Campus Connect_Anniversary Edition (1)

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Risks, Challenges, and

Opportunities in the Adoption

of Generative AI

enerative AI’ and “Prompt Engineering” have

become the buzzwords in today’s rapidly evolving

tech landscape. Beyond just ChatGPT, we see

the widespread adoption of AI models like Midjourney,

Stable Diffusion, Github Copilot, and others. With the

continuous influx of large language models (LLMs) into the

market, the competition in the AI space is escalating. For

instance,

consider

Anthropic

AI’s

Claude,

which

is

backed by Google. It boasts a higher token limit

and excels in code generation when compared to

ChatGPT. At the same time, ChatGPT stands out

with its extensive language support and a plethora

of plugins designed for specific tasks. And let’s not forget

about

HuggingGPT,

which

integrates

LLMs

to

tackle complex tasks. The list of potential use cases

for these AI models is vast. However, the critical

question remains: how do these advancements in AI

technology impact us and various industries at large?

PREDOMINANT CONCERNS

• Susceptibility to bias: If the training data used is

of subpar quality or exhibits inherent bias, the model

is prone to generate biased responses. As a result,

industries are diligently working to implement governance

mechanisms to ensure that the outputs generated

by these models are not offensive or discriminatory.

• Carbon footprint: The training of such models demands

massive computational power, contributing to significant

environmental costs. As per nnlabs.org, GPT-3, which

boasts of a staggering 175 billion parameters, needed 355

years of computational time on a single processor and

used 284,000 kWh of energy during its training process. To

provide some context, a typical Indian household utilizes

approximately 900 kWh of electricity on a monthly basis.

• Incorrect information: These large language models

have been known to produce factually incorrect information

on

numerous

occasions.

The

most

infamous

among them was when Google Bard listed down

the discoveries made by James Webb Space Telescope.

It remains to be seen how industries will navigate

and

mitigate

this

challenge

effectively.

BUSINESS CHALLENGES

• Content Ownership: One major concern with generative

AI is the ownership of content and the potential leakage

of sensitive company data. This apprehension has led

companies such as JP Morgan, Amazon, and Verizon to

ban the usage of ChatGPT within their organizations.

• Fear of Job Loss: There is a prevailing fear of job

displacement due to the widespread adoption of AI

technologies. However, this fear is not unique to AI. Historically,

the introduction of new technologies, be it locomotives, the

internet, or smartphones, has disrupted job markets while

simultaneously creating new categories of employment

opportunities. As Sam Altman, the CEO of OpenAI, points

out, generative AI excels at tasks but struggle to perform

complete jobs. Thus, human intervention remains crucial.

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