sidhellman/clean_data.txt · Datasets at Hugging Face

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years to become integrated into business productivity
tools.5 To an extent, the febrile current discussion
around generative AI, however, obscures some
attributes that will shape its impact.
First, generative AI is a rapidly evolving field: it did not
spring into existence with the release of ChatGPT
and its development is far from complete. McKinsey
describes a rush to throw money at all things generative
AI between 2017 and 2022. During those years, private
investments in the technology rose at an average annual
compound rate of 74%, far outstripping the equivalent
growth rate for AI as a whole of 29%.6 Similarly, tools and
products incorporating generative AI have continued to
develop rapidly in recent months (see Figure 3).
Second, generative AI looks set to build on and
supplement existing technologies, rather than
necessarily replace them on a grand scale. As Chia
notes, you definitely need a clear use case to start
trying out any new technology. Any such cases will
arise from what generative AI can do compared to
existing tools.
These may be more limited than popular imagination
appreciates. Tools enabled by older versions of AI,
for example are now called just software, says John
Mileham, chief technology officer at Betterment, a
robo-advisor that assists users in automated investing.
And such tools have already played a crucial role in
the ongoing digitalization of financial services firms
and other companies. Theres little point in deploying
generative AI where less advanced technologies are
doing the job as effectively and at low cost. Indeed,
some analysts project that, while generative AI will have
a very large economic impact, it will be markedly less
than that of earlier iterations of AI.7
Its not magic
The use cases will arise from what generative AI
can deliver that other tools cannot. The striking new
capacity of generative AI is best illustrated by a brief
Figure 2: The breakout technology of 2023
ChatGPT gained more than 100 million active users across the globe within a span of two months, a much
faster rate than that of other platforms.
Source: Compiled by MIT Technology Review Insights, based on data from Generative Artificial Intelligence in Finance: Risk Considerations, International
Monetary Fund, 2023.
70
60
50
40
30
20
10
0
ChatGPT
TikTok
Uber
Instagram
Telegram
Pinterest
Facebook
Spotify
Twitter
Months to gain 100 million users
8
MIT Technology Review Insights
comparison with older forms of AI. Put simply, older
AI tools can train themselves on huge amounts of
structured data and can answer specific questions
asked in programming-like language.
Generative AI can learn from an even larger body of
informationincluding unstructured dataand it can
create apparently novel content in response to natural
language questions. Older AI tools, for example, can
tell users if something is a cat. Generative AI can
generate a new image of a cat.
In short, generative AIs strength is that it allows users
to ask questions in natural language and receive
output that provides readily comprehensive answers
in different formats based on a huge corpus of
information. This can involve, among other things, the
generation of new text, pictures, computer code, or
even large data sets.
The potential value of this creativity is substantial but
should not be overestimated. For example, generative
AI can generate a data set based on existing ones, says
Rama Cont, chair of mathematical finance and head of
the Oxford mathematical and computational finance
group at Oxford University. However, while it may be
even 100 times larger, it wont have more information,
he explains. It can extrapolate to certain situations,
provided you have similar data sets on which to train it.
In the cat example, the picture will rely on existing
knowledge of cats but will be unable to create something
new. Such extrapolation of existing data is a nice
feature of generative AI, says Cont, but not magic.
Figure 3: Six months in the life of generative AI
Starting in late 2022, new iterations of generative AI technology have been released several times a month.
Timeline of major large language model (LLM) developments following Chat GPTs launch
Nov. 2022
Dec.

Jan. 2023 Feb. March April

years to become integrated into business productivity

tools.5 To an extent, the febrile current discussion

around generative AI, however, obscures some

attributes that will shape its impact.

First, generative AI is a rapidly evolving field: it did not

spring into existence with the release of ChatGPT

and its development is far from complete. McKinsey

describes a rush to throw money at all things generative

AI between 2017 and 2022. During those years, private

investments in the technology rose at an average annual

compound rate of 74%, far outstripping the equivalent

growth rate for AI as a whole of 29%.6 Similarly, tools and

products incorporating generative AI have continued to

develop rapidly in recent months (see Figure 3).

Second, generative AI looks set to build on and

supplement existing technologies, rather than

necessarily replace them on a grand scale. As Chia

notes, you definitely need a clear use case to start

trying out any new technology. Any such cases will

arise from what generative AI can do compared to

existing tools.

These may be more limited than popular imagination

appreciates. Tools enabled by older versions of AI,

for example are now called just software, says John

Mileham, chief technology officer at Betterment, a

robo-advisor that assists users in automated investing.

And such tools have already played a crucial role in

the ongoing digitalization of financial services firms

and other companies. Theres little point in deploying

generative AI where less advanced technologies are

doing the job as effectively and at low cost. Indeed,

some analysts project that, while generative AI will have

a very large economic impact, it will be markedly less

than that of earlier iterations of AI.7

Its not magic

The use cases will arise from what generative AI

can deliver that other tools cannot. The striking new

capacity of generative AI is best illustrated by a brief

Figure 2: The breakout technology of 2023

ChatGPT gained more than 100 million active users across the globe within a span of two months, a much

faster rate than that of other platforms.

Source: Compiled by MIT Technology Review Insights, based on data from Generative Artificial Intelligence in Finance: Risk Considerations, International

Monetary Fund, 2023.

70

60

50

40

30

20

10

0

ChatGPT

TikTok

Uber

Instagram

Facebook

Spotify

Twitter

Months to gain 100 million users

8

MIT Technology Review Insights

comparison with older forms of AI. Put simply, older

AI tools can train themselves on huge amounts of

structured data and can answer specific questions

asked in programming-like language.

Generative AI can learn from an even larger body of

informationincluding unstructured dataand it can

create apparently novel content in response to natural

language questions. Older AI tools, for example, can

tell users if something is a cat. Generative AI can

generate a new image of a cat.

In short, generative AIs strength is that it allows users

to ask questions in natural language and receive

output that provides readily comprehensive answers

in different formats based on a huge corpus of

information. This can involve, among other things, the

generation of new text, pictures, computer code, or

even large data sets.

The potential value of this creativity is substantial but

should not be overestimated. For example, generative

AI can generate a data set based on existing ones, says

Rama Cont, chair of mathematical finance and head of

the Oxford mathematical and computational finance

group at Oxford University. However, while it may be

even 100 times larger, it wont have more information,

he explains. It can extrapolate to certain situations,

provided you have similar data sets on which to train it.

In the cat example, the picture will rely on existing

knowledge of cats but will be unable to create something

new. Such extrapolation of existing data is a nice

feature of generative AI, says Cont, but not magic.

Figure 3: Six months in the life of generative AI

Starting in late 2022, new iterations of generative AI technology have been released several times a month.

Timeline of major large language model (LLM) developments following Chat GPTs launch

Nov. 2022

Dec.

Jan. 2023 Feb. March April