SFTS2019: DLT will take years to implement
10 May 2019 London
Image: Shutterstock
Distributed ledger technology (DLT) will take years to be implemented, an industry expert predicted at the 麻豆传媒 Finance Technology Symposium in London.
The panellist explained that unlike DLT, 鈥渁rtificial intelligence (AI) is everywhere today and there are probably five models waiting to be deployed鈥�.
Discussing technology further, another speaker cited: 鈥淢achine learning is a subdomain of AI and it is about looking at statistics to try and find patterns to predict the future.鈥�
The speaker added: 鈥淔or me, machine learning is like the holy grail and yet there are so many issues around standardised data that we need to get through first.鈥�
One speaker referred to a quote which suggests that computers are incredibly fast accurate and stupid, while humans are inaccurate slow and brilliant and only together can they work really well.
The speaker highlighted that there is a role for humans to be involved with machine learning especially when the costs of the results being wrong are high.
They added: 鈥淐hange is happening faster and we need to take it upon ourselves to do a bit of self-learning [for technology]. I would encourage you all to pursue and investigate these ideas.鈥�
Additionally, one speaker discussed the opportunities of technology and said: 鈥淭here are problems in securities lending that can be solved by technology. It can be done very differently in five years time and still be called securities lending."
The panellist explained that unlike DLT, 鈥渁rtificial intelligence (AI) is everywhere today and there are probably five models waiting to be deployed鈥�.
Discussing technology further, another speaker cited: 鈥淢achine learning is a subdomain of AI and it is about looking at statistics to try and find patterns to predict the future.鈥�
The speaker added: 鈥淔or me, machine learning is like the holy grail and yet there are so many issues around standardised data that we need to get through first.鈥�
One speaker referred to a quote which suggests that computers are incredibly fast accurate and stupid, while humans are inaccurate slow and brilliant and only together can they work really well.
The speaker highlighted that there is a role for humans to be involved with machine learning especially when the costs of the results being wrong are high.
They added: 鈥淐hange is happening faster and we need to take it upon ourselves to do a bit of self-learning [for technology]. I would encourage you all to pursue and investigate these ideas.鈥�
Additionally, one speaker discussed the opportunities of technology and said: 鈥淭here are problems in securities lending that can be solved by technology. It can be done very differently in five years time and still be called securities lending."
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