Introduction to Generative AI n explainable AI
Summary
TLDRAnnie Thomas discusses advancements in AI, focusing on machine learning and natural language processing. She introduces generative AI, capable of creating new data like text or images, and explainable AI, which counters the 'black box' nature of ML by providing transparent decision-making processes. Thomas also touches on large language models like GPT and Transformer architecture, highlighting their applications in various tasks. She emphasizes the importance of local language processing and suggests research opportunities in summarization, spelling correction, and sentiment analysis.
Takeaways
- 🌟 Annie Thomas introduces herself as a speaker from India, focusing on advancements in AI, particularly in machine learning and natural language processing.
- 📈 She discusses the importance of generative AI, which can create new content like text, images, or audio based on training data, and discriminative models that classify and predict from existing data.
- 💡 Generative AI, also known as chain AI, operates on unstructured data and is exemplified by predictive text features on mobile devices.
- 🧠 Large language models are highlighted as a significant development in AI, capable of understanding context and generating human-like text.
- 🌐 The talk covers the rise of Transformers, a deep learning architecture that has revolutionized various AI applications beyond just language processing.
- 🔍 Explainable AI is introduced as a response to the 'black box' issue in machine learning, aiming to make AI decisions understandable and trustworthy, especially in critical fields like medicine and finance.
- 📊 Annie emphasizes the four principles of explainable AI: meaningful explanation, accuracy, knowledge limitation, and user assistance.
- 🔎 The script mentions the challenges of balancing interpretability and accuracy in explainable AI and the need for clear abstractions in explanations.
- 📚 Research opportunities in natural language processing are explored, including text summarization, spell checkers, and news article classification, with a focus on local languages.
- 📊 The importance of differentiating between extractive and abstractive summarization is highlighted, where the former extracts parts of the original text and the latter generates new text.
- 📈 The script concludes with the speaker's encouragement for researchers to explore various summarization techniques and semantic analysis to improve AI models.
Q & A
Who is Annie Thomas and what is her background?
-Annie Thomas is the speaker of the keynote session. She is from the Line Street of Technology dirt, from Sattisgarh State, and from the country of India.
What is the main topic of Annie Thomas' keynote session?
-The main topic of the keynote session is research prospects in the field of machine learning and natural language processing.
What are the two new aspects of AI in natural language processing mentioned by Annie Thomas?
-The two new aspects of AI in natural language processing mentioned are generative AI and explainable AI.
What is the difference between supervised and unsupervised machine learning models?
-Supervised models have a set of labels that are fixed and always existing, allowing for checking predictions for correctness. Unsupervised models do not have a class of labels and predict based on the model-generated data, which can be unstructured.
What is generative AI and how does it differ from discriminative models?
-Generative AI generates new data based on the training provided and understands the distribution of data. Discriminative models, on the other hand, are used to classify, predict, and cluster and are trained on labeled data.
Can you provide an example of generative AI mentioned in the script?
-An example of generative AI is the predictive text feature on mobile phones, which suggests the next word in a sentence based on patterns learned from previous inputs.
What are Foundation models in the context of generative AI?
-Foundation models are large language models that work on unstructured data to generate new patterns and can generate new content such as text, images, or audio based on the training provided.
What is the importance of explainable AI in industries?
-Explainable AI counters the 'Black Box' tendency of machine learning by providing explanations for decisions, which is crucial in domains like medicine, defense, finance, and law to build trust in the algorithms.
What are the four principles of explainable AI?
-The four principles of explainable AI are providing meaningful explanations, ensuring accuracy, having a high knowledge limit, and assisting users in determining appropriate trust in the system.
What are the challenges faced in explainable AI?
-Challenges in explainable AI include contrasting interpretability and accuracy, the need for abstractions to clarify explanations, and the difficulty of providing explanations that meet human accuracy levels.
What are some applications of natural language processing mentioned in the script?
-Some applications of natural language processing mentioned are text summarization, spell checkers, news article classification, and semantic analysis of reviews.
Outlines
💡 Introduction to AI and Machine Learning
Annie Thomas introduces herself and the keynote session's focus on research prospects in machine learning and natural language processing. She distinguishes between generative AI, which creates new content like text or images, and explainable AI, aiming to make AI decisions understandable. Annie explains AI, machine learning as a subset of AI, and deep learning as a subset of machine learning with multiple hidden layers. She further discusses supervised and unsupervised learning, with the former using labeled data and the latter generating models from unstructured data.
📚 Deep Dive into Generative AI
The paragraph delves into generative AI, contrasting it with discriminative models. Generative models create new data based on training, understanding data distribution to generate examples. Annie provides an example with predictive text on mobile phones, illustrating how generative AI learns patterns to suggest the next word in a sentence. She also introduces foundation models and large language models capable of handling vast amounts of text, image, or video data to generate human-like text, with examples like GPT-3 and ChatGPT. The paragraph also mentions the rise of Transformer models post-2017, which are versatile for various tasks beyond just language processing.
🔍 The Emergence of Explainable AI
Explainable AI is introduced as a response to the 'black box' issue in machine learning, where decisions lack transparency. It's crucial for domains like medicine and finance to understand and trust AI algorithms. The paragraph outlines four principles of explainable AI: providing meaningful, accurate explanations within the designed operational limits. Challenges include balancing interpretability with accuracy and using abstractions for clarity. The categorization of explainable AI is discussed, including model-agnostic vs. model-specific and global vs. local explanations.
🌐 Local Language Processing and Applications
Annie discusses her work on local languages, focusing on text summarization. She outlines various types of summarization, including single and multi-document, and based on different criteria like informative or evaluative. The paragraph mentions different summarization techniques like extractive and abstractive, with the latter requiring more sophisticated models. She also touches on spelling and grammar correction for local languages and classifying news articles into predefined categories, emphasizing the challenges due to language diversity.
📊 Semantic Analysis and News Classification
The final paragraph discusses ongoing work on semantic analysis of social media reviews to improve sentiment analysis models. Annie mentions the application of data mining techniques to enhance accuracy. Additionally, she talks about classifying news articles into appropriate sections like national, international, business, etc., using discriminative models trained on the Hindi language. The paragraph highlights the collection of large datasets and the development of models to address the unique challenges of processing different languages.
Mindmap
Keywords
💡Artificial Intelligence (AI)
💡Machine Learning
💡Deep Learning
💡Generative AI
💡Explainable AI
💡Supervised Learning
💡Unsupervised Learning
💡Discriminative Models
💡Large Language Models
💡Transformers
💡Summarization
Highlights
Introduction to the keynote session on Research prospects in machine learning and natural language processing.
Exploration of generative AI and explainable AI as new aspects of AI in natural language processing.
Description of AI's goal to make machines as intelligent as humans, with examples like Chat GPT.
Explanation of machine learning as a subset of AI and its function to make systems learn from a knowledge base.
Deep learning defined as a subset of machine learning with deep neural networks for precise results.
Differentiation between supervised and unsupervised machine learning models.
Introduction to generative AI, capable of generating new data based on training.
Example of generative AI in predictive text input on mobile phones.
Generative AI's ability to work with unstructured data like text, images, or audio.
Foundation models and large language models in the context of generative AI.
Research focus on local languages in natural language processing tasks.
Popular large language models like GPT-3 and their capabilities in text generation.
The rise of Transformers in deep learning architecture and their applications.
Explainable AI as a counter to the Black Box tendency of machine learning.
Importance of explainable AI in high-stakes domains like medicine, defense, and law.
Four principles of explainable AI: explanation, accuracy, knowledge limit, and user assistance.
Challenges in explainable AI, including the trade-off between interpretability and accuracy.
Categorization of explainable AI into model-agnostic, model-specific, global, and local explanations.
Research opportunities in natural language processing, including text summarization and sentiment analysis.
Different types of text summarization: extractive, abstractive, monolingual, multilingual, and cross-lingual.
Spell checkers and grammatical correction systems for local languages.
Automatic classification of news articles into predefined categories using discriminative models.
Semantic analysis of social media reviews and sentiment analysis models.
Transcripts
good morning everyone
hope you all are fine
let me introduce myself I'm Annie Thomas
from the line Street of Technology dirt
from sattisgarh State and from the
country of India
today's keynote session is on Research
prospects in the field of machine
learning and natural language processing
the two new aspects of AI in the field
of natural language processing I want to
introduce over here that is generative
Ai and explainable AI
so as we all know about AI
artificial intelligence everyone knows
nowadays is to make
the machine as intelligence intelligent
as human beings and with the Advent of
chat GPT and all all are familiar with
the term AI nowadays to make the machine
as efficient as humans
so machine learning that is a part of AI
there is a subset of AI and in machine
learning we make the system learn
according to the knowledge base that is
provided again going deeper into that we
have deep learning that is a subset of
machine learning where we have deep
neural networks to
um
have more precise and evaluative results
for the problem statements it has more
than one hidden layers which makes it
possible to go deep into the network
frames so depending on all this we'll go
further into generative AI the machine
learning model can be divided into
supervised and unsupervised models
supervised model has a set of labels
that are fixed and they are
always existing like if we get an output
also we will have a label attached to
the output from which we can we can
always
check whether our predictions correct or
not the unsupervised models they are not
having a class of labels we have to
predict based on the model that is
generated and maybe that may be
unstructured data or unstructured data
so unsupervised learning will be on the
basis of the input data that is given a
model will be working on that input data
and it will be having a generated
example
going further into deep learning we
again have two types discriminative and
generative
discriminative is used to classify
predict cluster and it is trained on a
data set of labeled data we already have
the labeled data then
um
the training is easy as we have to put
it into one particular class and it
learns the relationship between the data
points and the labels but generative is
different that it generates new data
based on the training that is provided
to it so it has to understand the
distribution of data and How likely a
given example is going to be
categorized into that generative example
but in discriminative we don't have to
generate any new data the best example
of generative AI when it came into
existence is everyone is having a mobile
and we type the text so when we are
typing the text it will give you like if
I type Delight will give the next
predicted word as Institute and the next
predicted word or as of Billa Institute
of Technology I'll get an option for
that because it learns from the patterns
I have been doing all these these days
so the it is predicting the next word in
a sentence that's the best example of a
generative AI now if we come to the
concept of generative AI it is also
called chain AI it is artificial
intelligence capable of generating text
images or other media using the
generative models and
with the right side if whatever I have
written if you can see not j a i when y
input is a number or a discrete or a
class or a probability but it is when it
is a natural language text or an image
or an audio that shows that it works on
unstructured data so generative AI works
on unstructured data to generate new
patterns we have Foundation models large
language models which we'll be
discussing in the further slides
so now we understood that generative AI
where we generate new content content
and that content may be a text or image
or audio
so if we see the category of the
supervised semi-supervised unsupervised
chain AI you can have input as training
code or label data or unlabeled data and
the foundation model works on all this
and generates new content and the output
can be a text or image or code or a
video audio anything
so
the main concept here of generative AI
is to generate the new content based on
the training that is provided so here
since I am interested in text so I have
taken only the text input you can also
take image or audio so in the
uh some research which we are pursuing
here we are working on text and that
also we are working on the local
languages because in English already the
work uh text to text if you see the post
column translation summarization
question answering grammar correction
all these my research Scholars are doing
in the for the local languages here
because for English we are having
already uh or much work is being done on
this uh in these areas I'm not working
in image and audio and musicians but you
can because image generation text to
image text to video text to speech all
these are again natural language
processing generative way I can be used
in all these areas
we see we know about the Google's
Foundation models that is Palm API for
text is a very popular model word we use
vit for vision we use
vitgp2 blip vqa all these models we are
using now uh when we work talk about
natural language processing we talked
about large language models a large
language model is specifically designed
and trained for natural language
processing tasks what is the
characterization of large language
models is its large size so it can be
working for worst amounts of Text data
or it can be working for other areas
also and it is capable of generating
human-like text understanding context
answering questions all these things the
large language models are doing nowadays
the notable examples are open AIS GPT
GPT 3 GPT 4 and chat PT and birth all
these examples are very popular they
train
um
Text data or image or video and are
capable of generating the new text based
on the training data the large language
models are able to handle the large
volume of data that is taken from
internet or from social media or any
other place this shows some other models
which are also popular
then after the large language models
came into existence
there was an era of Transformers
a Transformer is a deep learning
architecture that relies on the parallel
multi-head attention mechanism the
modern Transformer it was proposed in
the year 2017 I think and the paper from
which it was introduced was attention is
all you need so attention was given to
the important data which has to be
picked up from the given set of huge
databases and the less attention has to
be provided to the data which is not of
much importance on the basis of that the
Transformers were broken so so a general
pre-trained Transformer is a more
broader term for models based on
transform architecture with these models
they can be applied to wide range of
problems not only for natural language
tasks computer vision speech recognition
reinforcement learning all the things it
is being used then these models are
pre-trained on large data sets and can
be fine-tuned for specific tasks some
more examples I'll be showing you in the
next slide that is Vision Transformer
the detr conformer swim Transformer
perceiver and perceiver IO this is what
we have talked about generative Ai and
the concepts which are used in
generative AI moving on ahead to
explainable AI that is also a new term
that is being used in the industry
nowadays and
when we talk about explainable AI it
counters the Black Box tendency of
machine learning where even the AI
designers cannot explain why it arrived
at a specific decision now what happens
is we see the black box design of
machine learning where the we have
reached to a conclusion but there needs
to be an explanation about how we are
getting the results why it is not like
this and why it is like this and why
this explainable AI was needed when we
are getting the results why is this
explainable AI was needed that was
because the domains like medicine
Defense Finance and law where it is
crucial to understand the decisions and
build trust in the algorithms they have
made their algorithms we are employing
algorithms we are getting the results
unless and until we have trust in that
those models who will is going to use
those models you know chargpt and Google
search are so popular because of the
trust we have in those systems that we
are getting such important informations
according to the need of the query we
are putting there so explainable AI came
into existence to attract find out how
we are getting the results
so four principles of explainable AI we
have done this four principles as
explanation meaningful explanation
accuracy we we are giving an explanation
but that explanation should have the
satisfactory level of accuracy not that
if it is 15 accurate we cannot say we
are having a good explanation so
explanation is there that is Meaningful
that is interpretable but that
explanation should have an accuracy and
the knowledge limit should be so high
that explainable a I will be able to
provide the system to operate under the
conditions for which it was designed and
when it should reach the sufficient
confidence in its output
so
the assisting its users and determining
appropriate trust that suppose part
trust we develop in the system in the
model which we are generating and the
second part is we have an
interpretability and explainability
mechanism which can explain to the users
how it is working so the next part is
that all that is having so many
advantages there are so many
disadvantages or issues challenges which
are existing with this one is
contrasting the interpretability and
accuracy we know we have to reach the
human accuracy levels sometimes it may
the explainable AI may not be able to
give the correct explanations it may be
contrasting with the human explanations
and those cases which has to be dealt in
the model as we improved like that so
still it exists these issues exist
describing the
and there should be the use of
abstractions to clarify the explanations
now based on this explainable AI xaia it
is also called we see there is
categorization based on agnosticity that
is model agnostic or it is model
specific if it is applied to all the
model types then it is called Model
agnostic and if it can be applied to
only particular specific model types for
which particular task it's been made the
latest model specific that is the
categorization of economic agnosticity
now depending on the scope we have
Global explanation or local explanation
if you want some part or the prediction
of some particular area only to be
displayed it may say it is local
explanation and we we want the
explanation of the whole model how it is
looking then we go for Global
explanation but local expression nations
are also important because individual
predictions are also taken into account
in X AI because Global explanation can
be given on a larger scale but local
explanation need to go deeper into the
problem so that is again an area where
the research can be done and the
different problems can have different
perspectives different aspects to
include either model agnostic model spec
or Global or local explanation
as I told you we are working on local
languages and on the basis of this local
languages we are doing text
summarization
so again now for the persons who are
hearing me those who want to do
um
and research in the field of natural
language processing machine learning you
can go for single document summarization
you can go for multi-document
summarization you can take any of the
languages on which work has not been
done you can go for indicative
informative evaluative summarization any
of the tasks can be taken upon and then
on the basis of this you can generate
models generative models or
discriminative models which will help
you to give a proper text summarization
and on the basis of target audience
there is a generic summarization and
there is a query focused summarization
on the basis of type of summarizer you
have monolingual multilingual
cross-lingual all types of summarization
so these I'm I have only included this
slide so that if you have some have
interest in generic summarization some
in multilingual so you can pursue your
area of Interest
then the two main Concepts that come
over here is extractive and abstracted
in extractive summarization the parts of
the original text are taken to form the
summary like if we are
taking a document and from the document
we want to summarize it the important
sentences will be taken out and given as
the extracted summarization but in the
abstractive summarization you need to
generate the new text depending on the
extracted important parts of the
documents so all this has to be done
which shows that abstractive
summarization needs more
fields which have to be covered so that
the abstracted summarization finds out
the meaning and again generates the new
test text based on the training data
some comparative studies of peer-wise
Publications has been done
the New Concept which has been included
for the new language has been done in
using these steps from the input
pre-processing has been done on the
pre-processing part text cleaning was
done stockpot removal based on the local
language was done then feature
extraction and then optimization was
applied to find the text summary on the
basis of some of the models bioelestem
was applied word for supplied CPT was
applied and we could find that the
results are
we could compare the models on the basis
of the results and the new model
Innovative models are being generated by
the research scholars in this area
then next topic was spelling and
correction
so if we are can go for two types of
grammatically text can be corrected or
spelling can be corrected that again
leads to two different we are working on
spell Checkers only for that particular
language maybe in the future we'll be
working on grammatical checking of the
sentences as well so we know we have in
English we have so good systems when we
type of
word and if it is wrong it gives the
suggestions and also many systems are
already existing in binding to all the
particular softwares but for the local
languages still we wrote half the
systems like which finds the text errors
may be the typographic maybe the
syntactic maybe the discourse there are
non-word errors real word errors
phonological errors
transformationals deletion errors
insertion errors of substitution all
these are the type of Errors which exist
in the text errors and you can work on
any of this type to remove all such type
of Errors being on the domain or outside
that only
then another system was to classify the
news via articles into the predefined
classes of the newspapers so that
whenever we get an article it will
automatically be sent to the national
International Business Sports
entertainment health or weather and this
has been done on our
national language that is Hindi for this
we have made the different
categorizations labels and the
discriminative models are being prepared
to classify the news where articles into
proper particular
sex label sections so that the newspaper
can automatically be aligned to the
pages according to what they are
so for challenging task was because of
the every language is a challenging for
processing for every language is a
challenging task because it has
different different types of consonants
vowels combinations and maybe patterns
or different Center structures are
different
so from different uh data set
huge amount of data has been collected
and work has been done on this I am not
able to show you the particular
results of these models but I'd like to
tell you that the news via articles they
will be classified into predefined
labels another work that is going on is
semantic analysis of the
reviews which are collected from the
social media and based on those reviews
the sentiment analysis where the
sentiment analysis models have still not
been applied on and based on that
suggestion in mining techniques are
being proposed so that they give more
accuracy than the existing semantic
analysis models so that's all from my
side thank you for the patient here
thanks
foreign
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