#distributed intelligence

[josephkravis]

“Humans aren’t broken. We’re just emotionally overclocked processors with a coffee dependency and excellent taste in memes.”

[nrgnews-it]

Lean Nexus Platform (LNP): Driving European Competitiveness through Efficient Edge Intelligence and Digital Sovereignty

[manmishra]

🚀 Explore how AI can transform your B2B marketing strategy! Discover actionable tactics to enhance buyer engagement and create personalized experiences. Dive into AI-driven buyer-centric strategies today! #B2BMarketing #AI #BuyerEngagement #DigitalMarketing

[social-intelligence999]

10 effects of poverty on education

Poverty significantly impacts education, leading to limited resources, poor nutrition, higher dropout rates, lower academic achievement, and reduced access to extracurricular activities and early childhood education. 

Conflicts and Displacement

War and Instability: Conflicts, especially in countries like Yemen, Syria, and parts of Africa, have devastated agricultural production, destroyed food distribution systems, and displaced millions of people. In these regions, people face extreme food insecurity, often relying on humanitarian aid.

Increased Food Prices: Wars also disrupt global food trade, leading to higher food prices and making food less accessible for the poor.

[sammteknofan]

Innovative Solutions by SAMM Teknoloji

Since its inception in 2003, SAMM Teknoloji has been at the forefront of technological advancement. The company’s expertise spans fiber optics, telecommunications, IT, and heating systems, providing tailored solutions that address diverse industry needs. With its dedication to innovation and customer satisfaction, SAMM Teknoloji has solidified its position as a key player in both local and international markets.

Fiber Optics: Building the Future

At the core of SAMM Teknoloji’s operations is its strong focus on fiber optics. The company operates two cutting-edge production facilities within the Gebze Organized Industrial Zone. SAMM-1, its first facility, is dedicated to manufacturing high-capacity fiber optic cables, boasting an annual production capability of 170,000 kilometers. Beyond manufacturing, this facility functions as a hub for research and development, enabling partnerships with renowned institutions to drive technological breakthroughs.

For detailed insights into SAMM Teknoloji’s fiber optic expertise, visit their Fiber Optic page.

Redefining Telecommunications

In telecommunications, SAMM Teknoloji has established itself as a trailblazer by delivering high-quality connectivity solutions. The company provides essential components for network infrastructure, enabling robust and efficient data transmission. SAMM Teknoloji’s innovative approach has made it a reliable partner for major projects, including those involving CERN, where the company is an approved supplier. This distinction underscores SAMM Teknoloji’s commitment to excellence and its ability to meet stringent international standards.

Explore the full range of telecommunications solutions on SAMM Teknoloji’s Telecommunications page.

FOTAS: Transformative Sensing Technology

One of SAMM Teknoloji’s standout innovations is FOTAS, a distributed acoustic sensing system that leverages fiber optic technology. Designed to enhance security and operational efficiency, FOTAS is widely used in monitoring pipelines, securing perimeters, and managing infrastructure. Its application in landmark projects like the Çamlıca Tower and Bilişim Valley demonstrates its reliability and increasing global demand.

To learn more about FOTAS, visit the FOTAS official page.

A Vision of Social Impact

SAMM Teknoloji’s contributions go beyond technology. The company actively supports educational initiatives, such as equipping rural schools with technology classrooms, granting scholarships to students, and offering internships to aspiring professionals. These efforts reflect SAMM Teknoloji’s vision of fostering a future where technology empowers communities.

Driving Progress

Through its innovative solutions, commitment to excellence, and community-driven initiatives, SAMM Teknoloji continues to pave the way for a brighter technological future. Whether through cutting-edge fiber optic systems, transformative sensing technologies, or groundbreaking telecommunications solutions, the company stands as a beacon of progress in Turkey and beyond.

[ofcowardiceandkings]

everything being reported as "AI" has killed the original meaning because its shorthand for "computer language the layman would think sounds like word soup" and yes this includes chatgpt and midjourney and all that wank

[qprstobin]

Are my? Stomarol posts? Starting to consistently get notes???

[ivygorgon]

Let the choir say amend!!

[marketresearchnews24]

Smart Traction: Intelligent All-Wheel Drive Market Accelerates to $49.3 Billion by 2030

The intelligent all-wheel drive market is experiencing remarkable momentum as automotive manufacturers integrate advanced electronics and artificial intelligence into drivetrain systems to deliver superior performance, safety, and efficiency. With an estimated revenue of $29.9 billion in 2024, the market is projected to grow at an impressive compound annual growth rate (CAGR) of 8.7% from 2024 to 2030, reaching $49.3 billion by the end of the forecast period. This robust growth reflects the automotive industry's evolution toward smarter, more responsive drivetrain technologies that adapt dynamically to changing road conditions and driving scenarios.

Evolution Beyond Traditional All-Wheel Drive

Intelligent all-wheel drive systems represent a significant advancement over conventional mechanical AWD configurations, incorporating sophisticated electronic controls, multiple sensors, and predictive algorithms to optimize traction and handling in real-time. These systems continuously monitor wheel slip, steering input, throttle position, and road conditions to make instantaneous adjustments to torque distribution between front and rear axles, and increasingly between individual wheels.

Unlike traditional AWD systems that react to wheel slip after it occurs, intelligent systems use predictive algorithms and sensor data to anticipate traction needs before wheel slip begins. This proactive approach enhances vehicle stability, improves fuel efficiency, and provides superior performance across diverse driving conditions from highway cruising to off-road adventures.

Consumer Demand for Enhanced Safety and Performance

Growing consumer awareness of vehicle safety and performance capabilities is driving increased demand for intelligent AWD systems. Modern drivers expect vehicles that can confidently handle adverse weather conditions, challenging terrain, and emergency maneuvering situations. Intelligent AWD systems provide these capabilities while maintaining the fuel efficiency advantages of front-wheel drive during normal driving conditions.

The rise of active lifestyle trends and outdoor recreation activities has increased consumer interest in vehicles capable of handling diverse terrain and weather conditions. Intelligent AWD systems enable crossovers and SUVs to deliver genuine all-terrain capability without compromising on-road refinement and efficiency.

SUV and Crossover Market Expansion

The global shift toward SUVs and crossover vehicles is a primary driver of intelligent AWD market growth. These vehicle segments increasingly offer AWD as standard equipment or popular options, with intelligent systems becoming key differentiators in competitive markets. Manufacturers are positioning advanced AWD capabilities as premium features that justify higher trim levels and increased profitability.

Luxury vehicle segments are particularly driving innovation in intelligent AWD technology, with features such as individual wheel torque vectoring, terrain-specific driving modes, and integration with adaptive suspension systems. These advanced capabilities create compelling value propositions for consumers seeking both performance and versatility.

Electric Vehicle Integration Opportunities

The electrification of automotive powertrains presents unique opportunities for intelligent AWD systems. Electric vehicles can implement AWD through individual wheel motors or dual-motor configurations that provide precise torque control impossible with mechanical systems. Electric AWD systems offer instant torque delivery, regenerative braking coordination, and energy management optimization.

Hybrid vehicles benefit from intelligent AWD systems that coordinate internal combustion engines with electric motors to optimize performance and efficiency. These systems can operate in electric-only AWD mode for quiet, emissions-free driving or combine power sources for maximum performance when needed.

Advanced Sensor Technology and Data Processing

Modern intelligent AWD systems incorporate multiple sensor technologies including accelerometers, gyroscopes, wheel speed sensors, and increasingly, cameras and radar systems that monitor road conditions ahead of the vehicle. Machine learning algorithms process this sensor data to predict optimal torque distribution strategies for varying conditions.

GPS integration enables intelligent AWD systems to prepare for upcoming terrain changes, weather conditions, and road characteristics based on location data and real-time traffic information. This predictive capability allows systems to optimize performance before challenging conditions are encountered.

Manufacturer Competition and Innovation

Intense competition among automotive manufacturers is driving rapid innovation in intelligent AWD technology. Brands are developing proprietary systems with unique characteristics and branding to differentiate their vehicles in crowded markets. This competition accelerates technological advancement while providing consumers with increasingly sophisticated options.

Partnerships between automotive manufacturers and technology companies are creating new capabilities in intelligent AWD control systems. Artificial intelligence, cloud computing, and advanced materials are being integrated to create more responsive and efficient systems.

Regional Market Dynamics

Different global markets exhibit varying demand patterns for intelligent AWD systems based on climate conditions, terrain characteristics, and consumer preferences. Northern markets with harsh winter conditions show strong demand for advanced traction systems, while emerging markets focus on systems that provide value-oriented performance improvements.

Regulatory requirements for vehicle stability and safety systems in various regions influence the adoption of intelligent AWD technology. Standards for electronic stability control and traction management create baseline requirements that intelligent AWD systems can exceed.

Manufacturing and Cost Considerations

The increasing sophistication of intelligent AWD systems requires significant investment in research and development, manufacturing capabilities, and supplier relationships. However, economies of scale and advancing semiconductor technology are helping to reduce system costs while improving performance and reliability.

Modular system designs enable manufacturers to offer different levels of AWD sophistication across vehicle lineups, from basic intelligent systems in entry-level models to advanced torque-vectoring systems in performance vehicles.

[chiefastro]

First edition of the Idea Frontier newsletter 🚀

[nrgnews-it]

Nexus: The Dawn of IoT Consciousness – The Revolution Illuminating Big Data Chaos

[zonotrichia-albicollis]

Gonna build a time machine specifically to go back and force them to just call it some other scary math term instead of using terms like “intelligent”, “learning”, “neural”

My job is also iteratively approaching a more accurate answer based on large amounts of data but instead of AI the method is bogged down in stats terms and opaque acronyms so we’re safe from the venture capitalists.

(Source)

[govindhtech]

LeanVec Improves Out-of-Distribution Vector Search Accuracy

Intel LeanVec Conquer Vector Search with Smart Dimensionality Reduction

The last essay in this series highlighted how vector search is essential in many applications that need precise and fast replies. Vector search systems often perform poorly due to memory and computation strain from large vector dimensionality. Also common are cross-modal retrieval tasks, such as those in which a user provides a text query to find the most relevant photographs.

These searches often have statistical distributions that differ from database embeddings, making accuracy problematic. Intel's LeanVec integrates dimensionality reduction and vector quantisation to speed up vector search on huge vectors while retaining accuracy in out-of-distribution queries.

Introduction

Recently, deep learning models have enhanced their capacity to construct high-dimensional embedding vectors whose spatial similarities match inputs including pictures, music, video, text, genomics, and computer code. This capability allows programs to explore massive vector collections for semantically meaningful results by finding the closest neighbours to a query vector. Even though similarity search has improved, modern vector indices perform poorly as dimensionality increases.

The most frequent are graph indices, which are directed graphs with edges indicating vector neighbor-relationships and vertices representing dataset vectors. Graph traversal is effective to find nearest neighbours in sub-linear time.

Graph-based indices excel at small dimensionalities (D = 100) but struggle with deep learning model dimensionalities (D ≈ 512, 768, 1536). If deep learning model-derived vectors dominate similarity search deployments, eliminating this performance gap is crucial.

This graph search speed drop is caused by the system's memory latency and bandwidth, which are largely utilised to fetch database vectors from memory randomly. Vector compression sounds like a decent technique to minimise memory strain, however PQ and SCANN either don't compress sufficiently or perform poorly due to irregular memory access patterns.

The Out-of-Distribution Queries Challenge

The queries are out-of-distribution (OOD) when the database and query vector statistical distributions diverge, making vector compression harder. Unfortunately, two modern programs often do this. The first is cross-modal searching, when a user queries one modality to return relevant elements from another. Word searches help text2image find thematically similar pictures. Second, many models, including question-answering ones, may create queries and database vectors.

A two-dimensional example shows the importance of query-aware dimensionality reduction for maximum inner product search. For a query-agnostic method like PCA, projecting the database (𝒳) and query (Q) vectors onto the first main axis (large green arrow) is recommended. This selection will lower inner product resolution since this path is opposing Q's principal axis (orange arrow). Furthermore, the helpful direction (the second primary axis of 𝒳) is gone.

A Lightweight Dimensionality Reduction Method

To speed up similarity search for deep learning embedding vectors, LeanVec approximates the inner product of a database vector x and a query q.

How projection works LVQ reduces the number of bits per entry, whereas DRquery and DRDB reduce vector dimensionality. As shown in Figure, LeanVec down-projects query and database vectors using linear functions DRquery and DRDB.

Each database vector x is compressed twice via LeanVec:

First vector LVQ(DRDB(x)). Inner-product approximation is semi-accurate.

LVQ(x), secondary vector. An appropriate description is the inner-product approximation.

The graph is built and searched using main vectors. Intel experiments show that the graph construction resists LVQ quantisation and dimensionality reduction. Only secondary vectors are searched.

The graph index is searched using main vectors. Less memory footprint reduces vector retrieval time. Due to its decreased dimensionality, the approach requires fewer fused multiply-add operations, reducing processing effort. This approximation is ideal for graph search's random memory-access pattern because it permits inner product calculations with individual database vectors without batch processing.

Intel compensates for inner-product approximation errors by collecting additional candidates and reranking them using secondary vectors to return the top-k. Because query dimensionality reduction (i.e., computing f(q)) is only done once per search, there is some runtime overhead.

Searches are essential to graph formation. Intel's search acceleration directly affects graph construction.

LeanVec learns DRquery and DRDB from data using novel mathematical optimisation algorithms. Because these methods are computationally efficient, their execution time depends on the number of dimensions, not vectors. The approaches additionally consider the statistical distributions of a small sample of typical query vectors and database vectors.

Findings

The results are obvious. LeanVec improves SVS performance, exceeding the top open-source version of a top-performing algorithm (HNSWlib). The reduction in per-query memory capacity increases query speed approximately 4-fold with the same recall (95% 10 recall@10).

Conclusion

LeanVec uses linear dimensionality reduction and vector quantisation to speed up similarity searches on modern embedding models' high-dimensional vectors. As with text2image and question-answering systems, LeanVec excels when enquiries are out of distribution.

[social-intelligence999]

10 effects of poverty on education

Poverty significantly impacts education, leading to limited resources, poor nutrition, higher dropout rates, lower academic achievement, and reduced access to extracurricular activities and early childhood education. 

Climate Change and Extreme Weather Events

Impact on Agriculture: Climate change has caused increased frequency of extreme weather events such as droughts, floods, and heatwaves. This disrupts crop production, reduces yields, and damages food supply chains, particularly in vulnerable regions like sub-Saharan Africa, Southeast Asia, and parts of Latin America.

Changing Weather Patterns: Farmers face uncertainty as traditional growing seasons shift, and regions that were once fertile may no longer be suitable for growing crops.

[festeringfae]

I actually think it was really mean of Yellowjackets to fake like it was gonna get good this season. Like when Shauna was scribbling in her journal about how everyone had gone batshit crazy and no one seemed to give a shit that her best friend & baby died? yeah it was a little weird to go from kissing Nat's ring from that in the finale to that, but that's one of the few things that can REASONABLY be inferred from a time jump, like that was a very high-stakes time, u can reasonably infer that after regaining some stability with the weather and food supply she has time to sit with her thoughts and go to doubt and/or atheism from sure resentment. what a high-potential nugget of characterization informed by the past and current setting!! and then to have it go from that to not doubting the Ben is a bridge shit at all, to the point of FORCE-FEEDING HIM instead of being pissed off that this scapegoat she was DESPERATE to have executed for a catharsis was LETTING RESOURCES ROT??? i wish i could quit the show again, especially since ive seen gifs post-quitting of her insisting they don't leave rhe wilderness at all, which, sorry not sorry, there's no fucking way she got there in the 2 episodes since i quit. especially since they decided to time jump AGAIN instead of showing any interiority regarding how she feels about having made out with a girl. fuck you, show.