privacy and security

Social media is an ant mill (Internet is a disaster) (Ep. 303)

Internet followed nature. Until it didn't. And became the disaster we know.
 
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About Apple's Privacy (Ep. 302)

Paragon's spyware hacked fully updated iPhones without a single click. Apple just spent $2B on tech that reads your silent speech. And your iCloud? Governments can request it with paperwork. This is what the privacy brand actually buys you.
 
Buy me a coffee https://ko-fi.com/datascience
 
✨ Connect with us! Personal newsletter:
https://defragzone.substack.com
📩 Newsletter: https://datascienceathome.substack.com
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📘LinkedIn: https://www.linkedin.com/in/fragadaleta/
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Facebook: https://www.facebook.com/datascienceAH
LinkedIn: https://www.linkedin.com/company/data-science-at-home-podcast
Discord Channel: https://discord.gg/4UNKGf3
 
NEW TO DATA SCIENCE AT HOME?
Welcome! Data Science at Home explores the latest in AI, data science, and machine learning. Whether you’re a data professional, tech enthusiast, or just curious about the field, our podcast delivers insights, interviews, and discussions.
Learn more at https://datascienceathome.com
 
SEND US MAIL!
We love hearing from you!
Send us mail at: hello@datascienceathome.com
Don’t forget to like, subscribe, and hit the 🔔 for updates on the latest in AI and data science!
 

Humans vs. Bots: Are You Talking to a Machine Right Now? (Ep. 273)

In this episode of Data Science at Home, host Francesco Gadaleta dives deep into the evolving world of AI-generated content detection with experts Souradip Chakraborty, Ph.D. grad student at the University of Maryland, and Amrit Singh Bedi, CS faculty at the University of Central Florida. 
Together, they explore the growing importance of distinguishing human-written from AI-generated text, discussing real-world examples from social media to news. How reliable are current detection tools like DetectGPT? What are the ethical and technical challenges ahead as AI continues to advance? And is the balance between innovation and regulation tipping in the right direction? 
 
Tune in for insights on the future of AI text detection and the broader implications for media, academia, and policy.
 
Chapters 
 
00:00 - Intro 
00:23 - Guests: Souradip Chakraborty and Amrit Singh Bedi 
01:25 - Distinguish Text Generation By AI 
04:33 - Research on Safety and Alignment of Generative Model 
06:01 - Tools to Detect Generated AI Text  
11:28 - Water Marking
18:27 - Challenges in Detecting Large Documents Generated by AI 
23:34 - Number of Tokens 
26:22 - Adversarial Attack
29:01 - True Positive and False Positive of Detectors 
31:01 - Limit of Technologies 
41:01 - Future of AI Detection Techniques 
46:04 - Closing Thought
 
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Love, Loss, and Algorithms: The Dangerous Realism of AI (Ep. 270)

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In this episode of Data Science at Home, we confront a tragic story highlighting the ethical and emotional complexities of AI technology. A U.S. teenager recently took his own life after developing a deep emotional attachment to an AI chatbot emulating a character from Game of Thrones. This devastating event has sparked urgent discussions on the mental health risks, ethical responsibilities, and potential regulations surrounding AI chatbots, especially as they become increasingly lifelike.
 
🎙️ Topics Covered:
AI & Emotional Attachment: How hyper-realistic AI chatbots can foster intense emotional bonds with users, especially vulnerable groups like adolescents.
Mental Health Risks: The potential for AI to unintentionally contribute to mental health issues, and the challenges of diagnosing such impacts. Ethical & Legal Accountability: How companies like Character AI are being held accountable and the ethical questions raised by emotionally persuasive AI.
 
🚨 Analogies Explored:
From VR to CGI and deepfakes, we discuss how hyper-realism in AI parallels other immersive technologies and why its emotional impact can be particularly disorienting and even harmful.
 
🛠️ Possible Mitigations:
We cover potential solutions like age verification, content monitoring, transparency in AI design, and ethical audits that could mitigate some of the risks involved with hyper-realistic AI interactions. 👀 Key Takeaways: As AI becomes more realistic, it brings both immense potential and serious responsibility. Join us as we dive into the ethical landscape of AI—analyzing how we can ensure this technology enriches human lives without crossing lines that could harm us emotionally and psychologically. Stay curious, stay critical, and make sure to subscribe for more no-nonsense tech talk!
 
Chapters
00:00 - Intro
02:21 - Emotions In Artificial Intelligence
04:00 - Unregulated Influence and Misleading Interaction
06:32 - Overwhelming Realism In AI
10:54 - Virtual Reality
13:25 - Hyper-Realistic CGI Movies
15:38 - Deep Fake Technology
18:11 - Regulations To Mitigate AI Risks
22:50 - Conclusion
 
#AI#ArtificialIntelligence#MentalHealth#AIEthics#podcast#AIRegulation#EmotionalAI#HyperRealisticAI#TechTalk#AIChatbots#Deepfakes#VirtualReality#TechEthics#DataScience#AIDiscussion #StayCuriousStayCritical

What Big Tech Isn’t Telling You About AI (Ep. 267)

Are AI giants really building trustworthy systems? A groundbreaking transparency report by Stanford, MIT, and Princeton says no. In this episode, we expose the shocking lack of transparency in AI development and how it impacts bias, safety, and trust in the technology. We’ll break down Gary Marcus’s demands for more openness and what consumers should know about the AI products shaping their lives.
 
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Cool links
https://mitpress.mit.edu/9780262551069/taming-silicon-valley/
http://garymarcus.com/index.html

Data Guardians: How Enterprises Can Master Privacy with MetaRouter (Ep. 261)

In this insightful episode, we dive deep into the pressing issue of data privacy, where 86% of U.S. consumers express growing concerns and 40% don't trust companies to handle their data ethically. Join us as we chat with the Vice President of Engineering at MetaRouter, a cutting-edge platform enabling enterprises to regain control over their customer data. We explore how MetaRouter empowers businesses to manage data in a 1st-party context, ensuring ethical, compliant handling while navigating the complexities of privacy regulations.
 
Sponsors
Intrepid AI (https://intrepid.ai) is an AI assisted all-in-one platform for robotics teams. Build robotics applications in minutes, not months
 
References
https://www.metarouter.io/post/mastering-data-governance-why-governance-at-the-point-of-collection-is-a-must-have 
https://hubs.ly/Q02HwJly0 
https://www.metarouter.io/post/why-privacy-sandbox-is-a-good-paradigm-shift-for-consumers 

Harnessing AI for Cybersecurity: Expert Tips from QFunction (Ep. 258)

In this episode, we sit down with Ryan Smith, Founder of QFunction LLC, to explore how AI and machine learning are revolutionizing cybersecurity. With over 8 years of experience, including work at NASA's Jet Propulsion Laboratory, Ryan shares insights on the future of threat detection and prevention, the challenges businesses face in maintaining effective cybersecurity, and the ethical considerations of AI implementation. Learn about cost-effective strategies for small businesses, the importance of collaboration in combating cyber threats, and how QFunction tailors its AI solutions to meet diverse industry needs.
 
Sponsors
Arctic Wolf Learn what the new year holds for ransomware as a service, Active Directory, artificial intelligence and more when you download the 2024 Arctic Wolf Labs Predictions Report today at arcticwolf.com/datascience
Intrepid AI (https://intrepid.ai) is an AI assisted all-in-one platform for robotics teams. Build robotics applications in minutes, not months.
QFunction  does cybersecurity differently. By relying on scientific breakthroughs in AI and machine learning, QFunction works within your existing security stack to detect anomalies and threats within your data
 
References
QFunction Blog
Threat Hunting Firewall Logs
Threat Hunting Linux Logs
QFunction GitHub

Attacking LLMs for fun and profit (Ep. 239)

As a continuation of Episode 238, I explain some effective and fun attacks to conduct against LLMs. Such attacks are even more effective on models served locally, that are hardly controlled by human feedback.
Have great fun and learn them responsibly.
 
References
https://www.jailbreakchat.com/
https://www.reddit.com/r/ChatGPT/comments/10tevu1/new_jailbreak_proudly_unveiling_the_tried_and/
https://arxiv.org/abs/2305.13860
 

Private machine learning done right (Ep. 207)

There are many solutions to private machine learning. I am pretty confident when I say that the one we are speaking in this episode is probably one of the most feasible and reliable.I am with Daniel Huynh, CEO of Mithril Security,  a graduate from Ecole Polytechnique with a specialisation in AI and data science. He worked at Microsoft on Privacy Enhancing Technologies under the office of the CTO of Microsoft France. He has written articles on Homomorphic Encryptions with the CKKS explained series (https://blog.openmined.org/ckks-explained-part-1-simple-encoding-and-decoding/). He is now focusing on Confidential Computing at Mithril Security and has written extensive articles on the topic: https://blog.mithrilsecurity.io/. 
In this show we speak about confidential computing, SGX and private machine learning
 
References
Mithril Security: https://www.mithrilsecurity.io/ 
BindAI GitHub: https://github.com/mithril-security/blindai 
Use cases for BlindAI:Deploy Transformers models with confidentiality: https://blog.mithrilsecurity.io/transformers-with-confidentiality/
Confidential medical image analysis with COVID-Net and BlindAI: https://blog.mithrilsecurity.io/confidential-covidnet-with-blindai/ 
Build a privacy-by-design voice assistant with BlindAI: https://blog.mithrilsecurity.io/privacy-voice-ai-with-blindai/ 
Confidential Computing Explained: https://blog.mithrilsecurity.io/confidential-computing-explained-part-1-introduction/ 
Confidential Computing Consortium: https://confidentialcomputing.io/ 
Confidential Computing White Papers: https://confidentialcomputing.io/white-papers-reports/ 
List of Intel processors with Intel SGX:https://www.intel.com/content/www/us/en/support/articles/000028173/processors.html 
https://github.com/ayeks/SGX-hardware 
Azure Confidential Computing VMs with SGX:Azure Docs: https://docs.microsoft.com/en-us/azure/confidential-computing/confidential-computing-enclaves 
How to deploy BlindAI on Azure: https://docs.mithrilsecurity.io/getting-started/cloud-deployment/azure-dcsv3 
Confidential Computing 101: https://www.youtube.com/watch?v=77U12Ss38Zc 
Rust: https://www.rust-lang.org/ 
ONNX: https://github.com/onnx/onnx
Tract, a Rust inference engine for ONNX models: https://github.com/sonos/tract
 

Capturing Data at the Edge (Ep. 180)

In this episode I speak with Manavalan Krishnan from Tsecond about capturing massive amounts of data at the edge with security and reliability in mind.
 
This episode is brought to you by Tsecond
The growth of data being created at static and moving edges across industries such as air travel, ocean and space exploration, shipping and freight, oil and gas, media, and more proposes numerous challenges in capturing, processing, and analyzing large amounts of data.
and by Amethix Technologies
Amethix use advanced Artificial Intelligence and Machine Learning to build data platforms and predictive engines in domain like finance, healthcare, pharmaceuticals, logistics, energy. Amethix provide solutions to collect and secure data with higher transparency and disintermediation, and build the statistical models that will support your business.
 
References
https://tsecond.us/company/manavalan-krishnan/