Posts Tagged ‘ArtificialIntelligence’
[KotlinConf2025] LangChain4j with Quarkus
In a collaboration between Red Hat and Twilio, Max Rydahl Andersen and Konstantin Pavlov presented an illuminating session on the powerful combination of LangChain4j and Quarkus for building AI-driven applications with Kotlin. The talk addressed the burgeoning demand for integrating artificial intelligence into modern software and the common difficulties developers encounter, such as complex setups and performance bottlenecks. By merging Kotlin’s expressive power, Quarkus’s rapid runtime, and LangChain4j’s AI capabilities, the presenters demonstrated a streamlined and effective solution for creating cutting-edge applications.
A Synergistic Approach to AI Integration
The core of the session focused on the seamless synergy between the three technologies. Andersen and Pavlov detailed how Kotlin’s idiomatic features simplify the development of AI workflows. They presented a compelling case for using LangChain4j, a versatile framework for building language model-based applications, within the Quarkus ecosystem. Quarkus, with its fast startup times and low memory footprint, proved to be an ideal runtime for these resource-intensive applications. The presenters walked through practical code samples, illustrating how to set up the environment, manage dependencies, and orchestrate AI tools efficiently. They emphasized that this integrated approach significantly reduces the friction typically associated with AI development, allowing engineers to focus on business logic rather than infrastructural challenges.
Enhancing Performance and Productivity
The talk also addressed the critical aspect of performance. The presenters demonstrated how the combination of LangChain4j and Quarkus enables the creation of high-performing, AI-powered applications. They discussed the importance of leveraging Quarkus’s native compilation capabilities, which can lead to dramatic improvements in startup time and resource utilization. Additionally, they touched on the ongoing work to optimize the Kotlin compiler’s interaction with the Quarkus build system. Andersen noted that while the current process is efficient, there are continuous efforts to further reduce build times and enhance developer productivity. This commitment to performance underscores the value of this tech stack for developers who need to build scalable and responsive AI solutions.
The Path Forward
Looking ahead, Andersen and Pavlov outlined the future roadmap for LangChain4j and its integration with Quarkus. They highlighted upcoming features, such as the native asynchronous API, which will provide enhanced support for Kotlin coroutines. While acknowledging the importance of coroutines for certain use cases, they also reminded the audience that traditional blocking and virtual threads remain perfectly viable and often preferred for a majority of applications. They also extended an open invitation to the community to contribute to the project, emphasizing that the development of these tools is a collaborative effort. The session concluded with a powerful message: this technology stack is not just about building applications; it’s about empowering developers to confidently tackle the next generation of AI-driven projects.
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[GoogleIO2025] Google I/O ’25 Keynote
Keynote Speakers
Sundar Pichai serves as the Chief Executive Officer of Alphabet Inc. and Google, overseeing the company’s strategic direction with a focus on artificial intelligence integration across products and services. Born in India, he holds degrees from the Indian Institute of Technology Kharagpur, Stanford University, and the Wharton School, and has been instrumental in advancing Google’s cloud computing and AI initiatives since joining the firm in 2004.
Demis Hassabis acts as the Co-Founder and Chief Executive Officer of Google DeepMind, leading efforts in artificial general intelligence and breakthroughs in areas like protein folding and game-playing AI. A former child chess prodigy with a PhD in cognitive neuroscience from University College London, he has received knighthood for his contributions to science and technology.
Liz Reid holds the position of Vice President of Search at Google, directing product management and engineering for core search functionalities. She joined Google in 2003 as its first female engineer in the New York office and has spearheaded innovations in local search and AI-enhanced experiences.
Johanna Voolich functions as the Chief Product Officer at YouTube, guiding product strategies for the platform’s global user base. With extensive experience at Google in search, Android, and Workspace, she emphasizes AI-driven enhancements for content creation and consumption.
Dave Burke previously served as Vice President of Engineering for Android at Google, contributing to the platform’s development for over a decade before transitioning to advisory roles in AI and biotechnology.
Donald Glover is an acclaimed American actor, musician, writer, and director, known professionally as Childish Gambino in his music career. Born in 1983, he has garnered multiple Emmy and Grammy awards for his work in television series like Atlanta and music albums exploring diverse themes.
Sameer Samat operates as President of the Android Ecosystem at Google, responsible for the operating system’s user and developer experiences worldwide. Holding a bachelor’s degree in computer science from the University of California San Diego, he has held leadership roles in product management across Google’s mobile and ecosystem divisions.
Abstract
This examination delves into the pivotal announcements from the Google I/O 2025 keynote, centering on breakthroughs in artificial intelligence models, agentic systems, search enhancements, generative media, and extended reality platforms. It dissects the underlying methodologies driving these advancements, their contextual evolution from research prototypes to practical implementations, and the far-reaching implications for technological accessibility, societal problem-solving, and ethical AI deployment. By analyzing demonstrations and strategic integrations, the discourse illuminates how Google’s full-stack approach fosters rapid innovation while addressing real-world challenges.
Evolution of AI Models and Infrastructure
The keynote commences with Sundar Pichai highlighting the accelerated pace of AI development within Google’s ecosystem, emphasizing the transition from foundational research to widespread application. Central to this narrative is the Gemini model family, which has seen substantial enhancements since its inception. Pichai notes the deployment of over a dozen models and features in the past year, underscoring a methodology that prioritizes swift iteration and integration. For instance, the Gemini 2.5 Pro model achieves top rankings on benchmarks like the Ella Marina leaderboard, reflecting a 300-point increase in ELO scores—a metric evaluating model performance across diverse tasks.
This progress is underpinned by Google’s proprietary infrastructure, exemplified by the seventh-generation TPU named Ironwood. Designed for both training and inference at scale, it offers a tenfold performance boost over predecessors, enabling 42.5 exaflops per pod. Such hardware advancements facilitate cost reductions and efficiency gains, allowing models to process outputs at unprecedented speeds—Gemini models dominate the top three positions for tokens per second on leading leaderboards. The implications extend to democratizing AI, as lower prices and higher performance make advanced capabilities accessible to developers and users alike.
Demis Hassabis elaborates on the intelligence layer, positioning Gemini 2.5 Pro as the world’s premier foundation model. Updated previews have empowered creators to generate interactive applications from sketches or simulate urban environments, demonstrating multimodal reasoning that spans text, code, and visuals. The incorporation of LearnM, a specialized educational model, elevates its utility in learning scenarios, topping relevant benchmarks. Meanwhile, the refined Gemini 2.5 Flash serves as an efficient alternative, appealing to developers for its balance of speed and affordability.
Methodologically, these models leverage vast datasets and advanced training techniques, including reinforcement learning from human feedback, to enhance reasoning and contextual understanding. The context of this evolution lies in Google’s commitment to a full-stack AI strategy, integrating hardware, software, and research. Implications include fostering an ecosystem where AI augments human creativity, though challenges like computational resource demands necessitate ongoing optimizations to ensure equitable access.
Agentic Systems and Personalization Strategies
A significant portion of the presentation explores agentic AI, where systems autonomously execute tasks while remaining under user oversight. Pichai introduces concepts like Project Starline evolving into Google Beam, a 3D video platform that merges multiple camera feeds via AI to create immersive communications. This innovation, collaborating with HP, employs real-time rendering at 60 frames per second, implying enhanced remote interactions that mimic physical presence.
Building on this, Project Astra’s capabilities migrate to Gemini Live, enabling contextual awareness through camera and screen sharing. Demonstrations reveal its application in everyday scenarios, such as interview preparation or fitness training. The introduction of multitasking in Project Mariner allows oversight of up to ten tasks, utilizing “teach and repeat” mechanisms where agents learn from single demonstrations. Available via the Gemini API, this tool invites developer experimentation, with partners like UiPath integrating it for automation.
The agent ecosystem is bolstered by protocols like the open agent-to-agent framework and Model Context Protocol (MCP) compatibility in the Gemini SDK, facilitating inter-agent communication and service access. In practice, agent mode in the Gemini app exemplifies this by sourcing apartment listings, applying filters, and scheduling tours—streamlining complex workflows.
Personalization emerges as a complementary frontier, with “personal context” allowing models to draw from user data across Google apps, ensuring privacy through user controls. An example in Gmail illustrates personalized smart replies that emulate individual styles by analyzing past communications and documents. This methodology relies on secure data handling and fine-tuned models, implying deeper user engagement but raising ethical considerations around data consent and bias mitigation.
Overall, these agentic and personalized approaches shift AI from reactive tools to proactive assistants, contextualized within Google’s product suite. The implications are transformative for productivity, yet require robust governance to balance utility with user autonomy.
Innovations in Search and Information Retrieval
Liz Reid advances the discussion on search evolution, framing AI Overviews and AI Mode as pivotal shifts. With over 1.5 billion monthly users, AI Overviews synthesize responses from web content, enhancing query resolution. AI Mode extends this into conversational interfaces, supporting complex, multi-step inquiries like travel planning by integrating reasoning, tool usage, and web interaction.
Methodologically, this involves grounding models in real-time data, ensuring factual accuracy through citations and diverse perspectives. Demonstrations showcase handling ambiguous queries, such as dietary planning, by breaking them into sub-tasks and verifying outputs. The introduction of video understanding allows analysis of uploaded content, providing step-by-step guidance.
Contextually, these features address information overload in an era of abundant data, implying improved user satisfaction—evidenced by higher engagement metrics. However, implications include potential disruptions to content ecosystems, necessitating transparency in sourcing to maintain trust.
Generative Media and Creative Tools
Johanna Voolich and Donald Glover spotlight generative media, with Imagine 3 and V3 models enabling high-fidelity image and video creation. Imagine 3’s stylistic versatility and V3’s narrative consistency allow seamless editing, as Glover illustrates in crafting a short film.
The Flow tool democratizes filmmaking by generating clips from prompts, supporting extensions and refinements. Methodologically, these leverage diffusion-based architectures trained on vast datasets, ensuring coherence across outputs.
Context lies in empowering creators, with implications for industries like entertainment—potentially lowering barriers but raising concerns over authenticity and intellectual property. Subscription plans like Google AI Pro and Ultra provide access, fostering experimentation.
Android XR Platform and Ecosystem Expansion
Sameer Samat introduces Android XR, optimized for headsets and glasses, integrating Gemini for contextual assistance. Project Muhan with Samsung offers immersive experiences, while glasses prototypes enable hands-free interactions like navigation and translation.
Partnerships with Gentle Monster and Warby Parker emphasize style, with developer previews forthcoming. Methodologically, this builds on Android’s ecosystem, ensuring app compatibility.
Implications include redefining human-computer interaction, enhancing accessibility, but demanding advancements in battery life and privacy.
Societal Impacts and Prospective Horizons
The keynote culminates in applications like Firesat for wildfire detection and drone relief during disasters, showcasing AI’s role in societal challenges. Pichai envisions near-term realizations in robotics, medicine, quantum computing, and autonomous vehicles.
This forward-looking context underscores ethical deployment, with implications for global equity. Personal anecdotes reinforce technology’s inspirational potential, urging collaborative progress.
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[AWSReInventPartnerSessions2024] Revolutionizing Enterprise Resource Planning through AI-Infused Cloud-Native SaaS Architectures: The SAP and AWS Convergence
Lecturer
Lauren Houon directs the Grow with SAP product marketing team at SAP, formulating strategies for cloud ERP market penetration. Elena Toader leads go-to-market operations for Grow with SAP, coordinating deployment acceleration and partner ecosystem development.
Abstract
This analytical discourse unveils the strategic integration of Grow with SAP within the AWS Marketplace, presenting a transformative procurement model for cloud enterprise resource planning. It systematically addresses prevailing organizational impediments—agility deficits, process fragmentation, transparency shortages, security vulnerabilities, and legacy system constraints—through a tripartite framework emphasizing operational simplification, business expansion, and success assurance. Customer case studies illustrate rapid value realization, cost optimization, and resistance mitigation, while technical specifications underscore reliability and extensibility.
Tripartite Strategic Framework for Cloud ERP Transformation
Contemporary enterprises grapple with multifaceted operational challenges that undermine competitiveness. Organizational inflexibility impedes adaptation to structural shifts or geographic expansion; disconnected systems spawn inefficiencies; opaque data flows obstruct automation; digital threats escalate; outdated platforms restrict scalability.
Grow with SAP on AWS counters these through marketplace-enabled acquisition—a pioneering development reflecting deepened SAP-AWS collaboration. The offering crystallizes around three interdependent pillars.
Operational Simplification deploys agile business templates, automates workflows via fifty years of embedded industry best practices, integrates artificial intelligence for enhanced transparency and strategic prioritization, and delivers continuous security/compliance updates across ninety-plus certifications.
Business Expansion accommodates multinational operations through fifty-nine out-of-the-box localizations, thirty-three languages, and localization-as-a-service for additional jurisdictions. The platform further supports mergers, divestitures, and subsidiary management within unified governance structures.
Success Assurance manifests through deployment methodologies yielding go-live timelines of eight to twelve weeks, extensible Business Technology Platform for intellectual property encapsulation, and SaaS characteristics including 99.9% availability, elastic scaling across three-tier landscapes, and biannual feature releases.
Empirical Validation via Diverse Customer Implementations
Practical efficacy emerges through heterogeneous customer narratives spanning multiple sectors.
MOD Pizza initiated its SAP journey with human resources modernization, subsequently recognizing inextricable finance-HR interdependencies. Integration enabled predictive impact assessment across four hundred monthly transactions, fostering cross-functional collaboration and process streamlining.
Aair, a major industrial raw materials distributor, replaced decade-old on-premises infrastructure plagued by talent retention difficulties and paper-based warehouse operations. Grow with SAP digitized twelve facilities, eliminating manual invoicing while revitalizing information technology career prospects.
Western Sugar Cooperative confronted thirty-year legacy ERP entrenchment compounded by employee change resistance. Methodological guidance and embedded best practices facilitated disruption-minimized transition, achieving five percent information technology cost reduction and twenty percent efficiency improvement.
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apiVersion: sap.btp/v1
kind: ExtensionModule
metadata:
name: custom-localization
spec:
targetCountries: ["additional-jurisdictions"]
languageSupport: ["extended-set"]
deploymentTimeline: "8-weeks"
Industry breadth—encompassing quick-service dining, industrial distribution, agricultural processing—validates the platform’s versatile end-to-end process coverage. Partner ecosystem contributions from Accenture, Deloitte, Cognitus, Navigator, and Syntax amplify implementation expertise.
Strategic Implications and Enterprise Transformation Pathways
The marketplace procurement model democratizes access to sophisticated ERP capabilities, compressing adoption cycles while preserving customization flexibility. Tripartite pillar alignment ensures that simplification catalyzes expansion, which success assurance sustains.
Organizational consequences include liberated strategic focus through automation, regulatory compliance through perpetual updates, and scalable growth infrastructure. The paradigm shifts enterprise resource planning from administrative overhead to competitive differentiator, with artificial intelligence integration promising continual value augmentation.
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[DefCon32] Changing Global Threat Landscape
Rob Joyce, a distinguished former National Security Agency (NSA) official, joined Jeff Moss, known as The Dark Tangent and founder of DEF CON, for a riveting fireside chat at DEF CON 32. Their discussion delved into the dynamic evolution of global cyber threats, with a particular focus on the transformative role of artificial intelligence (AI) in reshaping cybersecurity. Rob, recently retired after 34 years at the NSA, brought a wealth of experience from roles such as Cybersecurity Coordinator at the White House and head of the NSA’s Tailored Access Operations. Jeff facilitated a conversation that explored how AI is redefining defense strategies and the broader implications for global security, offering insights into the challenges and opportunities ahead.
The Evolution of Cyber Threats
Rob began by reflecting on his extensive career at the NSA, where he witnessed the transformation of cyber threats from isolated incidents to sophisticated, state-sponsored campaigns. He highlighted how adversaries now leverage AI to enhance attack vectors, such as spear-phishing and polymorphic malware, which adapt dynamically to evade detection. Rob emphasized that the scale and speed of these threats demand a shift from reactive to proactive defenses, underscoring the importance of understanding adversaries’ intentions through signals intelligence. His experience during the Iraq War as an issue manager provided a unique perspective on the strategic use of cyber intelligence to counter evolving threats.
AI’s Dual Role in Cybersecurity
The conversation pivoted to AI’s dual nature as both a tool for attackers and defenders. Rob explained how AI enables rapid analysis of vast datasets, allowing defenders to identify patterns and anomalies that would be impossible for human analysts alone. However, he cautioned that adversaries exploit similar capabilities to craft advanced persistent threats (APTs) and automate large-scale attacks. Jeff probed the balance between automation and human oversight, to which Rob responded that AI-driven tools, like those developed by the NSA, are critical for scaling defenses, particularly for protecting critical infrastructure. The integration of AI, he noted, is essential to keep pace with the growing complexity of cyber threats.
Strengthening Defenses Through Collaboration
Rob stressed the importance of bipartisan support for cybersecurity, noting that stopping foreign adversaries is a shared goal across administrations. He highlighted the role of the Office of the National Cyber Director (ONCD) in convening agencies to synchronize efforts, citing examples where ground-up collaboration among agencies has led to effective threat mitigation. Jeff asked about the resource gap, and Rob acknowledged that the scope of threats often outpaces available resources. He advocated for widespread adoption of two-factor authentication and secure software development practices, such as moving away from memory-unsafe languages, to build more defensible systems.
Building a Resilient Future
Concluding, Rob expressed optimism about the trajectory of cybersecurity, emphasizing that automation can alleviate the burden on security teams, particularly for 24/7 operations. He underscored the need for robust teams and innovative technologies to address the relentless pace of vulnerabilities exploited by attackers. Jeff echoed this sentiment, encouraging the DEF CON community to contribute to shaping a secure digital landscape. Their dialogue highlighted the critical role of collaboration between government, industry, and the hacker community in navigating the ever-changing threat landscape.
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[DevoxxBE2023] Making Your @Beans Intelligent: Spring AI Innovations
At DevoxxBE2023, Dr. Mark Pollack delivered an insightful presentation on integrating artificial intelligence into Java applications using Spring AI, a project inspired by advancements in AI frameworks like LangChain and LlamaIndex. Mark, a seasoned Spring developer since 2003 and leader of the Spring Data project, explored how Java developers can harness pre-trained AI models to create intelligent applications that address real-world challenges. His talk introduced the audience to Spring AI’s capabilities, from simple “Hello World” examples to sophisticated use cases like question-and-answer systems over custom documents.
The Genesis of Spring AI
Mark began by sharing his journey into AI, sparked by the transformative impact of ChatGPT. Unlike traditional AI development, which often required extensive data cleaning and model training, pre-trained models like those from OpenAI offer accessible APIs and vast knowledge bases, enabling developers to focus on application engineering rather than data science. Mark highlighted how Spring AI emerged from his exploration of code generation, leveraging the structured nature of code within these models to create a framework tailored for Java developers. This framework abstracts the complexity of AI model interactions, making it easier to integrate AI into Spring-based applications.
Spring AI draws inspiration from Python’s AI ecosystem but adapts these concepts to Java’s idioms, emphasizing component abstractions and pluggability. Mark emphasized that this is not a direct port but a reimagination, aligning with the Spring ecosystem’s strengths in enterprise integration and batch processing. This approach positions Spring AI as a bridge between Java’s robust software engineering practices and the dynamic world of AI.
Core Components of AI Applications
A significant portion of Mark’s presentation focused on the architecture of AI applications, which extends beyond merely calling a model. He introduced a conceptual framework involving contextual data, AI frameworks, and models. Contextual data, akin to ETL (Extract, Transform, Load) processes, involves parsing and transforming data—such as PDFs—into embeddings stored in vector databases. These embeddings enable efficient similarity searches, crucial for use cases like question-and-answer systems.
Mark demonstrated a simple AI client in Spring AI, which abstracts interactions with various AI models, including OpenAI, Hugging Face, Amazon Bedrock, and Google Vertex. This portability allows developers to switch models without significant code changes. He also showcased the Spring CLI, a tool inspired by JavaScript’s Create React App, which simplifies project setup by generating starter code from existing repositories.
Prompt Engineering and Its Importance
Prompt engineering emerged as a critical theme in Mark’s talk. He explained that crafting effective prompts is essential for directing AI models to produce desired outputs, such as JSON-formatted responses or specific styles of answers. Spring AI’s PromptTemplate class facilitates this by allowing developers to create reusable, stateful templates with placeholders for dynamic content. Mark illustrated this with a demo where a prompt template generated a joke about a raccoon, highlighting the importance of roles (system and user) in defining the context and tone of AI responses.
He also touched on the concept of “dogfooding,” where AI models are used to refine prompts, creating a feedback loop that enhances their effectiveness. This iterative process, combined with evaluation techniques, ensures that applications deliver accurate and relevant responses, addressing challenges like model hallucinations—where AI generates plausible but incorrect information.
Retrieval Augmented Generation (RAG)
Mark introduced Retrieval Augmented Generation (RAG), a technique to overcome the limitations of AI models’ context windows, which restrict the amount of data they can process. RAG involves pre-processing data into smaller fragments, converting them into embeddings, and storing them in vector databases for similarity searches. This approach allows developers to provide only relevant data to the model, improving efficiency and accuracy.
In a demo, Mark showcased RAG with a bicycle shop dataset, where a question about city-commuting bikes retrieved relevant product descriptions from a vector store. This process mirrors traditional search engines but leverages AI to synthesize answers, demonstrating how Spring AI integrates with vector databases like Milvus and PostgreSQL to handle complex queries.
Real-World Applications and Future Directions
Mark highlighted practical applications of Spring AI, such as enabling question-and-answer systems for financial documents, medical records, or government programs like Medicaid. These use cases illustrate AI’s potential to make complex information more accessible, particularly for non-technical users. He also discussed the importance of evaluation in AI development, advocating for automated scoring mechanisms to assess response quality beyond simple test passing.
Looking forward, Mark outlined Spring AI’s roadmap, emphasizing robust core abstractions and support for a growing number of models and vector databases. He encouraged developers to explore the project’s GitHub repository and participate in its evolution, underscoring the rapid pace of AI advancements and the need for community involvement.