What is Artificial Intelligence? How Does AI Work?

What is Artificial Intelligence?

Artificial intelligence (AI) is wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. AI is an interdisciplinary science with multiple approaches, but advancements in machine learning and deep learning are creating a paradigm shift in virtually every sector of the tech industry.

HOW DOES ARTIFICIAL INTELLIGENCE WORK?

Can machines think? — Alan Turing, 1950

Less than a decade after breaking the Nazi encryption machine Enigma and helping the Allied Forces win World War II, mathematician Alan Turing changed history a second time with a simple question: “Can machines think?”

Turing’s paper “Computing Machinery and Intelligence” (1950), and it’s subsequent Turing Test, established the fundamental goal and vision of artificial intelligence.

what is artificial intelligenceAt it’s core, AI is the branch of computer science that aims to answer Turing’s question in the affirmative. It is the endeavor to replicate or simulate human intelligence in machines.

The expansive goal of artificial intelligence has given rise to many questions and debates. So much so, that no singular definition of the field is universally accepted.

The major limitation in defining AI as simply “building machines that are intelligent” is that it doesn’t actually explain what artificial intelligence is? What makes a machine intelligent?

In their groundbreaking textbook Artificial Intelligence: A Modern Approach, authors Stuart Russell and Peter Norvig approach the question by unifying their work around the theme of intelligent agents in machines. With this in mind, AI is “the study of agents that receive percepts from the environment and perform actions.” (Russel and Norvig viii)

Norvig and Russell go on to explore four different approaches that have historically defined the field of AI:

  1. Thinking humanly
  2. Thinking rationally
  3. Acting humanly 
  4. Acting rationally

The first two ideas concern thought processes and reasoning, while the others deal with behavior. Norvig and Russell focus particularly on rational agents that act to achieve the best outcome, noting “all the skills needed for the Turing Test also allow an agent to act rationally.” (Russel and Norvig 4).

Patrick Winston, the Ford professor of artificial intelligence and computer science at MIT, defines AI as  “algorithms enabled by constraints, exposed by representations that support models targeted at loops that tie thinking, perception and action together.”

While these definitions may seem abstract to the average person, they help focus the field as an area of computer science and provide a blueprint for infusing machines and programs with machine learning and other subsets of artificial intelligence.

While addressing a crowd at the Japan AI Experience in 2017,  DataRobot CEO Jeremy Achin began his speech by offering the following definition of how AI is used today:

“AI is a computer system able to perform tasks that ordinarily require human intelligence… Many of these artificial intelligence systems are powered by machine learning, some of them are powered by deep learning and some of them are powered by very boring things like rules.”

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HOW IS AI USED?

Artificial intelligence generally falls under two broad categories:

  • Narrow AI: Sometimes referred to as “Weak AI,” this kind of artificial intelligence operates within a limited context and is a simulation of human intelligence. Narrow AI is often focused on performing a single task extremely well and while these machines may seem intelligent, they are operating under far more constraints and limitations than even the most basic human intelligence.
  • Artificial General Intelligence (AGI): AGI, sometimes referred to as “Strong AI,” is the kind of artificial intelligence we see in the movies, like the robots from Westworld or Data from Star Trek: The Next Generation. AGI is a machine with general intelligence and, much like a human being, it can apply that intelligence to solve any problem.

ARTIFICIAL INTELLIGENCE EXAMPLES

  • Smart assistants (like Siri and Alexa)
  • Disease mapping and prediction tools
  • Manufacturing and drone robots
  • Optimized, personalized healthcare treatment recommendations
  • Conversational bots for marketing and customer service
  • Robo-advisors for stock trading
  • Spam filters on email
  • Social media monitoring tools for dangerous content or false news
  • Song or TV show recommendations from Spotify and Netflix

Narrow Artificial Intelligence

Narrow AI is all around us and is easily the most successful realization of artificial intelligence to date. With its focus on performing specific tasks, Narrow AI has experienced numerous breakthroughs in the last decade that have had “significant societal benefits and have contributed to the economic vitality of the nation,” according to “Preparing for the Future of Artificial Intelligence,” a 2016 report released by the Obama Administration.

A few examples of Narrow AI include:

  • Google search
  • Image recognition software
  • Siri, Alexa and other personal assistants
  • Self-driving cars
  • IBM’s Watson

Machine Learning & Deep Learning

Much of Narrow AI is powered by breakthroughs in machine learning and deep learning. Understanding the difference between artificial intelligence, machine learning and deep learning can be confusing. Venture capitalist Frank Chen provides a good overview of how to distinguish between them, noting:

“Artificial intelligence is a set of algorithms and intelligence to try to mimic human intelligence. Machine learning is one of them, and deep learning is one of those machine learning techniques.”

Simply put, machine learning feeds a computer data and uses statistical techniques to help it “learn” how to get progressively better at a task, without having been specifically programmed for that task, eliminating the need for millions of lines of written code. Machine learning consists of both supervised learning (using labeled data sets) and unsupervised learning (using unlabeled data sets).

Deep learning is a type of machine learning that runs inputs through a biologically-inspired neural network architecture. The neural networks contain a number of hidden layers through which the data is processed, allowing the machine to go “deep” in its learning, making connections and weighting input for the best results.

Artificial General Intelligence

The creation of a machine with human-level intelligence that can be applied to any task is the Holy Grail for many AI researchers, but the quest for AGI has been fraught with difficulty.

The search for a “universal algorithm for learning and acting in any environment,” (Russel and Norvig 27) isn’t new, but time hasn’t eased the difficulty of essentially creating a machine with a full set of cognitive abilities.

AGI has long been the muse of dystopian science fiction, in which super-intelligent robots overrun humanity, but experts agree it’s not something we need to worry about anytime soon.

HISTORY OF AI

Intelligent robots and artificial beings first appeared in the ancient Greek myths of Antiquity. Aristotle’s development of the syllogism and it’s use of deductive reasoning was a key moment in mankind’s quest to understand its own intelligence. While the roots are long and deep, the history of artificial intelligence as we think of it today spans less than a century. The following is a quick look at some of the most important events in AI.

1943

  • Warren McCullough and Walter Pitts publish “A Logical Calculus of Ideas Immanent in Nervous Activity.” The paper proposed the first mathematic model for building a neural network.

1949

  • In his book The Organization of Behavior: A Neuropsychological Theory, Donald Hebb proposes the theory that neural pathways are created from experiences and that connections between neurons become stronger the more frequently they’re used. Hebbian learning continues to be an important model in AI.

1950

  • Alan Turing publishes “Computing Machinery and Intelligence, proposing what is now known as the Turing Test, a method for determining if a machine is intelligent.
  • Harvard undergraduates Marvin Minsky and Dean Edmonds build SNARC, the first neural network computer.
  • Claude Shannon publishes the paper “Programming a Computer for Playing Chess.”
  • Isaac Asimov publishes the “Three Laws of Robotics.”

1952

  • Arthur Samuel develops a self-learning program to play checkers.

1954

  • The Georgetown-IBM machine translation experiment automatically translates 60 carefully selected Russian sentences into English.

1956

  • The phrase artificial intelligence is coined at the “Dartmouth Summer Research Project on Artificial Intelligence.” Led by John McCarthy, the conference, which defined the scope and goals of AI, is widely considered to be the birth of artificial intelligence as we know it today.
  • Allen Newell and Herbert Simon demonstrate Logic Theorist (LT), the first reasoning program.

1958

  • John McCarthy develops the AI programming language Lisp and publishes the paper “Programs with Common Sense.” The paper proposed the hypothetical Advice Taker, a complete AI system with the ability to learn from experience as effectively as humans do.

1959

  • Allen Newell, Herbert Simon and J.C. Shaw develop the General Problem Solver (GPS), a program designed to imitate human problem-solving.
  • Herbert Gelernter develops the Geometry Theorem Prover program.
  • Arthur Samuel coins the term machine learning while at IBM.
  • John McCarthy and Marvin Minsky found the MIT Artificial Intelligence Project.

1963

  • John McCarthy starts the AI Lab at Stanford.

1966

  • The Automatic Language Processing Advisory Committee (ALPAC) report by the U.S. government details the lack of progress in machine translations research, a major Cold War initiative with the promise of automatic and instantaneous translation of Russian. The ALPAC report leads to the cancellation of all government-funded MT projects.

1969

  • The first successful expert systems are developed in DENDRAL, a XX program, and MYCIN, designed to diagnose blood infections, are created at Stanford.

1972

  • The logic programming language PROLOG is created.

1973

  • The “Lighthill Report,” detailing the disappointments in AI research, is released by the British government and leads to severe cuts in funding for artificial intelligence projects.

1974-1980

  • Frustration with the progress of AI development leads to major DARPA cutbacks in academic grants. Combined with the earlier ALPAC report and the previous year’s “Lighthill Report,” artificial intelligence funding dries up and research stalls. This period is known as the “First AI Winter.”

1980

  • Digital Equipment Corporations develops R1 (also known as XCON), the first successful commercial expert system. Designed to configure orders for new computer systems, R1 kicks off an investment boom in expert systems that will last for much of the decade, effectively ending the first “AI Winter.”

1982

  • Japan’s Ministry of International Trade and Industry launches the ambitious Fifth Generation Computer Systems project. The goal of FGCS is to develop supercomputer-like performance and a platform for AI development.

1983

  • In response to Japan’s FGCS, the U.S. government launches the Strategic Computing Initiative to provide DARPA funded research in advanced computing and artificial intelligence.

1985

  • Companies are spending more than a billion dollars a year on expert systems and an entire industry known as the Lisp machine market springs up to support them. Companies like Symbolics and Lisp Machines Inc. build specialized computers to run on the AI programming language Lisp.

1987-1993

  • As computing technology improved, cheaper alternatives emerged and the Lisp machine market collapsed in 1987, ushering in the “Second AI Winter.” During this period, expert systems proved too expensive to maintain and update, eventually falling out of favor.
  • Japan terminates the FGCS project in 1992, citing failure in meeting the ambitious goals outlined a decade earlier.
  • DARPA ends the Strategic Computing Initiative in 1993 after spending nearly $1 billion and falling far short of expectations.

1991

  • U.S. forces deploy DART, an automated logistics planning and scheduling tool, during the Gulf War.

1997

  • IBM’s Deep Blue beats world chess champion Gary Kasparov

2005

  • STANLEY, a self-driving car, wins the DARPA Grand Challenge.
  • The U.S. military begins investing in autonomous robots like Boston Dynamic’s “Big Dog” and iRobot’s “PackBot.”

2008

  • Google makes breakthroughs in speech recognition and introduces the feature in its iPhone app.

2011

  • IBM’s Watson trounces the competition on Jeopardy!.  

2012

  • Andrew Ng, founder of the Google Brain Deep Learning project, feeds a neural network using deep learning algorithms 10 million YouTube videos as a training set. The neural network learned to recognize a cat without being told what a cat is, ushering in breakthrough era for neural networks and deep learning funding.

2014

  • Google makes first self-driving car to pass a state driving test.

2016

  • Google DeepMind’s AlphaGo defeats world champion Go player Lee Sedol. The complexity of the ancient Chinese game was seen as a major hurdle to clear in AI.

Is PI Network a scam providing no value to users? Possibly yes!

We don’t normally write about B2C topics but we make an exception in case of topics where we looked for answers and couldn’t easily find them. And this is an investment related topic so this disclaimer is necessary:

All investment strategies and investments involve risk of loss. Nothing contained in this website should be construed as investment advice. Any reference to an investment’s past or potential performance is not, and should not be construed as, a recommendation or as a guarantee of any specific outcome or profit.

We don’t expect anyone except the founders to benefits from PI Network because:

  • Users are currently putting value in the app without any (except maybe psychological) benefits:
    • The app does not provide any utility to its users. There is nothing to do in the app . Users hold on to it with the hope that they will sometime convert their virtual coins to actual value
    • The app works like a direct selling or affiliate marketing system, promising future rewards to users for bringing in new users. Some users put in additional time and effort to attract new users, such as numerous users adding their codes as comments to this article. We find it similar to Multi Level Marketing since it includes direct selling and provides increased potential benefits to early users (i.e. earlier users mine at an increased rate), however affiliate or direct selling are possibly better analogies.
    • Users are putting value into the app. There are hundreds of posts online saying PI Network can not be a scam because you do not put any money it. Users’ time and data are valuable to those users and they are spending these on the app. You can see the data collected by the app on its Play Store page.
  • We find it unlikely for the app to create value in the future unlike its claims:
    • The app creates limited value. Users create no value except for providing their information to the mobile app. The value of such data is unlikely to generate significant wealth for the large user base.
    • There is no visibility on its technology or blockchain. Most blockchain companies publish their code as open source so it can be validated which is not the case here. Currently it is no different than any mobile app in terms of the transparency it provides on its technology.
  • Some of its current practices are also used in scams:
    • Founders are already benefitting from the app. They launched optional video ads at launch to monetize the active user base. The app also has a KYC process of collecting passport information. Binding this to mobile IDs can be valuable information for the founders
    • Their marketing emphasizes the academic credentials of their users. Very similarly, a blockchain scam without blockchain infrastructure, OneCoin, relied on the McKinsey experience of its founder in its marketing.

After sharing these with Pi Network enthusiasts, I frequently hear that I do not get cryptos. That is not the case. I have been investing in cryptos since 2017, review new developments the crypto space.

How does Pi Network work?

It is an app where users

  • login every day and click a button to get digital currency. There is no proof of work being performed, they just login and click a button. This currency is not traded yet so currently holds no value.
  • level up by inviting more users to the platform. This makes them gain more digital currency per day. This is a common model in Pyramid Schemes and Multi level marketing.

Could Pi Networks’ currency be valuable in the future?

Of course. We have done an evidence based analysis here and there are also evidence that show that PI Networks is at least attempting to build something of value:

  • They have published a high level whitepaper outlining their ambitions without providing technical details on how their Pi Stack would work. One of their aims is to have others build apps on PI network to benefit from PI network users’ attention. This reminded us of the pay to surf models of the dot com boom where companies installed software on user devices and acted as middleman between users and advertisers without generating substantial benefit to either party.
  • They claim to have run a pilot in 2020 for people to exchange goods and services using Pi. We do not understand why they do these experiments instead of just creating a blockchain backed currency available on marketplaces. This should be doable with trivial effort as it has already been done by companies like electroneum.
  • According to their Linkedin page, they have 70 employees as of 2021. However, many of the people that list themselves as working there are app users with titles like “Cryptocurrency Trader”. We haven’t analyzed each profile but there seems to be a group of people working towards building something there. It could be the next version of the app or the blockchain network, that is hard to verify from outside the company
  • Its founders were educated at and worked at Stanford. Though this is certainly a good thing, people rarely notice that Warren Buffet, Jeff Bezos, the writer of this article and numerous business founders were educated at reputable universities (e.g. Ivy League universities for these examples). This is because their companies rarely use these facts. Based on our observations, business success is far more important and a better predictor of successful enterprises than academic credentials. And successful companies tend to speak about their business success rather than their founders’ academic credentials.
  • They have had significant growth. They have ±170k reviews and a good rating on Google Play Store. However, models similar to MLM tend to generate fast growth.

Could Pi Network make you rich?

Unlikely. For us, the question is why they don’t already launch the blockchain and the exchange. These are trivial engineering tasks. We have two theories:

  • They may be waiting for the user base to reach enough scale so they can generate value for advertisers. However, we are sceptical that large advertisers will show ads in a network where users login to make money by seeing ads. The concept isn’t new. Such websites existed since the early days of internet. However, none of them reached mass adoption. This is because it is more valuable for advertisers to advertise in websites which are used since they provide some value to users (e.g. information, connecting with friends etc.).
  • As some commenters like Jennifer Vanessa Kaiser highlighted, the founding team may be concerned that once the coin is published on an exchange, there would be a selling frenzy. Then, the coin would not be valuable enough for people to keep on logging in to click. Dreams are better motivators than actual value:

In short, your coins can be worth some value but don’t get your hopes up. Other experiments like ETN only make their users a few euros per month.

Are there free apps that pay users?

Yes, the Brave browser replaces ads on websites with its own ads and shares the value with its users. There are also other free-to-use apps but none of them currently provide any tradeable coin for free.

Bee Network

It is a clone of the Pi Network concept with even less transparency. Just skip it unless you like providing data to anonymous people.

Electroneum ETN

Pi network as a concept is a clone of ETN without a tradable coin. However, ETN launched its coin on exchanges and has been tradable since 2017. However, it no longer provides free coins.

We got attacked after publishing this

As mentioned before, we are a B2B business and nothing hostile ever happened us, except a few poor attempts at copying our content, which we dealt with successfully.

However, for the first time, someone bought a misleading domain name that looks similar to ours and mirrored our entire website, about a month after we published this article. This was incompetent and unethical.

Now the mirror website is down but you can see a screenshot below. We don’t know who did it but we know that it must have been a reaction to what we wrote here.

THE CLONE WAS COPYING OUR WEBSITE IMMEDIATELY. WE ADDED “2” TO THE CATEGORY NAME ON THE RIGHT WHICH WAS IMMEDIATELY REFLECTED
IT WAS NOT A PERFECT CLONE, AS YOU CAN SEE THE SHARE IMAGES WERE NOT REPLICATED

The domain was bought about a month after we initially published this article:

So what should you do?

I wouldn’t bother installing the app. You can always make the argument that you only lose time by giving the app a try. However, this belief would lead the believer to follow any dishonest actor who promises future value. There is no scarcity of empty promises in the world, we try to spend our time more carefully.

However, if you already have the app, you can wait to see if the founders actually build a crypto currency.

Finally, if you came across this because you are looking for ways to become wealthy without putting significant effort, we recommend you to look for other ways. As Buddha said, “Our mere existence is suffering” and as Karl Marx is claimed to have said “Life is struggle”. We don’t see shortcuts but consistent effort by flexible and open minds tend to pay off. Instead of such schemes, you could look into learning new skills which tend to pay off better.

 

What does the Pi Network team say about this?

Nothing until now. We asked for comments via their contact us form.

How you can contribute to this discussion?

Please leave a comment, we are open to all view points. I am still learning about crypto and this is only an attempt to help people make informed decisions about their time. We publish comments as long as they:

  • do not contain pi-network codes. If we let that happen, the comments below would be filled with codes that don’t add any value to the discussion. If anyone wants to find codes, they can google them.
  • do not repeat already discussed points
  • do not use inappropriate or immature language
  • do not include major spelling mistakes

What Is BlockChain Technology? How Does It Work?

what is blockchain technology
BLOCKCHAIN IS MOST SIMPLY DEFINED AS A DECENTRALIZED, DISTRIBUTED LEDGER TECHNOLOGY THAT RECORDS THE PROVENANCE OF A DIGITAL ASSET.

What is Blockchain Technology?

Blockchain, sometimes referred to as Distributed Ledger Technology (DLT), makes the history of any digital asset unalterable and transparent through the use of decentralization and cryptographic hashing.

A simple analogy for understanding blockchain technology is a Google Doc. When we create a document and share it with a group of people, the document is distributed instead of copied or transferred. This creates a decentralized distribution chain that gives everyone access to the document at the same time. No one is locked out awaiting changes from another party, while all modifications to the doc are being recorded in real-time, making changes completely transparent.

Of course, blockchain is more complicated than a Google Doc, but the analogy is apt because it illustrates three critical ideas of the technology:

BLOCKCHAIN EXPLAINED: A QUICK OVERVIEW

  1. Digital assets are distributed instead of copied or transferred.
  2. The asset is decentralized, allowing full real-time access.
  3. A transparent ledger of changes preserves integrity of the document, which creates trust in the asset.

Blockchain is an especially promising and revolutionary technology because it helps reduce risk, stamps out fraud and brings transparency in a scaleable way for myriad uses.

How Does Blockchain Work?

The whole point of using a blockchain is to let people — in particular, people who don’t trust one another — share valuable data in a secure, tamperproof way.
— MIT Technology Review

Blockchain consists of three important concepts: blocks, nodes and miners.

Blocks

Every chain consists of multiple blocks and each block has three basic elements:

  • The data in the block.
  • A 32-bit whole number called a nonce. The nonce is randomly generated when a block is created, which then generates a block header hash.
  • The hash is a 256-bit number wedded to the nonce. It must start with a huge number of zeroes (i.e., be extremely small).

When the first block of a chain is created, a nonce generates the cryptographic hash. The data in the block is considered signed and forever tied to the nonce and hash unless it is mined.

Miners

Miners create new blocks on the chain through a process called mining.

In a blockchain every block has its own unique nonce and hash, but also references the hash of the previous block in the chain, so mining a block isn’t easy, especially on large chains.

Miners use special software to solve the incredibly complex math problem of finding a nonce that generates an accepted hash. Because the nonce is only 32 bits and the hash is 256, there are roughly four billion possible nonce-hash combinations that must be mined before the right one is found. When that happens miners are said to have found the “golden nonce” and their block is added to the chain.

Making a change to any block earlier in the chain requires re-mining not just the block with the change, but all of the blocks that come after. This is why it’s extremely difficult to manipulate blockchain technology. Think of it is as “safety in math” since finding golden nonces requires an enormous amount of time and computing power.

When a block is successfully mined, the change is accepted by all of the nodes on the network and the miner is rewarded financially.

Nodes

One of the most important concepts in blockchain technology is decentralization. No one computer or organization can own the chain. Instead, it is a distributed ledger via the nodes connected to the chain. Nodes can be any kind of electronic device that maintains copies of the blockchain and keeps the network functioning.

Every node has its own copy of the blockchain and the network must algorithmically approve any newly mined block for the chain to be updated, trusted and verified. Since blockchains are transparent, every action in the ledger can be easily checked and viewed. Each participant is given a unique alphanumeric identification number that shows their transactions.

Combining public information with a system of checks-and-balances helps the blockchain maintain integrity and creates trust among users. Essentially, blockchains can be thought of as the scaleability of trust via technology.

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USES

blockchain uses ethereum

Beyond Bitcoin: Ethereum Blockchain

Originally created as the ultra-transparent ledger system for Bitcoin to operate on, blockchain has long been associated with cryptocurrency, but the technology’s transparency and security has seen growing adoption in a number of areas, much of which can be traced back to the development of the Ethereum blockchain.

In late 2013, Russian-Canadian developer Vitalik Buterin published a white paper that proposed a platform combining traditional blockchain functionality with one key difference: the execution of computer code. Thus, the Ethereum Project was born.

Ethereum blockchain lets developers create sophisticated programs that can communicate with one another on the blockchain.

Tokens

Ethereum programmers can create tokens to represent any kind of digital asset, track its ownership and execute its functionality according to a set of programming instructions.

Tokens can be music files, contracts, concert tickets or even a patient’s medical records. This has broadened the potential of blockchain to permeate other sectors like media, government and identity security. Thousands of companies are currently researching and developing products and ecosystems that run entirely on the burgeoning technology.

Blockchain is challenging the current status quo of innovation by letting companies experiment with groundbreaking technology like peer-to-peer energy distribution or decentralized forms for news media. Much like the definition of blockchain, the uses for the ledger system will only evolve as technology evolves.

BLOCKCHAIN APPLICATIONS

Blockchain has a nearly endless amount of applications across almost every industry. The ledger technology can be applied to track fraud in finance, securely share patient medical records between healthcare professionals and even acts as a better way to track intellectual property in business and music rights for artists.
HISTORY

history of blockchain

History of Blockchain

Although blockchain is a new technology, it already boasts a rich and interesting history. The following is a brief timeline of some of the most important and notable events in the development of blockchain.

2008

2009

  • The first successful Bitcoin (BTC) transaction occurs between computer scientist Hal Finney and the mysterious Satoshi Nakamoto.

2010

  • Florida-based programmer Laszlo Hanycez completes the first ever purchase using Bitcoin — two Papa John’s pizzas. Hanycez transferred 10,000 BTC’s, worth about $60 at the time. Today it’s worth $80 million.
  • The market cap of Bitcoin officially exceeds $1 million.

2011

  • 1 BTC = $1USD, giving the cryptocurrency parity with the US dollar.
  • Electronic Frontier Foundation, Wikileaks and other organizations start accepting Bitcoin as donations.

2012

  • Blockchain and cryptocurrency are mentioned in popular television shows like The Good Wife, injecting blockchain into pop culture.
  • Bitcoin Magazine launched by early Bitcoin developer Vitalik Buterin.

2013

  • BTC market cap surpassed $1 billion.
  • Bitcoin reached $100/BTC for first time.
  • Buterin publishes “Ethereum Project” paper suggesting that blockchain has other possibilities besides Bitcoin (e.g., smart contracts).

2014

  • Gaming company Zynga, The D Las Vegas Hotel and Overstock.com all start accepting Bitcoin as payment.
  • Buterin’s Ethereum Project is crowdfunded via an Initial Coin Offering (ICO) raising over $18 million in BTC and opening up new avenues for blockchain.
  • R3, a group of over 200 blockchain firms, is formed to discover new ways blockchain can be implemented in technology.
  • PayPal announces Bitcoin integration.

2015

  • Number of merchants accepting BTC exceeds 100,000.
  • NASDAQ and San-Francisco blockchain company Chain team up to test the technology for trading shares in private companies.

2016

  • Tech giant IBM announces a blockchain strategy for cloud-based business solutions.
  • Government of Japan recognizes the legitimacy of blockchain and cryptocurrencies.

2017

  • Bitcoin reaches $1,000/BTC for first time.
  • Cryptocurrency market cap reaches $150 billion.
  • JP Morgan CEO Jamie Dimon says he believes in blockchain as a future technology, giving the ledger system a vote-of-confidence from Wall Street.
  • Bitcoin reaches its all-time high at $19,783.21/BTC.
  • Dubai announces its government will be blockchain-powered by 2020.

2018

  • Facebook commits to starting a blockchain group and also hints at the possibility of creating its own cryptocurrency.
  • IBM develops a blockchain-based banking platform with large banks like Citi and Barclays signing on.