AI Literate Citizen

AI Literate Citizen

By Melissa DrewEducation
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AI Literate Citizen episodes

  • (68) Devadharshinin Murugan - Conversation with an Associate (Python) Developer

    AI technologies impact all of us, no matter where we are in our life journey. From high school students to full-time retirement on the beach, we each have a different perspective. How we navigate forward depends on our own experiences with AI technologies.Meet Devadharshinin in her first interview ever!! She received her bachelor's degree in Data Science and started the M.B.A. program in Business (Artificial) Intelligence in the Summer of 2024. At the same time, she works full-time at Mavdero Techservices Pvt Ltd as a software (python) developer. This episode is hearing the perspective of someone continuing her (AI) educational journey and, at the same time, just starting her professional journey. Topics covered are: - Developer vs Data Science- MBA in AI Business Strategy- Value of automation- The software she uses in her job - Learning on the job- Her pick of the most exciting feature she is working on- What is an AI Agent?Mavdero is a technology provider for intelligent automation services using artificial intelligence, RPA, and machine learning. The Devadharshinin is working on contract management automation.

    23 min
  • (67) Melissa Drew - AI 101, What is AI, Really?

    'AI technologies' is a generic phrase that combines 7 different types of AI technologies in a broader group. If you are new to AI or interested in a refresher, this is a good use of 15 minutes.

    This episode is a debrief and definition of key terms: - AI compared to AI Model - Narrow AI compared to General Intelligent and Super intelligent AIComponents of AI Technologies:- Machine Learning- Deep Learning- Natural Language Processing (NLP)- Neural Networks- Robotics Processing Automation (RPA)- Computer Vision- Generative AI

    16 min
  • (66) Susan Walsh - The Human Touch of Automating Spend

    This episode discusses an often overlooked area of data cleansing and its enduring value, even in the age of AI technologies. In our data-driven world, companies are racing to adopt AI to automate processes at the expense of pausing longer to evaluate potential adverse side effects. This is why the human element remains a critical component.


    This conversation aims to dive deeper into the benefits of data cleansing with human expertise, challenging the premise that AI is the only solution. With our guest this week, Susan Walsh, we explore how human intervention continues to add value.


    While AI can offer speed and efficiency, it cannot fully understand the contextual use of the same data set across industries. Leveraging human intelligence in conjunction with automation tools strengthens the outcomes.


    Sprinkled with real-world stories, the conversation will uncover the 'behind the scenes' human-driven data cleansing methodologies. This episode promises to offer a different perspective on AI and explain why the human element will be around for a while.

    32 min
  • (65) Julienne Ryan - Communication in a World of Technology

    Julienne B. Ryan shares valuable insights on the importance of effective communication in a digital world post-COVID. She highlights the impact of AI technologies on communication, including the limitations and potential inaccuracies of AI language models like ChatGPT. Ms. Ryan emphasizes the need to balance technology's benefits with the importance of the human experience.

    In her discussion, Ms. Ryan stresses the significance of critical thinking, trust, and authenticity in using technology for communication. She also explores creativity, experimentation, and failure in the context of technology and communication. Furthermore, Ms. Ryan highlights the importance of empathy and nuance in effective communication.
    Drawing from her consultant practice, Ms. Ryan encourages the audience to approach technology-assisted communication with a critical mindset while trusting their instincts and experiences. Her insights are more relevant than ever as we navigate a virtual and digital world.

    31 min
  • (64) Adita Karkera - Shaping the Future of the Public Sector Through Data

    Adita Karkera, "..motivated and inspired in how the public sector can cater to the needs of our citizens. My heart lies in the public sector."
    Data.gov was launched in 2009 and is the United States government's open data website. It provides access to datasets published by agencies across the federal government. It wasn't until 2018 that the federal government's mandate became a clear sign acknowledging the value and effectiveness of data.
    The 2018 Foundations for Evidence-Based Policymaking Act is a United States law that requires the federal government to modernize its data management practices. Its goal was to change the way data is used in the government to create policy. The law aims to improve access to federal data so agencies and policymakers can craft better, more effective policies and programs and deliver on services promised to the country.
    In 2019, as a follow-up to the prior 2018 Foundations Act, the head of each agency was tasked to appoint or designate a qualified #chiefdataofficer (CDO) without regard to political affiliation.
    Since then, a Federal Data Council has been established, bringing # CDOs across all federal government agencies. This interagency council has a charter establishing best practices to leverage data as a strategic asset. As of this recording, around 80+ individuals make up that council.
    Some of the other topics explored with Adita:
    - Are there challenges for the CDO in the public and private sectors?
    - Skills for a data-literate workforce
    - Understanding the 360-degree view of the citizen
    - Perception the public sector moves slowly may be an advantage rather than a hindrance
    - Potential risks in leveraging data can not be ignored, no matter how excited we are about emerging technologies such AI
    We need more women in the public sector to help shape our future. I am interested in serving the public after listening to Adita speak so passionately. Reach out directly to discuss the role of Women in Data in the Public Sector.   / aditakarkera  

    22 min
  • (63) Isabella Richard - Marketing Strategy for Emerging AI Products

    The strategy and executive of marketing is not commonly considered in the early part of the product development and lifecycle. The most common reasons include, but not limited to: 1. to ensure the product and service works before taking it commercial (internally or external) and 2. the product or service can evolve during the product development cycle.
    Our guest, Isabella has an expertise is working with new and emerging products after the fact. Often 1-4 years after the product or service has been created.
    "...a lot of times we (the marketer) miss the actual problem the product or services was created to solve, " Isabella mentions, "it is common to have a disconnect between engineering and marketing but also sales and marketing,"
    During this episode we discuss a marketing methodology:
    1. Business Challenge - What was this product created for?
    2. Refining the buyer persona - who are we targeting ? Who is going to use this product?
    3. Validate with Customers - Schedule time with potential buyers for feedback.
    4. Translate into what will resonate - Breaking through the noise.
    5. Educating value - What is the value proposition? It is more than features & capabilities
    6. Establish the story, but stay consistent.
    Other topics discussed were:
    - Educating new emerging technologies
    - Working around disadvantages:
    - What about good and negative publicity. Is it all good?
    - Startups. Are they spending their money wisely with lack of resources in marketing?
    - What do we mean when we talk about 'the story'
    - What approaches do we want to avoid?
    - Customer attention spans
    - Prioritize marketing efforts
    - Supplementing expertise with external content
    - Generation Z perspectives
    - Women on board of advisors at the university level, supporting the next generation
    - Lessons learned - women having a voice. You are there for a reason. You are where you are supposed to be. It is out
    Feel free to DM Isabella: https://www.linkedin.com/in/isabellarichard/

    30 min
  • (62) Laura Ellis - 'Little Miss Data' & the Evolution of Data Over Time

    From housing data on a single server to global, distributed storage with zettabytes of available data. Over the past 20 years we have continued to modify the underlying architecture to match our desire to store more data. We use this data in consumer hyper personalization, detect vulnerabilities and data breaches, and to have a higher level of confidence in the answers to our questions.

    As we collect more data to address the questions from today, consumer expectations shift again and have more complicated questions tomorrow. Just when we think we have enough data, the line in sand moves again. In fact, the line is moving so much these days, one could say it doesn't really exist anymore.
    My guest today is Laura Ellis, who originally thought she was destined to become a teacher. Today she is a data engineering & platform analytics executive who started her professional career in technology and recently received CDO executive certificate.
    She is also the founder of the Blog 'Little Miss Data', focusing on living out loud and talking about all things data. In her spare time, Laura produces and hosts the annual event (for the past 3 years) 'Data Mishaps'. This event is a virtual, safe space where data mistakes are shared to learn from each other. With ~400 participating in the last event it is becoming a much anticipated event each year.
    Topics discussed today are:
    - What is cybersecurity - the simple definition
    - Is the role of data engineering different than a data scientist
    - How has the interpretation of data changed over the years
    - Is there a business case for dark data
    - Pivoting into a data profession
    - The value of returning to school after 15+ years

    29 min
  • (61) Himali Kumar - Reoccurring Data (Quality) Audits

    We hear and understand that step 1 in developing a #dataorganization is to clean up the data. This often includes data harmonization from multiples sources, classification, mapping, labeling, etc. Most often it usually stops there. We cleaned it... we are done, right?
    No, not really. In my conversations with Himali Kumar an IT and Data Executive, new data is constantly be added into the enterprise data set. From information provided by new customers, inclusion of new data sources, changes in data sources such as POS system, customer preferences, etc.
    Question: How do you gather and ensure value in enterprise data if the data is constantly changing?
    If you are not leveraging a #datagovernance program with resources dedicated to continue to re-evaluate the data, then the organization is already falling behind or creating a negative perception between the company and the customer.
    Examples of challenges that can occur after the initial #datacleansing include:
    - duplicate customer data leading to sending the same message multiple times not realizing it was the same person (from a data perspective)
    - data schema issues leading to many man hours of rework
    - #dataaccuracy issues causing outages to internal and external stakeholders
    A decision-based organization is only as good as its data. If the data is constantly changing, then it is critical to continue to evaluate (or audit) the data to minimize gaps and mitigate data strategy challenges.
    My conversation with Himali, also highlighted:
    - Impacts of not performing regular reviews (audit) of the data
    - Three (3) areas critical to developing a data organization - the minimum to get started with
    - Establishing tolerances / risk thresholds to enterprise data
    - Who is really the end user?
    - Achievability of #dataquality

    25 min
  • (59) Bhuva Shakti - Championing Women's Financial Inclusion in Technology

    Bhuva Shaki is the Chief Sustainable Innovation Officer of Bhuva's Impact Global and
    Global Chief Ethics & Culture Officer (CECO) | Director of the Americas (Not-for-Profit), Women in AI.
    She embraces the concept of continuous learning and opportunities for personal growth: sustainability, entrepreneurships, private equity, venture capital. All of which intertwine to support her advocacy of others, helping startups expand sustainable innovation and championing women's financial inclusion with technology with a goal to eliminate exclusion.
    With such purposeful engagement in her continuous learning, I had to ask, which is first the education or the experience? Bhuva, replied experience first, then focus on specific areas of education that empahsizes your interest.
    This lead to the bulk of our conversation today, covered across the following four (4) questions:
    1. How can we eliminate bias and integrate ethics into financial services industry?
    2. 21st Century Skills are suppose to outline the grouping of skill sets our next generation needs as they enter the workforce. The phrase literacy tends to emphasis media, technology, and information literacy. There are so many other types of literacy the next generation should also focus on. How do we ensure the balance from those literacy skills while adding in others such as data literacy, data bias, financial literacy?
    3. Do governments need to play a role in order to improve of gender equity? More specficially, a recent article was referenced that real gains in gender equity will require technical equity as well and the role of government agencies is another variable required in the larger equation.
    4. Do you have recommendations to close that gap in the middle management of the workplace? More specifically, when we look at where women are in leadership roles today, we have improved at the level of VP and higher. We also see a lot of women leaving college and entering technical roles. There is a clear gap in recent studies in the middle section. If we don't have women throughout the oerganization do we risk our progression falling backward?
    5. Generative AI - from a textual perspective (not generating art). Is this going to help or hinder the financial customer, specifically from the lens of women-owed startups and financial inclusion.
    Finally, two (2) key takeaways for women-owned startups. What can they start doing after listening to this podcast to really change their financial equity?
    https://www.linkedin.com/in/bhuvashakti/

    https://www.linkedin.com/company/bhuvas-impact-global

    /https://www.bhuvas-impact.global/

    46 min

About AI Literate Citizen

From the publisher's feed

AI technologies are more accessible, enabling us to achieve more and do more. This can be empowering. These technologies are as powerful as the data that shapes them and the people who use them. To…

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