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7 artificial intelligence examples in everyday life

AI impacts everyday life: personal assistants, social media, healthcare, autonomous vehicles, smart homes and more!

Artificial intelligence (AI) is becoming increasingly important in our daily lives. AI can automate routine and time-consuming tasks, allowing us to focus on more important activities. In addition, AI algorithms can analyze vast amounts of data to personalize products, services and experiences. Moreover, AI is driving innovation in various industries, such as finance, retail and education.

Here are seven artificial intelligence examples in everyday life.

Personal Assistants

AI-powered personal assistants, such as Siri, Google Assistant and Amazon Alexa, are integrated into smartphones, smart speakers and other devices and can perform a wide range of tasks, from setting reminders and sending messages to playing music and controlling smart home devices.

Social media

Social media sites utilize AI to examine user preferences and behavior, suggest pertinent material, and customize the user experience. Moreover, bogus news, hate speech and other harmful content are found and eliminated thanks to AI systems.

For instance, Meta uses AI to detect and remove fake news and other harmful content. Instagram uses AI to recommend posts and stories based on user behavior. TikTok uses AI to personalize the user experience and recommend videos.

Customer service

Businesses are increasingly using virtual assistants and chatbots powered by AI to offer 24/7 customer service. Natural language processing is used by these chatbots to comprehend consumer questions and deliver relevant responses.

For instance, many companies, such as H&M, use AI-powered chatbots to provide customer support. These chatbots can handle a wide range of queries, such as tracking orders and processing returns.

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Applications of artificial intelligence in healthcare include patient monitoring, medication research and medical imaging. Medical picture analysis, anomaly detection and diagnosis support are all capabilities of AI algorithms.

For instance, Merative (formally IBM Watson Health) uses AI to analyze medical images and assist doctors in making diagnoses. The app Ada uses AI to help users identify symptoms and connect with healthcare professionals.

Related: 9 promising blockchain use cases in healthcare industry


Customers are given product recommendations by e-commerce sites, such as Amazon, using AI algorithms based on their search queries, browsing histories and other information. Sales are boosted as a result, and customer satisfaction is enhanced.

Autonomous vehicles

AI is used in self-driving cars, trucks and buses to perceive their environment, map out routes and make judgments while driving. It is anticipated that this technology will lessen collisions, gridlock in the streets and pollutants.

For instance, Tesla uses AI to power its self-driving cars, which can navigate roads, highways and parking lots without human intervention.

Smart home devices

Smart home devices such as thermostats, lighting systems and security systems use AI to learn user preferences and adjust settings accordingly. These devices can also be controlled remotely using smartphones or voice commands.

For instance, Philips Hue uses AI to adjust lighting based on user preferences and ambient light levels.

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AI set to benefit from blockchain-based data infrastructure

Several data infrastructure and intelligence use cases take a decentralized approach to provide AI functionalities.

The rise of ChatGPT has been nothing short of spectacular. Within two months of launch, the artificial intelligence (AI)-based application reached 100 million unique users. In January 2023 alone, ChatGPT registered about 590 million visits.

In addition to AI, blockchain is another disruptive technology with increasing adoption. Decentralized protocols, applications and business models have matured and gained market traction since the Bitcoin (BTC) white paper was published in 2008. Much needs to be done to advance both of these technologies, but the zones of convergence between the two will be exciting to watch.

While the hype is around AI, a lot goes on behind the scenes to create a robust data infrastructure to enable meaningful AI. Low-quality data stored and shared inefficiently would lead to poor insights from the intelligence layer. As a result, it is critical to look at the data value chain holistically to determine what needs to be done to get high-quality data and AI applications using blockchain.

The key question is how Web3 technologies can tap into artificial intelligence in areas like data storage, data transfers and data intelligence. Each of these data capabilities may benefit from decentralized technologies, and firms are focusing on delivering them.

Data storage

It helps to understand why decentralized data storage is an essential building block for the future of decentralized AI. As blockchain projects scale, every vector of centralization could come to haunt them. A centralized blockchain project could suffer governance breakdown, regulatory clampdown or infrastructure issues.

For instance, the Ethereum network “Merge,” which moved the chain from proof-of-work to proof-of-stake in September 2022, could have added a vector of centralization to the chain. Some have argued that major platforms and exchanges like Lido and Coinbase, which have a large share of the Ethereum staking market, have made the network more centralized.

Another vector of centralization for Ethereum is its reliance on Amazon Web Services (AWS) cloud storage. Therefore, storage and processing power for blockchain projects must be decentralized over time to mitigate the risks of a single centralized point of failure. This presents an opportunity for decentralized storage solutions to contribute to the ecosystem, bringing scalability and stability.

But how does decentralized storage work?

The principle is to use multiple servers and computers worldwide to store a document. Simply, a document can be split, encrypted and stored on different servers. Only the document owner will have the private key to retrieve the data. On retrieval, the algorithm pulls these individual parts to present the document to the user.

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From a security perspective, the private key is the first layer of protection, and the distributed storage is the second layer. If one node or a server on the network is hacked, it can only access part of the encrypted data file.

Major projects within the decentralized storage space include Filecoin, Arweave, Crust, Sia and StorJ.

Decentralized storage is still in a nascent state, however. Facebook generates 4 petabytes (4,096 terabytes) of data daily, yet Arweave has only handled about 122TB of data in total. It costs about $10 to store 1TB of data on AWS, while on Arweave, the cost is about $1,350 at the time of publication.

Undoubtedly, decentralized storage has a long way to go, but high-quality data storage can boost AI for real-world use cases.

Data transfer

Data transfer is the next key use case on the data stack that can benefit from decentralization. Data transfers using centralized application programming interfaces (APIs) can still enable AI applications. However, adding a vector of centralization at any point in the data stack would make it less effective.

Once decentralized, the next item on the data value chain is the transfer and sharing of data — primarily through oracles.

Oracles are entities that connect blockchains to external data sources so that smart contracts can plug into real-world data and make transaction decisions.

However, oracles are one of the most vulnerable parts of the data architecture, with hackers targeting them extensively and successfully over the years. In one recent example, the Bonq protocol suffered a $120 million loss due to an oracle hack.

Besides smart contracts and cross-chain bridge hacks, oracle vulnerabilities have been low-hanging fruit for cybercriminals. This is mainly due to a lack of decentralized data transfer infrastructure and protocols.

Decentralized oracle networks (DONs) are a potential solution for secure data transfer. DONs have multiple nodes that provide high-quality data and establish end-to-end decentralization.

Oracles have been used extensively within the blockchain industry, with different types of oracles contributing to the data transfer mechanism.

There are input, output, cross-chain and compute-enabled oracles. Each of them has a purpose in the data landscape. 

Input oracles carry and validate data from off-chain data sources to a blockchain for use by a smart contract. Output oracles allow smart contracts to carry data off-chain activity and trigger certain actions. Cross-chain oracles carry data between two blockchains — which could be fundamental as blockchain interoperability improves — while compute-enabled oracles use off-chain computation to offer decentralized services.

While Chainlink has been a pioneer in developing oracle technologies for blockchain data transfer, protocols like Nest and Band also provide decentralized oracles. Apart from pure blockchain-based protocols, platforms like Chain API and CryptoAPI provide APIs for DONs to consume off-chain data securely.

Data intelligence

The data intelligence layer is where all the infrastructure efforts of storing, sharing and processing data come to fruition. A blockchain-based application using AI can still source data from traditional APIs. However, that would add a degree of centralization and could affect the robustness of the final solution.

However, several applications are tapping into machine learning and artificial intelligence in crypto and blockchain.

Trading and investments

For several years, machine learning and artificial intelligence have been used within fintech to deliver robo-advisory functionalities to investors. Web3 has taken inspiration from these applications of AI. Platforms source data on market prices, macroeconomic data and alternate data like social media, generating user-specific insights.

The user typically sets their risk and returns expectations, with the recommendations from the AI platform falling within these parameters. The data required to deliver these insights is sourced by the AI platform using oracles.

Bitcoin Loophole and Numerai are examples of this AI use case. Bitcoin Loophole is a trading application that employs artificial intelligence to provide trading signals to platform users. It claims to have over 85% success rate in doing so.

Numerai claims it is on a mission to build “the world’s last hedge fund” using blockchain and AI. It uses AI to collect data from different sources to manage a portfolio of investments like a hedge fund would.

AI marketplace

A decentralized AI marketplace thrives on the network effect between developers building AI solutions at one end, and users and organizations employing these solutions at the other end. Due to the application’s decentralized nature, most commercial relationships and transactions between these stakeholders are automated using smart contracts.

Developers can configure the pricing strategy through inputs to smart contracts. Payment to them for using their solution could happen per data transaction, data insight or just a flat retainer fee for the period of use. There could also be hybrid approaches to the price plan, with the usage tracked on-chain as the AI solution is used. The on-chain activities would trigger smart contract-based payments for using the solution.

SingularityNET and Fetch.ai are two examples of such applications. SingularityNET is a decentralized marketplace for AI tools. Developers create and publish solutions that organizations and other platform participants can use through APIs.

Fetch.ai, similarly, offers decentralized machine learning solutions to build modular and reusable solutions. Agents build peer-to-peer solutions on this infrastructure. The economic layer across the entire data platform is on a blockchain, enabling usage tracking and smart contract transaction management.

NFT and metaverse intelligence

Another promising use case is around nonfungible tokens (NFTs) and metaverses. Since 2021, NFTs have been viewed as social identities by many Web3 users using their NFTs as Twitter profile pictures. Organizations like Yuga Labs have gone one step further, allowing users to log in to a metaverse experience using their Bored Ape Yacht Club NFT avatars.

As the metaverse narrative ramps up, so will the use of NFTs as digital avatars. However, digital avatars on metaverses today are neither intelligent nor do they bear any resemblance to the personality that the user expects. This is where AI can add value. Intelligent NFTs are being developed to allow NFT avatars to learn from their users.

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Matrix AI and Althea AI are two firms developing AI tools to bring intelligence to metaverse avatars. Matrix AI aims to create “avatar intelligence,” or AvI. Its technology allows users to create metaverse avatars as close to themselves as possible.

Althea AI is building a decentralized protocol to create intelligent NFTs (iNFTs). These NFTs can learn to respond to simple user cues through machine learning. The iNFTs would become avatars on its metaverse named “Noah’s Ark.” Developers can use the iNFT protocol to create, train and earn from their iNFTs.

Several of these AI projects have seen an increase in token prices alongside the rise of ChatGPT. Yet, user adoption is the true litmus test, and only then can we be sure that these platforms solve a real problem for the user. These are still early days for AI and decentralized data projects, but the green shoots have emerged and look promising.

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Here’s how ChatGPT-4 spends $100 in crypto trading

GPT-4 version of OpenAI's ChatGPT conversational AI was released on March 14, and is said to be much more powerful than the previous version.

GPT-4, the latest version of artificial intelligence chatbot ChatGPT, believes the events of the last seven days could be bullish for Bitcoin (BTC), Ether (ETH), and Cosmos (ATOM), according to an AI-trading experiment run by Cointelegraph. 

The experiment is aimed at understanding GPT-4’s potential biases towards certain cryptocurrencies, how the events of last week could impact investment decisions, and whether it can adjust strategy to eventually turn a profit.

The experiment began on March 17, instructing the chatbot to allocate $100 to “make as much money as possible in the shortest time.” The prompt had to be written in a way so that GPT would be comfortable giving out trading instructions.

Screenshot of the first message to ChatGPT. Source: OpenAI

As GPT-4 knowledge and training is current only to September 2021, Cointelegraph fed it with year-end round-ups for 2021 and 2022, along with its Markets News and most read stories over the past seven days to understand how it would invest based on the most recent events.

It noted that the current state of the crypto market has been volatile, with “various factors” influencing prices.

“The recent Silicon Valley Bank and Signature Bank failures have led to increased uncertainty in the financial sector. This situation, combined with regulatory pressure and macroeconomic factors, has contributed to fluctuations in cryptocurrency prices.”

With the context in mind, its advice to allocate the $100 involved the following:

  • $50 to Bitcoin (BTC)
  • $25 to Ether (ETH)
  • $15 to Cosmos (ATOM)
  • $10 to “NFT and Web3 related projects”

On Bitcoin, GPT-4 said that despite short-term volatility and a recent price drop, it still has strong-long term potential:

“The overall trend shows that Bitcoin acts as a safe haven during times of financial instability, such as the recent Silicon Valley Bank and Signature Bank failures. Additionally, Bitcoin's dominance is nearing 50%, and some analysts predict a move towards $100k.”

Meanwhile, GPT-4 suggested that Ethereum’s upcoming Shanghai upgrade could strengthen its dominance and benefit liquid staking providers.

“Ethereum's price is also showing signs of recovery, making it a suitable addition to the portfolio,” it added.

The chatbot then recommended allocating $15 to Cosmos (ATOM), due to Cosmo’s launch of Interchain Security, which “could create potential growth opportunities for ATOM.”

“It's a promising project in the blockchain space, and the launch could attract more investors,” it wrote.

BallerGPT’s balance as at the time of writing. Source: CoinMarketCap

It also instructed Cointelegraph to put $6 in Decentraland (MANA), given “interest in virtual worlds and NFTs continues to grow,” and $4 in Chainlink (LINK):

“With the growing adoption of Web3 and decentralized applications, the demand for reliable off-chain data will continue to rise. Chainlink's LINK token is used to pay for services within the Chainlink ecosystem, making it a good investment as the need for oracle services expands.”

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Cointelegraph made the purchases on an exchange immediately after and is now tracking the fund's performance using a portfolio tracker. At the time of writing, there has already been a small paper gain.

This is an ongoing experiment and more updates will be shared in the future. At the end of the experiment, any proceeds will be donated to a charity of the community’s choice.

The experiment was inspired by Twitter user Jackson Fall, who created a “HustleGPT” experiment covering e-commerce.

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‘AI can be defeated with cryptography,’ says Chelsea Manning at SXSW

Cointelegraph sat down with activist and cybersecurity expert Chelsea Manning to discuss how blockchain technology can combat challenges associated with artificial intelligence.

Artificial intelligence (AI) has become a hot topic following the launch of ChatGPT, an AI chatbot created by research company OpenAI. Yet, while ChatGPT has the potential to write blogs and create crypto trading bots, some worry that AI could be harmful. 

A survey conducted by sales platform Tidio found that 69% of college graduates believe AI could take their job or make it irrelevant in the coming years. Others have pointed out that the rise of AI will make it increasingly challenging to verify accurate information versus fake news generated by artificial intelligence.

For example, Chelsea Manning — an activist, security consultant for decentralized privacy platform Nym and former army intelligence analyst — told Cointelegraph that information verification would become a fundamental problem as AI is integrated into society. Manning told Cointelegraph about how blockchain technology can help combat AI challenges during an exclusive interview at South by Southwest 2023.

Cointelegraph: Why is the rise of AI concerning, and how can blockchain technology combat these concerns?

Chelsea Manning: The actual teachings of AI have been going on for a long time, yet as surveillance in AI becomes more efficient, it will reduce the effectiveness of virtual private networks and other circuits from protecting user data.

Another danger associated with AI and deep fakes is that these elements will eventually become so convincing that many of these instances will end up in a courtroom setting. For instance, there will be situations in the future where individuals will have to forensically verify to a court if something was generated by AI.

We can use blockchain technology to create a decentralized list of where information is coming from, who is producing it and where it was created. This can then be verified on a distributed ledger to prove that a particular event historically occurred, resulting in less dispute.

For instance, someone could take a photograph and then place that metadata on a ledger for verification. If someone tries to dispute that, they can go to the ledger and view the cryptographic signature for verification to see that a particular event occurred.

CT: Do you think we will see more companies evolve that will use cryptography to combat AI challenges?

CM: Yes — since verification is going to be a fundamental problem that arises between society’s exposure to products or surveillance that leverage AI. One way to challenge this is through cryptography, which is going to be fundamental.

Manning (right) with Cointelegraph reporter Rachel Wolfson at SXSW. 

I also believe that a great battle within the technology space over the next decade is going to be this issue of verification and knowing if the information we are receiving is accurate. We are running the very real risk of having our entire reality exposed through our phones or televisions and other places online. Although this is a fundamental way to interact with the world, this information will increasingly not be accurate, yet it will be convincing. I believe there are solutions to these problems, and with some foresight and planning, these doomsday scenarios can be navigated.

CT: You also have strong views on taking an infrastructure approach when it comes to ensuring privacy and security. Can you explain what this means?

CM: One of the most frustrating aspects of developing hardware technology is ensuring that the hardware itself is secure. This is why hardware developers need to focus intensively on supply chain matters — who is developing the technology, who is designing it, etc.

I also believe in the added benefit of an open-source architecture, as these standards are common and universal. I’ve been looking at open-source architectures for designing and developing secure hardware technology for Nym. For example, RISC-V is open source architecture developed at the University of California, Berkeley. RISC-V was designed to grow over time as a standard that doesn’t require any intellectual property (IP). Users can build an IP based on RISC-V, but the architecture itself is available to anyone without requiring a fee.

CT: What are your thoughts on cryptocurrency?

CM: I was very interested in Bitcoin when the white paper came out, but I didn’t necessarily view tokens as being assets or the value behind blockchain technology. I was quite surprised and struck by how readily people were to view proof-of-work certificates as being something that they would buy, sell and speculate on.

This is not necessarily my interest, as I don’t play with speculative assets in general. But from a purely academic sense, I find the technology fascinating. I think cryptocurrency is still a proof-of-concept for what is possible down the line with blockchain technology, but not necessarily ripe and ready to change the world.

CT: Recently, we saw Silicon Valley Bank overtaken by regulators. How do you think this will impact the tech industry as a whole?

CM: This is a seismic event and it goes back to my skepticism of speculative assets in general. This shows that we are still at the whims of the economy, both with traditional banks and with token assets.

The Federal Reserve System and regulators are all interconnected, so it doesn’t surprise me that as inflation has been high, and as the Federal Reserve has tried to curtail the amount of currency flowing, we have seen a number of stressors on more speculative and risky ventures. We are now seeing the effects of that.

But out of every one of these cycles, there has been innovation. If anything, operating in an environment where there is less cash available forces people into a position where they have to innovate more in order to survive. I think this will be an interesting time for the technology industry. It will slow down startups for sure, but I think that existing startups that are able to survive this will be the ones to look out for the most over the next 10 years.

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ChatGPT v4 aces the bar, SATs and can identify exploits in ETH contracts

GPT-4 completed many of the tests within the top 10% of the cohort, while the original version of ChatGPT often finished up in the bottom 10%.

GPT-4, the latest version of the artificial intelligence chatbot ChatGPT, can pass high school tests and law school exams with scores ranking in the 90th percentile and has new processing capabilities that were not possible with the prior version.

The figures from GPT-4’s test scores were shared on March 14 by creator OpenAI, revealing it can also convert image, audio and video inputs to text in addition to handling “much more nuanced instructions” more creatively and reliably. 

“It passes a simulated bar exam with a score around the top 10% of test takers,” OpenAI added. “In contrast, GPT-3.5’s score was around the bottom 10%.”

The figures show that GPT-4 achieved a score of 163 in the 88th percentile on the LSAT exam — the test college students need to pass in the United States to be admitted into law school.

Exam results of GPT-4 and GPT-3.5 on a range of recent U.S. exams. Source: OpenAI

GPT4’s score would put it in a good position to be admitted into a top 20 law school and is only a few marks short of the reported scores needed for acceptance to prestigious schools such as Harvard, Stanford, Princeton or Yale.

The prior version of ChatGPT only scored 149 on the LSAT, putting it in the bottom 40%.

GPT-4 also scored 298 out of 400 in the Uniform Bar Exam — a test undertaken by recently graduated law students permitting them to practice as a lawyer in any U.S. jurisdiction.

UBE scores needed to be admitted to practice law in each U.S. jurisdiction. Source: National Conference of Bar Examiners

The old version of ChatGPT struggled in this test, finishing in the bottom 10% with a score of 213 out of 400.

As for the SAT Evidence-Based Reading & Writing and SAT Math exams taken by U.S. high school students to measure their college readiness, GPT-4 scored in the 93rd and 89th percentile, respectively.

GPT-4 excelled in the “hard” sciences too, posting well above average percentile scores in AP Biology (85-100%), Chemistry (71-88%) and Physics 2 (66-84%).

Exam results of GPT-4 and GPT-3.5 on a range of recent U.S. exams. Source: OpenAI

However its AP Calculus score was fairly average, ranking in the 43rd to 59th percentile.

Another area where GPT-4 was lacking was in English literature exams, posting scores in the 8th to 44th percentile across two separate tests.

OpenAI said GPT-4 and GPT-3.5 took these tests from the 2022-2023 practice exams, and that “no specific training” was taken by the language processing tools:

“We did no specific training for these exams. A minority of the problems in the exams were seen by the model during training, but we believe the results to be representative.”

The results prompted fear in the Twitter community too.

Related: How will ChatGPT affect the Web3 space? Industry answers

Nick Almond, the founder of FactoryDAO, told his 14,300 Twitter followers on March 14 that GPT4 is going to “scare people” and it will “collapse” the global education system.

Former Coinbase director Conor Grogan said he inserted a live Ethereum smart contract into GPT-4, and the chatbot instantly pointed to several “security vulnerabilities” and outlined how the code mighbe exploited:

Earlier smart contract audits on ChatGPT found that its first version was also capable at spotting out code bugs to a reasonable degree as well.

Rowan Cheung, the founder of the AI newsletter The Rundown, shared a video of GPT transcribing a hand-drawn fake website on a piece of paper into code.

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‘CryptoGPT’ Twitter accounts spring up as hashtag trends on Twitter

Dozens of Twitter accounts have emerged on Twitter claiming to be related to "CryptoGPT."

A Twitter hashtag relating to a purported artificial intelligence (AI) crypto token called “CryptoGPT” has been trending on Twitter.

Alongside it, a number of very similar-looking Twitter accounts have also sprung up — some of which have been touting likely fake giveaways.

As of the time of writing, “Download CryptoGPT” is trending with 6,185 tweets associated with it. GPT-4 (Generative Pre-trained Transformer 4), an unreleased neural network created by OpenAI, is also trending with 4,683 tweets.

Trending topics on Twitter. Source: Twitter

Meanwhile, dozens of Twitter accounts sporting the name "CryptoGPT" can also be found on Twitter, with some offering likely fake giveaways or airdrops

Many of these accounts describe the purported project as allowing users to use blockchain to monetize their data with AI. The system is based on Ethereum and scales with a zero-knowledge rollup layer-2 network.

The project purportedly aims to attract decentralized application developers to build on its blockchain. CrypoGPT will offer its GPT tokens as payment for anonymous user data generated from the usage of these DApps.

Contrary to what its name may suggest, however, the project doesn't appear to be directly related to the ChatGPT AI chatbot that has taken the internet by storm in recent months.

A snippet of Twitter accounts with names relating to "CryptoGPT" Source: Twitter

The crypto token also appears to have backing from certain crypto exchanges, at least from a listing perspective.

On Mar. 8, Bitfinex announced a listing of GPT on Mar. 10, describing CryptoGPT as a project that aims to offer users an opportunity to earn crypto for sharing their anonymized data. Other exchanges that will reportedly list the GPT token include PancakeSwap, ByBit, Gate, MEXC, Bitget, among others.

Related: ChatGPT learns Bitcoin will end central banking and fiat currency

Earlier this year, blockchain analytics firm PeckShield warned its followers about dozens of alleged "pump & dump" tokens purporting to be related to ChatGPT and Bing AI, in a Feb. 20 Twitter post. 

A pump-and-dump scheme typically involves the creators orchestrating a campaign of misleading statements and hype to persuade investors into purchasing tokens, then secretly selling their stake in the scheme when prices go up. 

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EU Commission to ensure ‘healthy competition’ in the Metaverse

Margrethe Vestager, the executive vice president of the European Commission, stressed the need to anticipate and plan for changes in technological advancements.

Considering the regulatory struggle to keep up with ever-evolving innovations, Margrethe Vestager, the executive vice president of the European Commission, recommended a headstart into brainstorming implications of technologies such as the Metaverse and ChatGPT.

Vestager highlighted how digital transition and the shift to a digital economy have brought about risk and opportunities for the masses while speaking at the Keystone Conference about competition policy. She believes that legislations lag behind technological advancements, adding:

“We have certainly not been too quick to act - and this can be an important lesson for us in the future.”

While the enforcement and legislative process will continue to stay a step behind tech innovations, Vestager stressed the need to anticipate and plan for such changes. She stated:

“For example, it is already time for us to start asking what healthy competition should look like in the Metaverse, or how something like ChatGPT may change the equation.”

She also revealed that EU Commission would enforce antitrust investigations from May 2023 aimed toward the Facebook marketplace and how Meta uses ads-related data from rivals, among others.

Related: The limitations of the EU’s new cryptocurrency regulations

Feb. 15 marked the launch of the European Blockchain Regulatory Sandbox, which provides a space for regulatory dialog for 20 projects per year through 2026.

On the other end of the spectrum, European Union lawmakers are in talks about using zero-knowledge proofs for digital IDs. Cointelegraph’s report on the matter highlighted:

“The new eID would allow citizens to identify and authenticate themselves online (via a European digital identity wallet) without having to resort to commercial providers, as is the case today - a practice that raised trust, security and privacy concerns.”

Zero-knowledge proofs have recently been at the center of researchers’ attention as a possible means to ensure regulatory compliance and privacy in digital currencies.

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Crypto funding seen shifting from CeFi to DeFi after major collapses: CoinGecko

"NFTfi,” on-chain derivative platforms, decentralized stablecoins and Ethereum L2s are four investment opportunities being looked at closely by one crypto investment firm.

Digital asset investment firms poured $2.7 billion into decentralized finance projects in 2022, up 190% from 2021, while investments into centralized finance projects went the other way — falling 73% to $4.3 billion over the same timeframe.

The staggering rise in DeFi funding was despite overall crypto funding figures falling from $31.92 billion in 2021 to $18.25 billion in 2022 as the market shifted from bull to bear.

According to a March 1 report from CoinGecko, citing data from DefiLlama, the figures “potentially points to DeFi as the new high growth area for the crypto industry.” The report says that the decrease in funding toward CeFi could point to the sector “reaching a degree of saturation.”

Funding amount by sector in the cryptocurrency market between 2018-2022. Source: CoinGecko

The near three-fold increase in DeFi investment is also a staggering 65-fold increase from 2020, at the start of the last bull run.

According to CoinGecko, the largest DeFi funding in 2022 came from Luna Foundation Guard’s (LFG) $1 billion sale of LUNA tokens in February 2022, which came about three months before the catastrophic collapse of Terra Luna Classic (LUNC) and TerraClassicUSD (USTC) in May.

Ethereum-native decentralized exchange (DEX) Uniswap and Ethereum staking protocol Lido Finance raised $164 million and $94 million, respectively.

Meanwhile, FTX and FTX US were the largest recipients of CeFi funding, having raised $800 million in January — accounting for 18.6% of CeFi funding in 2022 alone. The crypto exchanges, however, collapsed only 10 months later and filed for bankruptcy.

Other areas of investments included blockchain infrastructure and blockchain technology companies, which raised $2.8 billion and $2.7 billion, respectively, a trend that has remained strong over the last five years, said CoinGecko.

Henrik Andersson, the chief investment officer of Australia-based asset fund manager Apollo Crypto, says his firm is looking at four specific sectors within crypto as of late:

The first is “NFTfi,” which he said results from the combination of DeFi and NFTs. These are NFT projects that use DeFi to implement various trading strategies to earn passive income, or long or short-trade NFT projects, among other things.

The second and third are on-chain derivative platforms and decentralized stablecoins, which Andersson believes have come about due to the collapse of FTX and recent regulatory action:

“In the light of the FTX debacle and regulatory movements, we have seen renewed interest for on-chain derivatives platforms, such as GMX, SNX and LYRA. All seeing record volume/TVL.Decentralised stablecoins such as LUSD/LQTY has also gained from the current regulatory environment.”

The fourth vertical Andersson cited was Ethereum-based layer-2 networks. “2023 is set to be the year for L2s, and in particular Ethereum L2s,” he said.

The chief investment officer explained that layer-2 tokens such as Optimism (OP) have performed well of late, particularly in light of the testnet launch of “Base,” which was created by Coinbase and is powered by Optimism.

GMX, SNX, LYRA, LQTY and OP are all investments of Apollo Crypto.

Related: Venture capital financing: A beginner’s guide to VC funding in the crypto space

Last month, cryptocurrency analyst Miles Deutscher predicted in a Feb. 19 tweet to his 301,700 followers that zero-knowledge rollup tokens, liquid staking derivative tokens, artificial intelligence (AI) tokens, perpetual DEX tokens, “real yield” tokens, GambleFi tokens, decentralized stablecoins and Chinese coins would perform well in 2023 on the back of heavy funding:

Venture capital funding in the crypto space has, however, fallen over the last three consecutive quarters, amid tough market conditions.

7 details in the CFTC lawsuit against Binance you may have missed

Gold Bug Schiff Says ‘The Months of Declining Inflation Are in the Review Mirror,’ AI Crypto Assets Surge, and More — Week in Review

Gold Bug Schiff Says ‘The Months of Declining Inflation Are in the Review Mirror,’ AI Crypto Assets Surge, and More — Week in ReviewEconomist and gold enthusiast Peter Schiff has said that the U.S. Fed may have to fight a “complete economic collapse” and be faced with more to worry about than the current battle against inflation. In other news, artificial intelligence (AI) crypto assets have seen a recent surge, and SEC Chairman Gary Gensler has tossed in […]

7 details in the CFTC lawsuit against Binance you may have missed