Posts in category: Reviews

With the recent launch of GHO on the Aave v3, we’re happy to announce the release of the GHO Risk analytics in the alpha version of the DeFi Risk Radar.

IntoTheBlock Risk Radar Expands to Stablecoins

A cornerstone to the decentralized finance (DeFi) ecosystem, stablecoins are a crucial tool to help users manage risk and returns in their strategies. However, economic risks such as de-pegging events and liquidations can cause substantial setbacks to a DeFi user’s strategy if not carefully monitored.

IntoTheBlock’s (ITB) newly released Risk Radar Dashboard for the highly anticipated GHO stablecoin is our first step in bringing advanced monitoring for economic risks related to stablecoins. The risk indicators in the dashboard are derived from the metrics used in our quant strategies that manage capital for many of the largest institutions and treasuries in the DeFi ecosystem. For more info on the Risk Radar vision feel free to read our CEO’s announcement of the initial alpha release.

GHO Risk Indicator Overview

Aave is one of the most well-known protocols in DeFi and the largest lending platform in the space with over $7 billion in TVL across 8 different blockchains. Aave’s design is to facilitate a permissionless method for users to lend and borrow assets through overcollateralized loans. As one of the most battle tested protocols in the space, the protocol decided to launch a collateralized debt position (CDP) stablecoin, GHO, as a new product for users.

Stablecoins can be notoriously difficult to manage as the crypto space has seen in the past with major de-pegging events and the infamous crash of Terra’s UST. These larger events are in addition to the daily economic risks a user can face such as liquidations of loans in lending markets or high slippage fees when attempting to withdraw assets from a liquidity pool on a decentralized exchange (DEX)

The 20 new metrics released in the GHO Risk Radar aim to provide a transparent way for users to navigate the risks that can be associated with stablecoins and help users make informed decisions about their strategies. Below, we will highlight a few of the indicators in the GHO Risk Radar release and how they can be used to navigate the market.

GHO Peg Performance

A key indicator to monitor for stablecoins is its ability to keep its peg. The GHO Peg Performance indicator tracks GHO peg performance against other stablecoins in a liquidity pool.  Above we can see that GHO has struggled slightly to keep the peg with other stablecoins such as crvUSD, FRAX, and USDC. This is often the case for a newly launched stablecoin as it expands its total supply and liquidity begins to deepen in the liquidity pools.

IntoTheBlock DeFi Risk Radar users will be able to monitor potential de-pegging events in near real-time, download the data to analyze previous instances and soon be able to receive alerts to be notified as they happen.

Collateral Distribution Behind Borrows

To monitor economic risks to GHO from a global perspective, users can use the Collateral Distribution Behind Borrows metric. The chart highlights the types and amounts of collateral used over time to mint GHO.

As can be seen in the chart, The most used collateral to mint GHO is wstETH with the second asset being WETH. This means that users would want to pay attention if there was a sharp decrease in the price of ETH as it could result in a substantial reduction in the supply of GHO. As GHO minters repay debts or get liquidated, the supply of the stablecoin could shrink in liquidity pools which could cause high slippage for users wanting to exit.

Whales Credit History

The behavior of whales in a market can have a significant impact on other users and the protocol’s overall health. If there are whales in the market that are known to have risky behavior, this information can help other users model their own risk profiles in the market accordingly.

The Whales Credit History indicator helps users identify GHO whales and understand how they have behaved in the past through their borrow, repayment, and liquidation history. Users can explore the whale address further by clicking the hyperlink that will take them to the etherscan page for that address.

From the current snapshot of GHO whales, we can see that the borrow share is well distributed with known addresses having minted more than 10% of the total GHO supply. Furthermore, we can see that no liquidations have happened on these addresses in the past, but some repayment. This can indicate that the current whales in the market are actively managing their risks.

Discover the GHO Risk Radar and Join the Conversation on Stablecoins
The launch of the GHO Risk Radar by IntoTheBlock represents a significant advancement in the monitoring and management of economic risks associated with stablecoins. With the inclusion of 20 comprehensive indicators, users can now navigate the DeFi space with a more informed and confident strategy.

Here are some movies about cryptocurrency and blockchain technology that you might find interesting:

Crypto (2019)

This film is about a young Wall Street banker who is demoted back to his hometown and is drawn into investigating a tangled web of corruption and fraud in Upstate New York

Bitcoin: The End of Money as We Know It (2015)

This documentary traces the history of money from the bartering societies of the ancient world to the trading floors of Wall St. It exposes the practices of central banks and the dubious financial actors who brought the world to its knees in the last crisis

The Blockchain and Us (2017)

A documentary about the blockchain, the underlying technology of bitcoin. Featuring interviews with experts in the finance and technology industries and the outlook of financial technology in the future.

Trust Machine: The Story of Blockchain (2018)

This documentary provides a comprehensive overview of how bitcoin works, as part of the blockchain technology, resulting in cryptocurrency and its possibilities: both good and evil.

Banking on Bitcoin (2016)

This documentary discusses the opposing thoughts and ideologies of individuals in the market regarding the growing usage of cryptocurrency, which, due to being decentralized, is considered to be a significant source of illegal activities like money laundering, buy/sell drugs, illegal weaponry, etc.

Bitcoin: Beyond The Bubble

This documentary explores what Bitcoin is. With the advent of Bitcoin, for the first time in history money is no longer controlled by banks or governments, but by the people who use it.

Earaser reborn 2022

Earaser reborn is an American action thriller film centers around a secret agency that specializes in engineering the fake deaths of witnesses that need to leave no trace of their existence.

Trust No One: The Hunt for the Crypto King

A group of investors-turned-sleuths try to unlock the suspicious death of cryptocurrency multimillionaire Gerry Cotten and the missing $250 million they believe he stole from them.

Come back here for more…

Bitcoin, the world’s most dominant cryptocurrency, has seen its price drop below the key $30,000 mark, which has sparked a heated debate among market participants. Several reasons are behind this recent downturn, all of which are essential to understand.

The first key reason is the overall lack of appetite for risk on the current crypto market. In recent times, the crypto sector has seen a sharp drop in the number of new investors entering the market. This decline can be attributed to a sense of caution among potential market participants as they await clarity on the regulatory stance toward digital assets, particularly in the United States.Source: TradingView

Investor uncertainty about the future of Bitcoin exchange-traded funds (ETFs) also plays a significant role. Despite numerous applications being filed with the U.S. Securities and Exchange Commission (SEC), there remains no clarity on whether any of these will be approved. This ambiguity has put a damper on institutional investment inflows into Bitcoin, further suppressing the digital asset’s price.

Moreover, the euphoria that the crypto market experienced back in June seems to be facing the reality check of decreased inflows. This has been accompanied by a cooling off of the decentralized finance (DeFi) and non-fungible token (NFT) sectors, which had previously attracted significant investment and attention. Neither field is showing significant growth trends in terms of total value locked (TVL) or inflows, indicating a potential shift in market sentiment.

On the positive side, Bitcoin’s spot trading volume has risen for the first time in three months. However, it remains around historic lows, suggesting that market participants are taking a wait-and-see approach before committing further funds to the sector.

GPT (Generative Pre-trained Transformer) is a type of artificial intelligence (AI) that can be used to develop natural-sounding text. It can understand the context and generate coherent sentences and paragraphs, making it a helpful tool for content writing in search engine optimization (SEO).

Some of the best use cases of ChatGPT for SEO include constructing regex, creating automations, and helping with technical SEO tasks like URL optimization, multilanguage support, page speed optimization, and code analysis.

Regular expressions construction by ChatGPT

ChatGPT can be used to construct regular expressions (regex) which are sequences of characters used to find patterns within text. For example, if you want to remove all the numbers from a sentence, you can type “removes all the numbers from the sentence,” and ChatGPT will generate the RegEx code `/ [^0-9]/g` for you. There are even courses available that teach you how to build a RegEx generating dashboard using the OpenAI API and a low code solution called Retool.

Content creation using ChatGPT

ChatGPT has a wide range of use cases. Some of the most common use cases include content creation, where ChatGPT can be used to generate high-quality content for websites, blogs, or social media platforms in a few seconds. It can also be used for translation services, where it can automatically translate text from one language to another. ChatGPT can also be used to create intelligent chatbots that can converse with users in natural language. Additionally, ChatGPT can be used for coding applications such as writing code for simple or repetitive tasks, debugging code by proposing possible causes of errors and presenting solutions to resolve them, code completion by anticipating the following lines or segments of code based on the context and current code, and code refactoring by recommending methods to enhance and refine the code structure, readability, and performance.

ChatGPT Translation services

ChatGPT can be used for translation services, where it can automatically translate text from one language to another. Unlike traditional translation tools like Google Translate, ChatGPT’s interactive nature makes it a standout translation tool. With other translation tools, you provide a text, you get a translation, and that’s it. With ChatGPT, you can customize translations to suit your specific needs and provide feedback on adjustments you’d love to see². For example, you can adjust the tone and style and take into account some cultural connotations and regional differences in the meaning of words.

The bottom line

GPT can be used to develop natural-sounding text and can help with tasks such as constructing regex, creating automations, and helping with technical SEO tasks. ChatGPT can be used to construct regular expressions (regex) which are sequences of characters used to find patterns within text. ChatGPT has a wide range of use cases including content creation, translation services, creating intelligent chatbots, and coding applications such as writing code for simple or repetitive tasks, debugging code by proposing possible causes of errors and presenting solutions to resolve them, code completion by anticipating the following lines or segments of code based on the context and current code, and code refactoring by recommending methods to enhance and refine the code structure, readability, and performance. Finally, ChatGPT can be used for translation services where it can automatically translate text from one language to another and that unlike traditional translation tools like Google Translate, ChatGPT’s interactive nature makes it a standout translation tool because you can customize translations to suit your specific needs and provide feedback on adjustments you’d love to see.

Heatbit is the world’s first personal heater that mines Bitcoin. It enables anyone to support the decentralized money revolution while being rewarded for heating in bitcoin². The device looks like a high-end space heater but uses integrated circuitry to process bitcoin transactions³.

Heatbit provides a green and energy-efficient solution to cryptocurrency mining, as emitted heat is recycled to replace other heating devices in homes. Heatbit was designed to require the same energy as a regular heater, at 1,400 W, and is suitable to heat spaces of up to 170 square feet².

How Heatbit works

Heatbit is a device that combines a heater, air purifier, and Bitcoin miner in one. The powerful processors at Heatbit’s core perform trillions of complex calculations per second, earning you Bitcoin. Similar to how your computer generates heat during intense tasks, mining creates warmth that is used to heat your indoor space. All the while, advanced HEPA and carbon filters eliminate dust, smoke, pet fur, and allergens for clean, fresh air.

At full mining capacity, Heatbit Mini uses 300 watts. The energy consumed for mining turns into heat to keep you cozy on cool days (about 1023 BTU/h). For extra warmth on colder days, Heatbit offers a heating boost up to 1,300 watts (about 4433 BTU/h). During summer, when using Heatbit Mini solely for air purification, it consumes less than 50 watts.

The Dunning–Kruger effect is a cognitive bias whereby people with low ability, expertise, or experience regarding a certain type of task or area of knowledge tend to overestimate their ability or knowledge. Some researchers also include the opposite effect for high performers: their tendency to underestimate their skills. In popular culture, the Dunning–Kruger effect is often misunderstood as a claim about general overconfidence of people with low intelligence instead of specific overconfidence of people unskilled at a particular task.

The Dunning–Kruger effect is usually measured by comparing self-assessment with a measure of objective performance. For example, the participants in a study may be asked to complete a quiz and then estimate how well they performed. This subjective assessment is then compared with how well they actually performed. This can happen in either relative or absolute terms, i.e., in comparison with one’s peer group as the percentage of peers outperformed or in comparison with objective standards as the number of questions answered correctly. The Dunning–Kruger effect appears in both cases, but is more pronounced in relative terms; the bottom quartile of performers tend to see themselves as being part of the top two quartiles. The initial study was published by David Dunning and Justin Kruger in 1999. It focused on logical reasoning, grammar, and social skills. Since then various other studies have been conducted across a wide range of tasks, including skills from fields such as business, politics, medicine, driving, aviation, spatial memory, examinations in school, and literacy.

Many models have been suggested to explain the Dunning-Kruger effect’s underlying causes. The original model by Dunning and Kruger holds that a lack of metacognitive abilities is responsible. This interpretation is based on the idea that poor performers have not yet acquired the ability to distinguish between good and bad performances. They tend to overrate themselves because they do not see the qualitative difference between their performances and the performances of others. This has also been termed the “dual-burden account” since the lack of skill is paired with the ignorance of this deficiency. Some researchers include the metacognitive component as part of the definition of the Dunning–Kruger effect and not just as an explanation distinct from it. Various researchers have criticized the metacognitive model and proposed alternative explanations. According to the statistical model, a statistical effect known as regression toward the mean together with the cognitive bias known as the better-than-average effect are responsible for the empirical findings. The rational model holds that overly positive prior beliefs about one’s skills are the source of false self-assessment. Another explanation claims that self-assessment is more difficult and error-prone for low performers because many of them have very similar skill levels. Another model sees lack of incentive to give accurate self-assessments as the source of error.

The Dunning–Kruger effect has been described as relevant for various practical matters, but disagreements exist about the magnitude of its influence. Inaccurate self-assessment can lead people to make bad decisions, such as choosing a career for which they are unfit or engaging in dangerous behavior. It may also inhibit the affected from addressing their shortcomings to improve themselves. In some cases, the associated overconfidence may have positive side effects, like increasing motivation and energy.

The last two weeks have been an absolute whirlwind in the world of generative artificial intelligence (AI), with groundbreaking new releases and cutting-edge integrations taking place. OpenAI released its highly anticipated GPT-4 model, Midjourney unveiled its latest V5 model, and Stanford released their Alpaca 7B language model. Meanwhile, Google launched generative AI across its entire Workspace suite, while Anthropic introduced its AI assistant Claude, and Microsoft integrated its powerful generative AI tool Copilot into the Microsoft 365 suite.

The pace of AI development and adoption is showing no signs of slowing down as businesses have begun to realize the value of AI and automation and the need to adopt these technologies to remain competitive in the market.

Despite the seemingly smooth progress of AI development, there are underlying challenges and bottlenecks that must be addressed. As more businesses and consumers embrace AI, a bottleneck is emerging around computing power. The computations required for AI systems is doubling every few months, while the supply of computing resources struggles to keep pace. Furthermore, the cost of training large-scale AI models continues to soar, with an increase of approximately 3100% per year over the past decade.

This trend towards rising costs and increasing resource demands needed to develop and train cutting-edge AI systems is resulting in centralization, where only entities with massive budgets are able to conduct research and produce models. However, several crypto-based projects are building decentralized solutions to address these issues using open compute and machine intelligence networks.

Primer on AI and ML

The advancement of AI is driven by three main factors:

• Algorithmic innovation: Researchers are constantly developing new algorithms and techniques that allow AI models to process and analyze data more efficiently and accurately.
• Data: AI models rely on large datasets as the fuel for their training, enabling them to learn from patterns and relationships within the data.
• Compute: The complex calculations necessary for training AI models require large amounts of computational processing power.

However, there are two main problems that are hindering the development of AI. Back in 2021, obtaining data was the top challenge facing AI companies in their pursuit of AI development. Last year, compute-related issues surpassed data as a challenge, specifically due to the lack of on-demand access to compute resources, driven by high levels of demand.

The second problem is related to inefficiencies in algorithmic innovation. While researchers continue to make incremental improvements to models by building on previous ones, the intelligence, or patterns, extracted by these models is always lost.

Let’s dig deeper into these problems.

Compute Bottleneck and Decentralized AI

Training foundational machine learning models requires extensive resources, often involving the use of large numbers of GPUs over extended periods of time. For instance, Stablility.AI needed 4,000 Nvidia A100 GPUs running in AWS’ cloud to train their AI models, which cost them more than $50 million in a month. OpenAI’s GPT-3, on the other hand, was trained using 1,000 Nvidia V100 GPUs, costing $12 million.

AI companies are often faced with two choices: investing in their own hardware and sacrificing scalability, or opting for cloud providers and paying inflated prices. While larger companies can afford to choose the latter, smaller companies may not have that luxury. With the rising cost of capital, startups are being forced to cut back on cloud spending, even as the cost of expanding infrastructure for large cloud providers remains largely unchanged.

The high cost of computing for AI creates significant obstacles for researchers and organizations pursuing advancements in the field. Currently, there is a pressing need for an affordable, on-demand serverless compute platform for ML work, which is absent in the traditional computing landscape. Fortunately, several crypto projects are working to develop decentralized machine learning compute networks that can address this need.

Blockchain and machine learning

Deep learning has gained huge traction in recent years because of its potential to make informed decisions. A large portion of today’s deep learning systems are based on centralized servers and fall short in providing operational transparency, traceability, reliability, security, and trusted data provenance features. Also, training deep learning models by utilizing centralized data is vulnerable to the single point of failure problem. In this paper, we explore the importance of integrating blockchain technology with deep learning. We review the existing literature focused on the integration of blockchain with deep learning. We classify and categorize the literature by devising a thematic taxonomy based on seven parameters; namely, blockchain type, deep learning models, deep learning specific consensus protocols, application area, services, data types, and deployment goals. We provide insightful discussions on the state-of-the-art blockchain-based deep learning frameworks by highlighting their strengths and weaknesses. Furthermore, we compare the existing blockchain-based deep learning frameworks based on four parameters such as blockchain type, consensus protocol, deep learning method, and dataset. Finally, we present important research challenges which need to be addressed to develop highly trustworthy deep learning frameworks.

Blockchain technology enables establishing the provenance of machine learning models, thus leading to trusted Artificial intelligence (AI) systems. Blockchain technology presents a robust system and can incentivize the participants who share their data (data trading) which is used to train machine learning models.

A digital identity is information used by computer systems to represent an external agent – a person, organization, application, or device. Digital identities allow access to services provided with computers to be automated and make it possible for computers to mediate relationships.

The use of digital identities is so widespread that many discussions refer to the entire collection of information generated by a person’s online activity as a “digital identity”. This includes usernames, passwords, search history, birthdate, social security number, and purchase history, especially where that information is publicly available and not anonymized and so can be used by others to discover that person’s civil identity. In this broader sense, a digital identity is a facet of a person’s social identity and is also referred to as online identity.

An individual’s digital identity is often linked to their civil or national identity and many countries have instituted national digital identity systems that provide digital identities to their citizenry.

The legal and social effects of digital identity are complex and challenging.

Personal Identifiable Information (PII)

Personal Identifiable Information (PII) is defined as: Any representation of information that permits the identity of an individual to whom the information applies to be reasonably inferred by either direct or indirect means.

The Digital Identity Crisis

To survive in our digitally transformed world, we’ve all gotten used to typing in personal details without a second thought and letting them drift wherever the internet winds may take them. Our names, addresses, emails, and other unique identifiers unlock everything from online banking to one-tap food delivery. But this same accessibility creates innumerable vulnerabilities for identity theft and even grand scale fraud attacks.

This contradiction—that our digital identities are both key to survival and constantly under attack—has created a fracture known as the Digital Identity Crisis. It’s a problem for everyone living in the digital age, but it’s up to businesses to solve it. And the stakes have never been higher.

The Ongoing Impact of the Digital Identity Crisis

The Digital Identity Crisis is causing chaos in how money and identities move across the web. It’s why many major hotels, car rental companies, and other large merchants have stopped accepting digital credit cards. It’s how 4.5 million fraudulent customer accounts were created at PayPal—causing a 25% stock slump. It’s why Robinhood created a list of digital banks from which it bans transfers: literally turns away money. And it’s the reason why so many underbanked, genuine customers get rejected during the digital onboarding processes, simply because they have a thin file of credit or a small digital identity footprint.

Companies have resigned themselves to these pull-up-the-drawbridge efforts as their best attempts to stop the citizen fraudsters, bot attacks, and sophisticated fraud rings that target and trade in valuable PII. The very fact that businesses would take such drastic measures shows how seriously they take the Digital Identity Crisis. Chime, for example, is a digital bank that has booming growth in both users and valuation; but major industries refuse to do business with them due to fraud worries, and in effect are locking themselves out of a robust revenue stream. And it’s not just the monetary impact of fraud that causes nightmares: brands’ reputations, consumer confidence, and regulator penalties are also at stake. How can businesses protect their reputations and revenue, without locking the door on genuine customers?

The Costly “Solutions” to Fraud

Fraud attacks can clobber a business in multiple ways. But while focused on preventing costly fraud loss, they’re often suffering from the even higher cost of false positives. Despite my career spent working with fraud teams, helping hundreds of companies fight fraud and reduce losses caused by cybercriminals, data breaches, and poor verification strategies, I am still constantly surprised by just how much money is lost each year due to fraud prevention systems declining genuine customers who are flagged as suspicious (aka, false positives). According to the Aite Group report, The E-Commerce Conundrum: Balancing False Declines and Fraud Prevention, between 2017-2019 more than 62% of merchants reported their false positive rates had increased. This same report predicted that those losses would grow to $443 billion within two years. This number is staggering, and typically far outweighs losses from fraudulent purchases.

Part of the difficulty in determining genuine customers lies in the fact that most fraud prevention technologies rely on static, historical data that is easily compromised. Many of today’s identity verification tools look backward: at where applicants live, what their credit score is, and other personal identifiable information (PII) connections. Meanwhile, PII is what fraudsters are harvesting and inputting at scale. So cybercriminals are increasingly able to use genuine PII data for fraudulent activities. They submit that PII, it gets through the fraud checks, and costs money. Meanwhile, genuine customers who just don’t have as lengthy PII trails—maybe they’ve moved a lot, or haven’t established long credit histories—get denied.

So, what do you do when the cure costs you more than the disease?

Digital Identity Verification Beyond Traditional Data

This is where new, highly accurate methods of pre-submit data analysis are driving real change across the fraud prevention industry. Pre-submit data is the information derived before PII is even submitted, pulled solely from the digital interactions of an online applicant as they fill out a form. When a prospective customer taps, types, or swipes information into an online form or application, these behaviors create pre-submit or behavioral data.

Behavioral data provides indicators of the intentions and experience that the user leaves behind with every interaction. It can provide deep insights into what users’ true intentions are (legitimate or nefarious), if they are who they say they are, and even the experience they have during their customer journey (confusion, frustration, confidence, etc).

This data has always been generated by digital applicants. For most organizations, this data is inherently captured–but not put to good use. Pre-submit behavioral analytics technology is often the critical missing piece in a business’ fraud detection and prevention system. Bringing with it the potential to save billions of dollars annually lost to fraud, false positives, and customer friction, many regard it as the true solution to solving the Digital Identity Crisis and overcoming the hazards of unreliable PII. Some behavioral analytics technology can even look at crowd-level behavior and find bot attacks before they do damage.

The Digital Identity Crisis is a real threat, but not insurmountable. Cybercriminals flourish when businesses stay in scramble mode, relying solely on static and likely compromised pools of PII data. Pre-submit and behavioral analytics are real-time checks that help businesses keep their eyes on the horizon for new opportunities, instead of always looking backwards at applicants’ history. Those who take advantage of this cutting-edge technology will also stay leaps and bounds ahead of the Digital Identity Crisis, and all the pain that comes with it.

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