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Category: robotics/AI – Page 1,767

These guys have a great idea…but In true Zuckerberg style how does one steal and supercharge the idea. With food having salmonella, people need to grow more food at home. What technology can be created that uses technology to help people in urban settings grow their own food. This will help many in a post covid world, and the food should be safer, and also may promote nutrition. nnAmerican farmers also are having trouble, and would see the loss in demand. Global food production needs to increase. Japan offered to boost the continent of Africa’s rice production through cooperation. The same cooperation needs to be done with American farmers to boost Africa’s food production. Technology would be used to partner American farmers with African village cooperatives. The farmers and cooperatives would work together and share profits. This way the American farmer has revenue coming from two markets and continents. The same model can also be used in Mexico to prevent immigration. This way American farmers would also have revenue coming from Central and South America, however people who normally would be farm workers would be partners, and make more than they would having to cross borders dangerously, to make less money. This model can both reduce poverty, as well and insure food security. The capital for investment would have to come from many sources. Crowdfunding is one that can be good as the money can be paid back with profit. This way a crowd fund investment would gain better returns than interest rates. The next of course would be USAID. A project can be developed, in which USAID provides American farmers with start up capital. They manage the project pay back the loans, while sharing profits. Agreements can be developed for certain periods of time, After which the American farmer turns the project over to the cooperatives…just thinking out of the box it is a bit crazy. The farmers would be like a new Peace Corps thing. #VillageEconomics nnPortfolio company #ApolloAgriculture was recently featured in a Forbes article highlighting their machine-learning and automated-operations technology that helps small-scale farmers access everything they need to maximize their profitability. #impactinvesting #agtech

Between 2011 and 2014, engineer and Stanford grad Eli Pollak worked in agricultural technology in the U.S. for a company called the Climate Corporation. The enterprise where he was one of the early employees (which in 2013 was acquired by Monsanto for over $1 billion) worked on providing customized recommendations to increase production of large scale commercial farmers. What caught Pollak’s eye during his tenure at the company, however, was that some countries were planting way more seeds, but producing dramatically less agricultural products than the U.S.

This prompted Pollak to team up with Climate Corporation colleague Earl St Sauver, and Benjamin Ngenga (who himself grew up on a farm) to start Apollo Agriculture, a Kenyan ag-tech company which uses machine learning and automated operations technology to help small-scale farmers access everything they need to maximize their profitability.

In late May, Apollo Agriculture raised $6 million in a Series A round. The round was led by Anthemis Exponential Ventures, with participation from Leaps by Bayer, Flourish Ventures (a venture of The Omidyar Group), Sage Hill Capital, To Ventures Food, Breyer Labs, and existing investors Accion Venture Lab and Newid Capital, among others.

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Many organizations will likely look to technology as they face budget cuts and need to reduce staff. “I don’t see us going back to the staffing levels we were at prior to COVID,” says Brian Pokorny, the director of information technologies for Otsego County in New York State, who has cut 10% of his staff because of pandemic-related budget issues. “So we need to look at things like AI to streamline government services and make us more efficient.”

For 23 years, Larry Collins worked in a booth on the Carquinez Bridge in the San Francisco Bay Area, collecting tolls. The fare changed over time, from a few bucks to $6, but the basics of the job stayed the same: Collins would make change, answer questions, give directions and greet commuters. “Sometimes, you’re the first person that people see in the morning,” says Collins, “and that human interaction can spark a lot of conversation.”

But one day in mid-March, as confirmed cases of the coronavirus were skyrocketing, Collins’ supervisor called and told him not to come into work the next day. The tollbooths were closing to protect the health of drivers and of toll collectors. Going forward, drivers would pay bridge tolls automatically via FasTrak tags mounted on their windshields or would receive bills sent to the address linked to their license plate. Collins’ job was disappearing, as were the jobs of around 185 other toll collectors at bridges in Northern California, all to be replaced by technology.

Machines have made jobs obsolete for centuries. The spinning jenny replaced weavers, buttons displaced elevator operators, and the Internet drove travel agencies out of business. One study estimates that about 400,000 jobs were lost to automation in U.S. factories from 1990 to 2007. But the drive to replace humans with machinery is accelerating as companies struggle to avoid workplace infections of COVID-19 and to keep operating costs low. The U.S. shed around 40 million jobs at the peak of the pandemic, and while some have come back, some will never return. One group of economists estimates that 42% of the jobs lost are gone forever.

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When the Covid-19 pandemic made face masks an everyday essential, Japanese startup Donut Robotics spotted an opportunity. They created a smart mask — a high-tech upgrade to standard face coverings, designed to make communication and social distancing easier.

In conjunction with an app, the C-Face Smart mask can transcribe dictation, amplify the wearer’s voice, and translate speech into eight different languages.

The cutouts on the front are vital for breathability, so the smart mask doesn’t offer protection against the coronavirus. Instead, it is designed to be worn over a standard face mask, explains Donut Robotics CEO Taisuke Ono. Made of white plastic and silicone, it has an embedded microphone that connects to the wearer’s smartphone via Bluetooth. The system can translate between Japanese and Chinese, Korean, Vietnamese, Indonesian, English, Spanish and French.

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A machine-learning algorithm that can predict the compositions of trend-defying new materials has been developed by RIKEN chemists1. It will be useful for finding materials for applications where there is a trade-off between two or more desirable properties.

Artificial intelligence has great potential to help scientists find new materials with desirable properties. A that has been trained with the compositions and properties of known materials can predict the properties of unknown materials, saving much time in the lab.

But discovering new materials for applications can be tricky because there is often a trade-off between two or more material properties. One example is organic materials for , where it is desired to maximize both the voltage and current, notes Kei Terayama, who was at the RIKEN Center for Advanced Intelligence Project and is now at Yokohama City University. “There’s a trade-off between voltage and current: a material that exhibits a high voltage will have a low current, whereas one with a high current will have a low voltage.”

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The Army is developing a system to allow autonomous ground robots to communicate with soldiers through natural conversations — and, in time, learn to respond to soldier instructions no matter how informal or potentially crass they may be.

Researchers from the U.S. Army Combat Capabilities Development Command’s Army Research Laboratory, working in collaboration with the University of Southern California’s Institute for Creative Technologies, have developed a new capability that allows conversational dialogue between soldiers and autonomous systems.

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The context: Studies show that when people and AI systems work together, they can outperform either one acting alone. Medical diagnostic systems are often checked over by human doctors, and content moderation systems filter what they can before requiring human assistance. But algorithms are rarely designed to optimize for this AI-to-human handover. If they were, the AI system would only defer to its human counterpart if the person could actually make a better decision.

The research: Researchers at MIT’s Computer Science and AI Laboratory (CSAIL) have now developed an AI system to do this kind of optimization based on strengths and weaknesses of the human collaborator. It uses two separate machine-learning models; one makes the actual decision, whether that’s diagnosing a patient or removing a social media post, and one predicts whether the AI or human is the better decision maker.

The latter model, which the researchers call “the rejector,” iteratively improves its predictions based on each decision maker’s track record over time. It can also take into account factors beyond performance, including a person’s time constraints or a doctor’s access to sensitive patient information not available to the AI system.

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Deepfakes are the most concerning use of AI for crime and terrorism, according to a new report from University College London.

The research team first identified 20 different ways AI could be used by criminals over the next 15 years. They then asked 31 AI experts to rank them by risk, based on their potential for harm, the money they could make, their ease of use, and how hard they are to stop.

Deepfakes — AI-generated videos of real people doing and saying fictional things — earned the top spot for two major reasons. Firstly, they’re hard to identify and prevent. Automated detection methods remain unreliable and deepfakes also getting better at fooling human eyes. A recent Facebook competition to detect them with algorithms led researchers to admit it’s “very much an unsolved problem.”

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