Posts Tagged ‘LLMs’
[NDCOslo2024] Mirror, Mirror: LLMs and the Illusion of Humanity – Jodie Burchell
In the mesmerizing mirror maze of machine mimicry, where words weave worlds indistinguishable from wit, Jodie Burchell, JetBrains’ data science developer advocate, shatters the spell of sentience in large language models (LLMs). A PhD psychologist turned NLP pioneer, Jodie probes the psychological ploys that propel projections of personhood onto probabilistic parsers, dissecting claims from consciousness to cognition. Her inquiry, anchored in academia and augmented by anecdotes, advises acuity: LLMs as linguistic lenses, not living likenesses, harnessing their heft while heeding hallucinations.
Jodie greets with gratitude for her gritty slot, her hipster cred in pre-prompt NLP notwithstanding. LLMs’ 2022 blaze beguiles: why bestow brains on bytes when other oracles oblige? Her hypothesis: humanity’s hall of mirrors, where models mirror our mores, eliciting empathy from echoes.
Psychological Projections: Perceiving Personhood in Parsers
Humans, Jodie hazards, hallucinate humanity: anthropomorphism’s ancient artifice, from pets to puppets. LLMs lure with language’s liquidity—coherent confessions conjure companionship. She cites stochastic parrots: parleying patterns, not pondering profundities, yet plausibility persuades.
Extraordinary assertions abound: Blake Lemoine’s LaMDA “alive,” Google’s Gemini “godhead.” Jodie juxtaposes: sentience’s scaffold—selfhood, suffering—sans in silicon. Chalmers’ conundrum: consciousness connotes qualia, quanta qualms quell in qubits.
Levels of Luminescence: From Language to Luminary
DeepMind’s AGI arc: Level 1 chatbots converse convincingly; Level 2 reasons reactively; Level 3 innovates imaginatively. LLMs linger at 1-2, lacking Level 4’s abstraction or 5’s autonomy. Jodie jests: jackdaws in jester’s garb, juggling jargon sans judgment.
Illusions intensify: theory of mind’s mirage, where models “infer” intents from inferences. Yet, benchmarks belie: ARC’s abstraction stumps, BIG-bench’s breadth baffles—brilliance brittle beyond basics.
Perils of Projection: Phishing and Philosophical Pitfalls
Prompt injections prey: upstream overrides oust origins, birthing bogus bounties—”Amazon voucher via arcane URL.” Jodie demonstrates: innocuous inquiries infected, innocuousness inverted into inducements. Robustness rankles: rebuttals rebuffed, ruses reiterated.
Her remedy: recognize reflections—lossy compressions of lore, not luminous lives. Demystify to deploy: distill data, detect delusions, design defensively.
Dispelling the Delusion: Harnessing Heuristics Humanely
Jodie’s jeremiad: myths mislead, magnifying misuses—overreach in oracles, oversight in safeguards. Her horizon: LLMs as lucid lenses, amplifying analysis while acknowledging artifice.
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[DefCon32] Threat Modeling in the Age of AI
As artificial intelligence (AI) reshapes technology, Adam Shostack, a renowned threat modeling expert, explores its implications for security. Speaking at the AppSec Village, Adam examines how traditional threat modeling adapts to large language models (LLMs), addressing real-world risks like biased hiring algorithms and deepfake misuse. His practical approach demystifies AI security, offering actionable strategies for researchers and developers to mitigate vulnerabilities in an AI-driven world.
Foundations of Threat Modeling
Adam introduces threat modeling’s four-question framework: what are we working on, what can go wrong, what are we going to do about it, and did we do a good job? This structured approach, applicable to any system, helps identify vulnerabilities in LLMs. By creating simplified system models, researchers can map AI components, such as training data and inference pipelines, to pinpoint potential failure points, ensuring a proactive stance against emerging threats.
AI-Specific Security Challenges
Delving into LLMs, Adam highlights unique risks stemming from their design, particularly the mingling of code and data. This architecture complicates secure deployment, as malicious inputs can exploit model behavior. Real-world issues, such as AI-driven resume screening biases or facial recognition errors leading to wrongful arrests, underscore the urgency of robust threat modeling. Adam notes that while LLMs excel at specific mitigation tasks, broad security questions yield poor results, necessitating precise queries.
Leveraging AI for Security Solutions
Adam explores how LLMs can enhance security practices. By generating mitigation code or test cases for specific vulnerabilities, AI can assist developers in fortifying systems. However, he cautions against over-reliance, as generic queries produce unreliable outcomes. His approach involves using AI to streamline threat identification while maintaining human oversight, ensuring that mitigations address tangible risks like data leaks or model poisoning.
Future Directions and Real-World Impact
Concluding, Adam dismisses apocalyptic AI fears but stresses immediate concerns, such as deepfake proliferation and biased decision-making. He advocates integrating threat modeling into AI development to address these issues early. By fostering a collaborative community effort, Adam encourages researchers to refine AI security practices, ensuring that LLMs serve as tools for progress rather than vectors for harm.
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[DevoxxGR2024] Meet Your New AI Best Friend: LangChain at Devoxx Greece 2024 by Henry Lagarde
At Devoxx Greece 2024, Henry Lagarde, a senior software engineer at Criteo, introduced audiences to LangChain, a versatile framework for building AI-powered applications. With infectious enthusiasm and live demonstrations, Henry showcased how LangChain simplifies interactions with large language models (LLMs), enabling developers to create context-aware, reasoning-driven tools. His talk, rooted in his experience at Criteo, a leader in retargeting and retail media, highlighted LangChain’s composability and community-driven evolution, offering a practical guide for AI integration.
LangChain’s Ecosystem and Composability
Henry began by defining LangChain as a framework for building context-aware reasoning applications. Unlike traditional LLM integrations, LangChain provides modular components—prompt templates, LLM abstractions, vector stores, text splitters, and document loaders—that integrate with external services rather than hosting them. This composability allows developers to switch LLMs seamlessly, adapting to changes in cost or performance without rewriting code. Henry emphasized LangChain’s open-source roots, launched in late 2022, and its rapid growth, with versions in Python, TypeScript, Java, and more, earning it the 2023 New Tool of the Year award.
The ecosystem extends beyond core modules to include LangServe for REST API deployment, LangSmith for monitoring, and a community hub for sharing prompts and agents. This holistic approach supports developers from prototyping to production, making LangChain a cornerstone for AI engineering.
Building a Chat Application
In a live demo, Henry showcased LangChain’s simplicity by recreating a ChatGPT-like application in under 10 lines of Python code. He instantiated an OpenAI client using GPT-3.5 Turbo, implemented chat history for context awareness, and used prompt templates to define system and human messages. By combining these components, he enabled streaming responses, mimicking ChatGPT’s real-time output without the $20 monthly subscription. This demonstration highlighted LangChain’s ability to handle memory, input/output formatting, and LLM interactions with minimal effort, empowering developers to build cost-effective alternatives.
Henry noted that LangChain’s abstractions, such as strong typing and output parsing, eliminate manual prompt engineering, ensuring robust integrations even when APIs change. The demo underscored the framework’s accessibility, inviting developers to experiment with its capabilities.
Creating an AI Agent for PowerPoint Generation
Henry’s second demo illustrated LangChain’s advanced features by building an AI agent to generate PowerPoint presentations. Using TypeScript, he configured a system prompt from LangSmith’s community hub, defining the agent’s tasks: researching a topic via the Serper API and generating a structured PowerPoint. He defined tools with Zod for runtime type checking, ensuring consistent outputs, and integrated callbacks for UI tracing and monitoring.
The agent, powered by Anthropic’s Claude model, performed internet research on Google Cloud, compiled findings, and generated a presentation with sourced information. Despite minor delays, the demo showcased LangChain’s ability to orchestrate complex workflows, combining research, data processing, and content creation. Henry’s use of LangSmith for prompt optimization and monitoring highlighted the framework’s production-ready capabilities.
Community and Cautions
Henry emphasized LangChain’s vibrant community, which drives its multi-language support and rapid evolution. He encouraged attendees to contribute, noting the framework’s open-source ethos and resources like GitHub for further exploration. However, he cautioned against over-reliance on LLMs, citing their occasional laziness or errors, as seen in ChatGPT’s simplistic responses. LangChain, he argued, augments developer workflows but requires careful integration to ensure reliability in production environments.
His vision for LangChain is one of empowerment, enabling developers to enhance applications incrementally while maintaining control over AI-driven processes. By sharing his demo code on GitHub, Henry invited attendees to experiment and contribute to LangChain’s growth.
Conclusion
Henry’s presentation at Devoxx Greece 2024 was a compelling introduction to LangChain’s potential. Through practical demos and insightful commentary, he demonstrated how the framework simplifies AI development, from basic chat applications to sophisticated agents. His emphasis on composability, community, and cautious integration resonated with developers eager to explore AI. As LangChain continues to evolve, Henry’s talk serves as a blueprint for harnessing its capabilities in real-world applications.
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[DotAI2024] DotAI 2024: Stanislas Polu – Tracing the Evolution of LLM Reasoning and Agency
Stanislas Polu, a trailblazing researcher and co-founder of Dust, offered a panoramic view of large language models’ ascent at DotAI 2024. With a background spanning Polytechnique, Stanford, and pivotal roles at Stripe and OpenAI—where he advanced mathematical reasoning in LLMs—Polu now steers Dust toward AI-augmented enterprise tools. His discourse framed the AI epoch as a societal phase shift, paralleling seismic transitions like agriculture or electrification, and dissected how LLMs’ cognitive prowess is reshaping work and innovation.
Societal Shifts Catalyzed by Emergent Intelligence
Polu likened the pre- to post-AI era to historical ruptures, pinpointing AlphaZero’s 2017 debut as the inflection. This system, ingesting mere rules to master Go and chess beyond human bounds, evoked extraterrestrial ingenuity—crunching simulations to forge strategies unattainable through rote play. ChatGPT’s 2022 emergence amplified this, birthing agents that orchestrate tasks autonomously, while recent milestones like an AI securing a bronze at the International Mathematical Olympiad signal prowess in abstract deduction.
These strides, Polu observed, provoke institutional ripples: Nobel nods to AI-driven physics and biology breakthroughs affirm computation’s ascendancy in discovery. Yet, deployment lags potential; in mid-2022, OpenAI’s revenues hovered in tens of millions, with scant workplace adoption. This chasm propelled Polu’s pivot from research to product, hypothesizing that interfaces, not algorithms, bottleneck utility.
Dust embodies this thesis, granting teams bespoke assistants attuned to proprietary data and actions. Unlike monolithic bots, specialized agents—narrowly scoped for tasks like query resolution or report synthesis—yield superior accuracy by mitigating retrieval noise and model hallucinations. Polu’s narrative stresses infrastructure’s role: plumbing data silos and action endpoints to empower models without exposing sensitivities.
Unlocking Workplace Transformation Through Tailored AI
At Dust’s core lies dual convictions: seamless enterprise integration and multiplicity of agents. The former demands robust pipes—secure data federation and API orchestration—while the latter champions modularity, where assistants evolve via iterative refinement, drawing from domain lore to eclipse generalists.
Polu recounted Dust’s genesis amid GPT’s hype, yet workplace AI remains nascent, mired in “pre-GPT” paradigms of siloed tools. His solution: hyper-focused agents that ingest contextual artifacts, execute workflows, and iterate on feedback loops. This architecture not only boosts efficacy but fosters emergent behaviors, like chaining assistants for complex pipelines.
Envision a sales team querying leads enriched by CRM insights, or engineers debugging via code-aware bots—scenarios where Dust’s agnosticism across models ensures longevity. Polu advocated starting small: automate a 30-minute drudgery with GPT or Dust, scaling from there. This pragmatic ethos, he contended, unlocks boundless augmentation, where AI amplifies human ingenuity rather than supplants it.
As enterprises grapple with AI’s dual-edged sword—efficiency gains versus integration hurdles—Polu’s blueprint charts a collaborative path. Dust’s trajectory, blending research rigor with product agility, heralds a workspace where intelligence permeates, propelling productivity into uncharted realms.