GENERATIVE
AI AND LABOUR MARKET – ECONOMY
News:
Generative AI: How will
it affect future jobs and workflows?
What's
in the news?
●
As companies struggle to understand the
implications and applications of generative AI (gen AI), one thing seems clear:
AI and its future iterations are not going anywhere.
Key
takeaways:
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A new report from the World Economic
Forum, Jobs of Tomorrow, highlights the potential impact of large language
models on job tasks.
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Generative AI has the capacity to enhance
job quality and foster job growth if managed responsibly.
Generative
AI:
●
Generative AI (GenAI) is the part of
Artificial Intelligence that can generate all kinds of data, including audio,
code, images, text, simulations, 3D objects, videos, and so forth. It takes
inspiration from existing data, but also generates new and unexpected outputs.
●
Recently, San Francisco-based AI start-up
OpenAI launched ChatGPT (Chat Generative Pre-Trained Transformer).
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Generative AI works by training a model on
a large dataset and then using that model to generate new, previously unseen
content that is similar to the training data.
●
This can be done through techniques such
as neural machine translation, image generation, and music generation.
Large
language Model:
●
A
large language model (LLM) is a type of artificial intelligence (AI) algorithm that uses deep learning techniques and massively large
data sets to understand, summarize, generate and predict new content.
●
The
term generative AI also is closely connected with
LLMs, which are, in fact, a type of generative AI that has been specifically
architected to help generate text-based content.
Positive
Implications:
- Reduce the burden of
human research: It can help shift through numerous
legal research materials and produce a pertinent, specific and actionable
summary. As a result, it can reduce the countless hours of human research
and enable them to focus on more complex and exciting problems.
- Help in designing:
It can also help create and simulate complex engineering, design, and
architecture. It can help speed up the iterative development and testing
of novel designs.
- Personalized Health
treatments: It can also help health professionals
with their medical diagnosis. AI can generate potential and alternative
treatments personalized to patients’ symptoms and medical history. For
instance, DeepMind Alpha Fold can predict the shape of the protein.
- Economic
Transformation: LLMs are predicted to contribute
$2.6 trillion to $4.4 trillion annually to the global economy.
- Enhanced
Communication: LLMs redefine human-machine
interaction, allowing for more natural and nuanced communication.
- Information
Democratization: Initiatives like the Jugalbandi
Chatbot exemplify LLMs’ power by making information accessible across
language barriers.
- Industry Disruption:
LLMs can transform various industries. For
example, content creation, customer service, translation and data analysis
can benefit from their capabilities.
- Efficiency Gains: Automation
of language tasks leads to efficiency improvements. This enables
businesses to allocate resources to higher-value activities.
- Educational Support:
LLMs hold educational potential. They can provide personalized tutoring,
answer queries and create engaging learning materials.
- Entertainment and
Creativity: LLMs contribute to generating
creative content, enhancing sectors like entertainment and creative
industries.
- Positive
Societal Impact: LLMs have the potential to improve accessibility,
foster innovation and address various societal challenges.
Concerns:
- Deep Fakes:
Generative AI, particularly machine learning approaches such as deepfakes,
can be used to generate synthetic media, such as images, videos and audio.
Such AI-generated content can be difficult or impossible to distinguish
from real media, posing serious ethical implications.
- Inaccuracy problem:
Generative AI uses machine learning to infer information, which brings the
potential inaccuracy problem to acknowledge.
- Increase
in Biases: Recent evidence suggests that larger and more sophisticated
systems are often more likely to absorb underlying social biases from
their training data. These AI biases can include sexist, racist, or
ableist approaches within online communities.
- Risk of
Unemployment:
This could happen if generative AI automates tasks or processes
previously performed by humans, leading to the displacement of human
workers.
- Plagiarism:
they are really just making new patterns from the millions of examples in
their training set. The results are a cut-and-paste synthesis drawn from
various sources—also known, when humans do it, as plagiarism. Either way,
what’s missing is uniqueness.
WAY
FORWARD:
- Ethical
considerations: PM Modi called for a framework for
ethical AI.
- International
collaboration in regulation: consensus between
the countries.
- Balanced values and
risks: Address tensions between privacy, security,
accountability, and freedom.
- Information
integrity: Extend the identity assurance framework principles to
information integrity. Validate the authenticity of information sources,
content integrity, and information validity.
Thus, it is important for
developers and users of generative AI to consider the potential impacts and
ensure that the technology is used ethically and responsibly.