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In this blog we will be discussing one of the most trending subject in the AI world i.e. Generative AI. With this blog we'll have a quick introduction on Generative AI and how we can leverage AI in our day to day activities at our organization. By end of this blog we will have a good insight of how generative AI is used to transform the AI world. But before we deep dive into the world of AI, let us consider on of the most highly debatable questions on the internet today i.e. "🤔💭 Is AI a threat or an opportunity? 🌟🚀🤖🔍". I will try my best to answer this question in this case study.
Agenda:
Introduction to Generative AI
What are Large Language Models (LLMs)
Exciting Journey into the World of LLMs
Potential risks & Challenges with AI.
Final wordict
Introduction to Generative AI
In this section we will explore the basics of generative AI, why is it trending everywhere on the internet and lastly we will discuss a couple if use cases.
OK so, what is generative AI? 🤔
Generative AI at a very high level is a sub-section of artificial intelligence (AI). AI refers to computer systems capable of performing complex tasks that historically only a human could do, such as reasoning, making decisions, or solving problems. It is an umbrella term that encompasses a wide variety of technologies, including machine learning (ML), deep learning, and natural language processing (NLP). If we dig deeper, we have ML where computers take an existing dataset as source and make decisions without being clearly programmed to do so. A small section of machine learning is called deep learning where we have the use of what is referred as Artificial Neural Network (ANN). ANN model involves computations and mathematics, which simulate the human–brain processes. Finally inside deep learning we have generative AI, also referred as GenAI.
GenAI is built on top of a certain AI models called Generative Models which mathematically approximate the underlying data. These models take huge data sets like text, pictures as input and apply deep learning in the second stage to recognize the data. Once the models have completed their analysis, then this can be used to perform multiple tasks including generating specific images based on the existing ones or utilizing the current text to make it more inviting.
Now one important question is to ask here is what suddenly led to the rise of astonishing applications. There are multiple reasons behind this but, let's discuss couple of important once here.
First of all, the availability of extensive and diverse open source data has been one of the important factor driving this sudden shift. These data can be used as source to train AI Models.
Secondly, in recent times there has been a huge advancement in deep learning models. These models involves computations and mathematics, which simulate the human–brain processes.
Lastly, there has been a significant advancement in computing resources like cloud computing which has indeed provided organizations to explore GenAI.
But, the question still remains *WHY NOW?*🤔 🤔 This is due to the precision, productiveness and availability has reached a pivotal moment making a dream use cases a reality. Now let's discuss some popular possible use cases which we as user might have experienced.
1. Customer Experience Personalization
This could be one of the most impactful use cases for GenAI. By using the power of AI, businesses can create custom and personalized experience for their customers. It can create personalized suggestions by understanding the customer's taste and can create relevant content or product recommendations. For instance, an e-commerce platform could use GenAI to create images and text for personalized product recommendations based on a customer's browsing history and previous purchases, enhancing their shopping experience.
2. Fraud Detection
It involves analyzing huge amount of data to identify patterns and behavior that indicate potential fraudulent behavior. It uses Natural language processing (NLP), a branch of AI, to understand and process human language. For example, GenAI can identify patterns of fraudulent reviews or fake feedback, helping businesses maintain the integrity of their online reputation. Generative AI can also analyze financial transactions and detect unusual patterns or anomalies indicating fraudulent activities, such as identity theft or unauthorized access by continuously monitoring transactions and applying predictive analytics.
3. Predictive Maintenance
Predictive maintenance involves analyzing historical and real-time data from machines and equipment to predict when maintenance is required. Business can detect abnormality and patterns that indicate by analyzing variety of parameters like performance metrics, temperatures etc. This enables them to schedule maintenance activities before a breakdown occurs, minimizing downtime and maximizing productivity.
Large Language Modules (LLMs)
Now, since we have explored some of the use cases of GenAI, let's deep dive into what is called "THE HEART" ♥♥♥ of GenAI i.e. Large Language Modules (LLMs).
So, what are LLMs? 🤔
LLMs are the models which are trained on huge data sets and are based on artificial neural networks that utilize the transformer architecture to achieve advance language processing capabilities. LLMs mainly consists of 3 major components, the encoder, the transformer model and the decoder. Let us now discuss these components briefly.
Encoder is responsible for understanding and extracting the relevant information from the input text and converts it into tokens. These tokens are then converted into numerical values which are further converted into token embedding, which help group similar tokens together. After the token embeddings are generated, they are trained using pre-trained transformer model. The Decoder then translates the tokens generated by the encoder back into human readable format. Currently, there are multiple LLMs available in market like Google's Gemini, OpenAI's GPT, Meta's BART etc. Now, let’s delve into the practical applications of LLMs.
1. Audio data analysis
LLMs are reinventing how audio data is handled by turning hours of recordings into actionable insights generating summaries, extracting key points, and answering queries about meetings, phone calls, videos, or podcasts.
2. Chatbots and virtual assistants
Chatbots and virtual assistants use LLMs to provide quality service to customers by providing assistance with troubleshooting and answering common questions (yet deeply personalized). Hence, enabling businesses to offer 24/7 support without extensive human resources.
3. Customer sentiment analysis
LLMs can be trained to understand voice and text sentiments to better respond to customer concerns and needs. It can also be used to analyze customer feedback, reviews, and social media mentions at scale to gain insights into public perception and emerging trends. For instance, here's a message from an angry customer who is saying that "xyz company's product was manufactured with the lowest grade material and didn't last for a week". Clearly, the customer is very disappointed and no business wants it. Instead of a human dealing with this situation, an automatic message based on customer's data can help resolve the situation.
4. Talent acquisition and recruiting
Organizations can use LLMs to filter out the applicant skills and identify the candidates best suited for the job. Not only does this help with identifying quality candidates, but it also makes the entire process far more efficient. For example, many companies are already using AI's "Applicant Tracking System (ATS)" for filter out applicants.
Exciting Journey into the World of LLMs
Types of LLMs
1. Open Source models, which can be fine-tuned according to the needs. This model offer flexibility for personalization and are often smaller in size which help to lower the cost of operation.
**Hallucination being a phenomenon where models might generate results which seems credible but in reality this happened due to inaccurate data or limitation in understanding the data.
2. Proprietary model, which are mostly offered to organization as these models are trained on extensive data sets. However, license to proprietary model comes with its unique set of challenges like usage and modification.
Fine tuning a model involves taking an already trained data set and apply further training to perform certain tasks. Usually, the foundation model is already trained on large data set, we take this foundation model and train further on a smaller data to improve its predictive capability. In order to have to best output, often multiple LLMs are used. A good example will be analyzing summary and sentiment analysis for multiple articles. In the first solution, all articles are parsed together which can lead to lengthy inputs hence increasing cost. A more effective solution is the second one where one LLM is used for summarization of article and another LLM is used for sentiment analysis. Hence, making the process more efficient and less prone to errors.
There is another concept called Retrieval Augmented Generation (RAG) which is a LLM application which allows us to use external data source to complete the task. This is extremely useful model as it prevents from training an already existing model whenever there is a new or change in data. With RAG we can created a search system. So, when we ask our question, this search system searches for any data that has already been considered into the database. It then returns any relevant information which is then paired with the initial question and then is fed into the model. This means we can spent less time in retaining models hence, reducing hallucination.
Potential Risks and Challenges
GenAI brings new risks and challenges for business and society
1. Legal Issue such as Privacy, Security, Intellectual property protection
2. Ethical issues like bias, misinformation
3. Social and environmental issues including impact on workforce and on the environmental
Let's discuss them briefly
1. Legal Issues
The first challenge that GenAI brings is privacy concerns. Current GenAI models lake a forgetting feature for personal data. As these models are trained on huge data sets there is a high possibility of exposing personal information breaching personal rights.
The second challenge is the issue of data leakage. GenAI have the ability to remember and reproduce the training data. This raises a threat when sensitive or confidential data is included in training data sets.
2. Ethical Issues
Individuals with malicious intent such as fraudsters or cyber attackers can exploit LLM to create destructive content. There are many probable threats to be aware of such as scam attacks or automated frauds.
3. Social Impact
AI may lead to job losses of the workforce -> economic inequalities and unemployment.
Final wordict
As we continue to explore the vast potential of Generative AI, it’s clear that this technology is not just a fleeting trend but a transformative force in the tech landscape. By understanding its fundamentals, we can harness its power to innovate and solve complex problems across various industries. Whether you’re a seasoned professional or just starting your journey, embracing Generative AI will undoubtedly open new doors and opportunities. Stay curious, keep experimenting, and let’s shape the future of technology together.
Thank you for joining me on this journey through the fundamentals of Generative AI. 🤖✨ I hope you found the insights valuable and inspiring. 🌟 Stay tuned for more deep dives into the world of AI, DevOps, and cutting-edge technology. 🚀📚 Until next time, keep exploring and innovating! 💡🔍🔧🌐💻
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