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Iowa State University researchers are growing two kinds of corn plants.

If you drive past the many fields near the universitys campus in Ames, you can see row after row of the first. But the second exists in a location that hasnt been completely explored yet: cyberspace.

The researchers, part of the AI Institute for Resilient Agriculture, are using photos, sensor data and artificial intelligence to create digital twins of corn plants that, through analysis, can lead to a better understanding of their real-life counterparts. They hope the resulting software and techniques will lead to better management, improved breeding, and ultimately, smarter crops.

We need to use lots of real-time, high-resolution data to make decisions, Patrick Schnable, an agronomy professor and director of Iowa States Plant Sciences Institute,told Agri-Pulse. Just collecting data for data's sake is not something that production ag wants. But data which is then linked to statistical models or other kinds of mathematical models that advise farmers on what to do has a lot of value.

The idea of machine learning systems that can improve or take over typical human tasks has been seeing increased attention over the past couple of years in many industries, including agriculture. In 2019, the National Science Foundation and several partner agencies, including the USDA, began establishing and funding AI institutes to research and advance artificial intelligence in fields like agriculture.

In their call for proposals, the organizations said AI could spur the next revolution in food and feed production.

The Green Revolution of the 1960s greatly enhanced food production and resulted in positive impacts on food security, human health, employment, and overall quality of life for many, the solicitation said. There were also unintended consequences on natural resource use, water and soil quality, and pest population expansion. An AI-based approach to agriculture can go much further by addressing whole food systems, inputs and outputs, internal and external consequences, and issues and challenges at micro, meso, and macro scales that include meeting policy requirements of ecosystem health.

Among the seven inaugural institutes established in 2020 were two focusing on agriculture: the AI Institute for Future Agricultural Resilience, Management and Sustainability at the University of Illinois at Urbana-Champaign, and the AI Institute for Next Generation Food Systems at the University of California, Davis. The 2021 lineup includesthe AIIRA and the Institute for Agricultural AI for Transformation Workforce and Decision Support (AgAID) at Washington State University.

Lakshmi Attigala, a senior scientist and lab manager at Iowa State University, prepares a corn plant to be photographed.

The AIIRA, which received $20 million in funding from these governmental organizations, plans to pool the expertise of researchers at Iowa State, Carnegie Mellon University, the University of Arizona, New York University, George Mason University, the Iowa Soybean Association, the University of Nebraska-Lincoln and the University of Missouri to study the intersection of plant science, agronomics and AI.

The institute hopes to develop AI algorithms that can take all of the collectible data from a field whether by ground robots, drones, or satellites and analyze it to create tools farmers can use to improve production of crops for resilience to the pressures brought about by climate change.

This is a game-changer, Baskar Ganapathysubramian, the director of the institute, told Agri-Pulse as he walked toward a nondescript white shed tucked between crop fields on the Iowa State University campus.

Scouting is based on the visual, he said. By using multimodal things, you can actually go beyond the visual and do early detection and early mitigation. That's not only sustainable, because you're going to use less of the chemicals needed, but also amazingly profitable.

Ganapathysubramian opened the door to reveal a flurry of activity. Directly inside, genetics graduate student Yawei Li held a protractor up to a corn plant in various positions, trying to measure the angles of its leaves.

Across the room, Lakshmi Attigala, a senior scientist and lab manager, grabbed a fully headed corn plant from a gray tote and walked it over to the labs makeshift photography studio, where a sheet of blue cloth hanging from the ceiling served as a backdrop.

She placed the corn plant in a small, rotating green vase ringed by light stands and adjusted its leaves, preparing it for a photo shoot. She gave it a unique number, 21-3N3125-1, which was printed on a piece of paper she attached to the front of it.

As the vase rotated, she used two cameras one hanging from the ceiling and the other sitting atop a tripod in front of the corn plant to take shots of the plant.

On the north side of the building two researchers senior staff member Zaki Jubery and graduate student Koushik Nagasubramanian placed eight more corn plants in a ring surrounding a terrestrial laser scanner. The scanner sends out a signal to detect point clouds, or find the exact dimensions of these plants based on which points the lasers bounce off.

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All three of these actions, while happening separately and in different parts of the room, feed data from the 80 corn plants scanned that day to a computer learning program that can study their features to learn what the plants look like. If cameras, lasers and sensors can collect enough data on corn plants, the software should be able to create near-identical models of them when fully developed.

The idea is that we perfect something from here and then we do that on a higher scale in the field, said Nagasubramanian. Thats a more complicated thing if you have plants in the background and you have changing light intensities and clouds.

The institute, which collaborates with the Genomes to Fields Initiative to phenotype corn hybrid varieties across 162 environments in North America, also monitors a corn field lined with cameras mounted on poles. The solar-powered cameras sit above the corn plants and take photos every 15 minutes to watch each one develop over time.

The resulting data can be fed to AI programs to get a better understanding of how these plants grow and what genetic traits they share.

Certainly it is going to help us understand for example, with the photography what is the genetic control of leaf angle. And then that would allow us to develop varieties with different leaf angles more readily, Schnable said.

Schnable said its too soon for the developing technology to be widely deployed in fields or used for breeding purposes and that for now, the research funding is limited. But he believes private companies will use AI technology to develop their own products.

These things do have significant impacts out there in the world, he said.

For more news, go to http://www.Agri-Pulse.com.

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New institute aims to unlock the secrets of corn using artificial intelligence - Agri-Pulse

Toms Ondarra

The brain is a generator of automatisms, which allow us to do things even though we cant explain how we do them. The goalkeeper who dives to clear that ball in the corner, the gymnast who throws the ribbon and catches it without looking after several somersaults, the tennis player who connects the passing shot on the run. None of them think about (or know), while executing these movements, the mathematical model or the laws of physics that determine these trajectories, yet nevertheless, based on some basic concepts and millions of repetitions, they are capable of doing them.

But sometimes, something happens that truncates that ability. Like Simone Biles at the Tokyo Olympics, sometimes the brain loses its automatisms. The gymnasts lose their axis, the golfers their swing, the tennis players their serve. The concepts have not been forgotten, but the automatisms fail. And if you have to think, it no longer works. To recover, they have to slowly rebuild their automatisms, until they are able, once again, to play without thinking.

Computers, unlike the brain, need explicit models. In order to send a rocket to the Moon, complex trajectories are designed with high precision. And to study the effect of an economic policy measure, a mathematical model is designed that simulates the functioning of the economy. Computers need instructions, they do not know how to generate automatisms. Thats how they differ from humans.

Artificial intelligence started like this, giving instructions to the computer. To translate a document, a model was designed that replicated the grammar of the language. To play chess, a program was designed that replicated the rules of the game. But it soon became clear this road was very limited. How do you write a program to teach a computer what a cat is? Or to detect a tumor on an X-ray? Human intelligence is different, it does not work with models. A baby is not taught to recognize the face of her parents. But after a few days, by dint of seeing them, she is able to do it.

Data is also the limit of artificial intelligence, because the power of an algorithm is limited to its database. Thats why artificial intelligence replaces tasks, not jobs or business strategies

Thats because the brain is a machine for predicting the immediate present, based on trial and error. Each action and its consequence generate a neural connection, each repetition of that action reinforces that neural connection, and based on repetitions the connection is consolidated and the brain learns.

Artificial intelligence has evolved towards the prediction of the present. The immense improvement in the processing capacity of computers, and the exponential increase in the data available for analysis more than 90 percent of the data available today has been created in recent years makes it possible for computers to operate in a similar way to the brain.

Text translation is done based on the analysis of millions of translations, and the computer learns to predict which word or phrase in one language is related to another in another language. Facial recognition takes advantage of the digitization and tagging of millions of photos, which enables relational analysis of images. Autonomous driving systems are built with the digitization and analysis of the actions of human drivers, to be able to predict and replicate their behavior. Any activity that can be digitized and tagged can be turned into a prediction exercise, and therefore automated.

Artificial intelligence reaches the most unexpected corners. For example, this summer I witnessed how, in one of the most famous Rioja wineries in Spain, harvested grapes that are in poor condition are no longer discarded manually, but with an artificial intelligence system: the computer has been trained to recognize images of grapes in poor condition, the cameras detect them on the conveyor belt and activate a system of pressurized air jets that eliminates them before reaching the pressing barrel.

Computers perform arithmetic and probability analysis better than humans, but humans are superior in value judgments and intangible decisions

The economics of artificial intelligence is the economics of prediction. The computer reduced the cost of arithmetic operations, making the prediction process cheaper. The improvement of internet connections exponentially expanded the volume of data available to apply this arithmetic. The combination of more powerful computers and faster internet connections makes the system globally scalable, making prediction infinitely cheaper and more accurate, allowing many activities to be converted into prediction exercises.

Data, be it images, videos, or texts, is the raw material of artificial intelligence, the fundamental element for learning and training algorithms. Every time you send a message or upload a photo to the internet you are helping develop or improve artificial intelligence algorithms. The famous cookies, and internet searches, capture patterns of digital behavior that will serve as training for algorithms. Data regulation is not just a matter of privacy, but also of ownership of this fundamental raw material.

Data, in the world of statistics and econometrics, shows diminishing returns: once a model is estimated, one more data point does not materially improve its prediction ability. But in the world of artificial intelligence, data shows increasing returns: with little data you cannot do facial recognition, or autonomous driving systems. But the accumulation of data at some point makes it possible and economically viable, and from there the improvements are exponential. This explains the interest of technology companies in companies that, although not profitable, are generators of data. The exclusivity of the data, more than the details of the algorithms, is the key to success in artificial intelligence.

Data is also the limit of artificial intelligence, because the power of an algorithm is limited to its database. Thats why artificial intelligence replaces tasks, not jobs or business strategies. The key to technological progress is the combination of machines and humans. The best chess players are not humans, nor computers, but humans with the help of computers. Computers perform arithmetic and probability analysis better than humans, but humans are superior in value judgments and intangible decisions, because the accumulated experience in their brains their database is far superior in quantity and especially in diversity to that of computers. And that allows them to react to an unforeseen event for which the algorithm was not trained. It also facilitates creativity, which almost always springs from interdisciplinary connections think of molecular cuisine, for example. Therefore, it is essential to educate citizens so that they know how to operate with computers computing should be as mandatory as a second language but without forgetting the humanistic subjects and abstract reasoning that provide that agility and creative advantage.

Technological progress is the source of growth and, therefore, of job creation. But you have to be well prepared to take advantage of it.

Twitter: @angelubide

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The economics of artificial intelligence - EL PAS in English

Quick, before we reach the singularity! This week, the UN released a report on the state of Artificial Intelligence (AI), and in a nutshell, theyre not liking what they see. The report comes from UN High Commissioner for Human Rights Michelle Bachelet, and it doesnt mince words. Lets check it out!

Basically, the UN has found that both private companies and states/countries themselves are using AI technology that violates international human rights laws. Specifically, theyre worried that AI-based profiling, automated decision-making, and other machine-learning technologies can have disastrous consequences for people.

In addition to violating privacy laws, these technologies can affect a persons rights to health, education, freedom of movement, freedom of peaceful assembly and association, and freedom of expression. So yeah, not a great way to be using our newfound tech.

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#AI incl. profiling, automated decision-making & machine-learning affects peoples right to privacy and other rights, such as rights to health, education, freedom of movement, freedom of peaceful assembly & association, and freedom of expression. https://t.co/VmmR75aKzD pic.twitter.com/Xs9zzFGIbs

UN Human Rights (@UNHumanRights) September 15, 2021

Some specific examples of these issues include getting denied social security benefits due to faulty AIor even being arrested thanks to flawed facial recognition tools. Yeah, this is starting to sound more and more like a sci-fi movie, but these are legitimate problems.

Well use targeted ads as a quick example of how things can go wrong with Big Data and AI. As youre browsing around the internet, your interests and activity are tracked and accumulated by social media companies, advertisers, and whoever else has the cash to access it. Boom, two weeks later, and that thing you thought you needed (or maybe even didnt) is right there, waiting for you to buy it.

Now, thats not really a problem in and of itself, but were going to continue the analogy. Lets say you were browsing the internet looking for gifts for friends, researching a school project, or whatever else. Well then, the AI cant distinguish your intent from your behaviour it takes it at face value. All of a sudden, your friend sees you scrolling through Instagram and their birthday present is the first sponsored ad.

Shopping is one thing, but political actions, personal health decisions, and other deeply important behaviour go down online as well. And as long as AI operates indiscriminately and without oversight, the risks for mistakes with grave consequences continue to grow.

The UN has seen this, and theyre (justifiably) freaked out about it. While we cant see the worlds leading countries or international corporations taking their advice any time soon, were happy theyve said something about it. If youd like to check out the report for yourself, just click here.

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The UN wants the world to pump the brakes on Artificial Intelligence - Curiocity

On 14 September 2021, eight international organisations joined forces to launch a new portal promoting global co-operation on artificial intelligence (AI). The portal is a one-stop shop for data, research findings and good practices in AI policy.

The objective of the portal is to help policymakers and the wider public navigate the international AI governance landscape. It provides access to the necessary tools and information, such as projects, research and reports to promote trustworthy and responsible AI that is aligned with human rights at global, national and local level.

Key partners in this joint effort include the Council of Europe, the European Commission, the European Union Agency for Fundamental Rights, the Inter-American Development Bank, the Organisation for Economic Co-operation and Development (OECD), the United Nations (UN), the United Nations Educational, Scientific and Cultural Organization (UNESCO), and the World Bank Group.

Access the website: https://globalpolicy.ai

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Artificial Intelligence A New Portal to Promote Global Cooperation Launched with 8 International Organisations - Council of Europe

Artificial intelligence (AI) is sometimes referred to as a herald of the fourth industrial revolution. That revolution is already here. Whenever you say Hey Siri or glance at your phone in order to unlock it, youre using AI. Its current and potential applications are numerous, including medical diagnosis and predictive technologies that enhance user interactions.

As chairwoman of the U.S. House Committee on Science, Space, and Technology, I am particularly interested in the potential for AI to accelerate innovation and discovery across the science and engineering disciplines. Just last year, DeepMind announced that its AI system AlphaFold had solved a protein-folding challenge that had stumped biologists for half a century. It is clear that not only will AI technologies be integral to improving the lives of Americans, but they will also help determine Americas standing in the world in the decades to come.

However, the vision of AIs role in humanitys future isnt all rosy. Increasingly autonomous devices and growing amounts of data will exacerbate traditional concerns, such as privacy and cybersecurity.Other potential dangers of AI have also arrived, appearing as patterns of algorithmic bias that often reflect our societys systemic racial and gender-based biases. We have seen discriminatory outcomes in AI systems that predict credit scores, health care risks, and recruitment potential. These are domains where we must mitigate the risk of bias in our decision-making, and the tools we use to augment that decision-making.

Technological progress does not have to come at the expense of safety, security, fairness, or transparency. In fact, embedding our values into technological development is central to our economic competitiveness and national security. Our federal government has the responsibility to work with private industry to ensure that we are able to maximize the benefits of AI technology for society while simultaneously managing its emerging risks.

To this end, the Science Committee has engaged in efforts to promote trustworthy AI. Last year, one of our signature achievements was passing the bipartisan National Artificial Intelligence Initiative Act, which directs the Department of Commerces National Institute of Standards and Technology (NIST) to develop a process for managing AI risks.

NIST may not be the most well-known government institution, but it has long conducted critical work on standard-setting and measurement research that is used by federal agencies and private industry. Over the past year, NIST has conducted a series of workshops examining topics like AI trustworthiness, bias, explainability, and evaluation. These workshops are geared at helping industry professionals understand how to detect, catalogue, and ultimately prevent the harmful outcomes that erode public trust in AI technology.

Most recently, NIST has been working to construct a voluntary Risk Management Framework that is intended to support the development and deployment of safe and trustworthy AI. This framework will be important for informing the work of both public and private sector AI researchers as they pursue their game-changing research. NIST is soliciting public comments until Sept. 15, 2021 and will develop the framework in several iterations, allowing for continued input. Interested stakeholders should submit comments and/or participate in the ongoing processes at NIST.

We know that AI has the potential to benefit society and make the world a better place. In order for the U.S. to be a true global leader in this technology, we have to ensure that the AI we create does just that.

Eddie Bernice JohnsonEddie Bernice JohnsonUS must not only lead in artificial intelligence, but also in its ethical application Our approach to schizophrenia is failing House passes bills to boost science competitiveness with China MORE represents the 30th District of Texas and is chairwoman of the House Committee on Science, Space, and Technology.

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US must not only lead in artificial intelligence, but also in its ethical application | TheHill - The Hill