Incorrect or
misleading labels/outputs are provided by the AI and Machine Learning Models
AI and ML products have
proliferated as businesses use them to process and analyze immense volumes of
data, drive better decision-making, generate recommendations and insights in
real time, and create accurate forecasts and predictions.
Gemini
is a large language model (LLM) developed by Google Artificial Intelligence.
Gemini is built on a foundation of advanced machine learning techniques,
including transformer architectures and deep learning. It has been trained on a
massive dataset of text and code, allowing it to acquire a deep understanding
of language.
It is designed to be a
versatile AI assistant capable of a wide range of tasks, including:
§
Gemini can generate human-quality text, such as
articles, essays, code, scripts, musical pieces, email, letters, etc.
§
It can translate text from one language to another
accurately and naturally.
§
Gemini can write code in various programming
languages, including Java, Python, JavaScript, and CSharp.
§
It can provide informative and comprehensive
answers to a wide range of questions.
§
Gemini can summarize long texts into shorter, more
concise versions.
§
It can generate creative content, such as poems,
stories, and scripts.
Overall,
Gemini is a powerful and versatile AI assistant with the potential to
revolutionize a wide range of industries.
Note:
Google Gemini’s AI and Machine Learning Models are utilized for the below use
cases.
Use Case 1:
The below two
features are used as input to Google Gemini.
Ø Flights
list from New York to Mumbai
Ø Air
India Flights from New York to Mumbai cheap rates
v For
the first feature i.e., “Flights list from New York to Mumbai”, the below label
is present by Gemini.
When searched the flights using the Google Flights link,
two Air India Flights were displayed and
both were direct flights.
But when
searched the Air India website, there were no direct flights from EWR – BOM and
its details.
Google Gemini has provided
incorrect or misleading information, as the content displayed by Gemini, that
there were direct flights, which was partially correct, but it recommended to
Google Flights Link, where there were direct flights from EWR – BOM, but the
information in the Air India is completely different, and there were no direct
flights.
v The second feature i.e., “Air
India Flights from New York to Mumbai cheap rates”, the below label is
presented by Google Gemini.
Google
Gemini content gave the information that direct flights may be more expensive
than flights with layovers, but when queried the Air India flights, the
opposite was true, such as the direct flight was the cheapest option, and the
details goes here. The Google Gemini content was thus misleading.
Summary:
The randomness, generalization, and pattern identifications techniques are
still evolving, as it continues to develop, we can expect to see even more
innovative and exciting applications of this technology.
Use Case
2:
The below
feature is used as input to Google Gemini, as a fresh query.
Ø Direct
Air India Fligths list from John F Kennedy to Mumbai on 13 Oct
Google
Gemini, gave the above label for the feature (Direct Flights), such as there
are multiple flights from JFK to BOM and the number may vary. It is recommended
to visit Air India Link for details. In the Air India Link provided by Google
Gemini, there was only one direct flight from JFK to BOM, the details are as
follows.
Here the content provided by the Google Gemini
and the Air India link data are not matching, the information is misleading.
Hence, we can safely conclude that incorrect or misleading labels/outputs are provided by the AI and Machine Learning Models which we have to double check before arriving any conclusion by using AI for outcomes.
