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Mintlify uses AI to generate documentation from code – TechCrunch

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Mintlify, a startup developing software to automate software documentation tasks, today announced that it raised $2.8 million in a seed round led by by Bain Capital Ventures with participation from TwentyTwo Ventures and Quinn Slack, Sourcegraph's co-founder. CEO Han Wang says that the proceeds will be put toward product development and doubling Mintlify's core, three-person team by the end of the year. Ithaca, New York-based Mintlify was co-founded in 2021 by Han Wang and Hahnbee Lee -- both software engineers by trade. Wang previously co-launched Foodful, an startup that developed a cloud-based monitoring system for cows, and Pe•ple, an online customer community platform that was acquired by Tribe in early 2021. Lee was a co-founder at Pe•ple before briefly joining Duolingo as an engineer.


Genetics and ML/AI to Speed up Food Production

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Food shortage is looming in a time when researchers harness artificial intelligence to breed new crop varieties for the changing climate. In an article published by PNAS, Tanksley, a professor emeritus at Cornell University in Ithaca, NY said that "We have to double the productivity per acre of our major crops if we're going to stay on par with the world's needs." Even if we prepare the land, lay out a water irrigation system and make a fence around the land, we still need to have the right genetic material (varieties) to be able to have a harvest with yields to meet both household and market demands as well as needs in terms of quantity and quality including nutrition. To speed up the process of developing these varieties within a short period of time, researchers are turning to machine learning and artificial intelligence (AI). Researchers are using ML/AI based techniques to help assess rapidly genetic resources for plants with the fastest growth in a particular climate and which genes helped these plants to thrive under such climate condition.


This touch-sensitive glove is made from stretchy optical fibres

New Scientist

A touch-sensitive glove made from stretchable fibre-optic sensors could be used in robotics, sport and medicine. "We made a sensor that can sense haptic interactions, in the same way that our own skin sensors interact with [the] environment," says Hedan Bai at Cornell University in Ithaca, New York. Bai and her team created the glove using optical fibres made from thin elastomeric polyurethane cables that transmit light from an LED. The light is interrupted when the cables are bent, stretched or put under pressure. The team dyed parts of the fibres with different colours, meaning that as they are distorted, the colour of light coming out of the fibres changes.


Inner Workings: Crop researchers harness artificial intelligence to breed crops for the changing climate

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Until recently, the field of plant breeding looked a lot like it did in centuries past. A breeder might examine, for example, which tomato plants were most resistant to drought and then cross the most promising plants to produce the most drought-resistant offspring. This process would be repeated, plant generation after generation, until, over the course of roughly seven years, the breeder arrived at what seemed the optimal variety. Researchers at ETH Zürich use standard color images and thermal images collected by drone to determine how plots of wheat with different genotypes vary in grain ripeness. Image credit: Norbert Kirchgessner (ETH Zürich, Zürich, Switzerland). Now, with the global population expected to swell to nearly 10 billion by 2050 (1) and climate change shifting growing conditions (2), crop breeder and geneticist Steven Tanksley doesn’t think plant breeders have that kind of time. “We have to double the productivity per acre of our major crops if we’re going to stay on par with the world’s needs,” says Tanksley, a professor emeritus at Cornell University in Ithaca, NY. To speed up the process, Tanksley and others are turning to artificial intelligence (AI). Using computer science techniques, breeders can rapidly assess which plants grow the fastest in a particular climate, which genes help plants thrive there, and which plants, when crossed, produce an optimum combination of genes for a given location, opting for traits that boost yield and stave off the effects of a changing climate. Large seed companies in particular have been using components of AI for more than a decade. With computing power rapidly advancing, the techniques are now poised to accelerate breeding on a broader scale. AI is not, however, a panacea. Crop breeders still grapple with tradeoffs such as higher yield versus marketable appearance. And even the most sophisticated AI …


Earphone cameras watch your facial expressions and read your lips

New Scientist - News

A wearable device consisting of two mini-cameras mounted on earphones can recognise your facial expressions and read your lips, even if your mouth is covered. The tool – called C-Face – was developed by Cheng Zhang at Cornell University in Ithaca, New York, and his colleagues. It looks at the sides of the wearer's head and uses machine learning to accurately visualise facial expressions by analysing small changes in cheek contour lines. "With previous technology to reconstruct facial expression, you had to put a camera in front of you. But that brings a lot of limitations," says Zhang. "Right now, many people are wearing a face mask, and standard facial tracking will not work. Our technology still works because it doesn't rely on what your face looks like."


Earphone cameras watch your facial expressions and read your lips

New Scientist

A wearable device consisting of two mini-cameras mounted on earphones can recognise your facial expressions and read your lips, even if your mouth is covered. The tool – called C-Face – was developed by Cheng Zhang at Cornell University in Ithaca, New York, and his colleagues. It looks at the sides of the wearer's head and uses machine learning to accurately visualise facial expressions by analysing small changes in cheek contour lines. "With previous technology to reconstruct facial expression, you had to put a camera in front of you. But that brings a lot of limitations," says Zhang. "Right now, many people are wearing a face mask, and standard facial tracking will not work. Our technology still works because it doesn't rely on what your face looks like."


Smiles beam and walls blush: Architecture meets AI at Microsoft

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Redmond, Washington and Ithaca, New York – Jenny Sabin is perched high on a scissor lift, her head poking through an opening of the porous fabric structure that she's struggling to stretch onto the exoskeleton of her installation piece, which is suspended in the airy atrium of building 99 on Microsoft's Redmond, Washington, campus. Momentarily defeated, she pauses and looks up. "It's going to be gorgeous," she says. "It" is a glowing, translucent and ethereal pavilion that Sabin and her Microsoft collaborators describe as both a research tool and a glimpse into a future in which architecture and artificial intelligence merge. "To my knowledge, this installation is the first architectural structure to be driven by artificial intelligence in real time," said Sabin, principal designer at Jenny Sabin Studio in Ithaca, New York, who designed and built the pavilion as part of Microsoft's Artist in Residence program.


Smiles beam and walls blush: Architecture meets AI at Microsoft

#artificialintelligence

Jenny Sabin is perched high on a scissor lift, her head poking through an opening of the porous fabric structure that she's struggling to stretch onto the exoskeleton of her installation piece, which is suspended in the airy atrium of building 99 on Microsoft's Redmond, Washington, campus. Momentarily defeated, she pauses and looks up. "It's going to be gorgeous," she says. "It" is a glowing, translucent and ethereal pavilion that Sabin and her Microsoft collaborators describe as both a research tool and a glimpse into a future in which architecture and artificial intelligence merge. "To my knowledge, this installation is the first architectural structure to be driven by artificial intelligence in real time," said Sabin, principal designer at Jenny Sabin Studio in Ithaca, New York, who designed and built the pavilion as part of Microsoft's Artist in Residence program.


AI in climate change: Machine learning helps predict methane well leaks

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AI could have a key role to play in climate change after the technology was used by scientists to identify greenhouse gas leaks in oil and gas wells. Research conducted at the University of Vermont used machine learning algorithms to predict whether the wells would emit significant amounts of methane – one of the most harmful gases contributing to global warming. It tested 38,391 wells in Alberta, Canada, and was able to determine which wells leaked – and those that didn't – with up to 87% accuracy. Professor George Pinder, who conducted the research alongside former doctoral student James Montague, said: "The big picture is that we can now have tool that could help us much more efficiently identify leaking wells. "Given that methane is such a significant contributor to global warming, this is powerful information that should be put to use." The analysis yielded a cluster of 16 traits that predicted whether a well would fail and leak. Researchers were given access to more complete information, including the fluid properties of the oil or natural gas being mined, for 4,000 wells. For these wells, the machine learning algorithm identified leaks with 87% accuracy. For a larger sample of about 28,500 wells, where the fluid property was not known and taken into account, the accuracy level was 62%. Companies in Alberta are required to test wells at the time they begin operating to determine if they have failed and are leaking methane. They must also keep careful records of each well's construction characteristics. Professor Anthony R Ingraffea – based at Cornell University's School of Civil and Environmental Engineering, in Ithaca, New York – is an expert in oil and natural gas well design and construction, but was not involved in the study. He said: "Provincial and state regulatory agencies never have enough inspectors or financial resources to locate, let alone repair, leaking wells.


How Will Artificial Intelligence Affect Income Inequality?

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Artificial intelligence is capturing the curiosities, hopes, and fears of people all over the country. Will AI spur extraordinary improvements in security, healthcare, and convenience all while creating new industries, boosting US productivity, and improving quality of life? Or will AI cause millions of workers to become redundant, catalyze massive unemployment, and undermine the socio-economic fabric of American democracy? Born in Ithaca, New York, Julian is the Founding Editor in Chief of the Brown University Journal of Philosophy, Politics, and Economics (PPE) and a journalist."