New Orleans Artificial intelligences place in schools may be poised to grow, but school districts and companies have a long way to go before teachers buy into the concept.

At a session on the future of AI in school districts, held at the ISTE conference this week, a panel of leaders discussed its potential to shape classroom experiences and the many unresolved questions associated with the technology.

The mention of AI can intimidate teachers asits so often associated with complex code and sophisticated robotics. But AI is already a part of daily life in the way our phones recommend content to us or the ways that our smart home technology responds to our requests.

When AI is made relatable, thats when teachers buy into it, opening doors for successful implementation in the classroom, panelists said.

AI sounds so exotic right now, but it wasnt that long ago that even computer science in classrooms was blowing our minds, said Joseph South, chief learning officer for ISTE. South is a former director of the office of educational technology at the U.S. Department of Education.It doesnt matter how much we do out here. If the teacher doesnt believe in what youre bringing to the table, it will not be successful.Nneka McGee, South San Antonio Independent School District

The first step in getting educators comfortable with AI is to provide them the support to understand it, said Nancye Blair Black, ISTEs AI Explorations project lead, who moderated the panel. That kind of support needs to come from many sources, from federal officials down to the state level and individual districts.

We need to be talking about, What is AI? and it needs to be explained, she said. A lot of people think AI is magic, but we just need to understand these tools and their limitations and do more research to get people on board.

With the use of machine learning, AI technologies can adapt to individual students needs in real-time, tracking their progress and providing immediate feedback and data to teachers as well.

In instances where a student may be rushing through answering questions, AI technology can pick up on that and flag the student to slow down, the speakers said. This can provide a level of individual attention that cant be achieved by a teacher whos expected to be looking over every students shoulder simultaneously.

Others see reasons to be wary of AIs potential impact on teaching and learning. Many ed-tech advocates and academic researchers have raised serious concerns that the technology could have a negative impact on students.

One longstanding worry is that the data AI systems rely on can be inaccurate or even discriminatory, and that the algorithms put into AI programs make faulty assumptions about students and their educational interests and potential.

For instance, if AI is used to influence decisions about which lessons or academic programs students have access to, it could end up scuttling students opportunities, rather than enhancing them.

Nneka McGee, executive director for learning and innovation for the South San Antonio ISD, mentioned in the ISTE panel that a lot more research still has to be done on AI, regarding opportunity, data, and ethics.

Some districts that are more affluent will have more funding, so how do we provide opportunities for all students? she said.

We also need to look into the amount of data that is needed and collected for AI to run effectively. Your school will probably need a data- sharing agreement with the companies you work with.

A lot of research needs to be done on AIs data security and accessibility, as well as how to best integrate such technologies across the curriculum not just in STEM-focused courses.

Its important to start getting educators familiar with the AI and how it works, panelists said, because when used effectively, AI can increase student engagement in the classroom, and give teachers more time to customize lessons to individual student needs.

As AI picks up momentum within the education sphere, the speakers said that teachers need to start by learning the fundamentals of the technology and how it can be used in their classrooms.But a big share of the responsibilityalso falls on company officials developing new AI products, Black said.

When asked about advice for ed-tech organizations that are looking to expand into AI capabilities, Black emphasized the need for user-friendliness and an interface that can be seamlessly assimilated into existing curriculum and standards.

Hand [teachers] something they can use right away, not just another thing to pile on what they already have, she said.

McGee, of the South San Antonio ISD,urges companies to include teachers in every part of the process when it comes to pioneering AI.

Involve teachers because theyre on the front lines; theyre the first ones who see our students, she said. It doesnt matter how much we do out here. If the teacher doesnt believe in what youre bringing to the table, it will not be successful.

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Photo Credit: International Society for Technology in Education

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How to Make Teachers Informed Consumers of Artificial Intelligence - Market Brief - EdWeek

Artificial intelligence (AI) is often presented in binary terms in both popular culture and political analysis. Either it represents the key to a futuristic utopia defined by the integration of human intelligence and technological prowess, or it is the first step toward a dystopian rise of machines. This same binary thinking is practiced by academics, entrepreneurs, and even activists in relation to the application of AI in combating climate change. The technology industrys singular focus on AIs role in creating a new technological utopia obscures the ways that AI can exacerbate environmental degradation, often in ways that directly harm marginalized populations. In order to utilize AI in fighting climate change in a way that both embraces its technological promise and acknowledges its heavy energy use, the technology companies leading the AI charge need to explore solutions to the environmental impacts of AI.

AI can be a powerful tool to fight climate change. AI self-driving cars, for instance, may reduce emissions by 50 percent by 2050 by identifying the most efficient routes. Employing AI in agriculture produces higher yields; peanut farmers in India achieved a 30 percent larger harvest by using AI technology. In addition, AI can provide faster and more accurate analysis of satellite images that identify disaster-stricken areas in need of assistance or rainforest destruction. AI-driven data analysis can also help predict hazardous weather patterns and increase accountability by precisely monitoring whether governments and companies are sticking to their emissions targets.

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Energy and Environment

Yet AI and the broader internet and communications industry have increasingly come under fire for using exorbitant amounts of energy. Take data processing, for example. The supercomputers used to run cutting-edge AI programs are powered by the public electricity grid and supported by back up diesel-powered generators. Training a single AI system can emit over 250,000 pounds of carbon dioxide. In fact, the use of AI technology across all sectors produces carbon dioxide emissions at a level comparable to the aviation industry. These additional emissions disproportionately impact historically marginalized communities who often live in heavily polluted areas and are more directly affected by the health hazards of pollution.

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Recently, AI scientists and engineers have responded to these critiques and are considering new sources for powering data farms. However, even new, ostensibly more sustainable energy sources such as rechargeable batteries can exacerbate climate change and harm communities. Most rechargeable batteries are built using lithium, a rare earth metal whose extraction can have negative effects for marginalized communities. Lithium extraction, which is fueled by an increasing demand for cleaner energy sources, demands enormous water usage, to the tune of 500,000 gallons of water for every ton of lithium extracted. In Chile, the second largest producer of lithium in the world, indigenous communities like the Copiap people in the North often clash with mining companies over land and water rights. These mining activities are so water intensive, the Institute for Energy Research reports that in Salar de Atacama they consumed 65 percent of the regions water. This water loss damages and permanently depletes wetlands and water sources, which has caused native species of flora and fauna to become endangered and affected local populations. Portraying lithium as clean energy simply because it is less environmentally disastrous than diesel or coal is a false dichotomy, which discourages stakeholders from pursuing newer, greener energy sources.

The development of artificial intelligence technology is a symbol of incredible progress; however, progress is not one size fits all, and the companies developing these technologies have a responsibility to ensure that marginalized communities do not bear the brunt of the negative side effects of the AI revolution.

Some data farms have shifted to running entirely on clean energy. Icelands data farms, for example, largely run on clean energy powered by the islands hydroelectric and geothermal resources, and the country has become a popular location for new data centers. These data centers also dont need to be cooled by energy-intensive fans or air conditioningIcelands cold climate does the trick. However, Iceland is particularly well suited to hosting data processing centers, and most countries arent able to replicate the unique environmental conditions.

Large data companies can avoid the pitfalls of lithium batteries by using physical batteries. Made of concrete, these batteries store gravitational potential energy in elevated concrete blocks which can then be harnessed at any point. This isnt some far off ideain a Swiss valley two 35 ton concrete blocks are suspended by a 246 foot tower. These are an early prototype of what a physical battery could look like, and together, they hold enough energy to power two thousand homes (two megawatts). Physical batteries are a potential alternative to lithium batteries with a lower cost to the environment and marginalized communities, and which could be built from commonly available materials, such as concrete.

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The U.S. government, through the Department of Energy and the Defense Advanced Research Projects Agency (DARPA), has invested billions of dollars in improving lithium batteries, especially by creating solid-state lithium ion batteries, which could provide better safety, energy density, and lifespan compared to traditional lithium ion batteries. Some private companies have made commitments to expand their use of lithium ion technology in their facilities, including Google, which has created a pilot program to phase out diesel generators at some data centers and replace them with lithium ion batteries. These investments are not enough, especially at a time when electric vehicle manufacturers and the U.S. government are making multi-billion dollar investments in new kinds of batteries. Technology companies need to do more to help solve the energy use and storage issues posed by AI.

AI presents a number of advantages for solving the current climate crisis, but the potential environmental side effects are hard to ignore. Technology companies have often been lauded for their creativity and ingenuity, and they need to apply these skills to solve the problems associated with artificial intelligence.

Elsabet Jones is an Assistant Editor in the Council on Foreign Relations Education Department.

Baylee Easterday is a Program Associate with World Learning and a former intern in the Council on Foreign Relations Education Department.

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Artificial Intelligences Environmental Costs and Promise - Council on Foreign Relations

In 2019, guards on the borders of Greece, Hungary, and Latvia began testing an artificial-intelligence-powered lie detector. The system, called iBorderCtrl, analyzed facial movements to attempt to spot signs a person was lying to a border agent. The trial was propelled by nearly $5 million in European Union research funding, and almost 20 years of research at Manchester Metropolitan University, in the UK.

The trial sparked controversy. Polygraphs and other technologies built to detect lies from physical attributes have been widely declared unreliable by psychologists. Soon, errors were reported from iBorderCtrl, too. Media reports indicated that its lie-prediction algorithm didnt work, and the projects own website acknowledged that the technology may imply risks for fundamental human rights.

This month, Silent Talker, a company spun out of Manchester Met that made the technology underlying iBorderCtrl, dissolved. But thats not the end of the story. Lawyers, activists, and lawmakers are pushing for a European Union law to regulate AI, which would ban systems that claim to detect human deception in migrationciting iBorderCtrl as an example of what can go wrong. Former Silent Talker executives could not be reached for comment.

A ban on AI lie detectors at borders is one of thousands of amendments to the AI Act being considered by officials from EU nations and members of the European Parliament. The legislation is intended to protect EU citizens fundamental rights, like the right to live free from discrimination or to declare asylum. It labels some use cases of AI high-risk, some low-risk, and slaps an outright ban on others. Those lobbying to change the AI Act include human rights groups, trade unions, and companies like Google and Microsoft, which want the AI Act to draw a distinction between those who make general-purpose AI systems, and those who deploy them for specific uses.

Last month, advocacy groups including European Digital Rights and the Platform for International Cooperation on Undocumented Migrants called for the act to ban the use of AI polygraphs that measure things like eye movement, tone of voice, or facial expression at borders. Statewatch, a civil liberties nonprofit, released an analysis warning that the AI Act as written would allow use of systems like iBorderCtrl, adding to Europes existing publicly funded border AI ecosystem. The analysis calculated that over the past two decades, roughly half of the 341 million ($356 million) in funding for use of AI at the border, such as profiling migrants, went to private companies.

The use of AI lie detectors on borders effectively creates new immigration policy through technology, says Petra Molnar, associate director of the nonprofit Refugee Law Lab, labeling everyone as suspicious. You have to prove that you are a refugee, and you're assumed to be a liar unless proven otherwise, she says. That logic underpins everything. It underpins AI lie detectors, and it underpins more surveillance and pushback at borders.

Molnar, an immigration lawyer, says people often avoid eye contact with border or migration officials for innocuous reasonssuch as culture, religion, or traumabut doing so is sometimes misread as a signal a person is hiding something. Humans often struggle with cross-cultural communication or speaking to people who experienced trauma, she says, so why would people believe a machine can do better?

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The Fight Over Which Uses of Artificial Intelligence Europe Should Outlaw - WIRED

Machine learning is one of the artificial intelligence models frequently used for modeling, prediction, and management of swine farming. Machine learning models mainly include algorithms of a decision tree, clustering, a support vector machine, and the Markov chain model focused on disease detection, behaviour recognition for postural classification, and sound detection of animals. The researchers from North Carolina State University and Smithfield Premium Genetics* demonstrated the application of machine learning algorithms to estimate body weight in growing pigs from feeding behaviour and feed intake data.

Feed intake, feeder occupation time, and body weight information were collected from 655 pigs of 3 breeds (Duroc, Landrace, and Large White) from 75 to 166 days of age. 2 machine learning algorithms (long short-term memory network and random forest) were selected to forecast the body weight of pigs using 4 scenarios. Long short-term memory was used to accurately predict time series data due to its ability in learning and storing long term patterns in a sequence-dependent order and random forest approach was used as a representative algorithm in the machine learning space. The scenarios included an individually informed predictive scenario, an individually and group informed predictive scenario, a breed-specific individually and group informed predictive scenario, and a group informed predictive scenario. 4 models each implemented with 3 algorithms were constructed and trained by different subsets of data collected along the grow-finish period to predict the body weight of individuals or groups of pigs.

Overall, as pigs matured and gained weight, daily feed intake increased, while the daily number of visits and daily occupation time decreased. Overall, the individually informed predictive scenario achieved better predictive performances than the individually and group informed predictive scenarios in terms of correlation, accuracy, sensitivity, and specificity. The greatest correlation was 0.87, and the highest accuracy was 0.89 for the individually informed prediction, while they were 0.84 and 0.85 for the individually and group informed predictions, respectively. The effect of the addition of feeding behaviour and feed intake data varied across algorithms and scenarios from a small to moderate improvement in predictive performance.

This study demonstrated various roles of feeding behaviour and feed intake data in diverse predictive scenarios. The information collected from the period closest to the finishing stage was useful to achieve the best predictive performance across predictions. Artificial intelligence has the potential to connect feeding behaviour dynamics to body growth and to provide a promising picture of the feeding behaviour data involvement in the group-housed pigs body weight prediction. Artificial intelligence and machine learning can be used as management tools for swine farmers to evaluate and rank individual pigs to adjust feeding strategies during the growth period and to avoid sorting losses at the finishing stage while reducing labor and costs.

Some technologies and tools have been developed for data collection, data processing, and modeling algorithms to evaluate pigs feeding behaviour and feed intake. These technologies demonstrated great potential to enhance the swine industry efficiency on decision making. A standard database or method for data cleaning and selection is however required to minimise the time and costs of data processing.

* He Y, Tiezzi F, Howard J, Maltecca C. Predicting body weight in growing pigs from feeding behavior data using machine learning algorithms. Comput Electron Agric. 2021;184:106085. doi:10.1016/j.compag.2021.106085

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Artificial intelligence: a new paradigm in the swine industry - Pig Progress