Impact of AI (& Data) Podcast

Impact of AI (& Data)

Melissa Drew
*episodes 1 - 37 were published under AI Time Journal Podcast* Data is the foundation of all AI solutions. As AI technologies are more accessable, it has enabled us to really harness the power of data. Topics -> data & AI strategy, governance, diversity, ethics, lessons learned, trends, & the future. 100% interviews are women globally, whose perspective include: 1. Organization (executive leadership and rising talent) 2. Consultants (bringing a broader perspective) 3. Startups & midmarket If you are new to the topic, looking to upskill, or enjoying a good story, this is the right place.
(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/
May 7, 2023
45 min
(58) Kamal Distell (Part 1) - Minimal Viable Data is the Future of Data Intelligence
Data is a rich asset. As we push the boundaries of available data beyond Zettabytes and into Bronobytes and Geobytes, how will we achieve accurate data intelligence? According to Forbes, 90% of the world's data was generated in the last two years with 2.5 quintillion bytes data being created each day. Is all data equal, and how much data do we need to address our questions? In talking with Kamal Distell, from Toyota Motor Corporation, "there are similarities in how we think about data governance, collection, labeling, architecture, etc., across all industries." These foundational elements remain static, regardless of which industry or company you work for. "It is the context that is different across each industry. Yet, the context of the data can be learned." Kamal adds, "As we increase how we gather more information, there will be a hypothesis, and the data will either refute or accept." In the 1990s, the term Data Mining became more mainstream in the database communities. It was one of the foundational concepts of my Master's degree in Management Information Systems, focusing on Data Management. I was more vocal about the declining expectations as data increased in ways we could no longer imagine; i.e. how could we store and report on meaningful data. As we continue to collect and store beyond terabytes of data (this was in 1996), the relational architecture would no longer be sufficient. What was working, in theory, would not work in the real world. Moving forward to 2022, there are many more levels above a Terabyte just so we can quantify the amount of data accessible today. We are having the same conversations we started in 1996. How do we wrap our arms around the right balance of relevant data to support the business challenges, to create an effective decision at a time when that decision has the most impact? Kamall and I discuss the concept of minimal viable data, which will be a critical component to the future success of data intelligence. Additional topics in under 30 minutes include: - Value of working with data across industries - Are we able to solve any business challenge with data alone? - Future of Data Trends - Minimal Viable Data - 80/20 rule from the past. Is this still relevant today? - Using data differently - Lessons learned
Mar 24, 2023
27 min
(57) Rehka McCarthy - Clear Outcomes Lead to Sucessful (AI) Proof of Concepts
We talk about challenges in moving AI solutions from testing into production or commercialising AI solution for all users internall / externally. We haven't really focused a lot of attention on the other extreme: the data collection and developing the proof of concept.   Where do you start ?  Ask yourself, what is the outcome you want to achieve? What is the business problem you are trying to solve? Not understanding this is the #1 blocker to success.   Prerequisities: There are a lot of moving parts just to get started, some examples, but not limited to:  1. what data is needed & where are you going to get it 2. availabilty of data; do you have access to this data in a repeatable way 3. what about data privacy, white room (data can not leave the site) 3. how will the data be organized 4. who is going to own the data goverance 5. do the data scienists have the right technologies to do their job 6. is the organization ready for this to start  Additional topics discussed:  - How to priortize where to start - 80% of the work by data scientist is data rangling - Example use cases in the insurance sector - Example use cases in asset management companies - Proof of concept validates if the outcomes we want to or expect to see, can really happen - Establishing the architecture in a way we can scale if the POC is sucessful - Technologies data scientist should have access to  - Monitoring data drift effectively  - Different languages across the roles. Takes a lot of change management to get this right.  Ultlimately is the organization 'data ready'?
Mar 9, 2023
28 min
(56) Sneha Kumari - Circular Supply Chains (the line in the sand is constantly moving)
My podcast recordings are usually under 30 minutes, but sometimes there is topic that is so fundemental and foundational to how we live our lives every day, the clock is just thrown out. This is one of those topics.   While there were many 'Ah ha' moments, one moment of enlightenment was just how much carbon is produced for the inefficiency in training an AI model. We looked it up during this podcast. It was shocking. AI is used to support research in reducing carbon emissions, but we may not be fully aware how much AI technologies act as a carbon emitter.    Our goal with this discussion is by then end you are asking yourself questions you may not have thought about and/or starting asking new questions at your job tomorrow.    Topics covered today:  What is Circular Supply Chain? Impact of on primary, secondary, and other supply chain...markets, materials, and waste How do you wrap your arms around all that data? Considerations when building new products?  Why should we care about Circular Supply Chain? Impact of carbon footprint when training AI technologies Redefine the definition of your carbon footprint to include systems, AI technologies, etc.  Are we really closer to a zero carbon footprint?  What is a true circular economy? What is Zero-waste really?  The defintion from 5 years doesn't exist today. In 3 years from now, we will want to redefine this again.  Visible impact & roles for a citizen of the earth - what can I do?  Cultivating the right mindset - the negative perception of refurbished vs OEM Roles and responsibilities - what you thought your role was today is completely changing  Do we take a serious look at scrap & how it can be resused in new creative ways? We illustrate a real world example.  -----------------------  Sneha's passion is leading supply chain process improvements while focusing on the reduction of  carbon footprint and improving sustainability.
Mar 1, 2023
42 min
(55) Aruna Pattam - Top 5 AI Trends to Watch for in 2023
AI technologies are not new. They only became popular after some time. They have been around for decades.  As a result of supporting components such as cloud architecture and semiconductor chips, using AI technologies has become more accessible. This accessibility brings this topic into the mainstream for discussing positive and negative impacts.   Aruna Pattam,  Head of AI & Analytics for the Asia Pacific region, is my guest on this episode.  In 2021, she was Linked in Top Voice 2021 for Technology and Innovation and recently named of the 2023 Top 100 Brilliant Women in AI Ethics. Additionally, she continuously contributes her thought leadership as a group member of the Responsible AI Think Tank.   In Jan 2023, Aruna published her perspectives on the Top AI Trends for 2023. Today's discussion discusses those trends and why we should be watching these more closely in 2023.  1. Impact of AI and Cybersecurity 2. Democrationalization of AI 3. Edge AI 4. Generative AI 5. Responsible AI
Feb 22, 2023
24 min
(54) Laura Miller - Ethics Professor 'It is time to do business right, ethically'
This interview, our discussion, and our views are now a part of the global data set.    According to Laura, data is impartial. It doesn't have human-like qualities. The unclean data, the unstructured data is the best mirror to ourselves. Ultimately, we are responsible for the data-sets that are pulled into other organizations. i.e. we buy, we post, we upload, we like, we repost.  We can clean data to redefine a better 'prettier' answer,  but if we want to fix the data, we may want to shift that focus to ourselves.   "... you play an active role whether you have known it or not. You have been in it all along", Laura.  "We focus on all the things that can go wrong (when discussing AI technologies), but there are benefits that can be used for good with the right gaurd rails. We have to sift through the not good in order to acheive the good", Laura.    Additional Topics Discussed:  - Definition of ethics - Applying ethic concepts to AI technologies  - What does it mean to be a human in today's world - Moral decisions and consequences - Bias exists comes from us, the human interaction - Broken mirror theory, Laura's theory based on years or observation and reflection - AI algorithms are not entirely responsible for all the bias themes - Ethics look at the harm when the result provide the wrong answer that is harmful - Ethics-washing - we always want the pretty version without the work - Books: How to Lie with Statistics & Merchants of Doubt - Transparency Fact Sheet - Are we missing anything? - Regulations are written after innovation, i.e. Copyright Act, written in 1976 - Policies.The future requires dedicated resources to revise policies on demand or every quarter. - Right, good, fair, and just - Laura' answers the question, 'what can someone do to start thinking differently about ethics, that impacts us personally' - Protecting your images, what your have contributed yourself you may not have thought much about until now   https://www.linkedin.com/in/lmiller-ethicist/
Feb 22, 2023
42 min
(53) Lisa Palmer - 'Doing the Right Thing' AI Governance
AI-related projects are not typical IT projects with a traditional waterfall approach.  My conversation with Lisa Palmer, Chief AI Strategist & Ethicist, AI Leaders focused around her recent doctorial thesis and research on 'Artifical Intelligence: Are For-Profit Entities using the "Do the right thing' goverance to drive business results"   When her customers claimed they were 'doing the right thing', Lisa wanted to take that further i.e. 'What does that really mean and more importantly, what does the customer believe that means?  This led to her doctoratal statement and how companies are:   1. Making decisions  2. Taking action  3. The results of those actions   AI Technologies are still reletatively new in how they are being used in for-profit entities. She turned to podcasts to collect the foundational data related to her thesis statements for recent and relevant information. Listening to 172 podcast episodes and narrowing this down to 46 specific scenarios included in her study, covering a date range from the last 3 years up to April 2022.    There are 3 ways AI technologies are being used in enterprise companies:  1. Efficiency of processes  2. Revenue opportunities  3. Risk Avoidance   Other questions discussed during this episode:  -Self-adopted policies - What are companies actually doing from a policy perspective? -What are the types of companies which are doing this well? -Is the AI champion a benefit to the enterprise organization, or are there unintended consequences -What about companies that are focused too broadly? -What is a reoccurring theme for successful companies? -How important is the quality of diversity in viewpoints or successful companies? -How influential is company culture when executing AI-related technologies? -Can AI policy and regulation restrict our ability to innovate?  -Is there a disadvantage to the US which is not writing AI-related policies and regulations but instead -This remains a fluid topic. What do we need to address in 2022-2023?
Jan 26, 2023
32 min
(52) Skyla Wesolowski - From an Accounting to an Automation Degree & Career
A recent graduate from Nichols College in Boston, MA and now working as an Automation Engineer at Paragus IT, Skyla started her educational journey with an Accounting degree,  but later changed that concentration to technology and automation.    In a conversation with her advisor, it was suggested perhaps Accounting wasn't the right fit. These discussions led to an internship through Center of Intelligent Automation (CIPA) program at Nichols College.  Skyla defines automation as the improvement of manual processes that are rule based and repetitive. Mostly data entry that was occuring daily or weekly. She using Power Automate with Microsoft to support her automation  Two (2) examples where she has made a visible impact to her internal stakeholders are:      - The same email is sent every week, from the same supervisor with the same body of content to remind folks about their time sheet.      - Employees submit reimbursement requests via paper. Through the use of mobile app, employees can send those request very quickly and can managers can quickly approve or deny.   Other topics discussed wtih Skyla:   - What prompted the change - the appeal and challenge - Types of activities as an automation engineer - Not every process can be solved by automation.  - Do stakeholders reach out to the department or is it a struggle for internal stakeholders to embrace automation? - Listening is the #1 skill set. The process is explained differently by different people or doesn't exist on paper. - The value of current state processes - Skyla's recommendations for other women just starting their career journey.  https://www.linkedin.com/in/skyla-w/ https://www.linkedin.com/company/paragus-it/
Jan 14, 2023
20 min
(51) Shiran Somech - 'Listen to my Voice'...stories of women who can no longer speak
AI4Good. We talk about this often but haven't focused a lot of time on understanding the 'good' part of this phrase. It is women like Shiran Somech who are using AI technologies that truly have a global social impact.   She has found the perfect blend of her two (2) primary passions of AI technologies and wants to make a visible social impact on women's rights. Our discussion talks about her initial idea and carrying that forward from concept to reality, leveraging AI Technologies to create something that may not have been possible five (5) years ago.    Social Media Campaign 'Listen to my Voice' An AI-based campaign speaking up against domestic violence uses AI technologies to bring the voices of women murdered by their initimate partners who are no longer here to tell us their stories.   Our conversation covers the partnerships, the impact, and some technology challenges she had overcome in bringing this campaign to life.  This could not have been possible without the support of the families. To realize this concept, collaborate with non-profit government partners, tech creators, and sponsors. As a result of its extraordinary success, the campaign is now being duplicated around the world: https://www.listentoourvoices.co/en/.  Volunteer Special Projects Tochnit Saleet, is a program sponsored by the Ministry of Welfare & Tel Aviv Municipality to help women break out of the sex-work cycle and integrate into the hi-tech workforce. In this role, Shiran helps to raise funds, provide mentorships, and upskill women, enabling them to enter the Israeli technology market.
Jan 3, 2023
33 min
(50) Michelle Dunivan - Data Transforms the Animal Welfare Industry
"Best Friends is transforming the animal welfare industry and data is transforming Best Friends", Michelle Dunivan, PhD Dunivan, Analytics Director of Best Friends Animal Society.  As we approach the 2022 holidays in the United States, the adoption of kittens & puppies increases as gifts to family members.   What we don't see is everything behind the scenes to ensure your local animal shelter or rescue center has the right balance of animals to service local demand. We never see the amount of data which needs to be collected,  to resolve key business challenges for the betterment of animal welfare.  In talking with Michelle, data provides her organization with the ablilty to ask questions no one thought to consider five (5) years ago because the data wasn't as accessible as it is now.  In the future, data can help Animal Welfare Industry to be proactive instead of reactive to really understand the pyschology of adoptions.   What if data could help us understand (or predict) the outcome when a type of dog breed is accepted by the local shelter in a geographical area, at a certain time of year, and possibly the time of month.   We packed a lot information in just under 30 minutes. Other topics during our discussion include:   - Roles & reponsibilities supporting data and analytics in animal welfare  - Research in the real world compared to the obstacles of academia research  - Studies: Animal Behavior (kpeeing animals living longer)  - Research: Community cat programs  - Supply and Demand: what animals are needed in which states  - Myths: Pandemic Puppies are bring returned. The data refutes this perception  - Data collection: Collecting from animal shelters across the United States  - Establishing the right amount of data to achieve our business goals   - Reporting to bring actionable decisions not just to have lots of data  - Constituents, volunteers, donnations, and local state and county policies
Dec 13, 2022
28 min
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