Data Bytes Women in Data

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(Intro 00:00:00) Deep learning inspired by the human brain.

(00:00:29) Besa's leadership and analytics background.

(00:01:06) Excitement for Besa's interdisciplinary background.

(00:01:28) Besa's journey combining technology and human condition.

(00:03:49) AI's impact on healthcare discussed.

(00:05:41) Neuromorphic hardware and future technology.

(00:07:22) Modeling common sense and emotion in AI.

(00:08:26) Human uniqueness and AI creativity.

(00:10:41) AI in healthcare and elder care.

(00:14:08) Emotional AI and human attachments.

(00:15:24) AI in psychoanalysis and psychotherapy.

(00:17:14) Privacy risks with AI.

(00:19:35) AI's potential mental health pitfalls.

(00:22:00) Data protection laws for AI.

(00:23:12) Need for AI regulations.

(00:25:30) Educating regulators on AI.

(00:26:50) Importance of asking questions.

(00:30:10) Research interests in AI ethics.

(00:32:31) Advice: stay curious, ask questions.

(00:33:22) Encouragement to continue learning.


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(00:00:30 - 00:01:52) Welcome and Introduction

(00:01:52 - 00:04:07) Democratizing Data

(00:04:07 - 00:06:59) Balancing Architecture and Flexibility

(00:06:59 - 00:10:08) Internal vs. Consulting Support

(00:10:08 - 00:13:09) Measuring ROI in Data Projects

(00:13:09 - 00:16:26) Improving Business and Technical Team Collaboration

(00:16:26 - 00:20:20) In-Person vs. Virtual Collaboration

(00:20:20 - 00:22:21) Importance of Networking and Personal Growth

(00:22:21 - 00:27:31) Sandy's Career Turning Points

(00:27:31 - 00:29:33) Mentorship: Being a Great Mentor and Mentee

(00:29:33 - 00:32:38) Advancing Diversity and Equity in Tech Careers

(00:32:38 - 00:33:25) Closing Remarks

(00:33:25 - End) Outro and Membership Invitation






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(00:00:00) Importance of changing the way we teach math to be more inclusive.

(00:00:34) Introduction to the podcast guest, author and professor.

(00:00:58) Teaching at James Madison University; living in Alexandria, VA.

(00:01:28) Balancing in-person, hybrid, and online teaching formats.

(00:02:05) Overview of the book, "Essential Math for AI."

(00:02:27) Inspiration for writing the book; addressing the needs in AI education.

(00:04:11) Key concepts: types of intelligence, core mathematical foundations for AI.

(00:05:00) Starting points for learning math relevant to AI: calculus, linear algebra, probability.

(00:05:14) Connecting math learning to real-life applications for better understanding.

(00:06:12) Discussing the struggle with abstract math education and its real-world application.

(00:07:20) Changing how math is taught to retain more talented individuals.

(00:08:55) Addressing the fear of math and the impact on AI engagement.

(00:09:38) Overcoming math anxiety by setting clear goals and self-teaching.

(00:11:48) How AI advancements will change mathematics education.

(00:12:01) Benefits of AI in education, including automation and personalized learning.

(00:15:02) Historical perspective on the evolution of work and technology.

(00:15:29) Embracing fast technological advancements for societal benefits.

(00:17:20) Mathematics as a truth filter in the age of information.

(00:18:27) New book on prompting and data engineering.

(00:19:25) Exploring the intersection of AI models and data pipelines.

(00:21:09) The end-to-end story of AI from hardware to organizational strategy.

(00:21:45) The importance of understanding the bigger picture in AI.

(00:23:01) Journey into mathematics and AI, starting from Lebanon.

(00:25:44) Advice for young people navigating today's environment.

(00:26:08) Being true to oneself and seeking knowledgeable mentors.


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(Intro 00:00:00) Companies leaping into AI without solid data foundations.

(00:01:12) Importance of conferences for connecting and learning.

(00:01:26) Christina emphasizes energy and storytelling at tech conferences.

(00:02:17) LinkedIn connections making conference experiences better.

(00:02:33) Christina’s background at Google and Waze.

(00:03:13) Analytics ascendancy model explained.

(00:04:10) Common issue of companies jumping into AI prematurely.

(00:05:38) Importance of sequential steps in the analytics journey.

(00:06:23) Christina’s ACE framework (Advise, Create, Educate).

(00:07:06) Roles of advising, creating content, and educating.

(00:08:22) Handling C-suite pressure regarding AI hype.

(00:09:07) Evaluating current capabilities and setting expectations.

(00:10:27) Common pitfalls in the analytics journey.

(00:12:20) Challenges and risks in advanced analytics.

(00:13:57) Regulation and risk in finance and healthcare.

(00:14:59) Responsibility for assessing risk and regulation.

(00:15:19) Cross-functional nature of risk assessment.

(00:16:12) Advice on continuing the analytics journey.

(00:16:44) Maintaining a positive mindset and continuous learning.

(00:18:27) Future role of AI in analytics.

(00:19:41) AI’s potential and limitations in turbocharging analytics.

(00:21:49) Christina’s personal analytics journey.

(00:22:27) From studying statistics to founding Dare to Data.

(00:25:33) Advice for aspiring data professionals.

(00:25:37) Importance of curiosity, learning, and communication skills.

(00:27:14) Being a translator between business and technology.

(00:27:49) Christina’s SQL courses on LinkedIn Learning.

(00:28:35) Future courses and learning opportunities.


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(00:00:00) - Introduction by Sol Rashidi on data management challenges.(00:00:36) - Sadie welcomes Sol and discusses her background and accolades.
(00:02:21) - Sol addresses the impact of AI hype on C-suite roles and data governance.
(00:03:17) - Sol explains her approach to overcoming data challenges.(00:04:16) - Discussion on the evolving roles within the C-suite and the challenges of unclear division of labor.
(00:05:59) - Sol on the responsibilities of a chief AI officer and the practical challenges in strategy and delivery.
(00:07:05) - Sadie and Sol discuss the added complexity of new C-suite roles.
(00:08:16) - Sol outlines an ideal CDO role and its necessary scope.(00:10:58) - Sol discusses professional relationships within the C-suite and strategies for negotiation.
(00:14:28) - Sol promotes a course on transitioning from a practitioner to the C-suite.
(00:15:12) - Discussion on the challenges and strategies for effective leadership in the C-suite.
(00:18:23) - Sol on learning from failures and the importance of asking for what you want.
(00:21:17) - Discussion on why few women hold leadership positions and how to negotiate effectively.
(00:26:08) - Sadie discusses the role of tools like ChatGPT in professional communication.
(00:30:06) - Sol shares her plans post-retirement and her new book release.


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[00:00:00] Intro and discussion on information retrieval
[00:00:36] Sadie welcomes Harpreet, discussing his achievements and connections
[00:01:46] Early interactions and courses between Harpreet and Sadie
[00:02:27] Updates on Sadie's SQL course and new roles
[00:03:42] Harpreet discusses following curiosity in his career and AI's growth
[00:06:55] Future of data science roles and specialization within the field
[00:09:40] Unique skills of data scientists and transition to deep learning
[00:15:23] Discussion on benchmarks, datasets, and introduction to retrieval augmented generation (RAG)
[00:20:16] Explanation and potential applications of RAG models
[00:24:09] AI applications in various industries and predictions for future AI integration
[00:29:02] Harpreet's personal productivity gains from AI and new tools enhancing workflows
[00:34:58] Harpreet's podcast impact on his career and future plans
[00:40:44] Recommendations for staying updated in deep learning
[00:45:36] Harpreet invites listeners to join his new research initiative


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Support this podcast: https://podcasters.spotify.com/pod/show/women-in-data/support