The apparently AI-created/aided scientific submissions unearthed by the researchers include an extraordinary number of failed attempts at creative synonyms for known phrases in the machine learning sector: deep neural network: ‘profound neural organization’ artiﬁcial neural network: ‘(fake | counterfeit) neural organization’ network attack: ‘organization (ambush | assault)’ network connection: ‘organization association’ big data: ‘(enormous | huge | immense | colossal) information’ data warehouse: ‘information (stockroom | distribution center)’ artiﬁcial intelligence (AI): ‘(counterfeit | human-made) consciousness’ high performance computing: ‘elite ﬁguring’ graphics processing unit (GPU): ‘designs preparing unit’ central processing unit (CPU): ‘focal preparing unit’ face recognition: ‘facial acknowledgement’ mean square error: ‘mean square (mistake | blunder)’ mean absolute error: ‘mean (outright | supreme) (mistake | blunder)’ signal to noise: ‘(motion | ﬂag | indicator | sign | signal) to (clamor | commotion | noise)’ global parameters: ‘worldwide parameters’ random access: ‘(arbitrary | irregular) get right of passage to’ random forest: ‘(arbitrary | irregular) (backwoods | timberland | lush territory)’ random value: ‘(arbitrary | irregular) esteem’ ant colony: ‘subterranean insect (state | province | area | region | settlement)’ ant colony: ‘underground creepy crawly (state | province | area | region | settlement)’ naïve Bayes: ‘(credulous | innocent | gullible) Bayes’ personal digital assistant (PDA): ‘individual computerized collaborator’ In May of 2021 the researchers queried the Dimensions academic search engine in search of this kind of mangled, automated language, taking care to exclude legitimate phrases such as ‘enormous information’ (which is a valid phrase, and not a failed synonym for ‘big data’). At this point they observed that Microprocessors and Microsystems had the highest number of occurrences of mishandled paraphrasing. At the current time, it’s still possible to retrieve ( archive snapshot , 15/07/2021) a number of scientific papers for the nonsense phrase ‘profound neural organization’ (i.e. ‘deep neural network’), and others in the above list yield similar hits. Search results for ‘profound neural organization’ (‘deep neural network’) at Dimensions. Source: https://app.dimensions.ai/ The Microprocessors journal was founded in 1976, and renamed to Microprocessors and Microsystems two years later. The researchers studied a period covering February 2018 to June of 2021, and observed a steep rise in the volume of submissions over the last two years, and particularly over the last 6-8 months: Correlation or causation? The rise in submissions to the Microprocessors and Microsystems journal seems to coincide with the growth of ‘nonsense’ text and synonyms in apparently respectable submissions. Source: https://arxiv.org/pdf/2107.06751.pdf The final dataset gathered by the collaborators contains 1,078 full length articles obtained via the Elsevier subscription of University of Toulouse. Diminishing Editorial Oversight for Chinese Scientific Papers The paper observes that the time period allotted for editorial assessment of the flagged submissions becomes radically shortened in 2021, falling to below 40 days; a six-fold decrease in the standard time for peer review, evident from February of 2021. The greatest number of flagged papers originate from authors with affiliations to mainland China: out of 404 papers accepted in under 30 days, 97.5% are China-related. Conversely, in cases where the editorial process exceeded 40 days (615 papers), China-affiliated submissions represented only 9.5% of that category – a tenfold imbalance. The report ascribes the infiltration of the flagged papers to shortcomings in the editorial process, and a possible lack of resources in the face of a growing number of submissions. The researchers hypothesize that GPT-style generative models, and similar types of language generation frameworks, have been used to produce much of the text in the flagged papers; however, the way that a generative model abstracts its sources makes this difficult to prove, and the chief evidence lies in a common-sense evaluation of poor and unnecessary synonyms, and a meticulous examination of the submission’s logical coherence. The researchers further observe that the generative language models which they believe to be contributing to this flood of nonsense is capable not only of creating the problematic texts, but also recognizing them and flagging them systematically, in the same way that the researchers themselves have carried out manually. The work details such an implementation, using GPT-2, and offers a framework for future systems to identify problematic scientific submissions. The incidence of ‘polluted’ submissions is far higher in the Elsevier journal (72.1%) compared to other journals studied (13.6% maximum).https://www.unite.ai/ai-generated-language-is-beginning-to-pollute-scientific-literature/
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In 2020, industry-wide transaction banking revenue reached $1 trillion . According to Samantha Pelosi, SVP of Payments and Innovation at BAFT, the largest trade association for transaction banking: “The potential efficiency gains and democratization of finance associated with DeFi are attractive to traditional financial institutions. However, DeFi negates the need for relationships with trusted intermediaries, which makes the model disruptive and somewhat alien to these banks.” Virtually all major international commercial banks have at least piloted the use of blockchain for transaction banking services – which remain slow and cumbersome – but none of these pilots have involved DeFi. Rather, they focus on making bank processes more efficient and replacing traditional financial instruments with standardized digital assets. That means the approval and execution of transactions still ultimately go through the framework of traditional banking or more established fintechs. For example, a business’ credit risk is assessed based on financial statements and only applies to that specific business, without the ability to distribute risk across its system. The infrastructure around client support is also quite extensive, which means clients cannot be serviced without a high threshold cost. These practices hamper capital opportunities for larger enterprises and freeze out SMEs. DeFi platforms provide an alternative system, not simply a plug-in to existing banks. Their decentralized nature means transaction onboarding and market-based risk assessments are much easier to scale across a business’ wider system because access to relevant information is not dependent on centralized processing or a prior relationship. Prior to DeFi, a business would have to complete anti-money laundering and “know your customer” checks for every source of capital and convince their counterparts to onboard to the same transaction banking programmes. They also would not be able to present evidence of performance on their debt or payables outside of financial statements. DeFi allows for the exchange of trustable data across a system, mitigating these barriers to business financial services. Until now, however, most companies did not seriously consider DeFi as a viable alternative to their bank’s services because of the volatility of crypto-assets, regulatory uncertainty and the immature technology involved. Even Tesla’s purchase of $1.5 billion in bitcoin was motivated by the direct financial value of bitcoin as an asset, not by its transaction banking needs. While DeFi previously solved the complex requirements around portable digital ID for businesses and has a roadmap for providing access to financial performance track records in transaction banking, it completely lacks two crucial elements: a one-to-one exchange with fiat currency; and interoperability between different blockchains so that counterparties could freely interact with one another. The former is necessary for cryptocurrency to offer a stable store of value that can be used as currency and to have an easily accessible interface with the traditional financial system. Interoperability is crucial for transactions to occur at scale in the highly fragmented blockchain space. What is the World Economic Forum doing about blockchain? Blockchain is an early-stage technology that enables the decentralized and secure storage and transfer of information. It has the potential to be a powerful tool for tracking goods, data, documentation and transactions.https://www.weforum.org/agenda/2021/07/decentralized-finance-transaction-banking-smes/