Precision agriculture and smart farming are increasingly adopted to improve productivity, reduce input waste, and maintain high product quality under growing demand. These approaches integrate sensing, automation, and data-driven decision-making to improve crop yield and post-harvest quality (Gupta, Abdelsalam, Khorsandroo, and Mittal (2020)). In this context, autonomous robotic harvesting is a key enabling technology for horticulture, where labor shortages and high labor costs directly affect production and consistency. Despite progress in mechanization, many conventional harvesting methods (e.g., combine harvesters, reapers, and trunk shakers) are unsuitable for soft and delicate crops such as tomatoes and strawberries because large contact forces and impacts can bruise or damage the fruit (Cho, Iida, Suguri, Masuda, and Kurita (2014); Shojaei (2021)). Selective harvesting, where fruits are picked individually at the appropriate ripeness stage, is therefore preferred for high-value crops. However, selective harvesting remains challenging because a robot must (i) detect the target fruit under occlusion, (ii) estimate its pose and identify the pedicel cutting location, and (iii) execute grasping and detachment without damaging the fruit or plant. In real cultivation environments, tomatoes are often densely packed and partially occluded by leaves and branches, making perception and reliable manipulation difficult (Chen et al. (2015)). Consequently, integrated harvesting systems that combine compliant end-effectors, robust perception, and closed-loop control remain an active research topic (Comba, Gay, Piccarolo, and Ricauda Aimonino (2010); Ling, Zhao, Gong, Liu, and Wang (2019)). A wide range of end-effectors has been explored for harvesting and handling soft produce.

Robotic autonomy at centimeter scale requires compact and miniaturization-friendly actuation integrated with sensing and neural network processing assembly within a tiny form factor. Applications of such systems have witnessed significant advancements in recent years in fields such as healthcare, manufacturing, and post-disaster rescue. The system design at this scale puts stringent constraints on power consumption for both the sensory front-end and actuation back-end and the weight of the electronic assembly for robust operation. In this paper, we introduce FAVbot, the first autonomous mobile micro-robotic system integrated with a novel actuation mechanism and convolutional neural network (CNN) based computer vision - all integrated within a compact 3-cm form factor. The novel actuation mechanism utilizes mechanical resonance phenomenon to achieve frequency-controlled steering with a single piezoelectric actuator. Experimental results demonstrate the effectiveness of FAVbot's frequency-controlled actuation, which offers a diverse selection of resonance modes with different motion characteristics. The actuation system is complemented with the vision front-end where a camera along with a microcontroller supports object detection for closed-loop control and autonomous target tracking. This enables adaptive navigation in dynamic environments. This work contributes to the evolving landscape of neural network-enabled micro-robotic systems showing the smallest autonomous robot built using controllable multi-directional single-actuator mechanism.

The cartoon interviewer greets you on screen. He looks a little young to be asking questions about a job--sort of a cartoon version of Harry Potter, with dark hair and glasses. You can choose other interviewers to speak with instead, representing various genders and races with names like Benjamin, Leslie, and Kristin. Alex, the name given to this AI interviewer, asks about your professional experience, theoretical questions about programming, and then gives out a coding exercise. Alex is an AI interviewer developed by micro1, a US company that describes itself as an AI recruitment engine for engineers.

When Jahanzaib Ansari was looking for work in 2016, his resume was not the problem. Despite a CV boasting experience as a programmer and attending the University of Toronto, Ansari's job search soon hit a dead end. At the suggestion of a friend, he changed his first name on his resume and saw almost immediate results. "I wouldn't hear back from employers until my [colleague] said, 'Why don't you just Anglicize it?' I went with variations of Jason, Jordan, Jacob, and literally in four to six weeks, I got a job," says the CEO of Knockri, a technology firm that created an artificial intelligence tool that aims to reduce bias in the hiring process.

As Japan's second female astronaut to fly up in the Space Shuttle Discovery, Naoko Yamazaki didn't expect to spend a quarter of her time dusting, feeding mice and doing other menial jobs. It can cost more than $430 million a year to keep an astronaut in orbit, according to three-year-old startup called Gitai Inc. It's only possible to keep humans alive in outer space because of the money and effort poured into ensuring their safety. One way to bring down the cost and risks is to send an avatar -- a remotely controlled robot. "There's a need for robots that can help us," Yamazaki, 49, said.

Artificial intelligence (AI) has the potential to solve many routine business challenges -- from quickly spotting a few questionable charges in thousands of invoices to predicting consumers' needs and wants. But there may be a flipside to these advances. Privacy concerns are cropping up as companies feed more and more consumer and vendor data into advanced, AI-fuelled algorithms to create new bits of sensitive information, unbeknownst to affected consumers and employees. This means that AI may create personal data. When it does, "it's data that has not been provided with [an individual's] consent or even with knowledge", said Chantal Bernier, assistant and interim privacy commissioner in the Office of the Privacy Commissioner of Canada from 2008 until 2014 who now consults in the privacy and cybersecurity practice of global law firm Dentons.

Artificial intelligence (AI) has the potential to solve many routine business challenges -- from quickly spotting a few questionable charges in thousands of invoices to predicting consumers' needs and wants. But there may be a flipside to these advances. Privacy concerns are cropping up as companies feed more and more consumer and vendor data into advanced, AI-fuelled algorithms to create new bits of sensitive information, unbeknownst to affected consumers and employees. This means that AI may create personal data. When it does, "it's data that has not been provided with [an individual's] consent or even with knowledge", said Chantal Bernier, assistant and interim privacy commissioner in the Office of the Privacy Commissioner of Canada from 2008 until 2014 who now consults in the privacy and cybersecurity practice of global law firm Dentons.

Researchers from Georgia Tech have created microbots so tiny, they make a penny look massive -- and they could one day attack diseases in your body. According to the team's paper, which was accepted for publication in the Journal of Micromechanics and Microengineering, each of the 3D-printed microbots weighs just five milligrams, slightly more than a grain of sand. The team designed the bots to move in response to vibrations produced by anything from a small acoustic speaker to an attached piezoelectric actuator. "As the micro-bristle-bots move up and down, the vertical motion is translated into a directional movement by optimizing the design of the legs, which look like bristles," researcher Azadeh Ansari said in a press release. "The legs of the micro-robot are designed with specific angles that allow them to bend and move in one direction in resonant response to the vibration."

With the mass and moving parts that will define the IoT, can human beings really keep up? Or is it time to let the machines do the thinking? There is no point in connecting things unless those things send information that help those who own and operate those things optimize their value. Consider the difference between an office building before and after a full IoT installation. A legacy office building can operate with mechanical features: keys to unlock doors, switches to turn lights off and on, a network connecting phones and computers, a refrigerator and coffee maker in the kitchen.

Regional airports are ramping up their internal security measures as they say the most dangerous threats in today's aviation world come from within. Artificial intelligence will play a major role in staff clearance in cities like Dubai and Jeddah in the coming years as it will ensure swift analysis and predictions of potential criminal or terrorist behaviour, a security forum heard on Monday. "The concept of security has completely changed," said Farah Al Ansari, head of airport security at Dubai International Airport. "The threats used to be in restricted areas but now they've moved and affect people and government as they're more spread, like what happened in Brussels and Ataturk Airport in Istanbul, so we're upgrading our systems to be in line with modern technologies." The airport is currently working on staff entrance to ensure greater control by introducing biometrics, more advanced face recognition and artificial intelligence, as opposed to only CCTV cameras and police monitoring now.