#ai in agriculture industry

[mkcecollege]

ADVANCED ROBOTICS: THE FUTURE OF ENGINEERING AUTOMATION

Advanced robotics is transforming engineering by automating complex tasks with AI and machine learning. Industries like healthcare, manufacturing, and logistics benefit from intelligent machines that enhance efficiency and precision. Unlike traditional automation, AI-powered robots adapt, learn, and improve over time. At M.Kumarasamy College of Engineering (MKCE), students engage with cutting-edge robotics through hands-on projects. The institution’s labs foster innovation in autonomous systems and adaptive algorithms. Emerging trends like swarm robotics and soft robotics are revolutionizing automation. MKCE integrates interdisciplinary learning, merging robotics with AI and mechanical engineering. Industry partnerships ensure students gain real-world exposure to advanced technologies. The college also emphasizes sustainable robotics solutions for a greener future. As robotics continues to evolve, MKCE remains at the forefront of this transformative field.

To know more : https://mkce.ac.in/blog/advanced-robotics-as-the-next-frontier-in-engineering-automation/

[jcmarchi]

A nonflammable battery to power a safer, decarbonized future

New Post has been published on https://thedigitalinsider.com/a-nonflammable-battery-to-power-a-safer-decarbonized-future/

A nonflammable battery to power a safer, decarbonized future

Lithium-ion batteries are the workhorses of home electronics and are powering an electric revolution in transportation. But they are not suitable for every application.

A key drawback is their flammability and toxicity, which make large-scale lithium-ion energy storage a bad fit in densely populated city centers and near metal processing or chemical manufacturing plants.

Now Alsym Energy has developed a nonflammable, nontoxic alternative to lithium-ion batteries to help renewables like wind and solar bridge the gap in a broader range of sectors. The company’s electrodes use relatively stable, abundant materials, and its electrolyte is primarily water with some nontoxic add-ons.

“Renewables are intermittent, so you need storage, and to really solve the decarbonization problem, we need to be able to make these batteries anywhere at low cost,” says Alsym co-founder and MIT Professor Kripa Varanasi.

The company believes its batteries, which are currently being tested by potential customers around the world, hold enormous potential to decarbonize the high-emissions industrial manufacturing sector, and they see other applications ranging from mining to powering data centers, homes, and utilities.

“We are enabling a decarbonization of markets that was not possible before,” Alsym co-founder and CEO Mukesh Chatter says. “No chemical or steel plant would dare put a lithium battery close to their premises because of the flammability, and industrial emissions are a much bigger problem than passenger cars. With this approach, we’re able to offer a new path.”

Helping 1 billion people

Chatter started a telecommunications company with serial entrepreneurs and longtime members of the MIT community Ray Stata ’57, SM ’58 and Alec Dingee ’52 in 1997. Since the company was acquired in 1999, Chatter and his wife have started other ventures and invested in some startups, but after losing his mother to cancer in 2012, Chatter decided he wanted to maximize his impact by only working on technologies that could reach 1 billion people or more.

The problem Chatter decided to focus on was electricity access.

“The intent was to light up the homes of at least 1 billion people around the world who either did not have electricity, or only got it part of the time, condemning them basically to a life of poverty in the 19th century,” Chatter says. “When you don’t have access to electricity, you also don’t have the internet, cell phones, education, etc.”

To solve the problem, Chatter decided to fund research into a new kind of battery. The battery had to be cheap enough to be adopted in low-resource settings, safe enough to be deployed in crowded areas, and work well enough to support two light bulbs, a fan, a refrigerator, and an internet modem.

At first, Chatter was surprised how few takers he had to start the research, even from researchers at the top universities in the world.

“It’s a burning problem, but the risk of failure was so high that nobody wanted to take the chance,” Chatter recalls.

He finally found his partners in Varanasi, Rensselaer Polytechnic Institute Professor Nikhil Koratkar and Rensselaer researcher Rahul Mukherjee. Varanasi, who notes he’s been at MIT for 22 years, says the Institute’s culture gave him the confidence to tackle big problems.

“My students, postdocs, and colleagues are inspirational to me,” he says. “The MIT ecosystem infuses us with this resolve to go after problems that look insurmountable.”

Varanasi leads an interdisciplinary lab at MIT dedicated to understanding physicochemical and biological phenomena. His research has spurred the creation of materials, devices, products, and processes to tackle challenges in energy, agriculture, and other sectors, as well as startup companies to commercialize this work.

“Working at the interfaces of matter has unlocked numerous new research pathways across various fields, and MIT has provided me the creative freedom to explore, discover, and learn, and apply that knowledge to solve critical challenges,” he says. “I was able to draw significantly from my learnings as we set out to develop the new battery technology.”

Alsym’s founding team began by trying to design a battery from scratch based on new materials that could fit the parameters defined by Chatter. To make it nonflammable and nontoxic, the founders wanted to avoid lithium and cobalt.

After evaluating many different chemistries, the founders settled on Alsym’s current approach, which was finalized in 2020.

Although the full makeup of Alsym’s battery is still under wraps as the company waits to be granted patents, one of Alsym’s electrodes is made mostly of manganese oxide while the other is primarily made of a metal oxide. The electrolyte is primarily water.

There are several advantages to Alsym’s new battery chemistry. Because the battery is inherently safer and more sustainable than lithium-ion, the company doesn’t need the same safety protections or cooling equipment, and it can pack its batteries close to each other without fear of fires or explosions. Varanasi also says the battery can be manufactured in any of today’s lithium-ion plants with minimal changes and at significantly lower operating cost.

“We are very excited right now,” Chatter says. “We started out wanting to light up 1 billion people’s homes, and now in addition to the original goal we have a chance to impact the entire globe if we are successful at cutting back industrial emissions.”

A new platform for energy storage

Although the batteries don’t quite reach the energy density of lithium-ion batteries, Varanasi says Alsym is first among alternative chemistries at the system-level. He says 20-foot containers of Alsym’s batteries can provide 1.7 megawatt hours of electricity. The batteries can also fast-charge over four hours and can be configured to discharge over anywhere from two to 110 hours.

“We’re highly configurable, and that’s important because depending on where you are, you can sometimes run on two cycles a day with solar, and in combination with wind, you could truly get 24/7 electricity,” Chatter says. “The need to do multiday or long duration storage is a small part of the market, but we support that too.”

Alsym has been manufacturing prototypes at a small facility in Woburn, Massachusetts, for the last two years, and early this year it expanded its capacity and began to send samples to customers for field testing.

In addition to large utilities, the company is working with municipalities, generator manufacturers, and providers of behind-the-meter power for residential and commercial buildings. The company is also in discussion with a large chemical manufacturers and metal processing plants to provide energy storage system to reduce their carbon footprint, something they say was not feasible with lithium-ion batteries, due to their flammability, or with nonlithium batteries, due to their large space requirements.

Another critical area is data centers. With the growth of AI, the demand for data centers — and their energy consumption — is set to surge.

“We must power the AI and digitization revolution without compromising our planet,” says Varanasi, adding that lithium batteries are unsuitable for co-location with data centers due to flammability risks. “Alsym batteries are well-positioned to offer a safer, more sustainable alternative. Intermittency is also a key issue for electrolyzers used in green hydrogen production and other markets.”

Varanasi sees Alsym as a platform company, and Chatter says Alsym is already working on other battery chemistries that have higher densities and maintain performance at even more extreme temperatures.

“When you use a single material in any battery, and the whole world starts to use it, you run out of that material,” Varanasi says. “What we have is a platform that has enabled us to not just to come up with just one chemistry, but at least three or four chemistries targeted at different applications so no one particular set of materials will be stressed in terms of supply.”

[techdriveplay]

What Is the Future of Robotics in Everyday Life?

As technology continues to evolve at a rapid pace, many are asking, what is the future of robotics in everyday life? From automated vacuum cleaners to advanced AI assistants, robotics is steadily becoming an integral part of our daily routines. The blending of artificial intelligence with mechanical engineering is opening doors to possibilities that seemed like science fiction just a decade…

[brewscoop]

Discover how the nation's #1 brewer, Anheuser-Busch, is championing American farmers with the US Farmed Certification! Learn how this initiative supports local agriculture, ensures high-quality ingredients, and boosts sustainability. Check out the full story on how these efforts are shaping the future of US agriculture.

[kenresearch1]

Discover the Future of Farming: Global AI in Agriculture Industry is transforming agriculture through advanced technology and industry leaders like IBM and John Deere.

[greenthestral]

The Digital Green Economy: Paving the Way for a Sustainable Future

In recent years, the global community has witnessed a growing sense of urgency in addressing the pressing challenges posed by climate change and environmental degradation. Governments, organizations, and individuals have come to recognize the need for sustainable practices and innovative solutions to mitigate the impact of these issues. As a result, the concept of a green economy has gained significant traction and has become a focal point for discussions on sustainability and economic growth.

A green economy refers to an economic system that prioritizes environmental sustainability, resource efficiency, and social well-being. It aims to decouple economic growth from resource consumption and environmental degradation, instead promoting sustainable development that meets the needs of the present without compromising the ability of future generations to meet their own needs. The principles of a green economy include transitioning to renewable energy sources, promoting sustainable production and consumption patterns, and investing in green technologies and infrastructure.

However, as we enter further into the digital age, another powerful force has emerged—the digital green economy. This innovative approach combines the principles of sustainability with the transformative power of technology, paving the way for even more profound changes and opportunities.

The digital green economy harnesses the potential of digital technologies to drive sustainable development. It leverages advancements in areas such as artificial intelligence, the Internet of Things, data analytics, and cloud computing to create intelligent systems that optimize resource use, enhance energy efficiency, and reduce environmental impact.

One of the key advantages of the digital green economy is its ability to collect, analyze, and interpret vast amounts of data in real-time. The Internet of Things (IoT) enables the connection of various devices and sensors, allowing for the monitoring and control of energy consumption, waste management, and water usage. This level of connectivity and data-driven insights enable businesses and individuals to identify inefficiencies and make informed decisions that contribute to sustainability.

Artificial intelligence and machine learning algorithms are also pivotal in the digital green economy. These technologies can analyze complex datasets, identify patterns, and predict trends, allowing businesses to optimize their operations, reduce waste, and develop innovative solutions. For example, AI algorithms can optimize transportation routes, reducing fuel consumption and emissions, or predict energy demand, enabling renewable energy systems to adjust accordingly.

The digital green economy offers numerous advantages that contribute to shaping a sustainable future. Firstly, it helps reduce environmental impact. By leveraging digital technologies, businesses can lower their carbon footprint and minimize their use of natural resources. Smart grids, for instance, optimize energy distribution, reducing energy losses and dependence on fossil fuels. Additionally, remote working and teleconferencing technologies decrease the need for business travel, thus reducing transportation-related emissions.

Secondly, the digital green economy promotes resource conservation and efficiency. By using data-driven insights, companies can identify areas of improvement, enhance energy and water efficiency, and minimize material waste. This fosters a circular economy approach, where resources are utilized and reused in a sustainable manner, reducing the strain on the environment.

Moreover, the digital green economy presents significant economic opportunities. As businesses embrace sustainable practices and develop green technologies, new markets and industries emerge. The transition to renewable energy sources, for example, creates jobs in the renewable energy sector, clean technology development, and green infrastructure. This not only drives economic growth but also ensures that sustainability becomes a cornerstone of future prosperity.

Additionally, the digital green economy enhances resilience and adaptability in the face of climate change and other environmental challenges. By diversifying energy sources and embracing decentralized systems, communities can become more self-sufficient and less vulnerable to disruptions. The integration of renewable energy sources and microgrids, for example, can provide reliable power even during natural disasters, ensuring the continuous functioning of critical infrastructure.

Numerous digital green economy initiatives are already underway worldwide, demonstrating the potential of this transformative approach. Smart cities, for instance, leverage digital technologies to enhance urban sustainability. These initiatives integrate IoT devices, data analytics, and AI to optimize resource usage, improve transportation systems, and enhance citizen services. Barcelona's implementation of a smart irrigation system, adjusting watering schedules based on weather data to reduce water consumption in public parks, exemplifies the impact of such initiatives.

Furthermore, the integration of renewable energy sources into the existing energy grid is another significant aspect of the digital green economy. Through the use of smart grids and advanced energy management systems, renewable energy generation can be optimized and balanced with demand. Germany's Energiewende is a prime example, where digital technologies enable the efficient integration of wind and solar power into the national energy mix.

Precision agriculture is yet another domain where digital technologies are revolutionizing the sector and promoting sustainable practices. Precision agriculture utilizes sensors, drones, and AI algorithms to monitor crop health, optimize irrigation, and reduce the use of pesticides and fertilizers. This not only minimizes environmental impact but also enhances crop yields and farmer profitability.

However, as we delve into the potential of the digital green economy, it is essential to address certain challenges to ensure its widespread adoption and inclusivity. One of the primary challenges is the digital divide. Access to digital technologies and connectivity remains uneven globally, with underserved populations lacking the necessary infrastructure and skills. Bridging this divide is crucial to ensure that all communities can benefit from the digital green economy. Governments, businesses, and organizations must work together to improve internet access and provide training and support to ensure equal participation.

Another challenge is data privacy and security. The digital green economy relies on vast amounts of data to drive sustainable practices. It is imperative to establish robust cybersecurity measures and transparent data governance frameworks to protect sensitive information and maintain public trust.

Furthermore, the rapid proliferation of digital technologies also leads to an increase in electronic waste (e-waste). Proper e-waste management practices must be implemented to minimize environmental harm. This includes establishing recycling programs, promoting responsible disposal methods, and designing products that are durable and repairable.

The digital green economy represents a promising pathway towards a sustainable future. By leveraging digital technologies and integrating sustainable practices, we can reduce environmental impact, enhance resource efficiency, foster economic growth, and enhance resilience. The digital green economy offers numerous advantages, including reduced environmental impact, resource conservation, economic growth, and enhanced resilience. However, it is crucial to address challenges such as the digital divide, data privacy concerns, and e-waste management to ensure inclusivity and long-term success. By embracing the digital green economy, we can pave the way for a more sustainable and resilient world.

Defining the Digital Green Economy

The digital green economy refers to the integration of digital technologies and sustainable practices to promote environmentally friendly and resource-efficient solutions. It encompasses a wide range of sectors, including renewable energy, smart cities, circular economy, sustainable agriculture, and green transportation. The key objective is to leverage digital advancements to minimize environmental impact, reduce carbon emissions, and enhance resource conservation.

The Role of Digital Technologies

Digital technologies play a crucial role in driving the transition to a green economy. They enable the collection, analysis, and interpretation of vast amounts of data, facilitating informed decision-making and resource optimization. For instance, the Internet of Things (IoT) allows for real-time monitoring and control of energy consumption, waste management, and water usage, enabling businesses and individuals to identify and rectify inefficiencies.

Moreover, artificial intelligence (AI) and machine learning algorithms can analyze complex datasets to identify patterns and predict trends. This enables businesses to optimize their operations, reduce waste, and develop innovative solutions. For example, AI-powered algorithms can optimize transportation routes, reducing fuel consumption and emissions, or predict energy demand, enabling renewable energy systems to adjust accordingly.

Advantages of the Digital Green Economy

The digital green economy offers several advantages that contribute to a sustainable future:

Environmental Impact Reduction: By harnessing digital technologies, businesses can reduce their carbon footprint and environmental impact. For instance, smart grids can optimize energy distribution, reducing energy losses and reliance on fossil fuels. Additionally, remote working and teleconferencing technologies can decrease the need for business travel, lowering transportation-related emissions.

Resource Conservation: The digital green economy promotes resource efficiency by optimizing processes and reducing waste generation. Through data-driven insights, companies can identify areas of improvement, enhance energy and water efficiency, and minimize material waste. This fosters a circular economy approach, where resources are utilized and reused in a sustainable manner.

Economic Growth and Job Creation: The digital green economy presents significant opportunities for economic growth and job creation. As businesses embrace sustainable practices and develop innovative green technologies, new markets and industries emerge. This leads to the creation of jobs in sectors such as renewable energy, clean technology, and green infrastructure development.

Resilience and Adaptability: The digital green economy enhances resilience and adaptability in the face of climate change and other environmental challenges. By diversifying energy sources and embracing decentralized systems, communities can become more self-sufficient and less vulnerable to disruptions. For example, the integration of renewable energy sources and microgrids can provide reliable power even during natural disasters.

Examples of Digital Green Economy Initiatives

Numerous digital green economy initiatives are already underway worldwide, showcasing the potential of this transformative approach:

Smart Cities: Cities around the globe are leveraging digital technologies to enhance urban sustainability. Smart city initiatives integrate IoT devices, data analytics, and AI to optimize resource usage, improve transportation systems, and enhance citizen services. For example, Barcelona has implemented a smart irrigation system that adjusts watering schedules based on weather data, reducing water consumption in public parks.

Renewable Energy Integration: The digital green economy facilitates the integration of renewable energy sources into the existing energy grid. Through smart grids and advanced energy management systems, renewable energy generation can be optimized and balanced with demand. Germany's Energiewende is a prime example, where digital technologies enable the efficient integration of wind and solar power into the national energy mix.

Precision Agriculture: Digital technologies are revolutionizing the agricultural sector by promoting sustainable and resource-efficient practices. Precision agriculture utilizes sensors, drones, and AI algorithms to monitor crop health, optimize irrigation, and reduce the use of pesticides and fertilizers. This not only minimizes environmental impact but also enhances crop yields and farmer profitability.

Overcoming Challenges and Ensuring Inclusivity

While the digital green economy holds immense potential, it is essential to address certain challenges to ensure its widespread adoption and inclusivity. These challenges include:

Digital Divide: Access to digital technologies and connectivity remains uneven globally. Bridging the digital divide is crucial to ensure that all communities can benefit from the digital green economy. Governments, businesses, and organizations must work together to improve internet access and provide training and support for underserved populations.

Data Privacy and Security: As the digital green economy relies on vast amounts of data, ensuring data privacy and security is paramount. Robust cybersecurity measures and transparent data governance frameworks must be in place to protect sensitive information and maintain public trust.

E-Waste Management: The rapid proliferation of digital technologies also leads to an increase in electronic waste. Proper e-waste management practices must be implemented to minimize environmental harm. This includes recycling programs, responsible disposal methods, and product design that promotes durability and repairability.

Conclusion

The digital green economy represents a promising pathway towards a sustainable future. By leveraging digital technologies and sustainable practices, we can reduce environmental impact, enhance resource efficiency, and foster economic growth. From smart cities to renewable energy integration and precision agriculture, numerous initiatives exemplify the transformative power of the digital green economy. However, it is crucial to overcome challenges such as the digital divide, data privacy concerns, and e-waste management to ensure inclusivity and long-term success. By embracing the digital green economy, we can pave the way for a more sustainable and resilient world.

[mobiloitte7]

Empowering Agriculture with Digital Solutions

Revolutionize the agriculture industry with Mobiloitte's advanced IT solutions. Leverage the power of Blockchain, AI, IoT, and the Metaverse to optimize farming processes, increase yields, and ensure sustainability. Our mobile and web apps, along with DevOps and cloud expertise, empower seamless operations and data-driven decision-making. Embrace the digital era and unlock the potential of agriculture with Mobiloitte.

[mohitbisresearch]

Artificial Intelligence in Agriculture Market is expected to reach $4,096.1 million in 2027, AI in Agriculture Industry following a CAGR of 21.98% during 2022-2027.

[ashimbisresearch]

Comprehensive Research Forecast: Artificial Intelligence in Agriculture Market | BIS Research

The intersection of agriculture and artificial intelligence (AI) is reshaping the landscape of modern farming, ushering in an era of precision, efficiency, and sustainable practices.

The Rise of AI in Agriculture

Artificial intelligence has emerged as a transformative force in agriculture, offering solutions to age-old challenges and introducing unprecedented efficiency. The integration of AI technologies in farming practices holds the promise of optimized resource utilization, improved crop yields, and environmentally conscious cultivation.

The Global Artificial Intelligence in Agriculture Market was valued at $1,517.0 million in 2022 and is expected to reach $4,096.1 million in 2027, following a CAGR of 21.98% during 2022-2027.

Key Trends Shaping the Landscape

Precision Farming Revolution:

AI empowers precision farming by analyzing vast datasets, offering insights into crop health, soil conditions, and weather patterns.

Precision agriculture practices, guided by AI algorithms, enhance decision-making for optimal resource allocation and yield maximization.

Crop Monitoring and Disease Detection:

AI-powered sensors and imaging technologies enable real-time crop monitoring.

Advanced algorithms detect early signs of diseases, allowing farmers to implement timely interventions and minimize crop losses.

Autonomous Machinery and Robotics:

AI-driven autonomous machinery, including drones and robotic systems, is revolutionizing farm operations.

Automation of tasks such as planting, harvesting, and weeding enhances operational efficiency and reduces labor dependency.

Click on the link to download free insight on Artificial Intelligence in the Agriculture Industry.

Challenges and Solutions

Data Security and Privacy Concerns:

The influx of data in AI-driven agriculture raises concerns about data security and privacy.

Industry stakeholders are actively addressing these challenges through robust cybersecurity measures and transparent data handling practices.

Accessibility and Adoption Barriers:

While AI holds immense potential, ensuring its accessibility to all farmers remains a challenge.

Educational initiatives, government support, and collaborative efforts are crucial in overcoming barriers to AI adoption in agriculture.

Regional Dynamics

North America's Tech Prowess:

North America leads in AI adoption, with tech-savvy farmers embracing advanced solutions.

Government initiatives and strong technological infrastructure contribute to the region's prominence in the AI in Agriculture Market.

Asia-Pacific's Growing Landscape:

The Asia-Pacific region is witnessing a surge in AI adoption as awareness grows.

Increasing support for sustainable agriculture practices and the need for food security are driving the market in this region.

Future Outlook and Innovations

Integration with IoT and Big Data:

The synergy between AI, Internet of Things (IoT), and Big Data is a key trend shaping the future.

Real-time data from connected devices enhances AI capabilities, fostering more informed decision-making.

Advancements in Machine Learning:

Machine learning algorithms are evolving rapidly, offering more sophisticated analyses of agricultural data.

Continuous advancements in AI-driven machine learning contribute to the refinement of predictive modeling and crop management.

Conclusion

The Comprehensive Research Forecast on the Artificial Intelligence in Agriculture Market paints a vivid picture of an industry in flux, embracing innovation to meet the demands of a rapidly evolving world. As AI continues to carve its path in agriculture, the synergy between technology and cultivation promises a future where precision and sustainability go hand in hand.

[rwby-encrusted-blog]

There are two types of Mech, the Analog, and the Linked.

Analog mechs are the far, far cheaper of the two. Much less specialized, and infinitely more common, able to be found not just in military use, but in Agricultural and Industrial use as well.

To pilot one, all you need it four limbs (Including prosthetics) and good senses, and to score well in the Simulators to get your license.

Analog is a bit of a misnomer, a holdover word from yesteryear, simply meaning "Less sophisticated."

Analog Mechs work by scanning the Brain Activity of the pilot, comparing it to the physical input of the controls, and deciding what to do from there. They're considered old fashioned and less reliable than Linked Mechs, but they're reliable enough for the work they do. They are more easily replaced and retrofitted for different tasks.

They have simpler AI, and to exit one, you simply take the helmet off and open the hatch.

Linked Mechs are a whole other can of worms.

Each and every Linked Mech is custom built and fitted to their pilot, from the heaviest weapon, to the smallest nut, no one is the same as the other.

Linked Mech are Physically plugged into their pilot's nervous system, and they act as one. It can take hours of Decompression and Disconnection to remove a Linked Pilot.

Linked Mechs are truly nothing without their pilots.

Linked Pilots are the most terrifying people you may ever meet.

Their skin is mottled from the Oxygenator-Coolant that runs in their veins, the plugs and ports too deeply entwined in their flesh and body to be removed without serious disruption to their faculties, hairless and sterile from their conditioning and actions when inside their vessel, and that's only the physical differences.

Depending on how long they've been a Linked Pilot, their mental capabilities are affected in different ways.

'Young' pilots are simply too wary - able to pick up on the most minute details, in all five senses.

Their Mechs are still machine.

'Moderate' pilots have some neurological and mental degradation in addition to what they had before. often confused or forgetful outside of their mech, but still very aware of their surroundings, if not their place in time.

Their Mechs act protective of them, like a dog to it's beloved owner.

'Old' Pilots have all the earlier issues, compounded and worsened, and sometimes confusing themself for their mech, even going so far as to entering refill and Refueling areas of the hangars.

Their Mechs act the same, often trying to reach their pilot's quarters and the Cafeteria.

They may refuse to separate at time, feeling more comfortable together than apart.

and then there are the 'Fused' the oldest and rarest caste of Linked Pilot and Mech.

The Pilot and The Mech fused absolutely, inseparable for Neurological, Psychological, and physical reasons.

They are the same. They are one. A perfect fusion of the Biological and Mechanical, Electrochemical Intelligence and the Fissile-Logic Personality, Mirroring each other with every breath and ventilation protocol.

And they are still people.

The Fused still need social interaction and entertainment. They still need variety and novelty, comfort. All the things anyone else would.

Fused are the least common, but are the least likely to die. Too perfectly combined, Too well accompanied.

The Three times a 'Fused' has been separated, The pilot Died, the Mech 'Bricked' itself, like a Jail-broke phone, no matter how it was done.

Fused are the most loyal, true, and caring of all pilots.

Few ever meet the 'Pilot,' the meat within the metal, but the few that do are those most trusted by the Fused person, as the Fused is showing you their beating heart.

If you earn one's trust, and are given this deep, grand honor, I only have one piece of advice.

Do. Not. Break. That. Trust.

[allthecanadianpolitics]

The federal government will be investing $2.4 billion to accelerate Canada’s artificial intelligence (AI) sector, Prime Minister Justin Trudeau announced Sunday. The investment will be divided between a number of measures meant to advance job growth in the AI and tech industry and boost businesses’ productivity. “This announcement is a major investment in our future, in the future of workers, in making sure that every industry, and every generation, has the tools to succeed and prosper in the economy of tomorrow,” Trudeau said in a press release Sunday. Majority of the funds, $2 billion, will go toward increasing access to computing and technological infrastructure. Another $200 million is being invested into AI start-ups to accelerate the technology in “critical sectors” such as health care, agriculture and manufacturing, the release says. Additional funds will be put toward helping small and medium-sized businesses incorporate AI, with another $50 million being committed to help train workers whose jobs may be disrupted by the technology.

Continue Reading.

Tagging: @politicsofcanada

[jcmarchi]

1000 AI-powered machines: Vision AI on an industrial scale

New Post has been published on https://thedigitalinsider.com/1000-ai-powered-machines-vision-ai-on-an-industrial-scale/

1000 AI-powered machines: Vision AI on an industrial scale

This article is based on Bart Baekelandt’s brilliant talk at the Computer Vision Summit in London. Pro and Pro+ members can enjoy the complete recording here. For more exclusive content, head to your membership dashboard. 

Hi, I’m Bart Baekelandt, Head of Product Management at Robovision. 

Today, we’re going to talk about the lessons we’ve learned over the last 15 years of applying AI to robots and machines at scale in the real world. 

Robovision’s journey: From flawed to fantastic

First, let’s look at what these machines were like in the past.

15 years ago, our machine was very basic with extremely rudimentary computer vision capabilities. It used classical machine vision techniques and could only handle very basic tasks like recognizing a hand. Everything was hard-coded, so if you needed the machine to recognize something new, you’d have to recode the entire application. It was expensive and required highly skilled personnel.

Nowadays, we don’t have just one machine – we have entire populations of machines, with advanced recognition capabilities. There’s a continuous process of training AI models and applying them to production so the machines can tackle the problem at hand.

For example, there are machines that can take a seedling from a conveyor belt and plant it in a tray. We have entire fleets of these specialized machines. One day they’re trained to handle one type of seedling, and the next day they’re retrained to perform optimally for a different variety of plant. 

So yeah, a lot has happened in 15 years. We’ve gone from initially failing to scale AI, to figuring out how to apply AI at scale with minimal support from our side. As of today, we’ve produced over 1000 machines with game-changing industrial applications 

Let’s dive into a few of the key lessons we’ve picked up along the way.

Lesson one: AI success happens after the pilot

The first lesson is that AI success happens after the pilot phase. We learned this lesson the hard way in the initial stages of applying AI, around 2012.

Let me share a quick anecdote. When we were working on the machine that takes seedlings from a conveyor belt and plants them in trays, we spent a lot of time applying AI and building the algorithm to recognize the right breaking point on each seedling and plant it properly. 

Eventually, we nailed it – the algorithm worked perfectly. The machine builder who integrated it was happy, and the customer growing the seedlings was delighted because everything was functioning as intended.

However, the congratulations were short-lived. Within two weeks, we got a call – the system wasn’t picking the seedlings well anymore. What had happened? They were now trying to handle a different seedling variety, and the images looked just different enough that our AI model struggled. The robot started missing the plants entirely or planting them upside down.

We got new image data from the customer’s operations and retrained the model. Great, it worked again! But sure enough, two weeks later, we got another call reporting the same problem all over again. 

This highlighted a key problem. The machine builder wanted to sell to many customers, but we couldn’t feasibly support each one by perpetually retraining models on their unique data. That approach doesn’t scale. 

That painful lesson was the genesis of our products. We realized the end customers needed to be able to continuously retrain the models themselves without our assistance. So, we developed tooling for them to capture new data, convert it to retrained models, deploy those models to the machines, and interface with the machines for inference. 

Our product philosophy stems directly from those harsh real-world lessons about what’s required to successfully scale AI in real-world production.

Lesson two: It’s about getting the odd couple to work together

When you’re creating working AI solutions at scale, there typically are two types of people involved. They’re your classic “odd couple,” but they need to be able to collaborate effectively.

On one hand, you have the data scientists – they generally have advanced degrees like Masters in Engineering or even PhDs. Data scientists are driven by innovation. They live to solve complex problems and find solutions to new challenges. 

Once they’ve cracked the core issue, however, they tend to lose interest. They want to move on to the next big innovation, rather than focusing on continuous improvement cycles or incremental optimizations

On the other hand, you have the machine operators who run the manufacturing systems and processes where AI gets applied at scale – whether that’s a factory, greenhouse, or another facility. 

The machine operators have intricate knowledge of the products being handled by the machines. If you’re deploying AI to handle seedlings, for example, no one understands the nuances, variations, and defects of those plants better than the operator.

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[aa-predictions]

Economy Predictions: Part 1 (Post-Trump)

.　 . • ☆ . ° .• °:. *₊ ° . ☆

Prices will go up by at least 1.5% (accounting for inflation)

Social security collapse or severe downgrade

Trump tries tariffs, they backfire, and he backtracks

Hoover 2.0

We have the equivalent of a second Great Depression Reincarnated

Gas prices temporarily go down, while grocery and other cost-of-living prices skyrocket

Investments in AI companies will perform well, and investments in small businesses will perform more poorly

Some notable companies that haven’t shifted enough online for misc goods will collapse or go bankrupt

It will be much more difficult to be successful as a small business

More pressure on domestic factories causes an increase in demand for Hispanic workers, except ICE depleted available workers

The U.S dollar tanks in value and brings down other currencies dependent on it

The job market is garbage so American citizens and graduates emigrate for work

Massive hit to the agriculture industry as a result of immigration laws

Initially strict regulation becomes more lax again

They never address the temporary food shortage caused by lack of immigrant workers

[doomdoomofdoom]

I'm an american studies major (genuinely)

Drop a reference to agriculture in there and you've 100% nailed it.

I know that dissing art is probably a bad idea on the Artists Website but American folk art looks pretty easy conceptually. You just paint like a little cottage on a green hillside with a garden and a rusty truck and a flagpole. And boom that's Americana baby. If I knew how to paint I could be doing that.