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While many reports have been written about the impact of AI over the past several years, the AI100 reports are unique in that they are both written by AI insiders experts who create AI algorithms or study their influence on society as their main professional activity and that they are part of an ongoing, longitudinal, century-long study, said Peter Stone, a professor of computer science at the University of Texas at Austin, executive director of Sony AI America and chair of the AI100 standing committee. The 2021 report is critical to this longitudinal aspect of AI100 in that it links closely with the 2016 report by commenting on what's changed in the intervening five years. It also provides a wonderful template for future study panels to emulate by answering a set of questions that we expect future study panels to reevaluate at five-year intervals.

Eric Horvitz, chief scientific officer at Microsoft and co-founder of the One Hundred Year Study on AI, praised the work of the study panel.

"I'm impressed with the insights shared by the diverse panel of AI experts on this milestone report," Horvitz said. The 2021 report does a great job of describing where AI is today and where things are going, including an assessment of the frontiers of our current understandings and guidance on key opportunities and challenges ahead on the influences of AI on people and society.

In terms of AI advances, the panel noted substantial progress across subfields of AI, including speech and language processing, computer vision and other areas. Much of this progress has been driven by advances in machine learning techniques, particularly deep learning systems, which have made the leap in recent years from the academic setting to everyday applications.

In the area of natural language processing, for example, AI-driven systems are now able to not only recognize words, but understand how theyre used grammatically and how meanings can change in different contexts. That has enabled better web search, predictive text apps, chatbots and more. Some of these systems are now capable of producing original text that is difficult to distinguish from human-produced text.

Elsewhere, AI systems are diagnosing cancers and other conditions with accuracy that rivals trained pathologists. Research techniques using AI have produced new insights into the human genome and have sped the discovery of new pharmaceuticals. And while the long-promised self-driving cars are not yet in widespread use, AI-based driver-assist systems like lane-departure warnings and adaptive cruise control are standard equipment on most new cars.

Some recent AI progress may be overlooked by observers outside the field, but actually reflect dramatic strides in the underlying AI technologies, Littman says. One relatable example is the use of background images in video conferences, which became a ubiquitous part of many people's work-from-home lives during the COVID-19 pandemic.

To put you in front of a background image, the system has to distinguish you from the stuff behind you which is not easy to do just from an assemblage of pixels, Littman said. Being able to understand an image well enough to distinguish foreground from background is something that maybe could happen in the lab five years ago, but certainly wasnt something that could happen on everybodys computer, in real time and at high frame rates. Its a pretty striking advance.

As for the risks and dangers of AI, the panel does not envision a dystopian scenario in which super-intelligent machines take over the world. The real dangers of AI are a bit more subtle, but are no less concerning.

Some of the dangers cited in the report stem from deliberate misuse of AI deepfake images and video used to spread misinformation or harm peoples reputations, or online bots used to manipulate public discourse and opinion. Other dangers stem from an aura of neutrality and impartiality associated with AI decision-making in some corners of the public consciousness, resulting in systems being accepted as objective even though they may be the result of biased historical decisions or even blatant discrimination, the panel writes. This is a particular concern in areas like law enforcement, where crime prediction systems have been shown to adversely affect communities of color, or in health care, where embedded racial bias in insurance algorithms can affect peoples access to appropriate care.

As the use of AI increases, these kinds of problems are likely to become more widespread. The good news, Littman says, is that the field is taking these dangers seriously and actively seeking input from experts in psychology, public policy and other fields to explore ways of mitigating them. The makeup of the panel that produced the report reflects the widening perspective coming to the field, Littman says.

The panel consists of almost half social scientists and half computer science people, and I was very pleasantly surprised at how deep the knowledge about AI is among the social scientists, Littman said. We now have people who do work in a wide variety of different areas who are rightly considered AI experts. Thats a positive trend.

Moving forward, the panel concludes that governments, academia and industry will need to play expanded roles in making sure AI evolves to serve the greater good.

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New report assesses progress and risks of artificial intelligence - Brown University

Artificial intelligence-inspired policing technology and techniques like facial recognition software and digital surveillance continue to find traction and champions among law enforcement agencies, but at what cost to the public?

Some cities like Wilmington, North Carolina, have even adopted AI-driven policing, where technology like ShotSpotter identifies gunshots and their locations. The software also recommends to patrol officers next best action based on their current location, police data on past crime records, time of day, and housing and population density.

Rene Cummings, data activist in residence at the University of Virginias School of Data Science, warns that the rules of citizenship are changing with the development of AI-inspired policing technologies. She explains, If the rules are changing, then the public needs to have a voice and has the right to provide input on where we need to go with these technologies as well as demand solutions that are accountable, explainable and ethical.

As artificial intelligence is used toward the development of technology-based solutions, Cummings research questions the ethical use of technology to collect and track citizen data, aiming to hold agencies more accountable and to provide citizens greater transparency.

Law enforcement, national security, and defense agencies are spending a lot of money on surveillance tools with little oversight as to their impact on communities and an individuals right to privacy, Cummings said. Were creating a tool that would give citizens the ability to see how these powerful tools are used and how they impact our lives.

Cummings and a team of data science graduate students are developing an algorithmic tool to evaluate the impact of AI-inspired law enforcement technologies. Their goal is to create an algorithmic force score that would eventually be used in an application that tracks technologies currently used by law enforcement agencies by force and zip code.

Sarah Adams and Claire Setser, both students in the online M.S. in Data Science program, said they chose the project because they wanted to put their data science skills to work for the public good. Cummings praised their effort. The algorithmic foundation was created with tremendous effort by Sarah and Claire who went through massive amounts of existing data to create an algorithm force model.

Adams said she wanted to work on a capstone project that contributed to and supported the ongoing efforts toward increasing police accountability and citizen activism. Our cohort chose our capstone projects at the beginning of 2021, which was less than one year after the loss of George Floyd and our country had been in civil unrest for quite some time. I was inspired by Rene Cummings energy and passion for data ethics and its application in criminology.

Setser agreed. I was attracted to this capstone project because of the possibility to enact and help push for real change. Citizens have a right to understand the technologies that are used to police them and surveil their lives every day. The problem is that this information is not readily available, so the idea of creating a tool to educate the public and encourage dialogue was of great interest to me.

Students in the M.S. in Data Science program are required to complete a capstone project sponsored by corporate, government and non-profit organizations. Students collaborate closely with sponsors and faculty across disciplines to tackle applied problems and generate data-driven solutions. Capstone projects range in scope and focus, and past projects have explored health disparities, consumer behavior, election forecasting, disease diagnosis, mental health, credit card fraud and climate change.

The capstone project was a valuable opportunity to combine and implement almost all of the skills and knowledge that we gained throughout the program, Setser said. Its an opportunity to experience the data pipeline from beginning to end while providing your sponsor a better understanding of the data. This is incredibly rewarding.

The projects next stage is to fine-tune and test, and Cummings and her team hope to collaborate with UVA and the wider Charlottesville community. What makes this so exciting is that were creating something brand new and adding new insights into emerging technology. Sarah and Claire have been amazing, delivering something extraordinary in such a short space of time. It really speaks to their expertise, determination, and commitment toward AI for the public and social good.

Cummings joined the School of Data Science in 2020 as its first data activist in residence. She is a criminologist, criminal psychologist, therapeutic jurisprudence specialist, AI ethicist and AI strategist. Her research places her on the frontline of artificial intelligence for social good, justice-oriented AI design, and social justice in AI policy and governance. She is the founder of Urban AI and a community scholar at Columbia University.

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A Closer Look at Artificial Intelligence-Inspired Policing Technologies - University of Virginia

Artificial Intelligence is jockeying to become the focal point of U.S. technology innovation in coming years, and San Diego is among the cities well positioned to be a frontrunner in this looming AI race.

A new report from the Metropolitan Policy Program at the Brookings Institution ranked more than 360 cities based on their AI economic prowess.

Bay Area metros San Francisco and San Jose- topped the list, according to Brookings, a public policy think tank based in Washington, D.C. They were followed by 13 earlier adopter cities that managed to claw out a toehold in AI, including San Diego.

Not everywhere should be looking to artificial intelligence for a major change in its economy, but places like San Diego really need to, said Mark Muro, a Brookings fellow and co-author of the report. I think the costs of being out of position on it are pretty high for San Diego, and the benefits of leveraging it fully are really high.

To rank cities, Brookings combined data on federal research grants, AI academic papers, AI patents, job postings and AI-related companies, among other factors.

Besides San Diego, Los Angeles, Seattle, Boston, Austin, Washington, D.C., and Raleigh, N.C., are in strong positions. Smaller cities with significant AI footprints relative to their size include Santa Barbara, Santa Cruz, Boulder, Colo., Lincoln, Neb., and Santa Fe, N.M.

An additional 87 cities have the potential to become players but so far have limited AI activities, according to the study.

For most of us. AI is best known through recommendations that pop up on Amazon or Spotify, when smart speakers answer voice commands, or when navigation apps give turn-by-turn directions.

But AI is much more than that, with the potential to permeate thousands of industries. It could prevent power outages and help heal grids quickly, better route shipping to cut emissions, aid in medical diagnoses, and power navigation for self-driving vehicles.

Muro said Brookings undertook the research after receiving requests from economic development officials.

They watched the digitization of everything during the pandemic, he said. Theyre asking where do we stand on these advanced digital technologies? How do we engage with this?

As with other technologies, artificial intelligence tends to be clustered on the coasts. Of the 363 metro areas in the study, 261 had no significant AI footprint.

This is not everywhere, said Muro. But we think there can be a happy medium where we retain our coastal innovation centers while also taking steps to help other places make progress and counter some of this massive concentration.

In San Diego, companies such as Qualcomm, Oracle, Intuit, Teradata, Cubic, Viasat, Thermo Fisher and Illumina develop artificial intelligence and machine learning algorithms.

But key drivers of the regions AI prowess stems from the military and universities.

The Naval Information Warfare Systems Command (NAVWAR) is based locally, creating a magnet for defense contractors and cyber security firms working in AI.

San Diegos affiliation with the military has been extremely important, said Nate Kelley, senior researcher at the San Diego Regional Economic Development Corp. There are more and more contracts coming, particularly through NAVWAR. Those federal contracts tend to be large, and theyre multi-year. So, theyre less vulnerable to business cycles.

UC San Diego was an early researcher in neural networks, said Rajesh Gupta, director of the Halicioglu Data Sciences Institute. That work helped pave the way for the machine learning engines that banks use to uncover credit card transaction fraud.

Gupta thinks the Brookings report underestimates San Diegos AI capabilities. This summer, a new AI Research Institute at UCSD won a $20 million grant from the National Science Foundation to tackle big, complicated problems.

Among them: tapping artificial intelligence to cut the time and cost of designing semiconductors; finding ways to improve communications networks; and researching how robots interact with humans to make self-driving cars safer.

The San Diego Super Computer Center also performs research related to AI, and the San Diego Association of Governments (SANDAG) has been an early proponent of AI-based smart cities technologies, said Gupta.

We have a $39 million effort going on today basically on grid response and making it intelligent, said Gupta. Its smart buildings, smart parking, smart transportation. These are what will define the metropolitan areas of tomorrow with AI embedded in them.

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San Diego ranks relatively high in national ranking for artificial intelligence innovation - The San Diego Union-Tribune

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