Serendipity35

A decade or two ago when I was teaching one of my social media courses at NJIT, I used to ask students to write a short paper on what they thought was the first social medium or platform. It's one of those questions without a definitive answer and I received a variety of answers over the years. 

Now that we are even deeper into social media and students are even younger - this year's college freshman was born in the 21st century - the early days and history of social media is buried a bit deeper.

The most common answers go back to the 1970s and 80s with forums, bulletin boards and things like AOL's Instant Messenger.

In the early days of the World Wide Web, websites and fledgling social sites and tools were not commercialized. No advertising. How things have changed.

But there were always a few students who went pre-Internet.

On May 24, 1844, some electronic dots and dashes were tapped out by hand on a telegraph machine sending a first electronic message from Baltimore to Washington, D.C. Perhaps, Samuel Morse was prescient about what was to come with his scientific achievement since he wrote, “What hath God wrought?” This was communication and could be two-way but wasn't really a social network. Eventually, there did become a network of users and telegrams could be sent to multiple users.

Technology began to change very rapidly in the 20th Century. After the first super computers were created in the 1940s, scientists and engineers began to develop ways to create networks between those computers, and this would later lead to the birth of the Internet. 

A precursor of the electronic bulletin board system (BBS), known as Community Memory appeared in 1973 and true electronic BBSs arrived with the Computer Bulletin Board System in Chicago, which first came online early in 1978. BBS in big cities were running on TRS-80, Apple II, Atari, IBM PC, Commodore 64, Sinclair, and similar personal computers.

Let's back up a bit and look at the PLATO system launched in 1960. It was developed at the University of Illinois and then commercially marketed by Control Data Corporation. Later, it would offer early forms of social media features, In 1973, Notes (PLATO's message-forum application) was added and TERM-talk was an instant-messaging feature. The Talkomatic may be the first online chat room. There was also News Report, a crowdsourced online newspaper and blog. PLATO used Access Lists so that a note file or other application you created could be limited in access to a certain set of users, such as friends, classmates, or co-workers.

Some people point to the emergence in 1967-69 of the Advanced Research Projects Agency Network (ARPANET), an early digital network, created by the United States Department of Defense, that allowed scientists at four interconnected universities to share software, hardware, and other data. Though not intended to be "social," apparently social niceties did emerge and by the late-1970s non-government and business ideas passed back and forth and a network etiquette (netiquette) was described in a 1982 handbook on computing at MIT's Artificial Intelligence Laboratory.

ARPANET evolved into the Internet after the first Transmission Control Protocol (TCP) specification were witten by Vint Cerf, Yogen Dalal and Carl Sunshine in 1974. This was followed by Usenet, conceived by Tom Truscott and Jim Ellis in 1979 at the University of North Carolina at Chapel Hill and Duke University, and established in 1980.

1985 saw the introduction of The Well and GENie. GENie (General Electric Network for Information Exchange) was an online service created for GE and GENie was still used well into the late 1990s. It had 350,000 users at its peak and was only made redundant by the development of the World Wide Web.

In 1987, the National Science Foundation launched a more robust, nationwide digital network known as the NSFNET.

The IBM PC was introduced in 1981 and the subsequent models of both Apple Mac computers and PCs, better modems, and the slow increase of bandwidth allowed users to do more online. Compuserve, Prodigy and AOL were three of the largest BBS companies and were the first to migrate to the Internet in the 1990s.

The World Wide Web (WWW, or simply "the web") was added to the Internet in the mid-1990s. Message forums became Internet forums.

A number of platforms appeared tht had social tools inlcuding GeoCities (1994) Classmates.com (1995).

The first recognizable social media site might be Six Degrees which appeared in 1997. Users created profiles, give school affiliations and could "friend" other users. It differed from instant-messaging clients (such as ICQ and AOL's AIM) or chat clients (like IRC and iChat) because people used their real names.

It would be 2003 when Myspace launched and by 2006 it had become the most visited website on the planet. Sharing music was a big part of its appeal.

Mark Zuckerberg built a website called "Facemash" in 2003 while attending Harvard University to be used there. But it caught on, spread to other colleges and in June 2004 the company he had started around "TheFacebook" moved to Palo Alto, California. By 2008, it had eclipsed MySpace and in December, 2009, with 350 million registered users it became the most popular social platform in the world.

The original URL - thefacebook.com - still redirects to the renamed Facebook. Myspace was purchased by musician Justin Timberlake in 2011 for $35 million, but it failed to regain popularity.

If Google seems to be missing in this history it is because its attempts to enter social (Orkut and Google+) both failed. Google+ ended in 2018 with the final nail in its coffine being a data security breach that compromised the private information of nearly 500,000 Google+ users.

REFERENCES

online.maryville.edu/blog/evolution-social-media/ 

digitaltrends.com/features/the-history-of-social-networking/

Infographic via socialmediatoday.com

I read that the American rock band Kings of Leon is getting in on NFTs (non-fungible tokens). They are not the first. I looked into this term which I was not familiar with and found that the artist Grimes sold a bunch of NFTs for nearly $6 million and an NFT of LeBron James making a historic dunk for the Lakers garnered more than $200,000. The auction house Christie's got bids in the millions for the artist Beeple. 

NFT (sometimes pronounced niff-tees)stands for "non-fungible token" meaning a token that you can't exchange for another thing of equal value. Fungibility is the ability of a good or asset to be interchanged with other individual goods or assets of the same type. Fungible assets simplify the exchange and trade processes, as fungibility implies equal value between the assets.One comparison I found said to consider that you can exchange a $20 bill for two $10 bills. They are fungible. But an NFT is one of a kind.

These NFTs are used to create verifiable digital scarcity. They also give digital ownership. They seem to be used with things that require unique digital items like crypto art, digital collectibles, and online gaming.

This goes back to blockchain which has become an established way to provide proof of authenticity. Blockchain gets most of its attention because of its use with cryptocurrencies, like Bitcoin. Ownership is recorded on a blockchain which is a digital ledger.

NFTs use Ethereum, a decentralized, open-source blockchain featuring smart contract functionality. Ether is the native cryptocurrency of the platform. It is the second-largest cryptocurrency by market capitalization, after Bitcoin.

We heard recently that Elon Musk bought a lot of Bitcoin and will accept it as payment for his Tesla vehicles, and other vendors accept cryptocurrencies as payment. But NFTs are unlike cryptocurrencies because you can't exchange one NFT for another in the same way that you would with dollars. Its appeal is that each is unique and acts as a collector’s item that can’t be duplicated. They are rare by design, like limited editions and prints. 

And now, with music, proponents say that NFTs could help artists struggling with digital piracy, low streaming royalty rates and a lack of touring revenue from the last year of Covid-19 pandemic restrictions.

A recent episode of the PBS program NOVA took me back to my undergraduate statistics course. It was a course I didn't want to take because I have never been a math person and I assumed that is what the course was about. I was wrong. 

The interesting episode is on probability and prediction and its approach reminded me of the course which also turned out to be surprisingly interesting. Program and course were intended for non-math majors and the producers and professor focused on everyday examples.

I suggest you watch the NOVA episode. You will learn about things that are currently in the news and that you may not have associated with statistics, such as the wisdom of crowds, herd immunity, herd thinking and mob thinking.

For example, the wisdom of crowds is why when a contestant on a Who Wants to Be a Millionaire type of programs asks the audience and out of a few hundred people 85% answer "B," then there's an excllent chance that "B" is the correct answer. And larger samples get more accurate. Why is that?

One of the things I still recall from that class that the program highlighted was the law of large numbers. The law of large numbers states that as a sample size grows, its mean gets closer to the average of the whole population. It was proposed by the 16th century, mathematician Gerolama Cardano but was proven by Swiss mathematician Jakob Bernoulli in 1713.

It works for many situations from the stockmarket to a roulette wheel. I recall that we learned about the "Gambler’s Fallacy." The fallacy is that gamblers don't know enough math, or statistics. They stand by the wheel and see that red has won once and black has now won 5 times in a row. Red is due to win, right? Wrong. The red and black is the same as a coin flip. The odds are always 50/50. The casino knows that. They even list which color and numbers have come up on a screen to encourage you to believe the fallacy.

Flip the coin or spin the wheel 10 times and if could be heads or reds 9 times. Flip or spin 500 times and it will come out to be a lot closer to 50-50.

The "house edge" for American Roulette exists because there is that double zero on the wheel. That gives the house an edge of 2.70%. The edge for European roulette is 5.26%. 

Knowing about probability greatly increases your accuracy in making predictions. And more data makes that accuracy possible.

 

Julia for data visualization

A few friends and former students who are working as programmers have told me recently that I should write about Julia. Julia is not a person but a language. One person called this "the new Python" while another said it was the "Python killer."

Python is the so-far-unchallenged leader of AI programming languages and is used by almost 90% of data scientists, but it is probably not the future of machine learning. Programming languages, like all languages, fall out of favor and sometimes die. There is not much demand for the COBOL, FORTRAN and BASIC that was being taught when I was an undergrad.

Julia is faster than Python because it is designed to quickly implement the math concepts like linear algebra and matrix representations. It is an open source project with more than a thousand contributors and is available under the MIT license with the source code available on GitHub.

I have learned that you don’t need to know programming to do some AI. There are no-code AI tools like Obviously.AI, but programming is necessary for some devlopment.

The home site for Julia is julialang.org which has a lot of information.

An article I read at pub.towardsai.net led me to investiagte a free online course on computational thinking at MIT that is taught using Julia.

This is not a course on programming with Julia but almost all data and AI courses are taught in Python (perhaps a few using R and other languages) so this is unique as a course. The course itself uses as its topic the spread of COVID-19.and includes topics on analyzing COVID-19 data, modeling exponential growth, probability, random walk models, characterizing variability, optimization and fitting to data. Through this topic the course teaches how to understand and model exponential functions. That has much broader application into financial markets, compound interest, population growth, inflation, Moore’s Law, etc.

As that article notes, right now searching jobs on LinkedIn for “Python Developer” will turn up about 23,000 results, so there is a market for that skill set now. Searching “Julia Developer” will return few results now. You can find a LinkedIn group for Julia developers, called “The Julia Language,” so interest is there and the jobs are beginning to appear. A Julia specialits now has a big advantage in that there are fewer people with that skillset for the jobs that are appearing. The predictions (always a dangerous thing) are that Julia has a big role to play in the data & AI industry.