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14.12 Generative Articial Intelligence
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14.10 Natural Language Processing
Natural language processing (NLP) is a branch of computer science that can understand and communicate with human language. It uses machine learning for this
purpose to recognize texts and voices, although deep learning may be used for complex problems. NLP is trained using a large amount of text or voice data in the
machine learning model to learn the patterns and associative relationships of the
data. Once trained, the model can generate texts on new data. NLP is trained on a
continuous basis as new data are accumulated.
NLP works in several ways depending on the context it is used. For text processing, initially, the data are organized in a format for the model to understand. NLP,
however, cannot process the data in the input text format, and so it is broken into
small units called “tokens” such as words, sentences, phrases, etc., a process called
“tokenization.” The model then processes and interprets the data to its liking to create a meaningful text. It is the powerful force behind entities like Google Assistant,
Microsoft’s AI assistant, Amazon’s Alexa, etc. Speech recognition is a task of converting speech to text, but a successful conversion is achieved if the speech is clean
and not altered by obscure dialect, mispronunciation of words, incorrect grammar.
14.11 Large Language Model
A large language model (LLM) is a deep-learning algorithm that is pre-trained on a
massive data set to generate, translate, and process texts using natural language
processing (NLP). They are trained using unsupervised learning on a vast amount of
data and texts collected from media and literature, and the trained model recognizes
the previously unknown pattern from the unlabeled data. LLM training is quite
laborious and time-consuming due to the requirement of a massive amount of data,
which includes zillions of parameters that attributes to the term Large Language
Model. For example, LLM-GPT 4 has billions of parameters. LLM can write, code,
read, and draw, providing support for many industries in their operation. The scope
of LLM is so vast that many enterprises and institutions have adopted it to improve
their strategy. However, the requirement of massive data and concomitant large
capacity computers, along with experts in high-level computing OpenAI’s GPT-4
and Google’s Gemini are good examples of LLM that understand queries and generate text inputs based on them.
14.12 Generative Artificial Intelligence
A popular form of AI, Generative AI (GenAI), uses a machine learning AI model
that is trained to analyze the patterns in a large volume of available data and replicate those patterns to create new data, content, or information like text, images,
audios, and videos. It employs a neural network for training purposes, and following
training, it creates a new item similar to the item it was trained on. For example, if
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a poem is entered as input to the GenAI, the latter is trained on the data of the poem
and creates a new poem. Similarly, new texts, images, videos, or audios are created
in response to the user’s queries, resembling real-world samples. To generate highquality outputs, GenAI requires the training data to be comprehensive and diverse,
a robust model architecture, and an appropriate training process along with evaluation strategies. In this model, data storage and training of GenAI are carried out a
priori to identify and establish the patterns within the dataset.
Once the model is trained, prompts (see below) are then fed into the GenAI’s
algorithm to create new data. Prompts address the issues of reasons for the model’s
usage and the expected output from such use. For example, if the desired output is
a video of an event, the prompt may include any distinctive features of people present, decorative patterns, specic features of the event, etc. for appropriate production of a new video.
Evaluation of the GenAI output is typically carried out by using a different validation dataset, which is not used for training purposes. The purpose of using such
an unseen dataset is to determine how well the model performs with new, previously
unseen data. If the output is not up to expectation, additional data may be required
for retraining, or the model’s architecture may be ne-tuned.
GenAI is widely used in many applications, such as creating texts and images,
and translating text from one language to another. Because of its versatility, its use
in a variety of disciplines has increased dramatically over the years. OpenAI initially introduced the GenAI models like ChatGPT and DALL-E, which are chatbots
discussed below. Nowadays, big tech companies like Google, Microsoft, Amazon,
and Meta have launched their own GenAI tools to capitalize on the technology’s
rapid growth. Google’s Gemini, Microsoft’s Copilot, and OpenAI’s ChatGPT-4 are
examples of very useful GenAI models for nding a quick answer to a specic task.
GenAI has become the model of choice for content generation, creation of new
text, artwork, videos, audios, even personalized content, and interestingly, many
video games. It has facilitated the eld of drug discovery by predicting the drugtarget interaction, leading to the development of new drugs in a relatively short time.
Businesses, nancial institutions, industries, educational institutions, and social
media are adopting this model for their successful operation.
However, GenAI has its pitfalls too, like bad actors creating and spreading misinformation, deepfakes, hallucination (see below), mistrust, and infringement of
copyright and intellectual property. Moreover, if AI systems are not adequately
secured, they could become a target for cyberattacks, creating additional data security concerns.
14.13 Additional Terms ofInterest inAI
14.13.1 Prompt
An AI prompt is a query to infuse as input to LLM through GenAI to nd its answer.
A prompt can be a question, command, statement, code, etc. The query users enter
14.13 Additional Terms ofInterest inAI
to ask what they want from GenAI programs like ChatGPT (see below) or its imagemaking equivalents, like OpenAI’s Dall-E.GenAI scrounges through the data it is
already trained on and nds the appropriate response to the query. For an accurate
response from AI, prompts must be precise, effective, and understandable to the AI
model. An intelligent prompt can generate a story of interest, can create a blog post
on a specic topic of interest, and create advertising materials as prompted by business entrepreneurs, etc., and many other tasks. It can even generate codes for a
specic task.
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14.13.2 Token
In articial intelligence (AI), a token is the smallest unit of text or data that an AI
model requires to process the stored data to provide an answer to a question. Tokens
can be words, characters, subwords, or punctuation marks, and are the centerpiece
items in NLP.For the convenience of processing, text data is broken into smaller
units, considered tokens, to be processed by AI models. They can be a letter or a
whole phrase, which is fed into LLM for training to learn the pattern of data to nd
an answer to a query. According to OpenAI, a token contains roughly four characters of text. Tokens help enhance search algorithms, improve text classication, and
promote sentiment analysis.
14.13.3 Hallucination
In AI, hallucination occurs when a response to a query generated by an AI model,
especially an LLM, appears to be plausible but, in fact, incorrect, fake, or completely irrelevant to the input query. It can cause detrimental harm if one seeks reliable information, particularly in medical diagnosis. Hallucination is caused by
insufcient or poor quality data and limitation in the scope of the model.
14.13.4 Deepfake
Deepfakes are Images, photos, or videos created by AI algorithms designed to fool
people into thinking they are real. They use ML algorithms to analyze large amounts
of photos or recordings of a person. The algorithms learn to produce output that
resembles the examples they were fed and trained on.
Deepfakes can replace faces, change facial expressions, and create synthetic
faces. They can portray deceased actors in movies and spread lies against adversaries. False information and fake pornography can be generated by deepfakes. Because
it is difcult to decipher truth from falsehood, they can be used to the advantage of
perpetrators to sway public opinion and inuence an election. Deepfakes can cause
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havoc in the nancial world with irreparable damage. However, it can hold some
promise for counterterrorism.
14 Basics ofArticial Intelligence
14.13.5 Overfitting
When an AI model ts too close to the training dataset, the model cannot make
accurate predictions with any other dataset except the training data itself, the overtting occurs. In overtting, the model performs well on the training set but poorly
on the test/validation set. When training continues for a long time, it tends to learn
irrelevant information within the dataset that results in overtting. Small or noisy
dataset also causes this problem. The model cannot perform well in the classication and prediction of an intended job. Several steps are often taken to prevent overtting. Using more and cleaner data is an option to prevent overtting. Next is to
pause training early to avoid noises in the model. Proper feature selection in building a model may also be helpful in minimizing overtting.
14.13.6 Encoder andDecoder
Encoders and decoders are components of neural network architectures used to transform data from one format to another by compressing to a lower-dimensional entity.
Normally, an encoder transforms original input data (e.g., image, text, audio etc.) to
an output encoded representation (usually a vector). For example, an autoencoder
compresses an image into a latent code. Decoder, on the other hand, reconstructs an
output (a reconstructed image, translated sentence) from the encoded entity. A combined encoder-decoder structure is commonly used for various AI applications.
14.14 Computer andSoftware
To handle the vast quantity of data efciently and accurately, very fast computers
(supercomputers) with high-speed processing units and enormous memory capacity
are required. In personal and business computers, data processing is carried out by
a unit called the central processing unit (CPU). The CPUs have been described in
detail in Chap. 11. However, in AI application, more efcient units with vast storage
and high memory capacity (RAM) are required. Graphics processing units (GPUs)
are commonly used for the purpose, which are built by combining many (thousands) CPUs in parallel conguration. High quality GPUs are expensive, but efcient in computing. Besides commonly known computer’s internal storage and ash
drives, high-tech corporations offer long-term permanent cloud storages, which are
extremely useful in deep learning (DL) algorithms. Network-attached storages and
magnetic tapes are alternative choices for long-term permanent storage of AI data.
IBM’s Watson is a typical example of a superfast high capacity computer, which
was used in the gameshow, Jeopardy, against competitive players. Hewlett-Packard,
14.15 Chatbot
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Microsoft, and Super Micro Computer Inc. are just a few of many manufacturers
who are competing with one another to stay ahead in the game in building superfast
computers.
Computers have dual purposes—one to store a massive amount of collected data
and the other to carry out the software (algorithm) programs to achieve the intended
answer. The more information on a given topic is stored in the computer storage, the
more accurate the answer by AI to a question on that topic. High-capacity RAMs are
crucial for speedy access to and real-time processing of the data in AI applications.
The software needs to be robust, speedy, trustworthy, and cyberattack-proof. Also,
the software, and hence the computer, needs to be extremely fast to accomplish a
variety of tasks by different AI models.
14.15 Chatbot
Many of us are familiar with the term chatbot, which consists of a set of instructions
that simulate conversation with humans through texts or voice interactions. Besides
offering text or voice responses, they can build websites and codes, generate images,
and analyze documents. Chatbots are language models and use natural language
processing (NLP) as the core technology in guiding different chatbots (Bansal and
Khan 2018). Since the initial introduction of chatbot on November 30, 2022, by
OpenAI, several high-tech companies have introduced chatbots such as OpenAI’s
ChatGPT-3.5, ChatGPT-4, and ChatGPT 5, Google’s Gemini (formerly Bard),
Microsoft’s Copilot, Amazon’s Alexa, and Salesforce’s Einstein. ChatGPT-3.5 is
the initial version of the chatbot from OpenAI, where GPT stands for generative
pretrained transformer (Bhayana et al. 2023). This means the model works like
LLM using a neural network, is trained, and generates texts by predicting the next
most likely word or phrase.
ChatGPT-4o is an upgraded version of ChatGPT-3.5 along with a small default
version GPT-4o mini. Recently, OpenAI has introduced an upgraded version of
GPT 4.1 along with GPT 4.1 mini. While GPT 4.1 mini remains the default model
for all users, including those with a free account, ChatGPT-4.1 is available to only
paid subscribers. Initially, chatbots used to make erroneous responses, but with
time, AI technology has improved signicantly, resulting in more accurate chatbot
responses. All chatbots do not give the same answer to a query, because of nuances
in the use of datasets. Currie (2023) made a comparative study of relative performance by ChatGPT-3.5 and ChatGPT-4in examination sets and written assignments of nuclear medicine subjects, found that GPT-4 outperforms ChatGPT-3.5.
While ChatGPT-3.5 carries a minimal risk to academic integrity, ChatGPT-4
remains vulnerable to hallucination and fabrication. The latest version ChatGPT-5
has been released in 2025 by Open AI, offering better reasoning, accuracy and
memory. It handles texts, codes, images, audios, videos more efciently than GPT-4.
The operational principle of a chatbot is to ask one of these chatbots in a computer a question of specic interest, it will search the relevant dataset for the most
appropriate answer, which is then displayed on the computer screen. Those who are
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novices in computer operation but have some basic knowledge can enjoy the fun
and beauty of AI technology in a couple of simple steps. First, download and install
a basic chatbot like OpenAI’s ChatGPT, Microsoft’s Copilot, or Google’s Gemini,
on the desktop. However, it requires registration and login to access the program.
Some are free, while others charge a fee. There are upgraded versions available for
these chatbots for an additional charge. When the icon is clicked, a bar opens up and
the chatbot asks how it can help you. For simpler questions, login is not required to
access the program, but for a complex question the chatbox prompts you to login.
With login or no login, one types in a question of interest in the bar, and the computer displays the answer in seconds. If the response is not up to expectations, the
chatbot offers the option of different answers.
14.15.1 Example ofChatGPT
For the sake of interest, I am quoting below a simple example of the response (italics) given by ChatGPT when I asked for tips on a trip to the Taj Mahal in India.
A trip to the Taj Mahal is an extraordinary experience, lled with history, beauty,
and cultural richness. Located in Agra, India, this iconic monument is one of the
Seven Wonders of the World, known for its stunning white marble architecture and
intricate detailing.
Here’s what you can expect and plan for a visit:
Best Time to Visit
October to March: Ideal weather with cooler temperatures, making it comfortable to explore.
Early Morning or Late Afternoon: The Taj Mahal looks particularly magical
during sunrise or sunset when the marble reects beautiful colors.
Getting There
By Air: Agra has a small airport, but most international tourists y into Delhi
and then drive or take a train to Agra (about 3–4hours away).
By Train: The Gatimaan Express or Shatabdi Express are good train options
from Delhi.
By Car: A road trip from Delhi is common, via the Yamuna Expressway.
Tickets and Timings
Opening Hours: Sunrise to sunset, closed on Fridays.
Tickets: Available online or at the gate. There are different rates for international
tourists, Indian citizens, and children.
Taj Mahal Night Viewing: Available on full moon nights and the two days before
and after, offering a mesmerizing view of the monument under moonlight.
What to See
Main Mausoleum: The tomb of Mumtaz and Shah Jahan, with exquisite marble
work and precious stone inlay.
Gardens: Mughal-style gardens with symmetrical layouts, fountains, and lush
greenery.
14.17 Disadvantages ofAI
The Mosque and Guest House: Flanking the main structure, add to the architectural beauty.
Yamuna River View: Behind the Taj Mahal, the river provides a serene backdrop.
Nearby Attractions
Agra Fort: Another UNESCO World Heritage site, located a short distance from
the Taj Mahal.
Fatehpur Sikri: A historic Mughal city, about an hour from Agra.
Mehtab Bagh: For a fantastic view of the Taj Mahal from across the river.
Tips
Shoes: You will need to remove your shoes or wear shoe covers inside the
mausoleum.
Dress Modestly: Since the Taj Mahal is a tomb, respectful attire is
recommended.
Crowds: It can get quite crowded, so arriving early in the day helps avoid
the rush.
Would you like to know more details about the Taj Mahal or how to plan a
perfect trip there?
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14.16 Benefits ofAI
Automation of routine and repetitive tasks, solution of complex problems reducing
human errors are the obvious benets, which minimize human engagement in the
task and let humans focus on other priorities. Examples of AI benets are data entry,
customer service in the business, automation of driving (self-driving), diagnosis and
treatment of diseases, robot application in various institutions, automated selfservice, etc. While the ML model in AI can solve basic problems, the DL model is
very efcient in solving more complex tasks. Robotic automation can provide accurate results and reduce the time to complete a task. AI can improve customer relationships by providing prompt responses to customer queries. AI in manufacturing
can reduce assembly errors and production times while increasing production and
worker safety. Weather forecast (accurate prediction of hurricanes, tornadoes, ooding, snowfall, etc.) benets tremendously from AI applications. Other entities that
benet from the use of AI include nancial institutions, marketing, educational
institutions, gaming, and even military.
14.17 Disadvantages ofAI
Despite the tremendous benet of AI to human society in different walks of life, it
comes with risks and potential dangers. One of the concerns alludes to the displacement or even elimination of jobs for humans, the impact of which experts cannot
predict yet. Another concern reects biased human decisions that may be discriminatory against certain demographics. Also, AI can be used to generate fake news,
spreading disinformation, compromising social trust, and creating chaos.
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Furthermore, AI-generated material has the potential to infringe upon people’s
copyright and intellectual property rights.
14 Basics ofArticial Intelligence
14.18 Legal Implication
Because of the prolic growth of AI and its concomitant effects on human lives,
both benecially and adversely, lawmakers around the world are seriously engaged
in regulating its application and development. European Union passed a sweeping
Articial Intelligence Act to ensure safety, transparency, traceability, and nondiscriminatory aspects of AI.China and Brazil have followed suit. In the USA, the
Biden administration introduced an AI Bill of Rights followed by an Executive
Order on Safe, Secure, and Trustworthy AI in 2023, which was repealed by President
Trump in January 2025. Despite many attempts, Congress has failed to come up
with robust legislation.
14.19 Future ofAI
At present, despite immense development, the AI machine cannot yet talk, think, or
function like a human being. With the enormous ingenuity of human beings, it is
believed that AI will undergo continuous upgrades over the next several decades
making day-to-day life much easier and more comfortable, and thus improving the
quality of human life. A day will come when an AI machine will think, talk, and
respond like a normal human being.
14.20 Questions
1. Describe the principles of articial intelligence.
2. Describe the hierarchical relationship among articial intelligence, machine
learning, deep learning and convolutional neural network.
3. Database is a core requirement in articial intelligence. How is it generated?
4. What is the difference between supervised and unsupervised machine learning?
5. Describe the neural network and its operation in articial intelligence.
6. Indicate which AI model is appropriate to apply for the following tasks:
A. Chess game
B. Face of a man
C. Driverless autodriving
D. Translating a text
7. Does the generative AI use neural network? What is its distinct
characteristics?
8. Bias is used to adjust the function of a node by moving it up or down. True ____
or False_________
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9. Testing of an AI model is commonly performed by using
(a) Same dataset as the one used in training
(b) A new previously unseen dataset
(c) No dataset
10. Describe how backpropagation is carried out in AI application.
11. What is the loss function used in backpropagation, and how is it used?
12. What the difference between CPU and GPU?
13. What are the main attributes of radiomics in AI?
14. Explain the following terms: prompt, token, hallucination, deepfake.
15. What are the ethical and legal challenges in AI application?
16. How does generative adversarial network work?
17. Describe the function of a chatbot? Name some of the chatbots introduced by
several tech companies.
18. Describe how a feature map in CNN is generated?
19. What is the function of computer vision?
20. Explain overtting in AI and how it can be rectied.
21. Elucidate the benets and disadvantages of AI.
22. What are pretrained AI models and give some examples.
23. Explain the function of radiomics.
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