Podcast Index
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Daily Tech Headlines
Sarah Lane, Robb Dunewood and Jenn Cutter
Artificial Intelligence Masterclass
AI Masterclass
Starter Story
Starter Story
Tina Huang
Tina Huang
Solo Founders
Solo Founders
Talking HealthTech
Talking HealthTech
十字路口Crossing
Koji
CBN Tecnologia - Techtudo
CBN
Tecnologia
CBN
The Monocle Daily
Monocle
America’s Frontlines
Tom Sheffield
ShadowTalk: Powered by ReliaQuest
ReliaQuest
The Bitcoin Matrix
Cedric Youngelman
Daily Tech News Show
Tom Merritt
Investor's Row Podcast
The IRA Club
Cisco Podcast Network
Cisco
The Neuron: AI Explained
The Neuron
EA Forum Podcast (All audio)
EA Forum Team
The Product Podcast
Product School
Cyber Sitrep
Corvus Technologies, Digital Beachhead
Masters of the Nerdiverse Podcast
Mike G
Cyber Voices
Australian Information Security Association (AISA)
The Automation Podcast
Insights In Automation
The Good Tech Companies
HackerNoon
My Weird Prompts
Daniel Rosehill
OnPoint of View
OnPoint Community
The Everything Feed - All Packet Pushers Pods
Packet Pushers
Elon Musk Podcast
Stage Zero
Code with Jason
Jason Swett
Autonomous IT
Automox
Molly White's Citation Needed
Molly White
CyberWire Daily
N2K Networks
Engadget News + Next
Engadget
FYI - For Your Innovation
ARK Invest
Wendover Productions
Wendover Productions
Value Driven Data Science
Dr Genevieve Hayes
Tecnología
Dr Genevieve Hayes
AI misinformation is a new problem. Misleading data is not. Long before anyone had heard of a hallucination, organisations were making bad decisions based on cherry-picked statistics, misunderstood averages, and numbers ...
AI doesn't lie - at least, not intentionally. It just sounds completely confident while filling in the gaps with whatever seems most plausible. And in a world where AI outputs are increasingly being used to inform high-s...
In high-stakes decision-making, waiting for more data is often not an option. Yet many data scientists assume that without a large dataset, meaningful analysis is impossible. The good news is that rigorous, quantitative ...
Data scientists are trained to work with large datasets. But the decisions that truly make or break an organisation are rarely the ones with large datasets behind them. They are the high-stakes, one-off decisions made un...
AI has the potential to dramatically expand what data scientists can do. But used without care, it also has the potential to quietly erode the expertise that makes them valuable in the first place.In this Value Boost epi...
The question haunting every data scientist right now isn't whether AI will change their work, it's whether there will still be a place for them when it does. The answer, according to Tim Dietrich, isn't to compete with A...
These days, every organisation wants to describe themselves as "AI-first". But in the rush to find opportunities to use AI, it can be easy to forget that AI isn't always the right answer. In this Value Boost episode, San...
Organisations today have no shortage of AI ideas. What they lack is the ability to turn those ideas into production-ready systems that deliver real business value.For data scientists trying to get AI projects off the gro...
AI can get you to 60% of a finished output in minutes. But getting from 60% to 100% - the part where real insight lives - is where human expertise becomes the deciding factor. And the more expertise you bring, the furthe...
Most data scientists have been in this situation: you spend hours analysing a dataset, return to your stakeholder with your findings, and are met with a polite "that's interesting" - before your work disappears into a dr...
Building authority as a data professional doesn't require a large budget, a publisher, or even a large audience. But it does require a deliberate decision to share your thinking with the world and the patience to let tha...
Data scientists today are under pressure to adopt the latest tools - machine learning, LLMs, generative AI. But in the rush to embrace what's new, many are leaving some of the most powerful analytical tools sitting on th...
Over 100 episodes of conversations with world-class practitioners, a few ideas keep surfacing. Technical skill is necessary but never sufficient. The most valuable data professionals aren't the ones who build the best mo...
Biased machine learning models don't just produce poor predictions. They can damage reputations, derail projects, and in high-stakes fields like healthcare, potentially cause real harm. Yet many data scientists don't che...
When your machine learning model makes a decision that affects someone's medical treatment, financial security, or legal rights, "the algorithm said so" isn't good enough. Stakeholders need to understand why models make ...
Data scientists often jump straight to machine learning when tackling a new problem. But there's a foundational step that can dramatically increase your chances of project success and create more reliable business value....
Data scientists use optimisation every day when training machine learning models, without even thinking about it. But there's another type of optimisation - that many data scientists are unaware of - that can be used to ...
Building a model for an academic paper is one thing. Building a model that has to work perfectly during the Cricket World Cup with millions watching is something else entirely. There's no room for the kind of errors that...
For most data scientists, the idea of impacting the world through your work seems impossible. You may be developing technically brilliant solutions within your organisation, but seeing them become industry standards or i...
While the transition from academia to industry can be brutal for data scientists, academics don't show up in industry empty-handed. They bring powerful transferable skills that many industry-trained data scientists never...
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