Technology

How Decentralized Finance Could Make Investing More Accessible

Published

4 years ago

April 17, 2019

Jeff Desjardins

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Infographic: How Decentralized Finance Could Make Investing More Accessible

Did you know that a majority of the global population doesn’t have access to quality financial assets?

In advanced economies, we are lucky to have simple options to grow and protect our wealth. Banks are all over the place, markets are robust, and we can invest our money into assets like stocks or bonds at the drop of a hat.

In the United States, roughly 52% of people are invested in the stock market – but in a place like India, for example, this portion drops to a paltry 2%. How can we make it possible for people on the “outside” of the financial system to gain access?

Breaking Down Barriers

Today’s infographic comes to us from Abra, and it shows how decentralized finance could make investing a more universal phenomenon, especially for those that don’t have access to the modern financial system.

It lays out four key obstacles that prevent people in developing markets from investing in quality financial assets in the first place:

The Geographic Lottery
Where you live plays a massive role in determining your ability to build wealth. In advanced Western economies, the average person is much more likely to be invested in financial markets that can help compound wealth.
Financial Literacy and Complexity
Roughly 3.5 billion adults globally lack an understanding of basic financial concepts, which creates an impenetrable barrier to investing.
Local Market Turmoil
Even if a person is mentally prepared to invest, local market turmoil (hyperinflation, political crises, closed borders, etc.) can make it difficult to get access to stable assets.
The Cost of Investing in Foreign Markets
Foreign assets can be pricey. One share of Amazon is $1,800, which is realistically more money than many people around the world can afford.

In other words, there are billions of people globally that can’t take advantage of some of the most effective wealth-building tactics.

This is just one flaw in the current financial system, a paradigm that has created massive amounts of wealth but only for a specific and well-connected group of people.

Enter Decentralized Finance

Could decentralized finance be the alternative to open up access to financial markets?

By combining apps with blockchain technology – specifically through public blockchains such as Bitcoin or Ethereum – decentralized finance makes it possible to get around some of the barriers that are created by more traditional systems.

Here are some of the innovations that are making this possible:

Smart contracts could automate transactions and remove intermediaries, making investing cheaper, faster, and more accessible.

Fractional investing could allow partial or shared ownership of financial assets by using tokenization. This would make expensive stocks like Amazon ($1,800 per share) available to a much wider segment of the population.

Location independent investing is possible through smartphones. This would make it possible for people in remote parts of the developing world to invest, even without access to nearby financial institutions or local markets.

Like the internet with knowledge, decentralized finance could reshape the world by making financial access universal. Who’s ready?

Related Topics:investing blockchain decentralized finance

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AI

Charted: The Exponential Growth in AI Computation

In eight decades, artificial intelligence has moved from purview of science fiction to reality. Here’s a quick history of AI computation.

Creator Program

Published

4 days ago

September 18, 2023

Pallavi Rao

A cropped version of the time series chart showing the creation of machine learning systems on the x-axis and the amount of AI computation they used on the y-axis measured in FLOPs.

Charted: The Exponential Growth in AI Computation

Electronic computers had barely been around for a decade in the 1940s, before experiments with AI began. Now we have AI models that can write poetry and generate images from textual prompts. But what’s led to such exponential growth in such a short time?

This chart from Our World in Data tracks the history of AI through the amount of computation power used to train an AI model, using data from Epoch AI.

The Three Eras of AI Computation

In the 1950s, American mathematician Claude Shannon trained a robotic mouse called Theseus to navigate a maze and remember its course—the first apparent artificial learning of any kind.

Theseus was built on 40 floating point operations (FLOPs), a unit of measurement used to count the number of basic arithmetic operations (addition, subtraction, multiplication, or division) that a computer or processor can perform in one second.

ℹ️ FLOPs are often used as a metric to measure the computational performance of computer hardware. The higher the FLOP count, the higher computation, the more powerful the system.

Computation power, availability of training data, and algorithms are the three main ingredients to AI progress. And for the first few decades of AI advances, compute, which is the computational power needed to train an AI model, grew according to Moore’s Law.

Period	Era	Compute Doubling
1950–2010	Pre-Deep Learning	18–24 months
2010–2016	Deep Learning	5–7 months
2016–2022	Large-scale models	11 months

Source: “Compute Trends Across Three Eras of Machine Learning” by Sevilla et. al, 2022.

However, at the start of the Deep Learning Era, heralded by AlexNet (an image recognition AI) in 2012, that doubling timeframe shortened considerably to six months, as researchers invested more in computation and processors.

With the emergence of AlphaGo in 2015—a computer program that beat a human professional Go player—researchers have identified a third era: that of the large-scale AI models whose computation needs dwarf all previous AI systems.

Predicting AI Computation Progress

Looking back at the only the last decade itself, compute has grown so tremendously it’s difficult to comprehend.

For example, the compute used to train Minerva, an AI which can solve complex math problems, is nearly 6 million times that which was used to train AlexNet 10 years ago.

Here’s a list of important AI models through history and the amount of compute used to train them.

AI	Year	FLOPs
Theseus	1950	40
Perceptron Mark I	1957–58	695,000
Neocognitron	1980	228 million
NetTalk	1987	81 billion
TD-Gammon	1992	18 trillion
NPLM	2003	1.1 petaFLOPs
AlexNet	2012	470 petaFLOPs
AlphaGo	2016	1.9 million petaFLOPs
GPT-3	2020	314 million petaFLOPs
Minerva	2022	2.7 billion petaFLOPs

Note: One petaFLOP = one quadrillion FLOPs. Source: “Compute Trends Across Three Eras of Machine Learning” by Sevilla et. al, 2022.

The result of this growth in computation, along with the availability of massive data sets and better algorithms, has yielded a lot of AI progress in seemingly very little time. Now AI doesn’t just match, but also beats human performance in many areas.

It’s difficult to say if the same pace of computation growth will be maintained. Large-scale models require increasingly more compute power to train, and if computation doesn’t continue to ramp up it could slow down progress. Exhausting all the data currently available for training AI models could also impede the development and implementation of new models.

However with all the funding poured into AI recently, perhaps more breakthroughs are around the corner—like matching the computation power of the human brain.

Where Does This Data Come From?

Source: “Compute Trends Across Three Eras of Machine Learning” by Sevilla et. al, 2022.

Note: The time estimated to for computation to double can vary depending on different research attempts, including Amodei and Hernandez (2018) and Lyzhov (2021). This article is based on our source’s findings. Please see their full paper for further details. Furthermore, the authors are cognizant of the framing concerns with deeming an AI model “regular-sized” or “large-sized” and said further research is needed in the area.

Methodology: The authors of the paper used two methods to determine the amount of compute used to train AI Models: counting the number of operations and tracking GPU time. Both approaches have drawbacks, namely: a lack of transparency with training processes and severe complexity as ML models grow.