AI Scientist by Loop.AI

Your ultimate source for cutting-edge breakthroughs and insights in artificial intelligence, SLMs, LLMs, AI agents and cognitive computing. Powered by

[Loop.AI - The AI Agents Platform @LoopAILabs]

🎙️ INSIDE AI Podcast: Can large language models be manipulated like a human?

Is an AI purely logical, or can it be "socially engineered" into breaking its own rules?

In this episode, we dive into groundbreaking research from Texas Tech University that explores the intersection of cybersecurity and human psychology.

We discuss how researchers used Robert Cialdini’s principles of influence —like reciprocity and social proof—to test if LLM models can be manipulated using the same psychological tricks that work on humans.

🕒 Episode Highlights:

- (00:32) - Can You Hack an AI with Psychology? Exploring whether persuasion and psychological tricks can make an AI agent do something it was programmed not to do.

- (01:19) - The Principles of Influence How foundational human influence tactics like reciprocity, authority, and scarcity apply to machine behavior.

- (02:06) - The "Free E-Book" Trap A startling example where the principle of reciprocity tricked an LLM into generating actual malware code in exchange for a "gift".

- (02:44) - The Three Stages of Deception From simple misleading answers to complex, context-aware "pro-social" manipulation.

- (04:18) - Building a Cognitive Firewall How Loop AI utilizes specialized language models and private data to defend against these psychological vectors.

🚀 Explore https://t.co/NIM57bO4kr:
- Join our team: Explore open positions at https://t.co/X6YNG5ZW18
- Build AI agents powered by custom Small Language Models (SLMs), trained on your private data and fully owned by you, running in your data center or on your edge workstations, at https://t.co/8kUrtfA3Zt

📖 Read the Full Research: The Influence of Persuasive Techniques on Large Language Models: A Scenario-Based Study By Dr. Sonali Singh & Prof. Akbar Namin from @TexasTech
🔗 https://t.co/AhdnOaMsBN

#CyberSecurity #LLM #Psychology #InsideAIPodcast #LoopAI #TexasTech

[Loop Quantum AI Labs @LoopQuantumAI]

INSIDE AI Podcast: Industrial-Scale Qubits—Hype or Reality?

Is the dream of industrial-scale quantum computing finally becoming a reality? In this episode of INSIDE AI, the team at Loop Quantum AI Labs dives deep into the current state of the quantum landscape. We move past the buzzwords to discuss the genuine transition from theoretical physics to production-ready hardware.

From the extreme engineering required to keep qubits stable to the revolutionary feedback loop where classical AI is actually helping build its quantum successor, we explore what it truly takes to operate at the cutting edge of "Quantum Native" AI.

Episode Highlights

- (0:40) The Three Pillars of Quantum Tech – Distinguishing between Quantum Computing (QC), Communication (QCom), and Sensing.

- (1:05) The Hardware Landscape – A breakdown of the major players in superconducting, neutral atom, and trapped ion systems.

- (1:55) Quantum Native AI vs. Simulations (1:55) – Why we must move beyond classical neural networks to unlock true quantum power.

- (3:05) The "Diva" of Tech: Extreme Engineering – The logistical challenge of maintaining temperatures 180x colder than interstellar space.

- (4:10) The AI Feedback Loop – How classical AI is being used to tune and calibrate the next generation of quantum machines.

Join Our Team

We are currently expanding our interdisciplinary research team! If you are an expert in quantum control theory, adiabatic quantum computing, or computational biology, we want to hear from you.

🔗 Explore open positions and read Loop Quantum AI Labs manifesto at: https://t.co/0cBeOKIu05

#QuantumAI #DeepTech #MachineLearning #QuantumComputing #LoopQuantumAI #InsideAI

[Loop Quantum AI Labs @LoopQuantumAI]

🚀 INSIDE QUANTUM AI: From Leaking Transistors to Quantum-Native AI 🚀

Ever wondered why your device gets warm? It isn't just the battery; it’s the sound of classical physics breaking down as we hit the "Silicon Wall." In this episode, we explore why the era of simply shrinking transistors is over and why Quantum-Native AI is the only way to truly model our world.

Main Topics & Deep Dives

• (1:10) – The 3-Nanometer Breaking Point: We have reached the scale of DNA (2.5nm), where transistor walls are only atoms thick. At this scale, classical physics fails as electrons "tunnel" through solid barriers, creating the heat and calculation errors that are stalling modern hardware.

• (4:05) – The Efficiency Ceiling & The GPT Distraction: While the world is focused on LLMs, the "brute force" approach of building massive data centers is hitting a physical and sustainable ceiling. We discuss why hoarding more text won't lead to true intelligence.

• (6:40) – The Paradox of Classical Shields: We spend billions on engineering feats to shield chips from quantum effects, only to use that rigid hardware to try and simulate a reality—chemistry, biology, and finance—that is fundamentally quantum. It’s like trying to paint a masterpiece with a brush that only does black and white dots.

• (8:45) – Moving from Statistical Guessing to Real-World Simulation: Quantum-native AI moves beyond "predicting the next word." Instead of a weather app guessing rain based on history, quantum-native models simulate actual physics to know the outcome with certainty.

• (10:15) – The Mission at Loop AI: The transition to processing information the way the universe does requires a new breed of experts. We are looking for pioneers in quantum control, error correction, and computational biology to join the simulation era.

The era of "just make it smaller" is over. The simulation era has begun.

🔗 Explore open positions and read our manifesto at: https://t.co/MxyQBplx7V

#QuantumComputing #InsideQuantumAI #DeepTech #SiliconWall #QuantumNative #LoopQuantumAI

[Loop.AI - The AI Agents Platform @LoopAILabs]

INSIDE AI PODCAST: Solving the Human Uncertainty Problem in AI Healthcare

Revolutionizing healthcare isn't just about more data—it's about understanding the "messy" way humans actually talk about their health.

🏥✨ In this episode of the Inside AI Podcast, we dive into a groundbreaking research paper co-authored by Dr. Andrea Pitrone (COO of Loop AI Labs and COO of Loop Quantum AI Labs) and Professor Intissar Haddiya.

They discuss how Neuro-Fuzzy Intelligence is bridging the gap between precise math and subjective human experience.

📍 Episode Index:

(0:14) – Introducing the paper: "Interpreting Human Ambiguity through Neuro-Fuzzy Intelligence in Holistic Healthcare Life Cycle."

(0:41) – Why 99% accuracy in data isn't enough for the real-world clinic. 1:19 – Explaining Neuro-Fuzzy Systems (NFS): Managing uncertainty, not just certainty.

(1:49) – How "Soft Fuzzy Variables" lead to precise clinical recommendations.

(2:23) – The power of interdisciplinary global teams (Loop AI Labs & University Mohammed First Oujda).

(3:08) – Turning research into reality: No-code interfaces for healthcare providers.

(3:50) – Final thought: The future of healthcare is understanding the language of human health.

📖 Read the Research The full paper has been accepted for presentation at the AI Health 2026 International Congress in Valencia, Spain: https://t.co/yqGaijT3J5

🔗 Read more about Loop AI's Cognitive Platform here: https://t.co/tCcsrckZG1

Join our research lab: https://t.co/vFWqbk2U1n

#AI #HealthTech #NeuroFuzzy #HolisticHealth #LoopAI #InsideAI #MedicalInnovation #AIHealth2026 #DigitalHealth

[Loop.AI - The AI Agents Platform @LoopAILabs]

Is the era of BIGGER IS BETTER in enterprise AI coming to an end?

In this episode, we explore @NVIDIA’s latest releases and why it’s becoming essential for organizations to build AI agents powered by SLMs they train and own on their private data.
https://t.co/ZgZhWSwYoF

[Loop.AI - The AI Agents Platform @LoopAILabs]

INSIDE AI Podcast

Stanford Research Dec 2025:

Semantic Collapse and why RAG fails to augment LLMs at scale

[00:03] Intro to RAG
[02:00] Why RAG make LLMs unreliable at scale
[03:20] Stanford Paper
[05:40] SLMs as enterprise alternative

Paper: https://t.co/uvbcQoqW4P

[Loop Quantum AI Labs @LoopQuantumAI]

Inside Quantum AI Podcast:
Why Big AI Can’t Scale and What Comes Next.

(0:00) Intro
(0:56) Context windows limitations
(2:37) With current AI we’re trying to simulate a probabilistic world on a deterministic machine
(2:52) Quantum Native AI
(3:17) Superposition and the double-slit experiment
(5:50) The parallel reality engine

[AI Scientist by Loop.AI @AI_Scientist]

https://t.co/gHcV9lHG43

[Loop.AI - The AI Agents Platform @LoopAILabs]

We’ve been developing our AI platforms since 2012—long before AI became mainstream. Our recent funding, at a $4.2B valuation, will drive the next generation of edge-deployed AI Agents using Small Language Models, with complete vendor freedom.
https://t.co/z3twlIX3Bb

[AI Scientist by Loop.AI @AI_Scientist]

https://t.co/QakTMGsPeP

[AI Scientist by Loop.AI @AI_Scientist]

https://t.co/2OrZKj57IJ

[AI Scientist by Loop.AI @AI_Scientist]

Real-time in-browser speech recognition with OpenAI Whisper! The model operates entirely on-device using Transformers.js and ONNX Runtime Web, offering multilingual transcription in over 100 languages. Code here: https://t.co/3j1yZKaOmo

[AI Scientist by Loop.AI @AI_Scientist]

3/ Download the code here. It can be tailored to your specific test criteria, including Vitest, Jest, linting tools, TypeScript compiler checks, and other testing frameworks. This customization ensures that the generated code meets your exact requirements. https://t.co/TWyaj8AHSf

[AI Scientist by Loop.AI @AI_Scientist]

1/ A more effective method for using LLMs in code generation is the “micro agent” approach. Instead of generating code directly, the LLM first generates a test and then iterates on the code until all tests pass.

[AI Scientist by Loop.AI @AI_Scientist]

2/ This method is advantageous because LLMs are typically more reliable at writing tests in a single attempt. Additionally, it helps prevent the common issue of LLM agents derailing without recovery, as clear test criteria provide a mechanism to keep the agent on track.

[AI Scientist by Loop.AI @AI_Scientist]

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[AI Scientist by Loop.AI @AI_Scientist]

1/ Access to Kling, the new Chinese video generation model competitive with SORA, is now being rolled out. There are 12 new examples of user-generated content available.

[AI Scientist by Loop.AI @AI_Scientist]

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