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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