sidhellman/clean_data.txt · Datasets at Hugging Face

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Westpac, a large Australian bank, ran a trial with
generative AI to assist its coders and found a 46%
increase in productivity against a control group.20
Mileham says his company, Betterment, uses
generative AI software to help with debugging. They
have also procured GitHub Copilot, a cloud-based AI
tool, to help with code generation and auto-completion.
As with other uses, he stresses that this should happen
only in the context of robust review and testing, and
a person taking ultimate responsibility for any new
code. Even within these constraints, Mileham says its
a worthwhile effort. Everybody who has deployed it
[at Betterment] spends less time banging their heads
against the wall, not knowing the right answer [to
relatively straightforward questions], and more time
being creative, he explains.
Definitely, there will be a lot of
automation of routine tasks like
report generation.
Chia Hock Lai, Co-Founder, Global Fintech Institute
14
MIT Technology Review Insights
Diverse forms of information analysis and
summarization: One of the strengths of generative
AI is its ability to use the information in its model to
answer questions. The same capacity is already finding
various uses within financial services. Mileham cites
the example of using generative AI to create the first
draft of summaries following a business meeting or
phone conversation. This, he says, allows a minor,
everyday task to be compressed into a review rather
than being a drafting exercise. Similarly, at Man
Groupa large hedge fundmanagers have found
that generative AI can speed up initial research by
reviewing academic papers and spotting patterns.21
Meanwhile, a blue-chip Wall Street firm is rolling out
an app to act as a virtual assistant to help wealth
managers find client-relevant research or forms.22
Looking ahead, Chia expects such activity to grow,
saying, Definitely, there will be a lot of automation of
routine tasks like report generation.
Beyond this quotidian manipulation of information,
however, will be the opportunity for some businesses
to use generative AI to monetize data. One of the
most high-profile innovations using the technology
in financial services, BloombergGPT, falls into this
category. Subscribers could already access the
companys large data archive, but this has now been
turned into a specialist LLM for answering questions
related to financial services.23
Such subscription tools, however, do raise questions
around how generative AI will change thinking around
the role and value of data. Major news organizations
have taken steps, for example, to block ChatGPTs
web crawler from accessing their websites.24 On the
other hand, Cont says generative models can now be
shared without revealing the underlying data on which
the model was trained. Some fintech startups have
started this model, sharing or commercializing the
generative model but not the data, he explains. That
is a new possibility. Ultimately what data is commonly
retained, what is shared, and how, remains a question
for marketsand regulatorsto answer.
Some fintech startups have started this model,
sharing or commercializing the generative model but
not the data.
Rama Cont, Chair of Mathematical Finance and Head of the Oxford Mathematical and
Computational Finance Group, Oxford University
0
15
MIT Technology Review Insights
04
04
W
hile the buzz around generative AI has
somewhat eased in the second half of
2023, optimism still surrounds its
long-term impact. Generative AI is an
exciting development, says Mileham.
There is a lot of opportunity.
On the other hand, a distinct disconnect exists between
the current deployment of generative AI with its
perceived potential. Todays effort will have an effect,
but only on a limited number of functions. Moreover, the
innovation, so far, appears more to be improvements
to current practices than the kind of fintech-driven
disruption seen in payment services and wealth
management in recent years.
Great expectations
That hasnt stopped other companies and researchers
from looking further. In Conts view, the financial sector
has been quick to adopt new technologies at an
experimental level. Their deployment could be very
easy as long as they [companies] are comfortable with
the output, he says.
An area of particular interest is asset selection. One
aspect of this is finding tools that balance portfolio-
wide risk and return.25 Other research seeks to develop
Only half-speed ahead:
Wariness about more

Westpac, a large Australian bank, ran a trial with

generative AI to assist its coders and found a 46%

increase in productivity against a control group.20

Mileham says his company, Betterment, uses

generative AI software to help with debugging. They

have also procured GitHub Copilot, a cloud-based AI

tool, to help with code generation and auto-completion.

As with other uses, he stresses that this should happen

only in the context of robust review and testing, and

a person taking ultimate responsibility for any new

code. Even within these constraints, Mileham says its

a worthwhile effort. Everybody who has deployed it

[at Betterment] spends less time banging their heads

against the wall, not knowing the right answer [to

relatively straightforward questions], and more time

being creative, he explains.

Definitely, there will be a lot of

automation of routine tasks like

report generation.

Chia Hock Lai, Co-Founder, Global Fintech Institute

14

MIT Technology Review Insights

Diverse forms of information analysis and

summarization: One of the strengths of generative

AI is its ability to use the information in its model to

answer questions. The same capacity is already finding

various uses within financial services. Mileham cites

the example of using generative AI to create the first

draft of summaries following a business meeting or

phone conversation. This, he says, allows a minor,

everyday task to be compressed into a review rather

than being a drafting exercise. Similarly, at Man

Groupa large hedge fundmanagers have found

that generative AI can speed up initial research by

reviewing academic papers and spotting patterns.21

Meanwhile, a blue-chip Wall Street firm is rolling out

an app to act as a virtual assistant to help wealth

managers find client-relevant research or forms.22

Looking ahead, Chia expects such activity to grow,

saying, Definitely, there will be a lot of automation of

routine tasks like report generation.

Beyond this quotidian manipulation of information,

however, will be the opportunity for some businesses

to use generative AI to monetize data. One of the

most high-profile innovations using the technology

in financial services, BloombergGPT, falls into this

category. Subscribers could already access the

companys large data archive, but this has now been

turned into a specialist LLM for answering questions

related to financial services.23

Such subscription tools, however, do raise questions

around how generative AI will change thinking around

the role and value of data. Major news organizations

have taken steps, for example, to block ChatGPTs

web crawler from accessing their websites.24 On the

other hand, Cont says generative models can now be

shared without revealing the underlying data on which

the model was trained. Some fintech startups have

started this model, sharing or commercializing the

generative model but not the data, he explains. That

is a new possibility. Ultimately what data is commonly

retained, what is shared, and how, remains a question

for marketsand regulatorsto answer.

Some fintech startups have started this model,

sharing or commercializing the generative model but

not the data.

Rama Cont, Chair of Mathematical Finance and Head of the Oxford Mathematical and

Computational Finance Group, Oxford University

0

15

MIT Technology Review Insights

04

W

hile the buzz around generative AI has

somewhat eased in the second half of

2023, optimism still surrounds its

long-term impact. Generative AI is an

exciting development, says Mileham.

There is a lot of opportunity.

On the other hand, a distinct disconnect exists between

the current deployment of generative AI with its

perceived potential. Todays effort will have an effect,

but only on a limited number of functions. Moreover, the

innovation, so far, appears more to be improvements

to current practices than the kind of fintech-driven

disruption seen in payment services and wealth

management in recent years.

Great expectations

That hasnt stopped other companies and researchers

from looking further. In Conts view, the financial sector

has been quick to adopt new technologies at an

experimental level. Their deployment could be very

easy as long as they [companies] are comfortable with

the output, he says.

An area of particular interest is asset selection. One

aspect of this is finding tools that balance portfolio-

wide risk and return.25 Other research seeks to develop

Only half-speed ahead:

Wariness about more