought a war over operating systems that felt, at the time, existential. Proprietary systems, licensed, closed, controlled by a single vendor, against free and open-source software that anyone could inspect, modify, and redistribute. The rhetoric was religious. One side saw open source as unserious, insecure, communist even. The other saw closed software as a moral failing, a land grab against the digital commons. Everyone assumed there would be a winner.

There wasn’t. There was a stratification. Open systems did not defeat Windows on the desktop; they took the substrate instead, the servers, the cloud, the phones (Android sits on a Linux kernel), the embedded devices in your car and your router. Proprietary systems kept the consumer surface, where polish, integration, and accountability commanded a premium. Nobody won the war because the war was miscast: openness and closedness turned out not to be competing answers to one question, but correct answers to different questions. A hospital’s billing system and a hobbyist’s home server have different requirements. The market did not pick a philosophy. It routed each workload to the environment that suited it.

Credit: Generated by Gemini

I think this is roughly where AI models are heading, and faster than the OS era got there. Open-weight models, the ones you can download, inspect, fine-tune, and run on your own hardware, will take the substrate: the embedded systems, the regulated industries, the air-gapped environments, the long tail of specialized applications that no vendor will ever prioritize. Closed frontier models will keep the consumer and enterprise surface, where raw capability, convenience, and a vendor to sue still matter. Companies like Cohere have already built entire businesses on a fourth quadrant the manifestos barely mention, models deployed inside the customer’s own environment, for governments and banks that need capability and custody. The hybrid is not a compromise waiting to be resolved. The hybrid is the equilibrium.

But I want to be honest about where the analogy strains, because the strain is instructive. An operating system kernel has no capability overhang: you cannot fine-tune Linux into something that helps a bad actor design a pathogen. And operating systems could be deprecated, Windows XP was retired; a published model’s weights can never be unpublished. Every open release is a ratchet. These two disanalogies are, I think, the strongest honest case the control camp has, and any distribution enthusiast who cannot state them fairly has not earned their confidence. I will come back to what follows from them. It is less than the control camp thinks, and more than the distribution camp admits.

The axis that actually matters

Here is what a decade of arguing about open versus closed has obscured: for a large and growing class of real decisions, the binding question is not whether the weights are open. It is where the weights execute, and who can be compelled to produce what runs through them.

Let me make that concrete, because I live it.

I practice law in Ontario, and like a growing number of professionals, I run two AI environments in parallel. I use a hosted frontier model daily, for research, for drafting against anonymized facts, for stress-testing arguments, because it is the most capable reasoning instrument I have ever had access to, and pretending otherwise would be a disservice to my clients. And I also run capable models on hardware I own, inside walls I control, because there is a category of material that cannot cross certain boundaries at any price, at any capability level, under any terms of service.

Credit: Generated by Gemini

That second category is not a preference. It is law. Solicitor-client privilege is the client’s right, not the lawyer’s convenience, and it survives only if confidentiality is actually maintained. Canadian privacy statutes govern where personal information may flow and what safeguards must follow it. And when data sits on infrastructure in another jurisdiction, the question of who can compel its production, under what process, before which court, with what notice, stops being philosophical. A confidentiality obligation that depends on another country’s legal process is a different obligation than the one my clients think they have.

So here is the first uncomfortable claim of this essay: for a core slice of professional work, law, medicine, parts of finance and government, hosted frontier AI is not merely disfavored. It is structurally unusable, no matter how good it gets, and a meaningful part of the professions is currently pretending otherwise. The pretense mostly takes the form of not asking the question.

Notice what this does to the openness war. The reason I can run sovereign inference at all is that open-weight models exist, that is a genuine, concrete gift from the distribution camp, and I am a direct beneficiary. But notice also that what I needed from openness was not inspectability or ideological freedom. Weights alone do not tell you what a model learned; you do not get the training data, and interpretability remains a research frontier, not a shipped feature. What I needed was custody: the ability to execute the model where the law requires the data to stay. Openness happened to be the delivery mechanism for sovereignty. It is not the same thing as sovereignty. A closed model deployed inside my own walls under contract would solve my problem too; an open model that only ran on someone else’s cloud would not.

The axis that matters is custody and compellability. Open versus closed is merely one input into it.

Adoption proves nothing

There is an argument circulating at the highest levels of this industry that deserves to be retired, and it belongs to no single company, versions of it appear in the distribution camp’s manifestos and in every product keynote. It goes: people will not adopt AI systems they do not trust; therefore, if billions of people adopt these systems, alignment with users’ interests has effectively been demonstrated. The market becomes the safety case.

The last twenty years of the internet are a controlled experiment against this claim. Billions of people adopted engagement-optimized feeds. Adoption was total, enthusiastic, and sustained, and it demonstrated nothing about whether those systems served their users’ interests, because adoption measures perceived usefulness under information asymmetry, and the asymmetry is the entire problem. A system can be genuinely useful, honestly enjoyed, and quietly optimized for someone else’s objective, all at once. Usage statistics cannot distinguish these cases. That is not cynicism; it is measurement.

This cuts in every direction, which is why I like it. The distribution camp cannot cite a billion users as evidence that distributed AI produced a balance of power. The control camp cannot cite enterprise adoption as evidence that gated AI earned its gates. And those of us building sovereign systems cannot assume that self-hosting aligns a model with our interests either, it aligns the infrastructure with our interests, which is necessary and insufficient. Alignment, in every camp, remains a claim that must be demonstrated by mechanisms, not by market share.

In defense of invention

Now the part of the optimists’ case I want to defend, because I think the critics, and I have been among them, sometimes attack it lazily.

The optimists argue that AI’s greatest contribution will be invention rather than automation: not doing existing work cheaper, but making new things possible. The standard rebuttal is to point at the labor data, and the labor data is real. The early evidence shows something specific and troubling: not mass unemployment, but a narrowing of the entry-level door, young people in exposed occupations finding it markedly harder to get their first foothold, while their seniors’ employment holds. The ladder is losing its bottom rungs, and “people will adapt” is a sentence whose costs are paid by a cohort that didn’t choose it. Anyone writing cheerfully about this moment owes that cohort more than a transition-assistance clause.

But here is what the rebuttal misses: whether AI tilts toward automation or invention is not a law of nature awaiting discovery. It is the aggregate of millions of allocation decisions, what firms build, what buyers demand, what individuals choose to attempt with the new capacity in their hands. Compute is scarce; pointing it at inventing new value and pointing it at eliminating existing wages are competing uses. The invention thesis is not a prediction. It is a direction, and directions can be chosen. That is precisely why the hybrid ecosystem matters more than either camp’s total victory: a monoculture, open or closed, narrows who gets to invent and on whose terms. A stratified ecosystem, frontier models for those who need raw capability, sovereign models for those who need custody, small models for those who need cheapness at the edge, maximizes the surface area on which invention can occur. If you believe in the invention mechanism, the hybrid is not a compromise. It is the load-bearing condition.

A blip, perhaps

Let me end the argument with a conjecture I hold loosely, and then the point I hold firmly.

For most of human history, almost nobody had an employer. The baker, the smith, the farmer, the merchant, economic life was organized around families and small crews, and a person often worked several trades at once. Mass employment inside large corporations is, on the long timeline, astonishingly recent. The economist Ronald Coase gave us the reason it happened: firms exist because coordinating through markets is expensive, and when coordination costs are high, it is cheaper to bring people inside one organization. The corporation is not a law of nature either. It is a response to transaction costs.

AI collapses coordination costs. That is arguably the most fundamental thing it does. And if the corporation is a creature of coordination costs, then as those costs fall, the equilibrium size of the firm should fall with them. We may be watching the early innings of a reversal: large organizations needing fewer people, while small crews of family and friends, armed with capability that once required departments, take back the territory. A giant firm, whatever its resources, cannot allocate attention to a million niches. A million small crews can, because attention is the one resource that distribution actually distributes. It is possible that our great-grandchildren will look at the twentieth century’s centralized mass employment the way we look at feudal land tenure, not as the natural order, but as a phase.

I said I hold this loosely, and here is why: the smith owned his forge. The one-person studio of the coming era rents its compute, its distribution, and increasingly its intelligence from a handful of very large landlords. Whether the re-smalling of economic life produces independence or a new kind of tenancy depends on exactly the axis this essay has been about, who has custody of the tools. Which is, perhaps, the real reason the openness debate matters after all: not as a safety mechanism, but as the difference between owning your forge and renting it.

The humility we cannot afford to lose

I am genuinely agnostic on the question that most animates the two camps, whether the world should build the capacity to deliberately pace this technology. I find the concern about runaway dynamics serious, and I find the counterarguments serious, and I do not believe either side can honestly price the costs of what it proposes. I suspect some of what worries us is simply coming regardless. But I have written this essay anyway, because “it’s inevitable” is itself a claim that might be wrong, and if talking is what makes it not inevitable, then talking was the right call. That is not a paradox. That is just what taking your own fallibility seriously looks like.

The pace of this technology is the standard argument against humility: we must move fast, commit hard, decide now. I think the pace is the argument for it. When the cycle time of change is shorter than the cycle time of understanding, the number of confident positions that turn out wrong goes up, not down, and the cost of having welded your identity to one of them goes up with it. The appropriate response to moving faster is holding looser: strong positions, open hands. Ask what happens if the agenda you are pushing succeeds and you were wrong. Every camp in this debate should be able to answer that question about itself. Very few of the manifestos even acknowledge it exists.

We don’t know until we know. In the meantime, the most useful thing any of us can do is argue as if the other side might be right, because on the evidence of every previous technological argument we’ve had, parts of them are.

This study attempts to utilize the Google search trends data to investigate the potential impact that search trends might have on the commodities market value trends or vice versa.

In this study, I have used six commodities: traditional and some not so conventional. The traditional commodities include crude oil, gold, and Silver. The non-traditional ones are the top two digital currencies by market cap, namely Bitcoin (BTC) and Ethereum (ETH). One choice was given to the stock value of the GameStop Corporation. Some other public companies’ stock value was also investigated, but the findings were deemed insignificant and uninteresting and therefore are omitted from this report. The decision to include non-traditional commodities was made to explore their potential unique nature regarding the influence of the public’s behavior on their price portfolio.

Methods

The Google search trends data was collected and exported onto an Excel sheet (CSV) for 12 months from mid-March 2021, going backward in time. The market value of each commodity was also downloaded onto an Excel sheet for the same period. The resolution of the data, however, is different for each data set. In Google trends data, we have a weekly value vs. in the market value dataset, we have the daily values present. For this purpose, the Google trends data was adjusted to reflect both the mid-week and end of the week search trends and shifted on the graphs as seen in the results section of the report. Both scenarios (mid-week and end of the week adjustments) had all but the same effective results. Therefore to make the data visually more accessible, the mid-week adjusted version was plotted for this report.

Further, in the Bitcoin and Ethereum data, I have acquired finer resolution datasets around the week of interest that had the most activity and was the most volatile. The new datasets have the data for every day within 90 days around the peak point of interest for the Google trends dataset and the market price dataset.

All search data, except BTC and ETH’s fine resolutions, have been on the US and worldwide. For the fine resolution search data, only the US data was used. The worldwide and the US data for Bitcoin and Ethereum were comparable.

The market price data was extracted from Yahoo Finance.

Results

Here let us provide the results and graphs of our data collection on each commodity one by one.

In Silver’s case, as seen in the chart below, we observe a point of near-perfect overlap between price and search trends data

In Gold data, we can also see instances of near-perfect overlap between price and searches, and in some cases, we see instances that market price changes precede the search trends.

Gold is the only commodity that, in its data, we can find instances of the extreme difference between the search trend in the US and worldwide. This phenomenon remains unexplained in my study.

Oil data shows an instance of search trends lagging behind a dramatic price change at the beginning of the COVID-19 pandemic. We will attempt to explain this phenomenon in the discussion section of this report.

Bitcoin (BTC) shows a few instances that a search trend is preceding the price change. The uniqueness of this phenomenon in cryptocurrencies motivated me to provide a better resolution of 90 days dataset, with one data point for each day for both the search trends and the price trend. The graph for 12 months and the fine resolution can be found below.

Ethereum (ETH), similar to bitcoin, has instances of price trends following the search trends. Same as with Bitcoin, I also investigated a fine resolution for the Ethereum price and search trend. The fine resolution version of the graph spans 90 days and provides one data point for each day for both prices and search trends.

GameStop was the final commodity that I investigated. This commodity was also the only stock commodity of note among those I investigated (namely Tesla, Microsoft, Google, Amazon, etc.). In the discussion section, we attempt a deeper dive into the story behind the graph’s peak moment.

Discussion

In our observation, one pattern appears that more traditional commodities such as gold, crude oil, and Silver do not follow the search trend and population interest. This phenomenon might not necessarily be valid at a better resolution or smaller population; however, given that my search data source is Google Trends, this can only make observations into larger population trends. Therefore, traditional commodities’ market value has a mind of its own, driven by unknown forces to this study and not by mass’s behavior.

Although the cryptocurrencies of choice in this study do follow price trends preceding the search trends at times, the peaks seem to show the opposite behavior, meaning the peak price changes follow a massive surge in searches on Google. Of course, these searches do not hold the context in which the search was done. Ultimately, the digital currencies’ prices seem to initially get their influence from narrower sources, leading to a considerable surge in searches that drive the more significant price surge waves at the end.

GameStop stock is an excellent illustration of gradual few-day price changes leading to an interest in searches, which in turn drastically increases the stock price. The GameStop also had help by being in the news at the time. Some of the investors shorted it. However, as a way of protest, a group of independent individuals started making many small investments into GameStop, creating the early instances of gradual price change. This story makes its way into the news and sudden increase in searches. Following this increase, the price, of course, skyrockets.

Conclusion

The commodity market’s edge cases seem to be influenced by the population (search) trends. Specifically in their own edge cases. In this study, I have defined edge cases as non-established commodities (officially by the major organizations and/or governments) or up and coming commodities such as Bitcoin and Ethereum or commodities in turmoil and/or narrative driven, like GameStop.

It is worth noting that this is far from conclusive with the given datasets, and further in-depth studies are required to determine the validity of this claim. More likely, more data sources with much more sensitive nuances are needed to understand a complex organism such as the commodity market.

This study was done as a first step to determine whether anything of note can be found in the search vs. price trends. And a simple conclusion to that question, indicated in this study based on the results, is yes, there seem to be some relations of interest between market price trends and search trends.

Although I have already addressed Google Trends data limitations, I would like to put a more delicate note on it here. Google Trends data is a relative measure and does not provide a pure number of searchers. Although, in this case, we are mainly interested in the relative changes, it limits us in comparing the commodities with each other given the presented results.

As mentioned before, Google Trends entirely lacks the context in which a search was done. This will hinder our ability to consider, discuss and conclude with a great deal of certainty. We don’t know whether the individuals searching for the keywords are interested in making purchases, following the news, merely checking the prices, or something else.

Nevertheless, Google Trends’ data provided us with the ability to conclude there are relations of potential interest between some commodities’ price and their search and media trends within certain contexts.

References:

1. Google trends. (n.d.). Retrieved March 15, 2021, from https://trends.google.com/trends/?geo=US

Keywords: ETH, Ethereum, gold, bitcoin, oil, crude oil, Silver, GameStop.

2. Pisani, P. (2021, January 31). For GameStop day traders, the moment they’ve dreamed about. Retrieved March 15, 2021, from https://www.ctvnews.ca/business/for-gamestop-day-traders-the-moment-they-ve-dreamed-about-1.5289040

3. Yahoo finance — stock Market Live, Quotes, business & finance news. (n.d.). Retrieved March 15, 2021, from 

https://finance.yahoo.com/

Historical price of gold, Silver, ETH, BTC, oil, and GameStop archive used.

This topic entails the use of technology in self-reporting symptoms, primarily as a means to battle the COVID-19 pandemic. We do attempt to inform the reader of the potentials beyond the current pandemic.

The importance of such technologies, given their efficacy, became apparent very early into the COVID-19 pandemic due to the virus’s fast-moving nature and its varying effects on different individuals of different characteristics and demographics.

Studies that have been examined have used mobile applications for self-reporting, collecting and storing the questionnaires answered by individual reporters, and analyzed the collected data. The studies’ primary goal was to find patterns emerging out of the collected data and provide a proof of concept of the potential use case of such methods in combating the pandemic, with an eye on other potential use cases of similar methods outside the context of the pandemic — namely individualized medicine.

Many such studies started early on in mid-March. They were providing preliminary results as soon as mid to late April. Delivering some evidence in emerging patterns of population’s behavior and reported symptoms and its potential ramifications in the direction of the development and spread of the virus in some geographical regions before regional health authorities provided official reports. They further managed to find associations between symptoms and the odds ratio (OR) for a positive test and between risk factors and their OR with a positive test.

All studies that were considered have found complex symptoms (having two or more symptoms in specific categories of symptoms) are much more instructive in having predictive value than a single symptom in an individual. This phenomenon was due to the prevalence of some symptoms in other conditions unrelated to SARS-CoV-2 as well.

This approach proved to be adding to the arsenal of methods useful for battling COVID-19 due to its predictive power in all three areas mentioned above. The technique would also be helpful to move scarce resources to more vulnerable or in need places.

Combining other known but novel methods and technologies such as Machine Learning has furthered our understanding of the disease at a much faster pace than would be possible with more conventional methods.

The use cases beyond the COVID-19 pandemic, although briefly looked at, is worthy of further studies. The potentials include but are not limited to individualized medicine, the study of novel diseases, and further understanding of the known infections, especially in an individual or demographical context.

References:

1. M. Zens, A. Brammertz, J. Herpich, N. Südkamp, M. Hinterseer. App-Based Tracking of Self-Reported COVID-19 Symptoms: Analysis of Questionnaire Data. J Med Internet Res2020, 22, 9, e21956. DOI: 10.2196/21956

2. K. Yamamoto,T.Takahashi,M.Urasaki, Y. Nagayasu,T.Shimamoto, Y. Tateyama,K.Matsuzaki,D.Kobayashi, S. Kubo,S.Mito,T.Abe,H.Matsuura,T.Iwami T. Health Observation App for COVID-19 Symptom Tracking Integrated With Personal Health Records: Proof of Concept and Practical Use Study. JMIR Mhealth Uhealth2020, 8, 7, e19902. DOI: 10.2196/19902

3. D. A. Drew et al. Rapid implementation of mobile technology for real-time epidemiology of COVID-19. Science2020: 1362–1367

There are a lot of people who love playing video games. Which makes the understanding the effects of video game on players quite important. Playing video games has several advantages like distraction and high-level thinking skills. On the other hand, some people think that disadvantages of video games are more than its advantages.

Some believe that video games have positive effects on their life. First, video games relieve physical and emotional pain, depression, and aggressive behavior. As Scott C. Jones describes it “Games have kept my head above water, far more than I realized. They have served as pixelated balm on old, deep wounds.”. Playing video games develop a better perception of what is happening around players. Scott also says, “Games have fed me, clothed me and put a roof over my head.” Scott was molested by his neighbor and after he told his problem to his mother, she did not believe him. Scott found himself alone and to relieve his pain he used video games to escape his awful reality. This is how video games can give an opportunity for someone to solve a problem or at least ease it or escape from it even if temporarily. When people take the problems as if they were a game, they solve them with more determination and creativity as did Scott. According to Sheila Eugenio the author of the article “8 Cognitive Benefits of Playing Video Games for Kids” video games have many benefits including but not limited to Improves coordination, Improves problem-solving skills, Enhances memory, Improves attention and concentration, It is a great source of learning (as discussed before in this article as well), Improves the brain’s speed, Enhances multitasking skills. Furthermore, according to yet another article entitled “15 Surprising Benefits of Playing Video Games” games have positive effects on surgeons and makes them less prone to mistake than their “non-gaming counterparts”.

Scott gives elaborate story of how he managed to use games and the fantasy of the game, since when he was very young his family could not afford the game and he only had a book of the maze of the arcade games, to escape his horrible experience and take shelter. Some like Scott would argue this as one of the benefits of video games. After all he named the article, “Video games saved my life”. Scott’s account might be on the extreme side but in some for or other, others have shared similar stories of how such fantasies and games have helped them through tough times.

In many video games the skills required to move through the game involve abstract and high-level thinking and reasoning. These skills are not often taught at school or they are not engaging and effective enough for kids.

Ultimately the benefits of playing video games are plenty and we are far from exhausting them but as for almost everything there is the other side of the argument that we shall discuss.

Others say that there are many negative effects of video games. First, playing video games causes aggressiveness. Secondly, playing video games may impact children in ways such as attention problem. In fact, the players respond immediately to stimuli in the game because of pleasure hormone, so parents need to find the right balance in order to mitigate the negative consequences of video games.

On the other hand, there are plenty of potential physical negative consequence of playing video games such as shoulder pain, carpal tunnel, tendinitis etc. which of course done correctly can be mitigated.

From escapism and high-level thinking to cognitive benefits of video games and negative mental and physical consequences, there is no one adjective to box video games in. They are neither good nor bad. But is paramount that we understand these positive and negative consequences, so we amplify the positives and mitigate the negative in order to make them even more constructive.

After all, the story shared by Scott alone should pause any absolute critic of video games while noting that it remains a subjective story of one individual (though many share similar stories) and further scientific research need s to come into consideration.

References:

1. Jones, S. C. (2018, December 1). Video games saved my life. Retrieved March 25, 2020, from https://www.theglobeandmail.com/opinion/article-extra-life-how-i-was-saved-by-video-games/

2. 15 Surprising Benefits of Playing Video Games. (2019, July 8). Retrieved March 25, 2020, from https://www.mentalfloss.com/article/65008/15-surprising-benefits-playing-video-games

3. Bowler, J. (2017, April 26). 6 Scientific Benefits of Playing Videogames. Retrieved March 25, 2020, from https://www.sciencealert.com/6-scientific-benefits-of-playing-videogames

4. Eugenio, S. (2017, September 2). 8 Cognitive Benefits of Playing Video Games for Kids. Retrieved March 25, 2020, from https://www.engadget.com/2017-02-09-8-cognitive-benefits-of-playing-video-games-for-kids.html

5. Flynn, C., & Flynn, C. (2020, February 22). 7 Negative Health Effects of Video Games. Retrieved March 25, 2020, from https://bestlifeonline.com/negative-effects-video-games/

6. Smart Social. (2020, March 18). The Negative Effects of Video Games (Parent Guide). Retrieved March 25, 2020, from https://smartsocial.com/negative-effects-video-games/

The journey started from Planck’s introduction of quanta in 1900. Planck used the idea to make the classical theory work for black body radiation. He argued that whatever absorbs and emits light, absorbs and emits them at integer multiples of a certain amount of energy.

Later in 1905, Einstein extended the idea in his paper on the photoelectric effect where he suggested the quantum concept introduced by Planck is due to the fact that light is grainy.

In 1911, Walther Nernst declared the theory had become “so fruitful” that requires further attention. He went on to say, “it is the duty of science to take it seriously and to subject it to the careful investigation”. He managed a conference and gathered leading scientists of the time and urged them to examine this theory more closely. This event proved to be very successful and generated much excitement in the field.

Upon his return from the conference, Nobel laureate Ernest Rutherford discussed the conference with Niels Bohr, who had recently joined Rutherford’s lab. All this excitement caused those who were not present at the conference to also become interested. One such person was then 19-year-old Louis de Broglie, who had been convinced to convert to physics from civil service merely due to the excitement from the proceedings of the conference.

In another paper in 1916, Einstein added to his previous idea suggesting that light is absorbed and emitted in physically real quanta, having momentum and direction. He found himself having to compromise, however, when he realized that introducing statistics and probability was required to make the theory work (which he considered to be a big sacrifice). Nevertheless, he had hoped this would only be a temporary formulation and that it would soon be replaced by some deeper understanding of the concept.

Of course, at this time quantum theory consisted of a loosely related group of hypotheses and theorems which were only of interest to those deeply specialized in the field. This held true until 1925 when two major breakthroughs from Werner Heisenberg and Erwin Schrodinger generalized many of these concepts. In “On the Quantum-Mechanical Reinterpretation of Kinematic and Mechanical Relations”, Werner Heisenberg provided a method for calculating quantum states that did not involve particles or waves. This method used matrices and was called ”matrix mechanics”. However, matrix mechanics was considered difficult to use and almost impossible to visualize, causing resistance from many physicists.

On the other hand, Schrodinger’s work (while covering the same concepts) used the already familiar tools of classical mechanics. He developed a wave equation with continuous functions that unfolded in space and time.

In 1921 Schrodinger was a new lecturer at the University of Zurich where he was asked to deliver a formal talk. In his talk, which was called “What is a natural law?”, he suggested the possibility that “the laws of nature without exception have a statistical character”.

In the fall of 1925, Dutch physicist Debye asked Schrodinger to report on the published thesis of de Broglie. This was the paper in which de Broglie used Planck’s rule E = hν to connect the momentum of the electron with wavelengths. Schrodinger explained how de Broglie’s idea worked, with the right orbits being obtained if electrons had integer wavelengths. Debye didn’t like the idea and suggested that if “something were a wave it needed a proper wave equation”. Schrodinger took this remark seriously. After a lengthy absence working on the problem, he returned to Zurich on January 9 of 1926, and eventually completed the equation. Shortly after, he opened a talk with these words, “My colleague Debye suggested that one should have a wave equation, well I have found one!”. Schrodinger then published a series of six papers in 1926 titled “Quantization as a Problem of Proper Values”.

Schrodinger’s equation incorporated a wave function where Ψ related the wavelength to momentum and frequency to energy.

The Ψ included the complex number i, which disturbed Schrodinger at first since it would cause the introduction of the imaginary component. This term was problematic because its presence implied that the wave function had a dimension that was not observable. Schrodinger had initially hoped that Ψ would represent the vibration process in the atom and that the superposition of the waves would create a so-called wave packet (which would explain what was happening when Ψ acted like a single particle). Soon, however, Schrodinger came to a realization which he mentioned in the final part of his Quantization paper series: he realized Ψ function itself can’t be interpreted in terms of three-dimensional space directly. Nevertheless, the equation more or less did what he wanted and described the particle’s world in a more intuitive way.

In the summer of 1926 Max Born, who was Heisenberg’s supervisor, published his work on atomic collisions. Born regarded collisions as one of the key issues in understanding the atomic realm. He had struggled with the matrix mechanics approach and openly declared, ”Only Schrodinger’s formalism proved itself appropriate for this purpose. For this reason I am inclined to regard it as the most profound formulation of quantum laws”. However, Born could not make sense of Schrodinger’s claim that the Ψ function referred to an electron’s charge density. He concluded that Schrodinger’s equation did not tell us about the state of an event, but rather the probability of a state. With this theory, Born ushered in the era of different interpretations of waves and the Schrodinger equation.

Ever since then, the physicist’s view of the world has changed in that the person making the measurement is not merely an observer but a part of the system. All of this does require a rethinking of the nature of reality. Many scientists at the time considered it a sacrifice to bring probability into their equations in exchange for the greater precision afforded by quantum mechanics.