Why Blockchain hates, but needs the government to thrive

10

October

2019

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Blockchain’s trust and cost issue

This article will dive deeper into the problem of blockchain with trust and transaction costs. When blockchain advocates plea about trust they usually say things like “in code we trust”, but this is trust as verification. This is verification, because blockchain’s unique consensus protocol verifies if a block with transactions belongs to the distributed ledger or is tempered with (Rosic, 2017). Blockchain enthusiasts do not understand that trust is not the same as verification (Schneier, 2019).

Blockchain and trust
Kevin Werbach (2018) defines four systems that enable trust: peer-to-peer trust, leviathan trust, intermediary trust and distributed trust. Distributed trust is the foundation in the system underlying blockchain. Blockchain moves the traditional trust in people and institutions to trust in technology. You need to trust the algorithms, consensus protocols and the network. When that trust turns out to be displaced, there is no alternative solution (Munford, 2019). These are three frequent problems to illustrate this problem:

– If a wallet with cryptocurrencies gets hacked, you lose all your tokens and it is impossible to retrieve them (Cluley, 2019).
– If you forget your login credentials, you lose access to your coins and thereby lose your investment (Glaser, 2017).
– If there is a bug in the algorithm behind a smart contract, you lose all your coins. For example, the DAO-attack on a blockchain system based on Ethereums smart contracts caused the theft of $170 million worth of Ether (Buis, 2018).

Therefore you can say it is harder to trust technology than to trust people. A legal system is way more transparent and easy to understand than auditing a computer code to find a bug.

Blockchain and transaction costs
On the other hand, blockchain enthusiasts mention that institutions ask expensive fees while not adding any value (Hooper, 2018). This claim is legit to certain extent, but blockchain has higher transaction costs than institutions. The difference lies with the fact that the costs of blockchain transactions are hidden since the miners pay for the resources to mine an extra block. Furthermore, the energy used by miners creates huge environmental waste. The energy cost for mining bitcoin is currently more than twice the energy cost of mining copper or gold (Hern, 2018). Therefore, you can conclude that blockchain does not change the urgency to trust human institutions.

Economic theory views trust as a cost because it takes work to provide. Ronald Coase (1937) states that negotiations, contracts, arrangements and thereby trust are transaction costs. In reality systems with strong trust avoid the hidden costs that would be created if everyone was cheating the system and users would actively try avoiding being a victim of fraud or theft by others. These hidden costs are currently very high with blockchain technology (Orcutt, 2019). But how can we solve this problem?

Blockchain’s solution
Blockchain’s trust issue could be solved by government regulation. Blockchain is – despite its anarchic reputation – no exception and therefore the socio-technical system behind blockchain including the wallets and exchanges have to become regulated to protect consumers and create trust. Prosecutors will also have to contribute to creating trust by investigating the use of cryptocurrencies by criminals to fund terrorism, money laundering and other criminal activities. Governments with the most effective rules – not with the least rules – will attract economic activity and achieve success. In the end blockchain will have to combine algorithms and human activity. It is not sufficient to solely trust technology, built by human programmers after all. For mass-adoption and real economic value, there must be procedures and laws in place to hold humans accountable as well. This is the best solution for the blockchain community, but also for society as a whole. Do you agree?

References:
Cluley, G. (2019). ‘Cryptocurrency wallet GateHub hacked, nearly $10 million worth of Ripple (XRP) stolen’. Retrieved on 8 October 2019, from https://www.tripwire.com/state-of-security/featured/cryptocurrency-wallet-gatehub-hacked/
Coase, R. H. (1937). ‘The Nature of the Firm’, Economica, New Series, Vol. 4, No. 16. (Nov., 1937), pp. 386-405.
Buis, J. (2018). ‘How the $170 million Ethereum bug could have been prevented’. Retrieved on 8 October 2019, from https://hackernoon.com/how-the-170-million-ethereum-bug-could-have-been-prevented-819053c3b2cb
Glaser, A. (2017). ‘People Who Can’t Remember Their Bitcoin Passwords Are Really Freaking Out Now’. Retrieved on 8 October 2019, from https://slate.com/technology/2017/12/people-who-cant-remember-their-bitcoin-passwords-are-really-freaking-out.html
Hern, A. (2018). ‘Energy cost of ‘mining’ bitcoin more than twice that of copper or gold’. Retrieved on 8 October 2019, from https://www.theguardian.com/technology/2018/nov/05/energy-cost-of-mining-bitcoin-more-than-twice-that-of-copper-or-gold
Hooper, M. (2018). ‘Top five blockchain benefits transforming your industry’. Retrieved on 8 October 2019, from https://www.ibm.com/blogs/blockchain/2018/02/top-five-blockchain-benefits-transforming-your-industry/
Munford, M. (2019). ‘How I lost £25,000 when my cryptocurrency was stolen’. Retrieved on 8 October 2019, from https://www.bbc.com/news/business-49177705
Orcutt, M. (2019). ‘Once hailed as unhackable blockchains are now getting hacked’. Retrieved on 8 October 2019, from https://www.technologyreview.com/s/612974/once-hailed-as-unhackable-blockchains-are-now-getting-hacked/
Rosic, A. (2017). ‘Basic Primer: Blockchain Consensus’. Retrieved on 8 October 2019, from https://blockgeeks.com/guides/blockchain-consensus/
Schneier, B. (2019). ‘There is no good reason to trust blockchain technology’. Retrieved on 8 October 2019, from https://www.wired.com/story/theres-no-good-reason-to-trust-blockchain-technology/
Werbach, K. (2018). ‘The Blockchain and the New Architecture of Trust’, Information Policy, The MIT Press, Massachusetts.

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The future of EdTech: How AI will make universities obsolete within 10 years

6

October

2019

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Chinese AI startups show glimpse of the future of education

 
Zhou Yi, a thirteen year old from Hangzhou, was afraid of math. She had tried traditional services, but one day a company called Squirrel AI came to her middle school. Instead of a human teacher, an AI algorithm would compile her lessons and by the end of the semester her GPA rose from a 5.0 to a 6.25. Two years later, she scored a 8.5 at her final exam (Hao, 2019). How you may ask?

Shanghai-based EdTech company Squirrel AI opened 2,000 (franchised) learning centers and registered over a million students in five years – equal to 85% of students in Dutch higher education (CBS, 2019). For every course it offers, its engineering team works with a group of master teachers to divide a topic into over 10,000 elements, while a textbook might divide the same subject into 3,000 elements. Every element is then supported by video lectures, lecture notes and practice problems to give the student the best “student-centered” education possible (Bourne, 2019). You can learn more about the technology behind Squirrel AI in this keynote speech from the O’Reilly AI Conference below.

This may sound like distance future, but Squirrel is already getting traction outside of China by collaborating with MIT, Harvard and developing an OpenAI platform to eliminate the need for institutions to develop their own intelligent systems. They plan to export their technology to the United States and Europe by the end of 2021 (Kong Ho, 2019). Squirrel AI plan to change universities as we know them by replacing rote tasks, but also the class room. Together with Alo7 they added visual and sound analysis to generate summaries, measure the accuracy of the student’s English pronunciation and assess basic indicators of their effort and elation, such as the number of times they opened their mouth to speak and laugh (Hao, 2019).

To achieve this goal they have raised over $150 million in funding and gained unicorn status, surpassing $1 billion in valuation while expanding rapidly (Peng, 2018). This raises several questions: How long will it take till lectures or teachers will be replaced by algorithms? And what are the (dis)advantages of this development?

 

 

References:

Bourne, J. (2019). ‘How Squirrel AI is looking to provide adaptive learning to revolutionize education through AI and big data’. Retrieved 6 October 2019, from https://artificialintelligence-news.com/2019/04/30/how-squirrel-ai-is-looking-to-provide-adaptive-learning-to-revolutionise-education-through-ai-and-big-data/

CBS (2019). ‘Onderwijs in cijfers’. Retrieved 6 October 2019, from https://www.onderwijsincijfers.nl/kengetallen

Hao, K. (2019). ‘China has started a grand experiment in AI education. It could reshape how the world learns’. Retrieved 6 October 2019, from https://www.technologyreview.com/s/614057/china-squirrel-has-started-a-grand-experiment-in-ai-education-it-could-reshape-how-the

Kong Ho, C. (2019). ‘AI education unicorn Squirrel targets foreign markets with plans for mathematics, Mandarin lessons’. Retrieved 6 October 2019, from https://www.scmp.com/tech/start-ups/article/3018297/chinese-education-unicorn-squirrel-ai-targets-foreign-markets-plans

Peng, T. (2018). ‘Adaptive Learning Startup Squirrel AI Raises CN¥1B‘. Retrieved 6 October 2019, from https://medium.com/syncedreview/adaptive-learning-startup-squirrel-ai-raises-cn-1b-df275cbce068

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