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Business Insider
26-06-2025
- Business
- Business Insider
Agricultural weed control is a delicate process. AI tools could transform how farmers tackle it.
Weeds remain one of the most persistent problems in agriculture. But the biggest issue facing modern farmers isn't getting rid of weeds; mechanical tools and herbicides can do that. Instead, the difficulty lies in identifying and killing weeds without harming crops. Paul Mikesell is the founder of Carbon Robotics, a company that makes AI-powered robots for the agriculture sector, and the former director of infrastructure engineering at Uber. He's spent the past six years developing AI systems that try to solve the big weed problem. His company's solution is the LaserWeeder G2, a machine that automatically detects weeds and zaps them with a laser array. Mikesell told Business Insider that a neural network can be important "to not just find where the weeds are, but to find the perfect place to kill the weed." A neural network is a computational model inspired by how biological brains learn to process information, and is key to how modern AI systems function. Across the agricultural industry, AI tools are beginning to make a difference for farmers. That's good news for an industry struggling against foes like the climate crisis and shifts in trade. From complex robots to chatbots, farmers are testing out a range of tools to hone their processes and achieve goals once out of reach. Machine learning takes the field Mikesell's experience building autonomous vehicle infrastructure at Uber helped shape Carbon Robotics' approach to agricultural AI, applying that same technology to the farming tools he's developing now. The computer vision systems used in autonomous vehicles, including cars, tractors, and other agricultural equipment, often rely on neural networks known as convolutional neural networks. CNNs are a form of neural network that can be trained to detect patterns in images. Carbon Robotics uploads images of weeds to its own database, where human labelers manually identify weeds and crops. These image-label pairs are then used to train a weed-finding CNN that can detect weeds using the LaserWeeder's onboard cameras and computer hardware in the machine itself, meaning no internet connection is required. John Deere, the world's largest agricultural equipment company, also uses CNNs for multiple applications, including its autonomous tractors and See & Spray weed-detection systems. At CES 2025, the company showed its new second-generation "autonomy kit," which can partially or fully automate common tasks, including tillage and weed removal. Sarah Schinckel, the company's director of emerging technologies, said AI has already improved its agricultural equipment. In 2024, she said, John Deere's See & Spray system was used to spray over 1 million acres of farmland. Because the machine only sprays plants identified as weeds, the system was able to weed this acreage using 8 million gallons less herbicide than would typically be needed. "If you think about that savings, as well as just overall productivity and sustainability improvements for them, that's just a win for them all around," Schinckel said. The technology also gives farmers more staffing flexibility. Semi-autonomous harvesting equipment, for example, gives the human operator AI assistance that can adjust the equipment more quickly than a typical operator can react. "You can put somebody who maybe isn't an expert combine operator in a cab, and help them still achieve high performance," said Schinckel. Farmers fire up ChatGPT While big agricultural companies are building tools with complex CNNs and other types of machine learning, some farmers are making use of more accessible AI tools. Phillip Guthrie, a partner at the agriculture consulting firm Nine Creeks Consulting, often gives presentations on new technology in agriculture, including generative AI. He's already seeing farmers pick up ChatGPT for planning and advice. Guthrie recalled a conversation with a farmer who was having trouble with a data analytics platform he used to monitor and track weather at his farm. The analytics had never worked correctly for their operation, "so he just took the raw weather data, threw it into ChatGPT, and started doing analytics." The AI was able to handle the analytics tasks that prior software had failed to address. Guthrie expects more farmers to start using generative AI tools in similarly specific and creative ways, perhaps bypassing the companies that make specialized agri-tech software tools. Two visions for generative AI in agriculture AI techniques like CNNs, which are available today in autonomous agriculture equipment, represent a major leap in technology. Systems like the LaserWeeder G2 and John Deere See & Spray were impossible to imagine a decade ago. However, it's unclear how these task-specific examples of agricultural AI will fit with newer generative AI tools. Mikesell speculated that one solution could lie in integration. Carbon Robotics, like John Deere, doesn't use generative AI for its equipment and has no announced plans to do so. Still, he said that generative AI could become a "planning and human interface" used to operate equipment like the company's automated laser weeders. "I can say to the generative AI system that I want to clear this 2,000 acres," Mikesell said. "Then, it might come with a solution and say, why don't you deploy these laser weeders in this pattern?" Guthrie, meanwhile, thinks that generative AI could drive a "democratization" of the industry that larger companies may well miss out on. While the industry will always need heavy equipment, he said, farmers often express frustrations with the expensive, yet extremely specific, software available to the industry. "The last thing they need is another tool that does one thing. What they want is a tool that does everything," he said. Guthrie said with ever-improving generative AI, "You'll have farmers who could build their own tools, conduct their own analytics, do their own automations, and focus on what they want for themselves."
Time of India
13-06-2025
- Business
- Time of India
Scale AI founder Alexandr Wang joins Meta for AI research: Who is he and why is Zuckerberg investing on his $29 billion startup
Facebook owner Meta has invested in Scale AI in a deal that values the labelling startup of worth more than $29 billion. The CEO and co-founder of Scale AI is a 28-year-old Alexandr Wang who has emerged as one of the most influential figures in the artificial intelligence landscape. Scale AI initially was a data-labeling company and quickly became an underlying face of the AI revolution, quietly serving as an enabler of everything from self-driving cars to large language models (LLMs). Wang is now moving into a leadership role of high stakes at Meta, taking the helm at a newly created research lab aimed at creating artificial superintelligence (ASI). Why is Meta investing millions on 28-year-old Alexandr Wang startup Scale AI Meta is spending $15 billion to buy a 49% stake in Scale AI at a valuation of more than $29 billion, as reported by Reuters. This strategic acquisition marks Meta's aggressive push to surpass competitors like OpenAI, Google, and Microsoft. Wang will join Meta's new 50-person research lab to spearhead the creation of ASI; AI systems with intelligence greater than humans. The agreement also comprises seven to nine-figure pay packages to entice top AI talent away from top institutions and competitors. Meta's foray into superintelligence is at an important time. Though Meta made ambitious moves such as embedding AI in products like Ray-Ban smart glasses and open-sourcing its Llama models, it has trailed behind rivals in important AI developments. The company has been grappling with internal tension, employee turnover, and lackluster product releases. Its AI work was previously led by chief scientist Yann LeCun, who developed convolutional neural networks (CNNs). LeCun's distrust of LLMs and AGI, however, has opened up philosophical fault lines within Meta's AI leadership. by Taboola by Taboola Sponsored Links Sponsored Links Promoted Links Promoted Links You May Like Jours Fous à 65€ Hôtel PortAventura World Acheter Maintenant Undo Meet Alexandr Wang, Scale AI CEO and co-founder background Alexandr Wang, CEO and co-founder of Scale AI was born and raised in New Mexico by Chinese immigrant parents who were physicists at Los Alamos National Laboratory, Wang's intellectual heritage runs deep. He held a short-term stint at Quora prior to dropping out of MIT after his first year. He then joined Y Combinator spearheaded at the time by OpenAI CEO Sam Altman and founded Scale AI with co-founder Lucy Guo. Although Guo later exited the company, Wang continued to build Scale into a unicorn, securing investments from Peter Thiel's Founders Fund and reaching a $7 billion valuation by 2019. At just 24, he was named the youngest self-made billionaire. Alexandr Wang Scale AI's business model and rapid growth Scale AI founded in 2016 focused on organising and labeling vast datasets initially for autonomous vehicles. It emerged as the preeminent data infrastructure company for AI creators, hosting clients such as Waymo, Toyota, Honda, OpenAI, and Microsoft. It also serves the US government in interpreting satellite imagery. Scale had $870 million in revenue in 2024 and hopes to double that in 2025 and, possibly, hit a $25 billion valuation. However, increased competition from companies such as Surge AI, Labelbox, and Snorkel AI brings new challenges. Scale AI: Ethical issues and outsourcing techniques Even though Scale AI has been successful, it has also come under fire for outsourcing data labeling to the low-cost labor markets of Kenya, Venezuela, India, and the Philippines. Using its own platform, Remotasks, Scale trains the workers to do data annotation, typically in reportedly suboptimal working conditions, for less than $1 per hour. This has led to broader discussions concerning the ethics of human-in-the-loop AI systems. Alexandr Wang has also developed close relationships with the leading figures in the technology world such as OpenAI CEO Sam Altman and applied his increasing influence to open up in Washington DC. Alexandr Wang, who is not from a research background, has created a mighty AI business with an incisive business mind and revived Meta's AI vision better than the usual research-focused leadership. Also Read | World's most viral TikTok sensation Khaby lame leaves US after being detained by ICE AI Masterclass for Students. Upskill Young Ones Today!– Join Now
Indian Express
12-06-2025
- Business
- Indian Express
Who is Alexandr Wang, and why is Meta betting billions on his startup Scale AI?
Alexandr Wang is the CEO and co-founder of Scale AI, a data-labelling startup that helps other companies train and deploy cutting-edge AI models. Over the years, Wang has built his startup into the backbone of the AI boom, quietly enabling everything from autonomous vehicles to large language models (LLMs). Now, Wang finds himself at the centre of a potential $15 billion shake-up as Meta taps him to lead its newly formed research lab that will focus on building AI systems capable of 'superintelligence'. The $15 billion investment deal is also expected to bring other Scale AI employees to Meta, which is also reportedly offering seven to nine-figure compensation packages to AI researchers from OpenAI and Google who would like to be a part of its new 50-member artificial superintelligence lab. The new lab comes at a crucial time for Meta, which is perceived to be struggling to pull ahead of its competitors Google, Microsoft, and OpenAI in the high-stakes AI race. CEO Mark Zuckerberg has pushed for AI to be incorporated across the company's products such as its Ray Ban smart glasses as well as social media platforms Facebook, Instagram, and WhatsApp. Meta has also sought to define its competitive edge by developing open AI models, allowing developers to freely download and integrate the source code into their own tools. But internal issues such as employee turnover and underwhelming product launches have reportedly hampered Meta's AI efforts lately. So far, the company's research efforts have been overseen by its chief AI scientist, Turing Award winner Yann LeCun who is widely recognised for his groundbreaking research contributions on convolutional neural networks (CNNs). However, LeCun's views on AI are not aligned with others in Silicon Valley as he has argued that LLMs are not the path to artificial general intelligence (AGI). Now, Meta is betting on Wang to not only help it regain the lead in the AI race but also push toward another frontier known as artificial superintelligence (ASI) — a hypothetical AI system with intelligence exceeding that of the human brain. Alexandr Wang was born in New Mexico, US, to Chinese immigrant parents who worked at Los Alamos National Laboratory as nuclear physicists. Before heading to college, Wang reportedly worked at internet startup Quora. He dropped out of Massachusetts Institute of Technology (MIT) after just one year and joined Y Combinator, the popular startup accelerator that used to be led by OpenAI CEO Sam Altman. At Y Combinator, he teamed up with Quora alum Lucy Guo to start a new company called Scale AI in 2016. Two years later, both Wang and Guo were named in Forbes' 30 Under 30 list in enterprise technology. Guo shortly exited Scale AI 'due to differences in product vision and road map,' according to a report by Forbes. Wang continued running the startup which was minted as a unicorn in 2019 after raising $100 million in investment from Peter Thiel's Founders Fund followed by another $580 million fundraising round which put the company at a $7 billion valuation. At 24, Wang became the youngest self-made billionaire in the world. His co-founder, Lucy Guo, recently became the youngest self-made woman billionaire due to her stake in Scale AI. Wang was reportedly Sam Altman's roommate during the COVID-19 pandemic. The two AI industry leaders were also photographed sitting next to each other at US President Donald Trump's swearing-in ceremony in January this year. Scale AI was founded in 2016 as a startup that labelled mass quantities of data required to train AI systems, particularly autonomous vehicles (AV). As a result, most of its data services were primarily offered to self-driving automakers. This move to corner the market for supplying training data so that self-driving cars could tell the difference between various objects, is what made Scale AI well-positioned in the AI boom that was soon to follow. LLMs are trained on massive amounts of data to generate text and other content. Scale AI hires thousands of contract workers to sift through vast amounts of data, label the information, and clean the datasets that are then supplied to tech companies to train their complex AI models. Scale AI's client list includes major automakers such as Toyota and Honda as well as Waymo, Google's AV subsidiary. It has also partnered with Accenture to help the consulting giant build custom AI apps and models. OpenAI, Microsoft, and Toronto-based AI startup Cohere also count among Scale AI's customers, according to a report by Forbes. The US government has also reportedly sought Scale AI's data labelling and annotation services in order to help analyse satellite imagery in Ukraine. Last valued at nearly $14 billion, the company saw about $870 million in revenue in 2024. It further expects to more than double revenue this year to $2 billion, which would put Scale AI's valuation at $25 billion, according to a report by Bloomberg. However, the AI boom has also given rise to a wave of relatively new competitors such as Surge AI, which offers data labeling tools to AI companies, as well as data labeling startups Labelbox and Snorkel AI, which primarily cater to non-tech enterprises.
Int'l Business Times
12-06-2025
- Science
- Int'l Business Times
Smarter Fields Ahead: How Deep Learning Is Shaping Sustainable Agriculture
In today's fast-paced technological landscape, innovation in agriculture has become more than a necessity—it's an imperative for survival. Tenny Enoch Devadas, a technology expert interested in sustainable systems, examines how deep learning transforms agriculture into a data-driven, efficient, and environmentally responsible domain. His work focuses on applying artificial intelligence to optimize farm operations and sustainability, offering compelling insights into how farmers can thrive amid climate unpredictability. Forecasting the Future: Weather-Driven Farming A key innovation in the research is using deep learning, including CNNs and LSTMs, to enhance weather forecasting. These models process spatial and temporal data, enabling accurate local predictions that help farmers optimize planting, irrigation, and harvesting, reducing uncertainty and the risk of crop failure. Digging Deep: Understanding Soil from the Sky Deep learning is revolutionizing soil analysis. With input from drone-captured multispectral images and soil sensors, algorithms can determine soil moisture, nutrient levels, and organic content. Techniques like semantic segmentation using Fully Convolutional Networks (FCNs) allow for real-time assessments, enabling farmers to manage soil health better. This targeted approach reduces unnecessary use of fertilizers and enhances overall soil longevity. Smarter Choices: Data-Guided Crop Selection Choosing the right crop once relied on intuition, but hybrid CNN-LSTM models now analyze soil, weather, market trends, and yield history to guide decisions. Continuously learning from new data, these models boost yield, cut input costs, and more effectively align farming with market demand. Mitigating Risk: From Pest Alerts to Market Trends Deep learning enhances productivity and resilience in agriculture. Convolutional Neural Networks (CNNs) detect pests and diseases early using satellite or ground-level images, preventing outbreaks. Long-short-term memory (LSTM) models analyze time-series data to forecast extreme weather. Additionally, AI models track global market trends to predict price shifts, helping farmers plan harvests and sales with greater financial foresight. This proactive risk management supports stable yields and incomes. Efficiency on the Ground: Resource Optimization Deep learning enables precision agriculture, allowing farmers to optimize input use. AI tailors irrigation schedules to crop needs and distributes fertilizer based on soil nutrition, reducing costs and conserving resources. Drone imagery identifies water-stressed zones, while targeted pesticide application addresses specific threats. These practices lower pollution and create more sustainable, efficient farms. Stronger Chains: Logistics and Distribution Reinvented Beyond the farm, deep learning transforms supply chains. Yield predictions guide inventory planning, transportation logistics, and labor allocation, minimizing waste. AI also supports inter-regional crop exchanges by analyzing demand, forecasting prices, and recommending transport strategies, boosting national food availability. Harmonizing Regions: Balancing Supply and Demand AI-driven models integrate local climate, soil, and demographic data to align agricultural production with regional needs. These tools produce crop suitability maps, enhance pricing models, and support adaptive logistics, fostering a stable, responsive food system. Green Thinking: Long-Term Sustainability Goals Sustainability is central to deep learning's agricultural applications. From optimizing crop rotation for soil regeneration to recommending pollinator-friendly planting schedules, these models encourage environmentally conscious farming. They even help evaluate ecosystem services like carbon sequestration and biodiversity preservation. These efforts collectively reduce agriculture's ecological footprint while improving productivity. Rooted in Science: Soil Health and Biodiversity Algorithms provide actionable insights into crop rotation schedules and soil amendment needs. They help prevent degradation by monitoring microbial activity, nutrient levels, and erosion risks. On the biodiversity front, models can map habitats, identify conservation priorities, and recommend strategies that support beneficial insects and wildlife—all without compromising yield. In conclusion, Tenny Enoch Devadas envisions a sustainable agricultural future empowered by deep learning. This technology tackles food security, resource scarcity, and environmental challenges through precision farming and smart logistics. Collaboration among experts and farmers is key to adapting solutions locally and building a resilient, data-driven agricultural system.
The Hindu
25-05-2025
- Health
- The Hindu
MITS students develop portable, AI-powered skin disease detection device
The final-year ECE students of Madanapalle Institute of Technology and Science (MITS) have successfully developed a portable, AI-powered system for early detection of skin diseases. The MITS management said: 'The device leverages Convolutional Neural Networks (CNNs) to classify skin conditions such as melanoma, basal cell carcinoma, and others through a mobile or web interface. Built on Raspberry Pi, the device includes a buzzer, GSM module, and LCD for real-time alerts and display. In recognition of their achievement, the students were felicitated by the management, including Principal C. Yuvaraj and Correspondent N. Vijaya Bhaskar Choudary.
