Blog Detail

A Dummy’s Guide to Generative AI

Written by
Admin
Generative AI
Published on
January 1, 2025
Share
Table of Content

The recent spate of announcements by tech titans such as Microsoft, Google, Apple,OpenAI, NVidea, et al, has started a serious buzz among technology gurus andbusiness leaders. This buzz is a continuation of the overarching headlines emanatingout of Davos 2024, the consensus there that AI and Generative AI (this wasspecifically mentioned) as the means to, firstly, transform society and, secondly, toachieve greater revenues. While computer science graduates are revelling in theavailability of new AI technologies, most of us are not sure what the buzz is about.Sure, we are all using ChatGPT, but how is this going to transform our lives? Thisarticle attempts to unpack the technologies associated with AI, especially that ofGenerative AI that is at the heart of the buzz.


What is Generative AI?


To answer this, we need to go one step back and properly understand ArtificialIntelligence (AI). Broadly speaking AI can be equated to a discipline. Think of scienceas a discipline; within science we get chemistry, physics, microbiology, etc; in thesame way AI is a broad discipline, and within AI there are several subsets such as ML(Machine Learning), algorithms to perform specific tasks, Expert Systems (mimickinghuman expertise in specific topics to support decision making), Generative AI, etc.Generative AI (Gen AI) has been making significant strides, especially sinceDecember 2022. On 30 November 2022, OpenAI released ChatGPT, which reached100 million users in just 2 months, compared to 78 months for Google Translate, 20months for Instagram, and 9 months for TikTok. Generative AI is a majoradvancement, referring to AI that creates new content, such as text, images,language translations, audio, music, and code. While currently focused on theseoutputs, Gen AI’s potential is vast and could eventually encompass areas like urbanplanning, therapies, virtual sermons, and esoteric sciences. Generative AI isessentially a subset or specialized form of AI, akin to how chemistry is a subset ofscience. In AI terminology, these systems are called “models,” with ChatGPT beingone example.


Unpacking GPT


The term “Chat” in ChatGPT signifies a conversation, whether through text or voice,between the user and the system. “GPT” stands for Generative Pre-trainedTransformer. “Generative” refers to the AI’s ability to create original content, while“Pre-trained” highlights a core concept in AI where models are trained on vastdatasets to perform specific tasks, like translation between languages. For instance,a translation model can’t provide insights like a Ferrari’s speed, but it can explainlinguistic origins, such as Ferrari deriving from the Italian word for “blacksmith”. Thiscapability is honed through deep learning, where the model learns associations and context from extensive data. The training process involves predicting the next wordin a sequence based on prior words, which can sometimes lead to errors like“hallucinations” – unexpected outputs such as “the pillow is a tasty rice dish”. Thisdemonstrates how AI learns and operates within defined parameters without humanintuition.
The key here is that the model has to be trained on, firstly, vast amounts of data,and, secondly, with meticulous attention. And this leads us to another commonphrase or jargon used in the AI world – Large Language Models or LLMs. In fact, ChatGPT is a Large Language Model! If we have to define LLM, it could be defined as anext word prediction tool. From where do the developers of LLMs get data to carryout the Pre-training? They download an entire corpus of data mainly from websitessuch as Wikipedia, Quora, public social media, Github, Reddit, etc. it is moot tomention here that it cost OpenAI $1b (yup, one billion USD) to create and train ChatGPT – they were funded by Elon Musk, Microsoft, etc. Perhaps, that is why it not anopen-source model!!
Let’s now unpack the ‘T’ of ‘GPT’. This refers to Transformer. This is the ‘brain’ ofGen AI; Transformers may be defined as machine learning models; it is a neuralnetwork that contains 2 important components: an Encoder and a Decoder. Here’s asimple question that could be posted to ChatGPT: “What is a ciabatta loaf?”. Upontyping the question in ChatGPT, the question goes into the Transformer’s Encoder.The 2 operative words in the question are ‘ciabatta’ and ‘loaf’. The word ‘Ciabatta’has 2 possible contexts – footwear and Italian sour dough bread (Ciabatta meansslippers; since the bread is shaped like a slipper, it is called ‘ciabatta’).In the context of “loaf,” ChatGPT, a Pre-Trained model, would prioritize food itemsover other meanings. For instance, given “loaf,” it would likely choose “bread” over“footwear,” recognizing “ciabatta bread” as a specific example. The model processeswords sequentially and can predict associations like identifying ciabatta as an Italiansourdough bread. However, ChatGPT’s responses aren’t always flawless, as accuracydepends on its training and fine-tuning. Despite occasional errors, its answers areoften remarkably precise, reflecting meticulous development involving techniqueslike “attention,” which enhances its ability to focus on relevant details in dataprocessing.
Did you know that Gen AI has been in use well before the advent of ChatGPT? In2006 Google Translate was the first Gen AI tool available to the public; If you fed in,for example, “Directeur des Ventes” and asked Google Translate to translate theFrench into English, it would return “Sales Manager”. (By the way, Transformers wasfirst used by Google). And then in 2011 we were mesmerised by SIRI which was sucha popular ‘toy’ initially among iPhone users. Amazon’s Alexa followed, together withchatbots and virtual assistants that became a ubiquitous feature of our lives – theseare all GenAI models. As can be seen, we’ve been using Gen AI for a while, howeverno one told us that these ‘things’ were Generative AI models!

Similar Blogs

Stay Ahead in AI & Data Innovation

Stay informed with expert insights on AI, data governance, and emerging technologies. Explore thought leadership, industry trends, and the future of AI-driven innovation.
Blog

Demand Sensing Optimising Supply and Demand Mismatch

The goal of supply chain planning is to improve forecast accuracy and optimize inventory costs throughout the supply distribution network. Without proper planning, there is a chance of overstocking leading to high inventory costs or understocking leading to stock out situations causing revenue loss.


When a company produces more than the demand, the stock sits unsold in the inventory. Therefore, this increases the inventory holding cost, later leading to waste and obsolescence costs. When a company produces less than the customer demand, there is a revenue loss and in today’s competitive business environment this might also lead to future revenue losses.


Getting demand forecasting accurate is the key to success in today’s supply chain planning. However, there are various reasons why this demand-supply mismatch occurs and forecasting accuracies drop. Customers’ needs and requirements constantly change, maybe due to:

  • Introduction of new technology
  • Fast fashion
  • Promotional discounts
  • Point-of-sale
  • Weather
  • Strikes
  • Lockdowns


For example, when the first wave of the pandemic hit, people minimized their purchases like clothes, cosmetics, etc., thinking they won’t be using these items quite often. However, there was an exponential rise in the purchase of luxury goods as well as insurance (health and life). People also bought immunity boosters, comfort foods, groceries, digital services, and appliances. Additionally, there was a shift in how people perceived and bought commodities. This leads to uncertainties in aggregate demand. As companies try to fulfill the demand, there is a mismatch between supply and demand.

Traditional classical forecasting methods find it difficult to predict demand accurately in today’s dynamic business environment. However, Statistical forecast models rely solely on historical sales data and they fail to evaluate the impact of various other variables that impact sales demand. Product manufacturing and distribution must be aligned with supply-demand volume variabilities so that the companies can have accurate demand forecasts, close to the actual sales, preparing them to stock at the right place at the right time in the right quantities.

Using modern AI / ML technologies Demand Sensing has now made it possible to analyze the impact of these variables on sales demand and enable them to predict demand more accurately. Therefore, it is fast becoming an indispensable tool in supply chain planning for accurate demand forecasting. Moreover, it builds upon the classical traditional forecasting methods to develop baseline forecasts and then refines these forecasts for higher accuracy by taking into account other variables that impact the sales demand on a near real-time basis. Demand Sensing leads to better demand forecasting accuracy helping organizations to improve customer demand fulfillment, enhance revenues and optimize inventory throughout their distribution network and reduce costs.

Other than optimizing the inventory to meet demands, supply chains can also migrate to a just-in-time inventory management model to boost their responsiveness to consumer’s demands and lower their costs significantly.

Data Required for Demand Sensing

AL/ML-based Demand Sensing tools can make use of a variety of data available to predict demand more accurately. Such data includes (but not limited to):

  • Current Forecast
  • Actual Sales data
  • Weather
  • Demand disruption events like strikes, lockdown, curfew etc.
  • Point of Sales
  • Supply Factors
  • Extreme weather events like floods, cyclones, storms etc.
  • Promotions
  • Price

The variable may change for different businesses & organizations and any given variable can be modelled in Demand Sensing to analyze the impact on sales demand for greater accuracy.

The list above includes current data, historical data, internal data, and external data. Hence, this is exactly why AI/ML-based demand sensing is more accurate than traditional demand sensing. As large volumes of data are analyzed and processed quickly, predictions are specific making it easy for supply chains to make informed business decisions. An important factor to conduct demand sensing accurately is the availability of certain capabilities by supply chains. Let’s learn more about these capabilities.

Capabilities Required by Supply Chains for Demand Sensing

  • To template demand at an atomic level
  • To model demand variability
  • To calculate the impact of external variables
  • To process high volumes of data
  • To support a seamless environment
  • To drive process automation

Benefits of Demand Sensing

The major benefits of Demand Sensing for an organization are:

  • Greater Demand Forecasting accuracy
  • Reduced inventory and higher inventory turnover ratios.
  • Higher customer demand fulfillment leading to increased sales revenues
  • Enables citizen demand planners and supply planners.
  • Auto-modelling and Hyper parameter

Who Benefits the Most from Demand Sensing?

  • Retail/ CPG/ E-commerce
  • Distribution
  • Manufacturing/Supply chain/ Industrial automotive
  • Chemical/ Pharmaceutical
  • Food Processing
  • Transport/ Logistics
  • Natural Resources

Demand Sensing – Need of the Hour

As already discussed, demand sensing is required mandatorily by supply chains to manage and grow their business. In this dynamic market where most supply chains are opting for digital transformation and an automated process system, traditional methods to sense demand do not work efficiently. To gain a competitive edge and to keep the business running in the current unpredictable times, AI/ML-based demand sensing is the need of the hour.

How aptplan Can Help You

Aptus Data Labs’s AI/ML-based tool “aptplan” helps businesses access accurate demand sensing and forecasting data to plan their supply accurately. aptplan uses internal and external data with traditional techniques and advanced technology to train AI/ML models are used to predict accurate sales demand sensing on a real-time basis. It uses NLP technologies to collect a wide variety of unstructured data to convert into a structured format for use. Aptplan delivers highly accurate demand plans for better business decision-making and lower inventory costs. To know more or to request a demo, click on https://www.aptplan.ai/

Blog

The Challenges of Data Privacy and Security in the Age of Big Data

In the age of Big Data, privacy and security are major concerns for businesses and consumers alike. With the increasing amount of data being collected and analyzed, it is becoming increasingly important to ensure that the privacy and security of this data are protected. In this blog post, we will discuss the challenges of data privacy and security in the age of Big Data.


How to overcome these challenges

The amount of data being generated is increasing at an exponential rate. According to a report by IDC, the amount of data in the world will increase from 33 zettabytes in 2018 to 175 zettabytes by 2025. This data is being generated by various sources such as social media, online shopping, and IoT devices. Therefore, this data is valuable to businesses as it helps them make informed decisions and improve their products and services.


However, with the increased collection and analysis of data, there is a growing concern about data privacy and security. Additionally, a breach in data security can result in sensitive information being exposed, which can be harmful to individuals and businesses. In addition, the unauthorized access to data can result in financial losses, reputational damage, and legal repercussions.


The challenges of this are multi-faceted. Moreover, one of the main challenges is the lack of awareness and understanding of data privacy and security issues. According to a survey by KPMG, only 36% of businesses believe that, as they are adequately prepared to deal with a cyber-attack. Furthermore, this lack of preparedness can be attributed to a lack of understanding of data privacy and security issues.


Another challenge is the complexity of data privacy and security regulations. In addition, with the increasing amount of data being collected, there are various regulations that businesses need to comply with such as GDPR, CCPA, and HIPAA. These regulations can be complex and difficult to understand, especially for small and medium-sized businesses.


Furthermore, the growing amount of data being collected is also resulting in an increase in the number of cyber-attacks. According to a report by McAfee, there were 1.5 billion cyber-attacks in 2020, which is an increase of 20% from the previous year. This increase in cyber-attacks is a major challenge for businesses as they need to ensure that their data is protected from these attacks.


To overcome these challenges, businesses need to adopt a comprehensive approach to data privacy and security. This includes implementing data encryption, using secure networks, and implementing access controls. In addition, businesses need to ensure that their employees are trained on data privacy and security issues. They have a clear understanding of the regulations that they need to comply with.


In conclusion, data privacy and security are major concerns for businesses in the age of Big Data. The challenges of data privacy and security are multi-faceted and require a comprehensive approach. By adopting best practices for data privacy and security, businesses can ensure that their data is protected. Also, that they comply with the regulations that are in place.

Blog

Analytics solutions journey with D2D framework

In the age of Big Data, privacy and security are major concerns for businesses and consumers alike. With the increasing amount of data being collected and analyzed, it is becoming increasingly important to ensure that the privacy and security of this data are protected. In this blog post, we will discuss the challenges of data privacy and security in the age of Big Data.


How to overcome these challenges

The amount of data being generated is increasing at an exponential rate. According to a report by IDC, the amount of data in the world will increase from 33 zettabytes in 2018 to 175 zettabytes by 2025. This data is being generated by various sources such as social media, online shopping, and IoT devices. Therefore, this data is valuable to businesses as it helps them make informed decisions and improve their products and services.


However, with the increased collection and analysis of data, there is a growing concern about data privacy and security. Additionally, a breach in data security can result in sensitive information being exposed, which can be harmful to individuals and businesses. In addition, the unauthorized access to data can result in financial losses, reputational damage, and legal repercussions.


The challenges of this are multi-faceted. Moreover, one of the main challenges is the lack of awareness and understanding of data privacy and security issues. According to a survey by KPMG, only 36% of businesses believe that, as they are adequately prepared to deal with a cyber-attack. Furthermore, this lack of preparedness can be attributed to a lack of understanding of data privacy and security issues.


Another challenge is the complexity of data privacy and security regulations. In addition, with the increasing amount of data being collected, there are various regulations that businesses need to comply with such as GDPR, CCPA, and HIPAA. These regulations can be complex and difficult to understand, especially for small and medium-sized businesses.


Furthermore, the growing amount of data being collected is also resulting in an increase in the number of cyber-attacks. According to a report by McAfee, there were 1.5 billion cyber-attacks in 2020, which is an increase of 20% from the previous year. This increase in cyber-attacks is a major challenge for businesses as they need to ensure that their data is protected from these attacks.


To overcome these challenges, businesses need to adopt a comprehensive approach to data privacy and security. This includes implementing data encryption, using secure networks, and implementing access controls. In addition, businesses need to ensure that their employees are trained on data privacy and security issues. They have a clear understanding of the regulations that they need to comply with.
In conclusion, data privacy and security are major concerns for businesses in the age of Big Data. The challenges of data privacy and security are multi-faceted and require a comprehensive approach. By adopting best practices for data privacy and security, businesses can ensure that their data is protected. Also, that they comply with the regulations that are in place.