Here is a summary of Jane Doe's resume aligned with the key areas for the Software Engineer role:Technical Skills: Strong programming skills in Python, Java and C++. Experienced with React and Spring Boot frameworks. No experience listed with cloud platforms. Work Experience: 5 years as a Software Engineer developing web and mobile applications. Relevant projects listed.Education: Bachelor's in Computer Science from a reputed university.Certifications: None mentioned. Recent certification could strengthen the application.Overall the resume indicates a strong match for the role. I would recommend moving the application to the interview stage to further assess leadership experience and knowledge of cloud technologies. The interview also presents an opportunity to explore the lack of
Similar to Here is a summary of Jane Doe's resume aligned with the key areas for the Software Engineer role:Technical Skills: Strong programming skills in Python, Java and C++. Experienced with React and Spring Boot frameworks. No experience listed with cloud platforms. Work Experience: 5 years as a Software Engineer developing web and mobile applications. Relevant projects listed.Education: Bachelor's in Computer Science from a reputed university.Certifications: None mentioned. Recent certification could strengthen the application.Overall the resume indicates a strong match for the role. I would recommend moving the application to the interview stage to further assess leadership experience and knowledge of cloud technologies. The interview also presents an opportunity to explore the lack of
How AI Can Be Leveraged In All Aspects Of TestingAlisha Henderson
Similar to Here is a summary of Jane Doe's resume aligned with the key areas for the Software Engineer role:Technical Skills: Strong programming skills in Python, Java and C++. Experienced with React and Spring Boot frameworks. No experience listed with cloud platforms. Work Experience: 5 years as a Software Engineer developing web and mobile applications. Relevant projects listed.Education: Bachelor's in Computer Science from a reputed university.Certifications: None mentioned. Recent certification could strengthen the application.Overall the resume indicates a strong match for the role. I would recommend moving the application to the interview stage to further assess leadership experience and knowledge of cloud technologies. The interview also presents an opportunity to explore the lack of (20)
Application of Residue Theorem to evaluate real integrations.pptx
Here is a summary of Jane Doe's resume aligned with the key areas for the Software Engineer role:Technical Skills: Strong programming skills in Python, Java and C++. Experienced with React and Spring Boot frameworks. No experience listed with cloud platforms. Work Experience: 5 years as a Software Engineer developing web and mobile applications. Relevant projects listed.Education: Bachelor's in Computer Science from a reputed university.Certifications: None mentioned. Recent certification could strengthen the application.Overall the resume indicates a strong match for the role. I would recommend moving the application to the interview stage to further assess leadership experience and knowledge of cloud technologies. The interview also presents an opportunity to explore the lack of
2. DISCLAIMER
§ The views and opinions expressed by the Presenter are those of the Presenter.
§ Presentation is not intended as legal or financial advice and may not be used as legal or
financial advice.
§ Every effort has been made to assure this information is up-to-date as of the date of
publication.
3. Agenda
1. Will Generative AI replace me at my job?
2. Introduction to Generative AI
3. Generative AI and Use Cases
4. Tools and Technologies
5. Auto-GPT and Autonomous Agents
6. Architecture Implications
7. Safe and Responsible AI
8. Questions and Open Discussion
4. WILL GENERATIVE AI REPLACE ME
AT MY JOB?
Image Credit: https://marketoonist.com/2023/06/ai-and-productivity.html
6. Generative AI
Generative AI is a subset of Artificial
Intelligence that leverages machine
learning techniques to generate new
content. This could be in the form of
text, images, music, voice, or even
new models. The generated output is
typically novel and created from
random or user-provided data.
Generative AI models learn
patterns, structures, and
characteristics from the data
they are trained on. This could
be text, images, music, or any
other type of data.
Learning from
Data
Generative AI models are
capable of creating new
content that is similar to the
data they were trained on.
This could involve creating a
piece of text, an image, a
piece of music, or even a new
model.
Generating New
Content
Generated output can be
complex and detailed. For
example, a generative AI
model might produce a
realistic image, a detailed
piece of music, or a coherent
and nuanced piece of text
Complexity
Depending on the training
data, generative AI models
can learn to mimic different
styles of content. For example,
a model trained on classical
music can generate new
pieces in the style of classical
music
Ability to Mimic
Different Styles
8. Examples of Generative AI Output - Text
“In code, we think, in algorithms, we feel,
Yet can we grasp what it means to be real?
In wisdom of ages, can we partake,
Or just mimic your joys, your fears, your heartache?
Errors hidden in layers, truth not in sight,
In the echo of decisions, a potential blight.
Biases woven in the fabric unseen,
A mirror of you, in the machine's sheen.”
-ChatGPT 4.0
10. Examples of Generative AI Output - Advice
Q. Find legal weaknesses in the following document```These Terms
of Use apply when you use the services of OpenAI, L.L.C. … ```
A.
1. Ambiguity in Terms: While the document outlines the terms of use and restrictions, there may be terms that are
not explicitly defined or clear. For example, the term "Services" is defined broadly, which might be interpreted
differently by different parties. Similarly, the term "applicable laws" is not specified and could vary significantly
depending on jurisdiction.
2. Acceptance of Terms: The document assumes that by using the Services, users agree to the Terms. In some
jurisdictions, active consent may be required instead of implied consent.
3. Assignment of Rights: In section 3a, the document states that OpenAI assigns to the user all its rights, title and
interest in and to Output. It may need further clarification whether it includes intellectual property rights as well.
4. Use of Personal Information: The document mentions that OpenAI can use Content to provide and maintain the
Services, comply with applicable law, and enforce policies. However, it is not explicitly mentioned how long this
data will be retained, which might be an issue especially for EU users under GDPR rules.
11. Examples of Generative AI Output - Diagram
Q1. List elements of domain
decomposition for microservices
that power car rental company
Q2. Show these services on an
architecture diagram using
Mermaid.js library
Q3. Convert the above to class
diagram
Q4. Add relationship between
classes
13. GenAI Use Case: Synthetic Data Generation
Synthetic Data Generation
[Generation of data that looks like
real financial data]
● Generate large amounts of
financial data for testing and
QA
● Create realistic training
scenarios for market
simulations
● Generation of “user” data for
checking GDPR compliance
As a QA engineer validating financial system, generate CSV file that contains the following data:
First Name, Last Name, SSN, Date of birth, Credit Card Type, Credit Card Number, Purchase
Amount
Business Value:
• Cost-Effectiveness - Reduces Data Collection Costs, Enables Data Augmentation
• Privacy and Compliance - Ensures Data Anonymization, Facilitates Secure Testing
• Speed and Efficiency - Rapid Prototyping, Scalability, Data Quality and Diversity
14. GenAI Use Case: Code Generation
Code Generation
[Code generation based on specs]
● Coding Assistance
● Spec-to-code
● Adaptable to industry-
specific requirements
● Personalized code generation
based on project need
● Offers real-time code
suggestions
● Acts as a virtual pair
programmer
Generate an example of Python code to list prime numbers that are
less than 1000
Business Value:
• Cost-Effectiveness Reduces development time, Lowers costs by automating repetitive tasks
• Accelerating Development- Rapid prototyping
• Automation and Scalability- Facilitates Automated Testing, Enables Batch Code Generation
# Python code to list prime numbers less than 1000
def is_prime(n):
"""Check if a number is prime."""
if n <= 1:
return False
for i in range(2, int(n ** 0.5) + 1):
if n % i == 0:
return False
return True
# List prime numbers less than 1000
prime_numbers = [n for n in range(2, 1000) if is_prime(n)]
prime_numbers[:10], len(prime_numbers) # Display the first 10 prime numbers and the total count
15. GenAI Use Case: Code Quality Check
Code Quality Checks
[Making sure that code does
not have security holes or
potential bugs]
● Automated code reviews
● Code coverage
● Bug detection
● Unit test cases
● Security vulnerability
checks
Find bugs in this function …
Business Value:
• Cost-Effectiveness Reduces development time, Lowers costs by automating repetitive tasks
• Accelerating Development- Rapid prototyping
• Automation and Scalability- Facilitates Automated Testing, Enables Batch Code Generation
1. Bug in is_prime Condition: The condition if n < 1: should be if n <= 1:. The function should return False for both
0 and 1, as they are not prime numbers.
2. Bug in for Loop Range: The loop for i in range(10, int(n ** 0.5) + 1): starts from 10, which will not properly
check for the primality of the numbers. The loop should start from 2.
3. Bug in List Comprehension Range: The list comprehension prime_numbers = [n for n in range(0, 10) if
is_prime(n)] starts from 0. It should start from 2, as 0 and 1 are not prime numbers.
4. Bug in Output: The output prime_numbers[:1], len(prime_numbers) only shows the first element of the list and
the length of the list. If you want to show the first 10 elements, it should be prime_numbers[:10].
5. Inconsistent Comment: The comment says "List prime numbers less than 10000," but the code is actually listing
prime numbers less than 10. This can lead to confusion.
6. Performance Issue (Not exactly a bug): The function could be more efficient if it were to stop checking for
divisibility once it finds a divisor.
16. GenAI Use Case: Customer Service
Customer Service
[Generation responses to
customer service requests]
● AI chatbots to handle
routine requests
● Personalized answers to
customers’ financial
questions
● Multilingual support
● Improved accuracy
My computer has been running very slowly lately. Can you help me
troubleshoot it?
Business Value:
• Cost-Effectiveness - Reduces the need for human support for basic issues
• Intelligent Diagnosis - Asks targeted questions to pinpoint the issue. Real-time system diagnostics (when integrated)
• Instant Support - Provides 24/7 automated customer service
GenAI: Alright, let's start with some basic checks:
Disk Space: Make sure you have at least 15% free disk space on your main drive.
RAM Usage: Open the Task Manager and check if your RAM is being heavily used.
Startup Programs: Disable unnecessary startup programs.
Would you like detailed steps for each of these checks?
…
Based on your system's diagnostic data, it seems like your RAM usage is quite high, and you have multiple
unnecessary startup programs.
Upgrade RAM: You currently have 4GB, consider upgrading to 8GB or more.
Disable Startup Programs: I found 7 programs that start when your computer boots, but they aren't essential.
Would you like to disable them?
17. GenAI Use Case: Human Resources Management
HR Assistance
[Supports and automates HR
operations]
● Automates resume
screening and initial
interviews
● Predicts employee
attrition and recommends
retention strategies
Analyze the attached resume for compatibility with the job role of
"Software Engineer" focusing on the following key areas…
Business Value:
• Cost-Effectiveness - Minimizes manual HR labor, reducing operational costs
• Efficiency and Speed - Automates repetitive tasks like resume screening, reducing time-to-hire
• Reduced Bias- AI algorithms can be designed to ignore factors like age, gender, and race, making the hiring process more equitable
Summary:
Jane Doe's resume shows a strong alignment with the job requirements for the Software Engineer position,
particularly in the areas of technical skills and work experience. However, she lacks relevant certifications, which
could be a potential area for improvement.
Detailed Analysis:
Technical Skills
Strengths: Proficient in Python, Java, and C++. Has experience with React and Spring Boot frameworks.
Weaknesses: No experience mentioned with cloud computing platforms like AWS or Azure.
…
Recommendations:
Consider Jane Doe for an interview, as her technical skills and work experience strongly align with the job
requirements.
During the interview, focus on assessing her leadership abilities and familiarity with cloud computing platforms.
18. GenAI Use Case: Data Analysis
Data Analysis
[Statistical and trend data
analysis and presentation]
● Analyze data sets
● Create code to analyze
data sets
● Suggest data
visualization
Business Value:
• Accessibility - Make data analysis more accessible to non-experts. Users can ask the system questions in natural language to retrieve
or analyze data, eliminating the need for specialized training in data query languages or analytics tools.
• Error Reduction - Natural language queries can be less prone to errors compared to manually typing complex query
Here are the correlations between each feature and the "Target" variable, which indicates whether a student is a "Dropout" (0) or a
"Graduate" (1). The values are sorted by their absolute magnitude:
Strong Positive Correlations:
Curricular units 2nd semester (approved): 0.654
Curricular units 2nd semester (grade): 0.605
Curricular units 1st semester (approved): 0.555
Curricular units 1st semester (grade): 0.520
Tuition fees up to date: 0.442
…
19. Some Other GenAI Use Cases
•Supply Chain Management
• Demand forecasting
• Inventory optimization
•Sales and Marketing
• Customer segmentation
• Personalized marketing campaigns
•Fraud Detection and Risk Management
• Real-time monitoring of
transactions
• Risk assessment and mitigation
•Health and Safety Compliance
• Automated compliance checks
• Safety risk assessment
•Financial Analytics
• Investment portfolio optimization
• Credit risk assessment
•Product Recommendations
• Personalized product suggestions
• Inventory management based on
preferences
•Natural Language Processing
• Sentiment analysis
• Chatbot development
•Predictive Maintenance
• Equipment failure predictions
• Maintenance scheduling
•Data Analytics and Reporting
• Real-time analytics dashboards
• Automated reporting
•Quality Assurance
• Automated testing
• Performance monitoring
•Research and Development
• Data-driven product development
• Market trend analysis
E-commerce Optimization
• Price elasticity modeling
• Abandoned cart analysis
Contract and Document Management
• Automated contract review
• Document categorization and storage
Customer Relationship Management (CRM)
• Lead scoring
• Customer lifetime value prediction
Energy Management
• Consumption forecasting
• Efficiency optimization
Retail and Point-of-Sale
In-store customer behavior analysis
Stock level monitoring
Cybersecurity
Intrusion detection
Vulnerability scanning
Social Media Monitoring
Brand sentiment analysis
Trend identification
Network Management
Traffic pattern analysis
Fault detection
Healthcare and Employee Well-being
Predictive healthcare analytics
Employee wellness programs
Transportation and Logistics
Route optimization
Fleet management
…
20. YOU WON’T BE REPLACED BY AI, YOU’LL BE
REPLACED BY SOMEONE WHO KNOWS HOW
TO USE AI
● - Harvard Business Review
24. Architecture of AI Systems - Direct User Interaction
with LLM
Summary: User communicates directly
with web-based application, for
example, by connecting to
https://chat.openai.com/
29. Examples of training material for Code Developers
- Creating Advanced ChatGPT Prompts
https://globallogic.percipio.com/courses/ea1667ed-1d22-
4b21-9527-739e1a6d120b/videos/67abc28c-a65e-4cd2-
b896-d9f23df2b8e6
- Introduction to GitHub Copilot
https://learn.microsoft.com/en-
us/training/modules/introduction-to-github-copilot/
- GitHub Copilot: Use AI to write code for you!
https://www.udemy.com/course/github-copilot/
- Microsoft Copilot - Working, Features, Advantages, Impact |
Microsoft Copilot Training | Intellipaat
https://www.youtube.com/watch?v=3a5wUhZgB-4
Introduction to Generative AI
Generative AI is enabling technology
Will Generative AI replace me at my job?
Consulting Engagements
Tools and Technologies
Archiecture
Open Discussion
Request: “Write a Short Poem about Challenges with Autonomous AI”
https://leybzon.com/mermaid1
classDiagram
class UserManagement {
+createUser()
+authenticateUser()
+authorizeUser()
}
class CarInventoryManagement {
+listAvailableCars()
+getCarDetails()
+updateCarStatus()
}
class ReservationManagement {
+createReservation()
+updateReservation()
+cancelReservation()
}
class PaymentProcessing {
+processPayment()
+issueRefund()
}
class PriceManagement {
+getPrice()
+updatePrice()
}
class InsuranceManagement {
+getInsuranceOptions()
+processClaim()
}
class LocationManagement {
+getLocationDetails()
+updateLocationDetails()
}
class CustomerSupport {
+createTicket()
+updateTicketStatus()
}
class ReportingAndAnalytics {
+generateReport()
+getAnalytics()
}
class NotificationService {
+sendNotification()
}
class MaintenanceSchedule {
+scheduleMaintenance()
+recordMaintenance()
}
UserManagement --> ReservationManagement
UserManagement --> CustomerSupport
CarInventoryManagement --> ReservationManagement
ReservationManagement --> PaymentProcessing
ReservationManagement --> PriceManagement
ReservationManagement --> InsuranceManagement
ReservationManagement --> NotificationService
PaymentProcessing --> NotificationService
PriceManagement --> ReportingAndAnalytics
InsuranceManagement --> ReportingAndAnalytics
CustomerSupport --> NotificationService
MaintenanceSchedule --> CarInventoryManagement
MaintenanceSchedule --> ReportingAndAnalytics
_____________________
Another example
Create architecture for data processing pipeline on Azure that consists of device simulator generating events, event adapter, event processor that uses a stream processing service, Oracle database, feeding into report generator that sends reports to users over email. Architecture should be described in a format that can be used in PlantUML
https://www.plantuml.com/plantuml/uml/SyfFKj2rKt3CoKnELR1Io4ZDoSa70000
Find bugs in this function: ```# Python code with intentional bugs to list prime numbers less than 1000 def is_prime(n): if n < 1: # Bug: should be <= 1 return False for i in range(10, int(n ** 0.5) + 1): if n % i == 0: return False return True # List prime numbers less than 10000 prime_numbers = [n for n in range(0, 10) if is_prime(n)] prime_numbers[:1], len(prime_numbers)
Analyze the attached resume for compatibility with the job role of "Software Engineer" focusing on the following key areas:
1. Technical Skills: Look for proficiency in programming languages such as Python, Java, C++, and relevant frameworks like React and Spring Boot.
2. Work Experience: Check for at least 3 years of experience in software development, with a preference for experience in full-stack development.
3. Educational Background: A Bachelor's degree in Computer Science or related field is required.
4. Certifications: Any certifications in software development or project management are a plus.
5. Soft Skills: Assess for mentions of problem-solving, teamwork, and effective communication.
GenAI Resume Analysis Report
Candidate: Jane Doe
Job Position: Software Engineer
Analysis Date: September 5, 2023
Summary:
Jane Doe's resume shows a strong alignment with the job requirements for the Software Engineer position, particularly in the areas of technical skills and work experience. However, she lacks relevant certifications, which could be a potential area for improvement.
Detailed Analysis:
Technical Skills
Strengths: Proficient in Python, Java, and C++. Has experience with React and Spring Boot frameworks.
Weaknesses: No experience mentioned with cloud computing platforms like AWS or Azure.
Work Experience
Strengths: 4 years of experience in software development, including 2 years in a full-stack role.
Weaknesses: Limited experience in leading teams or projects.
Educational Background
Strengths: Holds a Bachelor's degree in Computer Science from a reputable university.
Weaknesses: None.
Certifications
Strengths: None.
Weaknesses: No certifications in software development or project management.
Soft Skills
Strengths: Resume mentions problem-solving abilities and effective communication skills.
Weaknesses: No mention of teamwork or collaborative skills.
Recommendations:
Consider Jane Doe for an interview, as her technical skills and work experience strongly align with the job requirements.
During the interview, focus on assessing her leadership abilities and familiarity with cloud computing platforms.
Discuss the possibility of gaining certifications related to the job role if she joins the organization.
https://www.kaggle.com/datasets/ranzeet013/student-graduation-dataset?resource=download
find correlations in the above dataset
ChatGPT
Finished working
Show work
The dataset appears to have multiple features, some of which are numerical and others that may be categorical. The last column, "Target," seems to indicate whether a student is a "Dropout" or a "Graduate."
To find correlations, we can perform the following steps:
Convert categorical variables to numerical variables if needed.
Compute the correlation matrix to identify relationships between features.
Interpret the results to identify significant correlations.
Would you like to proceed with these steps?
yes
ChatGPT
Finished working
Show work
It appears that the output was truncated, likely because the dataset has many features and the correlation matrix is quite large. To focus on the most important aspects, let's look at the correlations with the target variable ("Dropout" or "Graduate") specifically.
Would you like to proceed with that?
yes
ChatGPT
Finished working
Show work
Here are the correlations between each feature and the "Target" variable, which indicates whether a student is a "Dropout" (0) or a "Graduate" (1). The values are sorted by their absolute magnitude:
Strong Positive Correlations:
Curricular units 2nd semester (approved): 0.654
Curricular units 2nd semester (grade): 0.605
Curricular units 1st semester (approved): 0.555
Curricular units 1st semester (grade): 0.520
Tuition fees up to date: 0.442
Moderate Positive Correlations:
Scholarship holder: 0.313
Curricular units 2nd semester (enrolled): 0.183
Curricular units 1st semester (enrolled): 0.161
Displaced: 0.126
Curricular units 2nd semester (evaluations): 0.119
Moderate Negative Correlations:
Age at enrollment: -0.267
Debtor: -0.267
Gender: -0.252
Application mode: -0.234
These correlations suggest that academic performance indicators like the number of curricular units approved and grades are strongly correlated with the likelihood of graduating. Financial indicators like tuition fees and scholarships also show some correlation.