#active learning machine learning

[learning-software]

What are Smart Cities?

As Internet-of-Things (IoT) technology has gone mainstream, you may have heard of the smart home, but have you heard of the smart city? If not, you may be hearing more about these soon as smart cities using computer vision AI and a large network of information and communications technologies (ICT) are becoming a reality.

In a smart home, networked automation devices like smart thermostats, automated lighting and smartphone-controlled door locks allow homeowners to keep tabs on everything going on in and around the home using computers and smart devices. In a smart city, the same concept applies, just on a massive scale.

Increasing Municipal Efficiency

One of the biggest goals of smart cities is to improve efficiency. When large numbers of people, vehicles, residences, stores and government offices are crammed into tight quarters, trying to keep the gears of a city running smoothly can be difficult. Government agencies tasked with carrying out official duties tend to have a harder time as city populations grow, but the smart cities concept can help.

Think about this: In a traditional city, traffic management is a big job. If roadwork needs to be completed, traffic needs to be diverted and traffic signal operations may need to be changed. In a smart city, computer vision AI can monitor areas where traffic has been diverted to intelligently change traffic signals to create more efficient traffic patterns. This reduces the labor required for city officials and can reduce accidents as well.

Reduced Crime is a Benefit

Smart cities may also be safer due to connected technologies. In a traditional city setting, a gunshot that goes off in the commission of a crime may go undetected by people outside the immediate vicinity of the shot.

In a smart city, audio sensors that are trained to listen for gunshots can immediately detect the sound of the shot and pinpoint its location by measuring the echoes off of nearby structures. Simultaneously, the sensor can send a recording of the shot along with a video of the area and a notification to the nearest available police unit. A faster response time leads to more bad guys behind bars and safer communities for all.

Read a similar article about annotation services for computer vision here at this page.

[maddieandangel]

Consider: character-exclusive trinkets.

[undead-vamp]

I hate ai I hate ai I hate ai die die die die stop existing rn

[queen-mabs-revenge]

communist generative ai boosters on this website truly like

[jinglejails]

omg are u active again??

I'm active, but only in the sense that I'm not dead- lol. I'm an infrequent poster n' don't actively use or browse social media; but I crawl out of my cave like a malnourished sloth every once in a while to tack an ornament onto my Charlie Brown Christmas-like tree. Nothin' impressive nor frequent enough to be particularly noticeable, but if anyone enjoys whatever decoration I put up, it means a lot to me, n' every note n' kind comment is like a gift left under my tree (n' that includes your ask—thanks for askin'! -`(^ ^ o)).

Anyway, I'll post somethin' rather soon. Can't say when I'll post after that, but if ya' stick around, I hope you like whatever comes your way! If not—whoopsie-daisy!

Thanks again, n' I hope you have a wonderful day!

[aurosoulart]

my augmented reality community garden again - outside in the sunlight this time ☀

the idea for this one is to show how AR glasses could be used in future classrooms - with children exploring virtual gardens together and learning about plants via facts hidden in their roots. 🌻

[frank-olivier]

Bayesian Active Exploration: A New Frontier in Artificial Intelligence

The field of artificial intelligence has seen tremendous growth and advancements in recent years, with various techniques and paradigms emerging to tackle complex problems in the field of machine learning, computer vision, and natural language processing. Two of these concepts that have attracted a lot of attention are active inference and Bayesian mechanics. Although both techniques have been researched separately, their synergy has the potential to revolutionize AI by creating more efficient, accurate, and effective systems.

Traditional machine learning algorithms rely on a passive approach, where the system receives data and updates its parameters without actively influencing the data collection process. However, this approach can have limitations, especially in complex and dynamic environments. Active interference, on the other hand, allows AI systems to take an active role in selecting the most informative data points or actions to collect more relevant information. In this way, active inference allows systems to adapt to changing environments, reducing the need for labeled data and improving the efficiency of learning and decision-making.

One of the first milestones in active inference was the development of the "query by committee" algorithm by Freund et al. in 1997. This algorithm used a committee of models to determine the most meaningful data points to capture, laying the foundation for future active learning techniques. Another important milestone was the introduction of "uncertainty sampling" by Lewis and Gale in 1994, which selected data points with the highest uncertainty or ambiguity to capture more information.

Bayesian mechanics, on the other hand, provides a probabilistic framework for reasoning and decision-making under uncertainty. By modeling complex systems using probability distributions, Bayesian mechanics enables AI systems to quantify uncertainty and ambiguity, thereby making more informed decisions when faced with incomplete or noisy data. Bayesian inference, the process of updating the prior distribution using new data, is a powerful tool for learning and decision-making.

One of the first milestones in Bayesian mechanics was the development of Bayes' theorem by Thomas Bayes in 1763. This theorem provided a mathematical framework for updating the probability of a hypothesis based on new evidence. Another important milestone was the introduction of Bayesian networks by Pearl in 1988, which provided a structured approach to modeling complex systems using probability distributions.

While active inference and Bayesian mechanics each have their strengths, combining them has the potential to create a new generation of AI systems that can actively collect informative data and update their probabilistic models to make more informed decisions. The combination of active inference and Bayesian mechanics has numerous applications in AI, including robotics, computer vision, and natural language processing. In robotics, for example, active inference can be used to actively explore the environment, collect more informative data, and improve navigation and decision-making. In computer vision, active inference can be used to actively select the most informative images or viewpoints, improving object recognition or scene understanding.

Timeline:

1763: Bayes' theorem

1988: Bayesian networks

1994: Uncertainty Sampling

1997: Query by Committee algorithm

2017: Deep Bayesian Active Learning

2019: Bayesian Active Exploration

2020: Active Bayesian Inference for Deep Learning

2020: Bayesian Active Learning for Computer Vision

The synergy of active inference and Bayesian mechanics is expected to play a crucial role in shaping the next generation of AI systems. Some possible future developments in this area include:

- Combining active inference and Bayesian mechanics with other AI techniques, such as reinforcement learning and transfer learning, to create more powerful and flexible AI systems.

- Applying the synergy of active inference and Bayesian mechanics to new areas, such as healthcare, finance, and education, to improve decision-making and outcomes.

- Developing new algorithms and techniques that integrate active inference and Bayesian mechanics, such as Bayesian active learning for deep learning and Bayesian active exploration for robotics.

Dr. Sanjeev Namjosh: The Hidden Math Behind All Living Systems - On Active Inference, the Free Energy Principle, and Bayesian Mechanics (Machine Learning Street Talk, October 2024)

Saturday, October 26, 2024

[cultofthepigeon]

i love the invaders quest i love that henry can make peace/come to terms with/humanize cumans by just getting absolutely shit faced blasted off his ass drunk with them

[inverse-problem]

ooooo so much cool ultragift art to look at I have had a busy day though so I may not get around to it for a while zzzzz

[dodgebolts]

sorry for being incredibly ia I have had so much homework this past week junior year end of quarter is no fucking joke holy. That being said I hope dream gets the chance to maim whomever he likes 👍

[phightingconfessions]

LMAO WHOOPS

Anyways as I said

I hate really good slingshot players because they always get MVP (especially when I’m really close to getting it)

No hate if you main slingshot but they irritate me sometimes

- Machine Anon (again.)

.

[spacenutspod]

The Space, Astronomy & Science Podcast. SpaceTime Series 27 Episode 26 *The Brightest and Fastest Growing Black Hole Quasar Ever Seen Astronomers have uncovered a cosmic colossus: the most luminous quasar known, powered by a black hole 17 billion times the mass of the Sun and growing at a staggering rate. The quasar J0529-4351, situated over 12 billion light-years away, is a beacon from the early universe, challenging our understanding of black hole formation and growth. *Supernova 1987A's Hidden Heart: The Neutron Star Within NASA's Webb Space Telescope has pierced through the dusty veil of Supernova 1987A, revealing emissions indicative of a neutron star's presence. This discovery resolves a long-standing debate and provides a glimpse into the violent stellar processes that forge these dense remnants. *Surviving the Cosmic Rays: Earth's First Life and the Shield of Manganese How did life's early building blocks endure Earth's intense radiation? New research suggests that cell-like structures with manganese-based antioxidants could have been life's ancient protectors, enabling the survival and evolution of the first organisms in a gamma-ray-blasted world. *Leap Year Explained: Why February Gains an Extra Day As February 29 approaches, we demystify the leap year phenomenon. Learn how this calendrical correction ensures our timekeeping stays in harmony with Earth's orbit, and discover the historical and astronomical significance behind the extra day in February. Join us on SpaceTime as we delve into the depths of black holes, witness the aftermath of stellar explosions, and explore the primordial resilience of life on our planet. Tune in for a journey through the cosmos and the intricacies of our celestial calendar. Listen to SpaceTime on your favorite podcast app with our universal listen link: https://spacetimewithstuartgary.com/listen and access show links via https://linktr.ee/biteszHQ For more SpaceTime and show links: https://linktr.ee/biteszHQ For more space and astronomy podcasts visit our HQ at https://bitesz.com Become a supporter of this podcast: https://www.spreaker.com/podcast/spacetime-with-stuart-gary--2458531/support.

[oloreaa]

The urge to go nuts and buying a whole new wardrobe gets stronger every day

[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.

[somerunner]

Always sad when a minimalist blog (i.e. with next to no posts) likes one of my posts and doesn’t have likes public. How else can I tell if they’re on a tag-trawling spree, or if they’re just liking a bunch of random unrelated posts?

Especially if it’s a blog with posts that date back eeeearly. Like whoa, this is a long-lived scrolling blog.

[crypto28ro]

Dezvoltarea DAO cu suport AI: Optimizarea guvernanței și a deciziilor în organizațiile autonome descentralizate

Introducere Organizațiile Autonome Descentralizate (DAO) reprezintă o inovație majoră în domeniul guvernanței digitale, permițând comunităților să ia decizii colective fără a se baza pe o conducere centralizată. Totuși, provocările legate de coordonare, transparență și adaptabilitate rămân semnificative. Integrarea tehnologiilor de inteligență artificială (AI) în cadrul DAO poate transforma…