Lec 3: Role of AI in Product Management (Part 2)

Summary

Highlights

• Explained the AI Factory model and its components, crucial for digital transformation 🏗️
• Discussed the strategic role of product managers in embracing AI technologies 🦸‍♀️
• Explored various AI subfields like NLP, machine learning, and computer vision 🧠
• Highlighted the use of AI in enhancing decision-making and operational efficiency 🔍
• Demonstrated how AI tools are reshaping the competitive landscape of product management 🌍

Key Takeaways

• AI integration is pivotal for modern product management success 🚀
• The AI Factory model transforms traditional processes into AI-driven operations 🏭
• Product managers are strategic leaders in AI adoption 👩‍💼
• AI subfields like NLP, ML, and computer vision redefine product development 📈
• Understanding AI subfields is crucial for maximizing its potential and impact 💡

Overview

Chapters

• 00:00 - 00:30: Introduction to AI in Product Management The chapter introduces the online certification course on artificial intelligence in product management.
• 00:30 - 01:00: Role of AI in Product Management The chapter titled 'Role of AI in Product Management' discusses the integration of AI into traditional organizations to aid in digital transformation. It is a part of the module on AI in product management, specifically module three, part two of chapter 1. The chapter introduces the concept of the 'AI Factory' working model and explores its various components, illustrating how organizations can evolve to integrate AI effectively.
• 01:00 - 02:00: Components of AI Factory Working Model The chapter discusses the critical role of product managers in the transition from traditional frameworks to AI-assisted digital organizations. It highlights various subfields and types of AI, emphasizing the growing importance of AI in product management within the tech industry. The integration of AI is noted as a transformative factor in how product management is approached.
• 02:00 - 03:00: Importance of AI in Product Management The chapter discusses the importance of artificial intelligence (AI) in modern product management. It highlights that AI is not just a passing trend but a crucial strategy for success in today's technology-driven era. For companies transitioning into an AI-driven future, updating software strategies is essential to excel in product management. AI significantly contributes to organizations by promoting strategies that focus on accuracy, personalization, and scalability. The chapter emphasizes the strong connection between AI and lead product organizations, suggesting that AI should be an integral part of their strategic planning.
• 03:00 - 04:00: AI in Product-Led Growth and Strategy The chapter discusses the role of AI in product-led growth (PLG) and its strategic integration to fuel innovation and provide a competitive advantage. It emphasizes the need for organizations to incorporate AI into their strategies to remain competitive in today's fast-paced markets. The extent of a product manager's use of AI is considered crucial.
• 04:00 - 05:00: AI-Powered Organizations and Industrial Revolution The chapter explores the integration of AI within organizations and its transformative impact on industries. Key points include the discussion on how AI is fundamentally changing organizational functionalities and revolutionizing industries. It gives examples of major players like Netflix leading AI-driven operations, but also emphasizes that creating such capabilities is not limited to tech giants, suggesting a broader adoption across various sectors.
• 05:00 - 06:00: Paradigm Shift in Decision Making: The AI Factory The chapter discusses how the AI Factory working model is revolutionizing decision-making processes in organizations. Just as the Industrial Revolution brought scalable and repeatable production methods to manufacturing, the AI era is transforming how decisions are made by industrializing data collection. This marks a significant shift from traditional, individualized decision-making processes to a more structured, data-driven approach.
• 06:00 - 07:00: Role of Analytics in AI Factory The chapter titled "Role of Analytics in AI Factory" discusses how analytics and decision-making are transforming the landscape. It introduces the concept of the AI Factory, which signifies a paradigm shift in both decision-making and operational efficiency. Within this digital framework, traditional employee-driven processes are digitized, and decision-making is treated as an industrial process. The chapter emphasizes leveraging artificial intelligence by integrating AI, data processing, and analytics to empower organizations.
• 07:00 - 08:00: Digital Operating Models and AI Factories The chapter titled 'Digital Operating Models and AI Factories' discusses the use of scalable decision engines to drive innovation, improve customer experiences, and enhance overall performance. It emphasizes the pivotal role of analytics in an AI Factory. Analytics help systematically convert internal and external data into predictions, insights, and choices. These predictions, in turn, guide or automate various operational actions.
• 08:00 - 09:00: Virtuous Cycle of the AI Factory The chapter titled 'Virtuous Cycle of the AI Factory' discusses how leveraging analytics through the AI Factory facilitates enhanced scale, scope, and learning capacity within a digital framework. It explores digital operating models and their role in managing information flows and guiding the construction, delivery, and operation of both digital and physical products, with the AI Factory positioned as central to these processes.
• 09:00 - 10:00: AI Factory Approach and its Components This chapter discusses how critical processes and operational decisions are guided, shifting the role of humans to focus on tasks requiring creativity, intuition, and complex problem solving. It introduces the concept of the AI Factory's 'Virtuous Cycle', a flywheel fueled by customer centricity rather than profit maximization.
• 10:00 - 11:00: Data Pipeline in AI Factories The process of strengthening customer retention and satisfaction involves offering excellent customer experiences at competitive prices. When customers have positive experiences, they tend to return, thereby enhancing Amazon's flywheel effect.
• 11:00 - 12:00: Algorithm Development and Experimentation Platform The chapter discusses the cycle of positive customer experiences leading to repeated purchases, which increases traffic and attracts more third-party sellers. This, in turn, leads to greater selection choices and an enhanced customer experience, creating a cycle of growth. It also touches on how the company's growth contributes to a low-cost structure, which further fuels this cycle.
• 12:00 - 13:00: Software Infrastructure in AI Factories The chapter discusses the impact of lower cost structures on pricing and customer experience in AI Factories. It describes a cycle where reduced prices enhance customer experience, which in turn drives cost efficiency. Furthermore, it outlines the key component of an AI Factory as the integration of analytics and AI into a company’s core processes to ensure efficiency.
• 13:00 - 14:00: AI Factory's Role in Data Processing and Analytics The chapter discusses the essential components of an AI Factory, focusing on scalable operations, and highlights the importance of a reversed data pipeline, algorithm development, experimentation platforms, and software infrastructure. It emphasizes the data pipeline's crucial role in gathering, integrating, processing, and protecting large volumes of user data, ensuring smooth data flow and support for real-time operations in the system.
• 14:00 - 15:00: Core Value of AI Factory: Experimentation and Improvement The chapter discusses the core value of experimentation and improvement in an AI Factory setting. It emphasizes the importance of analytics and decision-making processes, which are supported by algorithm development that generates predictions crucial for operations. These algorithms are designed to continuously learn and adapt, thereby refining recommendations and enhancing user experiences over time. The chapter also highlights the role of a dedicated experimentation platform, which facilitates rigorous testing of hypotheses and the evaluation of their impact.
• 15:00 - 16:00: Product Managers in AI Revolution This chapter focuses on the role of product managers in the AI revolution, particularly how they can contribute to and manage the development process within an AI Factory setting. Key points include the importance of evaluating suggested changes to ensure they have the desired impact and the role of continuous improvement. Additionally, the chapter discusses the significance of software infrastructure in providing a scalable framework for data delivery and ensuring seamless interactions among users.
• 16:00 - 20:00: Subfields of AI This chapter discusses various subfields of AI, starting from data preparation which includes gathering, cleaning, and normalizing data. The processed data is then used for algorithm development involving techniques such as supervised learning, unsupervised learning, and reinforced learning. This leads to the creation of software infrastructure, encompassing software-enabled workflows, computing, storage, and analytics. This cycle then repeats itself, continually enhancing operations and the overall user experience.
• 20:00 - 21:00: Subfield: Natural Language Processing In the chapter titled 'Subfield: Natural Language Processing', the discussion focuses on how the AI Factory plays a crucial role in data processing and analytics. It explains the significance of automating data processing and integrating AI technologies to handle the analysis of large volumes of data. The AI Factory is presented as the central component of a digital operating model that enhances decision-making, boosts operational efficiency, and fosters continuous learning.
• 21:00 - 22:00: Subfield: Visual Perception The chapter discusses the core value of the "AI Factory" in modern digital firms, which centers on experimentation and improvement. It explains how the AI Factory supports rigorous experimentation protocols to test hypotheses about customer behavior, competitive responses, and process variations. Furthermore, data on usage, accuracy, and the impact of predictions are continuously fed back into the system, aiding further processing and improvement, illustrating an iterative process that enhances AI capabilities.
• 22:00 - 23:00: Subfield: Automatic Programming Subfield: Automatic Programming focuses on the evolving role of AI Factories in shaping business futures by continuously refining algorithms. It highlights the centrality of AI Factories in unlocking new business opportunities.
• 23:00 - 24:00: Subfield: Knowledge Representation This chapter discusses the evolving role of product managers in the age of AI technologies. Traditionally, product managers were responsible for overseeing product discovery, development, and delivery. However, with the integration of AI technologies, their roles are being fundamentally reshaped. Instead of being passive observers, product managers are becoming active drivers and strategists. They are now responsible for guiding the incorporation of AI solutions into their products and integrating AI as innovation leaders.
• 24:00 - 25:00: Subfield: Intelligent Robot The chapter discusses the transformative role of artificial intelligence in product development, especially within the realm of intelligent robotics. It emphasizes how AI not only helps keep pace with technological advancements but also establishes new benchmarks for efficiency, functionality, and user satisfaction. The chapter highlights the vast potential for product managers to harness AI, not just to refine process efficiencies but to explore groundbreaking possibilities and methodologies. This includes enhancing decision-making capabilities through machine learning and designing products leveraging natural language processing for a more intuitive user experience.
• 25:00 - 26:00: Subfield: Automated Reasoning This chapter discusses the subfield of Automated Reasoning, highlighting the transformative impact of AI on product management. It emphasizes that AI goes beyond just technical aspects, revolutionizing product managers' perspectives. They need to see AI not only as a tool for product development and delivery but also as an enhancement to ecosystem value. The chapter introduces the importance of understanding AI's subfields, crucial for maximizing its potential.
• 26:00 - 37:00: Subfield: Machine Learning and Its Techniques The chapter, titled 'Subfield: Machine Learning and Its Techniques,' emphasizes the importance of AI in product discovery and development, highlighting the necessity of maximizing AI's potential in product management. It introduces the subfields of AI, with a focus on natural language processing (NLP). NLP is described as a pivotal field that enables machines to comprehend and interpret human language, positioning it at the intersection of AI and human communication.
• 37:00 - 46:00: Subfield: Neural Networks This chapter delves into the field of Neural Networks, emphasizing Natural Language Processing (NLP) as a bridge between machine binary communication and human expressiveness. It highlights practical NLP applications such as language translation, autocorrection, and smart assistants on mobile devices, which enhance human-machine interaction by making it more intuitive and seamless. Additionally, it hints at the topic of visual perception, emphasizing a complex interaction between sensory inputs and cognitive processes.
• 46:00 - 50:00: Subfield: Computer Vision and Generative AI The chapter discusses the role of processing and contextual understanding in making sense of visual data within AI. It examines how AI analyzes data from sensors, cameras, microphones, and similar input devices to understand the surrounding environment. This capability enables machines to simulate human perceptions such as recognizing objects, comprehending speech, and interpreting visual and auditory signals. The chapter also touches on the topic of automatic programming.
• 50:00 - 53:00: Conclusion and References The chapter discusses automatic programming, also known as code generation. It explains how this process involves automatically generating computer programs or source code using high-level specifications, algorithms, and machine learning techniques. This method increases software development efficiency and reduces time consumption by eliminating the need for human intervention in writing repetitive or complex code. Automatic programming tools can effectively translate simplified inputs, like user requirements or system models, into executable code.

Lec 3: Role of AI in Product Management (Part 2) Transcription

• 00:00 - 00:30 [Music] [Music] welcome to this nbdl online certification course on artificial intelligence in product management now
• 00:30 - 01:00 we are talking about module three that is role of AI in product management part two so we are discussing this part within the introduction to AI in product management so this is module three and part two of chapter 1 in this module we will talk about how traditional organizations can digitally transform to support AI integration through the AI Factory working model then we will talk about what are the various components of the AI Factory working model
• 01:00 - 01:30 thereafter we will see how product managers play a crucial role in this transformation from tradition to AI assisted digital organization and what are the subfields of AI and their various types so now let us start with the importance of AI in product management in the ever evolving tech industry product management is experiencing a significant change due to the adoption of artificial intelligence integrating AI into product management
• 01:30 - 02:00 is no longer just a trend it is a crucial strategy for achieving success as companies transition into the aid driven era updating software strategies is vital for excelling in product management AI plays a key role in product Leed organizations by fostering strategies that are centered on accuracy personalization and scalability the connection between Ai and product Le
• 02:00 - 02:30 growth that is plg is clear presenting opportunities for the Strategic integration that can fuel Innovation and offer a Competitive Edge as AI influence on Modern product development grows organizations must incorporate it into their strategies to remain competitive in today's fast based markets therefore a product manages usage of AI largely dep dep on how much or to what extent
• 02:30 - 03:00 the organization has incorporated or integrated AI in its functioning the transformative impact of artificial intelligence is revolutionizing Industries and fundamentally changing how organizations function although major players like Netflix have led the charge with their aid driven operations the creation of such capabilities is not restricted only to to the tech Giants now let us look at AI
• 03:00 - 03:30 powered organizations that is the AI Factory working model the Industrial Revolution transformed manufacturing by introducing scalable and reputable production methods yet decision making processes remained largely traditional and individualized in today's AI driven era we are experiencing a profound shift where the industrialization of data collection
• 03:30 - 04:00 analytics and decision making is redefining the landscape the AI Factory represents a paradigm shift in decision making and operational efficiency within the digital forms it digitizes processes that were traditionally performed by employees and treats decision making as an industrial process leveraging the power of artificial intelligence by integrating AI data processing and analytics or organizations can Leverage The Power of
• 04:00 - 04:30 scalable decision engines to drive Innovation one improve customer experiences two and therefore enhance the overall performance now we will look at the role of analytics in the AI Factory analytics play a crucial role in the AI Factory by systematically converting internal and external data into predictions insights and choices these predictions guide or automate various operational actions within the
• 04:30 - 05:00 organization by leveraging analytics the AI Factory enables Superior scale scope and learning capacity within a digital form the next comes the digital operating models and AI factories digital operating models Encompass the management of information flows or the guidance of building delivery and operations of physical products in both cases the AI Factory sits at the core
• 05:00 - 05:30 guiding critical processes and operational decisions now this shifts it allows human to move to the periphery focusing on tasks that require creativity intution and complex problem solving then comes The Virtuous cycle of the AI Factory the amazing flywheel cycle has growth to its core but is fueled by focusing on the customers rather than the profits
• 05:30 - 06:00 by providing a great customer experience at reasonable prices it leads to happy customers who are more likely to come back thus pushing the cycle Amazon fly will goes in two cycles that merge the first is the initial cycle that is the availability of third party sellers lead to competition and wider product selection the second is competition and a wider selection of products allows for lower prices which fuels positive customer experience in in return
• 06:00 - 06:30 positive customer experiences results in repeated purchases that will bring in more traffic higher traffic brings in more third party Sellers and overall growth more sellers more selection choices that leads to better customer experience more customers come and therefore more sellers come so this is how this Cycle Works the second cycle the company's growth opens door for a lowcost structure
• 06:30 - 07:00 the lower cost of structure allows for lower prices of products lower prices once again improve customer experience and so on and so forth so that leads to lower cost structure which leads to lower prices and then it again leads to customer experience now what are the components of this AI Factory the AI Factory approach involves integrating analytics and AI into the core processes of the company ensuring efficient and
• 07:00 - 07:30 scalable operations the essential components of an AI Factory includes a reversed data pipeline algorithm development experimentation in platform and software infrastructure now what is the data pipeline in AI factories the data pipeline plays a crucial role in gathering integrating processing and safeguarding vast amount of user data it ensures the smooth flow of data through to the system supporting realtime
• 07:30 - 08:00 analytics and decision making processes the next is algorithm development that focuses on generating predictions that derive critical operating activities these algorithms continuously learn and adopt refining recommendations and enhancing the user experience over time the next is the experimentation platform the experimentation platform allows for rigorous testing of hypothesis and evaluation of the impact
• 08:00 - 08:30 of suggested changes it ensures that modifications have the intended effect and facilitates continuous Improvement the fourth is software infrastructure the software infrastructure component of an AI Factory provides a consistent and scalable framework for data delivery and connectivity it enables seamless interactions between internal and external users and ing efficient
• 08:30 - 09:00 operations and enhancing the overall user experience so now you see that here we are gathering cleaning and normalizing the data then this data goes to algorithm development that is the second thing that includes supervised learning unsupervised learning reinforced learning Etc then it goes to software infrastructure that is software enabled work flows Computing storage and analytics and then again the cycle goes back and here we have this
• 09:00 - 09:30 experimentation platforms and the product is deployed now we will talk about the AI factories role in data processing and analytics the AI Factory brings data processing and analytics addressing the challenge of analyzing vast amount of data by automating the process and integrating AI Technologies the AI Factory forms the core of a digital operating model driving decision making improving operational efficiency and en buing continuous learning in
• 09:30 - 10:00 modern digital firms now what are the core value of AI Factory experimentation and Improvement the AI Factory facilitates rigorous experimentation protocols to test hypothesis about customer Behavior competitive responses and process variations data on usage accuracy and impact of predictions are continuously fed back into the system for further processing and Improvement this iterative process enables the AI
• 10:00 - 10:30 Factory to adopt and refine its algorithms over time as the digital era continues to evolve the AI Factory will play a central role in shaping the future of businesses and unlocking new possibilities for Success now let us look at how product managers are in the Forefront of AI Revolution product managers have become pivotal figures in the AI Revolution uniquely positioned to Leverage ai's potential to drive product
• 10:30 - 11:00 development and isper Innovation the traditional responsibilities of product managers overseeing product Discovery deploy development and delivery are being fundamentally reshaped by the integration of AI Technologies rather than being passive observers product managers are now active drivers and strategists guiding the incorporation of AI Solutions into their products as Innovation leaders they are tasked with seamlessly integrating AI into their
• 11:00 - 11:30 product development processes not only to keep up with technological prog progress but to set new standards for efficiency functionality and user experience the potential of AI in the hands of product managers is immense extending Beyond process optimization to unlocking entirely new possibilities and approaches from improving decision making through machine learning to developing products that use natural language processing for more intuitive
• 11:30 - 12:00 user interactions AI empowers product managers to redefine what can be achieved now this shifts goes beyond technicalities it is a fundamental change in the way product managers think they must view AI both as a tool for developing and shipping products and as a capability to enhance the value of their ecosystem now let us look at the various sub fields of AI ai's vast scope include multiple sub fields each requiring a NST understanding to be
• 12:00 - 12:30 effectively applied in product Discovery and development this is crucial for maximizing ai's potential and ensuring its optimal use in product management so we will briefly discuss and understand the sub fields of AI so one is the natural language processing at the interaction of AI and human communication lies natural language processing a pivotal of field that empowers machines to comprehend and interpret human language
• 12:30 - 13:00 npl Bridges the gap between the binary world of machines and the non expressiveness of human communication task like language translation autoc correction and smart assistance on mobile devices epitomize the Practical applications of NLP making interactions with machines more intuitive and seemless the next comes visual perception it involves a complex interplay of sensory inputs cognitive
• 13:00 - 13:30 processing and contextual understanding to make sense of visual data perceptions within AI entails the analysis of data obtained from sensors cameras microphones or similar input devices to comprehend the surrounding environment this capability enables machines to simulate human perceptions by recognizing objects comprehending speech and interpreting Visual and auditory signals next comes automatic programming
• 13:30 - 14:00 automatic programming also known as code generation refers to the process of automatically generating computer programs or source code using high level specifications algorithms and machine learning techniques this approach makes software development more efficient and less time consuming as it eliminates the need for human intervention in writing repetitive or complex code automatic programming tools can translate simplified input such as user requirements or system models into
• 14:00 - 14:30 functional programs then comes knowledge representation knowledge representation in in AI refers to the way in which artificial intelligence systems store organize and utilize knowledge to solve complex problems it is a crucial aspect of AI enabling machines to mimic human understanding and reasoning knowledge representation involves the creation of data structures and models that can efficiently capture information about
• 14:30 - 15:00 the world making it accessible and usable to AI algorithms for decision making inference and learning the next is intelligent robot intelligent robot has a well- defined artificial brain which can arrange actions according to the purpose and also has sensors and effectors common Technologies mainly include core components Robo specific sensors Robo soft software test safety and reliability and other key
• 15:00 - 15:30 common Technologies key applications mainly include industrial robots service robots special environment service Robo and medical Rehabilitation robots demonstration applications are oriented to Industrial robot medical Rehabilitation robot and other fields then comes automated reasoning automated reasoning is used to prove two things first they prove that a system design or
• 15:30 - 16:00 implementation obeys its specifications second they prove it works the way it was intended to automated reasoning does this by producing proofs in formal logic supported by mathematical theorems or known truths automated reasoning uses mathematical logic based algorithm verification methods to produce proofs of security or correctness for all possible behaviors when you use automated reasoning you first present the system without a problem statement
• 16:00 - 16:30 then the automated reasoning system calculates and validates the Assumption with the problem statement the software does this until it exhaust all options another sub field is machine learning it is a subset of AI that relies on data and sophisticated algorithm enabling machines to evolve and enhance their decision-making capabilities over time a tangible manifestation of machine learning progess is evident in the the personalized product recommendations
• 16:30 - 17:00 algorithm is grafted on e-commerce platforms here machine learning distance integrated discerns integrate PL patterns from user Behavior optimizing the suggestions offered with each interaction machine learning algorithms can be categorized into three supervised unsupervised and reinforcement learning supervised learnings work by taking in clearly labeled data while being trained
• 17:00 - 17:30 and using that to learn and grow it uses the labeled data to predict outcomes for other data so that is supervised learning unsupervised learning algorithms are given data that is not labeled unsupervised learning algorithm use that unlabeled data to create models and evaluate the relationship between different data points in order to give more insights to the data reinforced learning algorithms learn by taking in
• 17:30 - 18:00 feedback from the results of its actions this is typically in the form of a reward a reinforcement algorithm is usually composed of two major parts an agent that performs an action and the environment in which the action is performed the cycle begins when the environment sends a state signal to the agent that cues the agent to perform a specific action within the environment we will discuss some algorithms techniques of machine learning one such technique is decision tree one of the
• 18:00 - 18:30 most common supervised learning algorithm decision trees get their name because of their tree like structure even though the tree is inverted the roots of the tree are the training databases and they lead to specific nodes which denote a test attribute nodes often leads to other nodes and a node that does not lead onward is called a leaf decision trees classify all the data into decision nodes it uses a selection criteria called attribute
• 18:30 - 19:00 selection measures which takes into account various measures for example would be entropy gain ratio Information Gain Etc using the root data and following the ASM that is the attribute selection measures the decision tree can classify the data it is given by following the training data into subnodes until it reaches the conclusion so this is a decision node this is the sub Tre to further decision node this is leaf this is leaf this is leaf
• 19:00 - 19:30 this is again a decision node so ultimately all these decision nodes will end in a leaf node so here you are with friends yes it is windy no cold above par no below par Branch now these are Leaf nodes walk or cart walk above par cold another machine learning technique is random Forest the random Forest algorithm is actually a broad collection of different decision trees leading to its name the random Forest builds
• 19:30 - 20:00 different decision trees and connects them to gain more accurate results this can be used for both classification and regression type of supervised learning while a solo decision tree has one outcome and a narrow range of groups the forest assures a more accurate result with a bigger number of groups and decisions it has the added benefit of adding Randomness to the model by finding the best feature among a random subset or features overall these
• 20:00 - 20:30 benefits create a model that has wide diversity that many data scientists favor so as we can see from the diagram the results of decision Tre 1 2 and three are combined which is then averaged out or the majority is considered as the final result so here we have this data data set this is decision Tre 1 results 1 2 3 averaging and then we get the final result next comes the principal component analysis as the number of features or dimensions
• 20:30 - 21:00 in a data set increases the amount of data required to obtain a statistically significant result increases exponentially principal component analysis works on the condition that while the data in a higher dimensional space is mapped to data in a lower Dimension space the variance of data in the lower Dimension space should be maximum it is used to examine the inter relationship among a set of variables and it is also known as a General factor analysis where regression determines a
• 21:00 - 21:30 line of best fit the main goal is to reduce the dimensionality of a data set by finding a new set of variables is smaller than the original set of variables retaining most of the samples information and useful for the regression and classification of data so this is PC2 this is the these are area and radius pc1 principal component this is the variance so the transformation goes from 2D to 1D PC2 pc1 is greater
• 21:30 - 22:00 than PC2 the next comes the K means from there it sorts the remaining data points into clusters based on their proximity to each other and the Cent data points for each cluster the algorithms takes the unlabeled data sets as inputs divides the data set into K numbers of clusters and repeat the process until it does not find the best clusters the value of K should be predetermined in this algorithm the K means clustering algorithm mainly
• 22:00 - 22:30 performs two tasks one is to determine the best value of K Center points or cids by an iterative process the second is assign each data point to its closest case Center those data points which are near to the particular K Center creates a cluster K means that the working of a k means algorithm is as follows first is select the number of case to decide the number of clusters second is to select random K points or cids assign each data
• 22:30 - 23:00 point to their closest centroid which will form the predefined K clusters then calculate the variance and place a new centroid in each cluster repeat the third steps which means reassigning each data point to the new closest centroid of each cluster so this are this is before the K means now these are the various clusters now this is after K means so now it becomes much more clear to understand the various clusters the
• 23:00 - 23:30 next comes the K nearest neighbor KNN algorithm can be used for regression as well as for classification but mostly it is used for the classification problems Suppose there are two categories category a and category B and we have a new data point X1 so this data point will lie in which of these categories to solve this type of problem we need a knnn algorithm with the help of KNN we can easily identify the category or class of a particular data set so this
• 23:30 - 24:00 is before KNN this is new data point the category B and category a now after knnn now this is category B new data point assigned to category 1 and this is category a next comes the linear regression linear regression is a supervised learning AI algorithm used for regression modeling it is mostly used for rediscovering the relationship between data points predictions and forecast casting much like support Vector machines it works by plotting
• 24:00 - 24:30 pieces of data on a chart with x-axis as the independent variable and Y AIS as the dependent variable the data points are then plotted out in the linear fashion to determine their relationships and forecast possible future data linear regression is one of the easiest and most popular machine learning algorithm it is uh statistical method that is used for predictive analysis linear regression makes predictions for continuous real or numeric variables
• 24:30 - 25:00 such as sales salary age product price Etc linear recognition algorithm shows a linear relationship between a dependent that is y and one or more independent that is X variables hence called as linear regression so this is independent variable X this is dependent variable Y and these are the points data points and they are fitted on on this line of regression since linear regression shows the linear
• 25:00 - 25:30 relationship which means it finds how the value of the dependent variable is changing according to the value of the independent variable the linear regression model provides a sloped straight line representing the relationship between the variables the next comes logistic regression a logistic regression algorithm usually uses a binary value that is 0 1 to estimate values from a set of independent variables the output of logistic regression is either either 1 or zero yes or no an example of this
• 25:30 - 26:00 would be a spam filter in email the filter uses logistic regression to Mark whether the an Incoming Email is Spam zero or not one logistic regression is only useful when the dependent variable is categorical that is either yes or no the logistic regression model is based on the logistic function which is a type of s shaped curve that Maps any continuous input to a probability value between 0 and 1 the logistic function allows us to
• 26:00 - 26:30 model the relationship between the independent variables and the probability of the dependent variables taking on the value of one the logistic regression model estimates the coefficient of the independent variable that are most predictive of the independent variable these coefficients are used to create a linear equation that is then transformed by the logistic function to produce a probability value for the dependent variables taking on the value of one so
• 26:30 - 27:00 this is that s-shaped curve that we have talked about earlier logistic regression is commonly used in fields such as Healthcare marketing finance and social sciences to predict the likelihood of an event occurring such as whether a patient has a certain disease or whether a customer will buy a product the next comes support Vector machines the support Vector machine that is svm algorithm is another common AI algorithm that can be used for either classification or regression but is most often used for
• 27:00 - 27:30 classification svm works by plotting each piece of data on a chart in N dimensional space where n is the number of data points thus the algorithm classifies the data points by finding the hyper plane that separates each class there can be more than one hyper plane so these are the positive hyper plane maximum margin negative hyper plane and these are the support vectors the main objective of a support Vector machine is to segregate the given data
• 27:30 - 28:00 sets in the best possible way the distance between the either nearest point is known as the margin the objective is to select a hyper plane with the maximum possible margin between support vectors in the given data set svm searches for the maximum marginal hyper plane generate hyperplanes which segregates the classes in the best way the figure on top shows three hyperplanes black blue and orange here the blue and orange have higher classification error but the black is
• 28:00 - 28:30 separating the two classes correctly select the right hyper plane with the maximum segregation from the either nearest data points as shown in the figure at the bottom next comes neural networks a neural network is a machine learning program or model that makes decisions in a manner similar to the human brain by using processes that mimic the way biological neurons work together to identify phenomena we options and arrive at conclusion every neural network consist of layers of
• 28:30 - 29:00 nodes or artificial neurons an input layer one or more hidden layers and the output layer so this is the input layer multiple hidden layers and this is the output layer each node connects to other and has its own Associated weight and threshold if the output of any individual node is above the specified threshold value that node is activated sending data to the next layer of the network otherwise no data is passed along to the next layer of the network neural networks relies on training data
• 29:00 - 29:30 to learn and improve their accuracy over time once they are fine-tuned for accuracy they are powerful Tools in computer science and artificial intelligence allowing us to classify and cluster data at a high velocity task and speech recognition or image recognition can take minutes versus hours one of the best known example of a neural network is Google search algorithm we will discuss a few types of neural networks in the next slides the first of this neural network is bolman machine a
• 29:30 - 30:00 bolman machine is an unsupervised deep learning model in which every node is connected to every other node it is type of recurrent neural network and the nodes make binary decisions with some level of bias these machines are not deterministic deep learning model they are stochastic or generative deep learning models there are two types of nodes in the ballman machine visible nodes these nodes which can and do measures and the hidden nodes these noes which we we cannot or do not measure although the node types are different
• 30:00 - 30:30 the ball span machine considers them as the same and everything works as one single system so these are the visible nodes and then there are hidden nodes the training data is fed into the wsman machine and the weights of the systems are adjusted accordingly balsman machine help us understand abnormalities by learning about the working of the system in normal conditions the next comes multiple layer perceptions a multiple layer perception that is MLP is a type of artificial neural
• 30:30 - 31:00 network consisting of multiple layers of neurons the neurons in the MLP typically uses non linear activation functions allowing the network to learn complex patterns in data MLPs are significant in machine learning because they can learn nonlinear relationships in data making them powerful models for tasks such as classification regression and pattern recognition there is one neuron or node it has one output layer with a single node for each output and it can have any
• 31:00 - 31:30 number of hidden layers and each hidden layer can have any number of nodes if it has more than one hidden layer it is called as deep artificial neural network and MLP is a typical example of feed forward artificial neural networks so deep learning can process next is deep learning deep learning can process an extensive array of data types including images and sounds including image and sound its application transcends the
• 31:30 - 32:00 contextual domain finding resonance and groundbreaking advancement such as the development of driverless car showcasing its abilities in object detention and decision making ushering in a new era of s sensory understanding for machines the next slide we will briefly explain how deep learning neutral networks work next comes recurrent neural networks it is a type of neural network where the output from the previous step is fed as input to the current Step In traditional
• 32:00 - 32:30 neural networks all the inputs and outputs are independent to each other still in cases where it is required to predict the next word of a sentence the previous words are required and hence there is a need to remember the previous words thus RNN comes into existence which solved this issue with the help of a hidden layer so this is what recurrent neural network look lies the main feature of RNN is its hidden state which remembers some information about a sequence the
• 32:30 - 33:00 state is also referred to as memory State since it remembers the previous inputs into the network it uses the same parameters for each input as it performs the same task on all inputs or hidden layers to produce the output this reduces complexity of parameters unlike other neural networks the next comes the convolutional neural network the convolutional neural network that is CNN also known as convet is a specialized type of deep learning algorithm mainly
• 33:00 - 33:30 designed for task that necessitates object recognition including image classification detection and segmentation CNN are employed in a variety of practical scenarios such as autonomous vehicles security cameras and others CNN mimics the human visual systems but are simpler lacking its complex feedback mechan mechanism and relying on supervised learning rather than unsupervised learning driving advances in computer vision despite
• 33:30 - 34:00 these differences then comes generative advisory network so this Gan represents a Cutting Edge approach to generative modeling within a deep learning often leveraging architectures like convolution neural network the goal of generative modeling is to autonomously identify patterns and input data enabling the model to produce new examples that feasibly resemble the original data sets generative adversity networks can be broken down into three
• 34:00 - 34:30 parts the first is generative to learn a generative model which describes how data is generated in terms of probabilistic models second is adversative results is compared with the actual image in the data set a mechanism known as a discriminator is used to apply a model that attempts to distinguish between real and fake images and the third is networks you use deep networks as artificial intelligence algorithms for training purposes so this is what it looks like here we have
• 34:30 - 35:00 latent random variable generator real data samples dis discriminator and condition is it correct the next comes the de belief Network they are sophisticated artificial neural networks used in the field of deep learning a subset of machine learning they are designed to discover and learn patterns within large data sets automatically we can imagine them as multi-layered networks where each layer is capable of making sense of the information received from the previous one gradually building
• 35:00 - 35:30 up a complex understanding of the overall data it is useful for task like images and spe recognition where the input data is high dimensional and requires a deep learning of understanding the architecture of dbn also makes them good at unsupervised learning where the goal is to understand and label input data without explicit guidance this characteristic is particularly useful in scenarios where labeled data is sces or when the goal is to explore the structure of the data
• 35:30 - 36:00 without any preconceived labels then another field sub field of AI is computer vision computer vision is a field of artificial intelligence that uses machine learning and neural networks to teach computers and systems to derive meaningful information from digital images videos and other visual inputs to make recommendations or take actions when they see defects or issues if AI enables computers think computer vision a enables them to see observe and
• 36:00 - 36:30 understand the next subfield is generative AI unleashing the power of creativity within AI generative AI exemplified by large language models like chat like gp4 or Google's Bard serves as an artistic force in the digital real this subfield thrives on generating content in response to prompts showcasing versatility in creating anything that anything from concise report summaries to engaging promotional emails generative AI is an Innovative tool simplifying the creative
• 36:30 - 37:00 capacities of machines now in this figure we summarize all these methods this is the AI ecosystem here we have generative Ai and large language models so to conclude in this module we have understood the digitization of traditional organization to support AI ecosystems with the help of a AI Factory working model and its various components thereafter we have discussed how product manag play a pivotal role in this transformation Journey from traditional
• 37:00 - 37:30 to the incorporation of AI finally we have understood the sub fields of AI and their various types which are all parts of the AI ecosystems and these are some of the references from which the material for this module was taken thank you [Music] a
• 37:30 - 38:00 [Music]