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The demand for agricultural products is surging in countries such as Brazil, India, the U.S., and China due to the rapid urbanization, surging disposable income, and changing consumption patterns of the booming population. On account of the soaring demand, these countries are leveraging artificial intelligence (AI) to increase their overall agricultural productivity. Owing to this reason, the AI in agriculture market is expected to progress at a robust CAGR of 24.8% during 20202030. According to P&S Intelligence, at this rate, the value of the market will rise from $852.2 million in 2019 to $8,379.5 million by 2030.

In recent years, the usage of smart sensors has increased tremendously in agriculture, as they enable farmers to map their fields accurately and apply crop treatment products to the areas that need them. Moreover, the development of several operation-specific sensors, including airflow sensors, location sensors, weather sensors, and soil moisture sensors, is assisting farmers in monitoring and optimizing their yields. Additionally, technology companies are developing smart sensors that are adaptable to the altering environmental conditions.

Additionally, the agrarian community is deploying drones in large numbers to monitor the growth and health of crops. Farmers use drones to scan the soil health, estimate the yield data, draft irrigation schedules, and apply fertilizers. Besides, the increasing support from the government has led to the widescale adoption of drones for modernizing agricultural practices. For example, in January 2019, the government of Maharashtra, India, partnered with the World Economic Forum (WEF) to enhance the agricultural yield by gathering insights about the farms through drones.

How Are AI-Powered Smart Sensors Improving Agricultural Practices?

Further, AI is being used in the agriculture sector to monitor the livestock in real-time. The utilization of AI solutions, such as facial recognition and image classification integrated with feeding patterns and condition score, enables dairy farms to individually monitor all the behavioral aspects of a herd. Moreover, farmers are using machine vision to recognize facial features and hide patterns, record the behavior and body temperature, and monitor the food and water intake of the livestock.

North America witnesses large-scale deployment of the AI technology in agricultural activities owing to the early adoption of computer vision and machine learning (ML) for soil management, precision farming, greenhouse management, and livestock management. Moreover, the increasing adoption of the internet of things (IoT) technology bolstered with computer vision will promote the application of AI solutions by the farming community. Besides, the existence of numerous technology vendors and sensor manufacturers in the region promotes the usage of the AI technologies in the agricultural space.

Furthermore, the Asia-Pacific (APAC) region is expected to adopt AI-enabled agricultural solutions at the fastest pace in the coming years. The high adoption rate of AI in China, Australia, India, and Japan will contribute significantly to the APAC AI in agriculture market in the future. Moreover, the entry of the Alibaba Group in the agricultural solution business, with its AI technology, will increase the penetration of these solutions in the Chinese agricultural industry. Additionally, India is utilizing such solutions due to the escalating effort by multinational companies (MNCs) and the government to spread awareness regarding data sciences and farm analytics among farmers.

Thus, the growing need to increase the crop yield and improve livestock management will fuel the adoption of AI-enabled solutions in the agricultural space.

Source: P&S Intelligence

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Farmers Increasing Their Crop Yield with Artificial Intelligence - Farmers Review Africa

Last month, DeepMind, a subsidiary of technology giant Alphabet, set Silicon Valley abuzz when it announced Gato, perhaps the most versatile artificial intelligence model in existence. Billed as a generalist agent, Gato can perform over 600 different tasks. It can drive a robot, caption images, identify objects in pictures, and more. It is probably the most advanced AI system on the planet that isnt dedicated to a singular function. And, to some computing experts, it is evidence that the industry is on the verge of reaching a long-awaited, much-hyped milestone: Artificial General Intelligence.

Unlike ordinary AI, Artificial General Intelligence wouldnt require giant troves of data to learn a task. Whereas ordinary artificial intelligence has to be pre-trained or programmed to solve a specific set of problems, a general intelligence can learn through intuition and experience.

An AGI would in theory be capable of learning anything that a human can, if given the same access to information. Basically, if you put an AGI on a chip and then put that chip into a robot, the robot could learn to play tennis the same way you or I do: by swinging a racket around and getting a feel for the game. That doesnt necessarily mean the robot would be sentient or capable of cognition. It wouldnt have thoughts or emotions, itd just be really good at learning to do new tasks without human aid.

This would be huge for humanity. Think about everything you could accomplish if you had a machine with the intellectual capacity of a human and the loyalty of a trusted canine companion a machine that could be physically adapted to suit any purpose. Thats the promise of AGI. Its C-3PO without the emotions, Lt. Commander Data without the curiosity, and Rosey the Robot without the personality. In the hands of the right developers, it could epitomize the idea of human-centered AI.

But how close, really, is the dream of AGI? And does Gato actually move us closer to it?

For a certain group of scientists and developers (Ill call this group the Scaling-Uber-Alles crowd, adopting a term coined by world-renowned AI expert Gary Marcus) Gato and similar systems based on transformer models of deep learning have already given us the blueprint for building AGI. Essentially, these transformers use humongous databases and billions or trillions of adjustable parameters to predict what will happen next in a sequence.

The Scaling-Uber-Alles crowd, which includes notable names such as OpenAIs Ilya Sutskever and the University of Texas at Austins Alex Dimakis, believes that transformers will inevitably lead to AGI; all that remains is to make them bigger and faster. As Nando de Freitas, a member of the team that created Gato, recently tweeted: Its all about scale now! The Game is Over! Its about making these models bigger, safer, compute efficient, faster at sampling, smarter memory De Freitas and company understand that theyll have to create new algorithms and architectures to support this growth, but they also seem to believe that an AGI will emerge on its own if we keep making models like Gato bigger.

Call me old-fashioned, but when a developer tells me their plan is to wait for an AGI to magically emerge from the miasma of big data like a mudfish from primordial soup, I tend to think theyre skipping a few steps. Apparently, Im not alone. A host of pundits and scientists, including Marcus, have argued that something fundamental is missing from the grandiose plans to build Gato-like AI into full-fledged generally intelligent machines.

If you put an AGI on a chip and then put that chip into a robot, the robot could learn to play tennis the same way you or I do: by swinging a racket around and getting a feel for the game.

I recently explained my thinking in a trilogy of essays for The Next Webs Neural vertical, where Im an editor. In short, a key premise of AGI is that it should be able to obtain its own data. But deep learning models, such as transformer AIs, are little more than machines designed to make inferences relative to the databases that have already been supplied to them. Theyre librarians and, as such, they are only as good as their training libraries.

A general intelligence could theoretically figure things out even if it had a tiny database. It would intuit the methodology to accomplish its task based on nothing more than its ability to choose which external data was and wasnt important, like a human deciding where to place their attention.

Gato is cool and theres nothing quite like it. But, essentially, it is a clever package that arguably presents the illusion of a general AI through the expert use of big data. Its giant database, for example, probably contains datasets built on the entire contents of websites such as Reddit and Wikipedia. Its amazing that humans have managed to do so much with simple algorithms just by forcing them to parse more data.

In fact, Gato is such an impressive way to fake general intelligence, it makes me wonder if we might be barking up the wrong tree. Many of the tasks Gato is capable of today were once believed to be something only an AGI could do. It feels like the more we accomplish with regular AI, the harder the challenge of building a general agent appears to be.

Call me old fashioned, but when a developer tells me their plan is to wait for an AGI to magically emerge from the miasma of big data like a mudfish from primordial soup, I tend to think theyre skipping a few steps.

For those reasons, Im skeptical that deep learning alone is the path to AGI. I believe well need more than bigger databases and additional parameters to tweak. Well need an entirely new conceptual approach to machine learning.

I do think that humanity will eventually succeed in the quest to build AGI. My best guess is that we will knock on AGIs door sometime around the early-to-mid 2100s, and that, when we do, well find that it looks quite different from what the scientists at DeepMind are envisioning.

But the beautiful thing about science is that you have to show your work, and, right now, DeepMind is doing just that. Its got every opportunity to prove me and the other naysayers wrong.

I truly, deeply hope it succeeds.

Tristan Greene is a futurist who believes in the power of human-centered technology. Hes currently the editor of The Next Webs futurism vertical, Neural.

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Opinion: The Long, Uncertain Road to Artificial General Intelligence - Undark Magazine

Sen. Ron Wyden, D-Ore., speaks during a Senate Finance Committee hearing on Oct. 19, 2021. Wyden says he has long been concerned about the algorithms used by his state's child welfare system. Mandel Ngan/AP hide caption

Sen. Ron Wyden, D-Ore., speaks during a Senate Finance Committee hearing on Oct. 19, 2021. Wyden says he has long been concerned about the algorithms used by his state's child welfare system.

Child welfare officials in Oregon will stop using an algorithm to help decide which families are investigated by social workers, opting instead for a new process that officials say will make better, more racially equitable decisions.

The move comes weeks after an Associated Press review of a separate algorithmic tool in Pennsylvania that had originally inspired Oregon officials to develop their model, and was found to have flagged a disproportionate number of Black children for "mandatory" neglect investigations when it first was in place.

Oregon's Department of Human Services announced to staff via email last month that after "extensive analysis" the agency's hotline workers would stop using the algorithm at the end of June to reduce disparities concerning which families are investigated for child abuse and neglect by child protective services.

"We are committed to continuous quality improvement and equity," Lacey Andresen, the agency's deputy director, said in the May 19 email.

Jake Sunderland, a department spokesman, said the existing algorithm would "no longer be necessary," since it can't be used with the state's new screening process. He declined to provide further details about why Oregon decided to replace the algorithm and would not elaborate on any related disparities that influenced the policy change.

Hotline workers' decisions about reports of child abuse and neglect mark a critical moment in the investigations process, when social workers first decide if families should face state intervention. The stakes are high not attending to an allegation could end with a child's death, but scrutinizing a family's life could set them up for separation.

From California to Colorado and Pennsylvania, as child welfare agencies use or consider implementing algorithms, an AP review identified concerns about transparency, reliability and racial disparities in the use of the technology, including their potential to harden bias in the child welfare system.

U.S. Sen. Ron Wyden, an Oregon Democrat, said he had long been concerned about the algorithms used by his state's child welfare system and reached out to the department again following the AP story to ask questions about racial bias a prevailing concern with the growing use of artificial intelligence tools in child protective services.

"Making decisions about what should happen to children and families is far too important a task to give untested algorithms," Wyden said in a statement. "I'm glad the Oregon Department of Human Services is taking the concerns I raised about racial bias seriously and is pausing the use of its screening tool."

Sunderland said Oregon child welfare officials had long been considering changing their investigations process before making the announcement last month.

He added that the state decided recently that the algorithm would be completely replaced by its new program, called the Structured Decision Making model, which aligns with many other child welfare jurisdictions across the country.

Oregon's Safety at Screening Tool was inspired by the influential Allegheny Family Screening Tool, which is named for the county surrounding Pittsburgh, and is aimed at predicting the risk that children face of winding up in foster care or being investigated in the future. It was first implemented in 2018. Social workers view the numerical risk scores the algorithm generates the higher the number, the greater the risk as they decide if a different social worker should go out to investigate the family.

But Oregon officials tweaked their original algorithm to only draw from internal child welfare data in calculating a family's risk, and tried to deliberately address racial bias in its design with a "fairness correction."

In response to Carnegie Mellon University researchers' findings that Allegheny County's algorithm initially flagged a disproportionate number of Black families for "mandatory" child neglect investigations, county officials called the research "hypothetical," and noted that social workers can always override the tool, which was never intended to be used on its own.

Wyden is a chief sponsor of a bill that seeks to establish transparency and national oversight of software, algorithms and other automated systems.

"With the livelihoods and safety of children and families at stake, technology used by the state must be equitable and I will continue to watchdog," Wyden said.

The second tool that Oregon developed an algorithm to help decide when foster care children can be reunified with their families remains on hiatus as researchers rework the model. Sunderland said the pilot was paused months ago due to inadequate data but that there is "no expectation that it will be unpaused soon."

In recent years while under scrutiny by a crisis oversight board ordered by the governor, the state agency currently preparing to hire its eighth new child welfare director in six years considered three additional algorithms, including predictive models that sought to assess a child's risk for death and severe injury, whether children should be placed in foster care, and if so, where. Sunderland said the child welfare department never built those tools, however.

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Oregon is dropping an artificial intelligence tool used in child welfare system - NPR

Greece is just one example of a population where the share of older people is expanding, and with it the incidences of neurodegenerative diseases. Among these, Alzheimers disease is the most prevalent, accounting for 70% of neurodegenerative disease cases in Greece. According to estimates published by the Alzheimer Society of Greece, 197,000 people are suffering from the disease at present. This number is expected to rise to 354,000 by 2050.

Dr. Andreas Papadopoulos1, a physician and scientific coordinator at Iatropolis Medical Group, a leading diagnostic provider near Athens, Greece, explains the key role of early diagnosis: The likelihood of developing Alzheimers may be only 1% to 2% at age 65. But then it doubles every five years. Existing drugs cannot reverse the course of the degeneration; they can only slow it down. This is why its crucial to make the right diagnosis in the preliminary stageswhen the first mild cognitive disorder appearsand to filter out Alzheimers patients2.

Diseases like Alzheimers or other neurodegenerative pathologies characteristically have a very slow progression, which makes is difficult to recognize and quantify pathological changes on brain MRI images at an early stage. In evaluating scans, some radiologists describe the process as one of guesstimation, as visual changes in the highly complex anatomy of the brain are not always possible to observe well with the human eye. This is where technical innovations such as artificial intelligence can offer support in interpreting clinical images.

One such tool is the AI-Rad Companion Brain MR3. Part of a family of AI-based, decision-support solutions for imaging, AI-Rad Companion Brain MR is a brain volumetry software that provides automatic volumetric quantification of different brain segments. It is able to segment them from each other: it isolates the hippocampi and the lobes of the brain and quantifies white matter and gray matter volumes for each segment individually. says Dr. Papadopoulos. In total, it has the capacity to segment, measure volumes, and highlight more than 40 regions of the brain.

Calculating volumetric properties manually can be an extremely laborious and time-consuming task. More importantly, it also involves a degree of precise observation that humans are simply not able to achieve. says Dr. Papadopoulos. Papadopoulos has always been an early adopter and welcomed technological innovations in imaging throughout his career. This AI-powered tool means that he can now also compare the quantifications with normative data from a healthy population. And its not all about the automation: the software displays the data in a structured report and generates a highlighted deviation map based on user settings. This allows the user to also monitor volumetric changes manually with all the key data prepared automatically in advance.

Opportunities for more accurate observation and evaluation of volumetric changes in the brain encourages Papadopoulos when he considers how important the early detection of neurodegenerative diseases is. He explains: In the early stages, the volumetric changes are small. In the hippocampus, for example, there is a volume reduction of 10% to 15%, which is very difficult for the eye to detect. But the objective calculations provided by the system could prove a big help.

The aim of AI is to relieve physicians of a considerable burden and, ultimately, to save time when optimally embedded in the workflow. An extremely valuable role for this particular AI-powered postprocessing tool is that it can visualize a deviation of the different structures that might be hard to identify with the naked eye. Papadopoulos already recognizes that the greatest advantage in his work is the objective framework that AI-Rad Companion Brain MR provides on which he can base his subjective assessment during an examination.

AI-Rad Companion4 from Siemens Healthineers supports clinicians in their daily routine of diagnostic decision-making. To maintain a continuous value stream, our AI-powered tools include regular software updates and upgrades that are deployed to the customers via the cloud. Customers can decide whether they want to integrate a fully cloud-based approach into their working environment leveraging all the benefits of the cloud or a hybrid approach that allows them to process imaging data within their own hospital IT setup.

The upcoming software version of AI-Rad Companion Brain MR will contain new algorithms that are capable of segmenting, quantifying, and visualizing white matter hyperintensities (WMH). Along with the McDonald criteria, reporting WHM aids in multiple sclerosis (MS) evaluation.

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Evaluating brain MRI scans with the help of artificial intelligence - MIT Technology Review

Mention artificial intelligence to pretty much anyone and there's a good chance that the term that once seemed magical now spawns a queasy feeling. It generates thoughts of a computer stealing your job, technology companies spying on us, and racial, gender and economic bias.

So, how do we bring the magic back to AI? Maybe it comes down to people and things that humans actually do pretty well: thinking and planning. That's one finding that will become clear in a review of the articles in this Quick Study packed with InformationWeek articles focused on AI ethics and bias.

Yes, there are ways to develop and utilize AI in ethical manners, but they involve thinking through how your organization will use AI, how you will test it, and what your training data looks like. In these articles AI experts and companies that have succeeded with AI share their advice.

What You Need to Know About AI Ethics

Honesty is the best policy. The same is true when it comes to artificial intelligence. With that in mind, a growing number of enterprises are starting to pay attention to how AI can be kept from making potentially harmful decisions.

Why AI Ethics Is Even More Important Now

Contact-tracing apps are fueling more AI ethics discussions, particularly around privacy. The longer term challenge is approaching AI ethics holistically.

Data Innovation in 2021: Supply Chain, Ethical AI, Data Pros in High Demand

Year in Review: In year two of the pandemic, enterprise data innovation pros put a focus on supply chain, ethical AI, automation, and more. From the automation to the supply chain to responsible/ethical AI, enterprises made progress in their efforts during 2021, but more work needs to be done.

The Tech Talent Chasm

How a changing world is forcing businesses to rethink everything, and in recruiting IT talent understand that great candidates want their employers to take AI ethics seriously.

3 Components CIOs Need to Create an Ethical AI Framework

CIOs shouldnt wait for an ethical AI framework to be mandatory. Whether buying the technology or building it, they need processes in place to embed ethics into their AI systems, according to PwC.

Why You Should Have an AI & Ethics Board

Guidelines are great -- but they need to be enforced. An ethics board is one way to ensure these principles are woven into product development and uses of internal data, according to the chief data officer of ADP.

How and Why Enterprises Must Tackle Ethical AI

Artificial intelligence is becoming more common in enterprises, but ensuring ethical and responsible AI is not always a priority. Here's how organizations can make sure that they are avoiding bias and protecting the rights of the individual.

Common AI Ethics Mistakes Companies Are Making

More organizations are embracing the concept of responsible AI, but faulty assumptions can impede success.

How IT Pros Can Lead the Fight for Data Ethics

Maintaining ethics means being alert on a continuum for issues. Heres how IT teams can play a pivotal role in protecting data ethics.

Ex-Googler's Ethical AI Startup Models More Inclusive Approach

Backed by big foundations, ethical AI startup DAIR promises a focus on AI directed by and in service of the many rather than controlled just by a few giant tech companies. How do its goals align with your enterprise's own AI ethics program?

The Cost of AI Bias: Lower Revenue, Lost Customers

A survey shows tech leadership's growing concern about AI bias and AI ethics, as negative events impact revenue, customer losses, and more.

What We Can Do About Biased AI

Biased artificial intelligence is a real issue. But how does it occur, what are the ramifications -- and what can we do about it?

How Fighting AI Bias Can Make Fintech Even More Inclusive

Digitized presumptions, encoded by very human creators, can introduce prejudice in new financial technology meant to be more accessible.

Im Not a Cat: The Human Side of Artificial Intelligence

Unconscious biases will be reflected in the data that feeds your AI and ML algorithms. Here are three simple actions to dismantle unconscious bias in AI.

When A Good Machine Learning Model Is So Bad

IT teams must work with managers who oversee data scientists, data engineers, and analysts to develop points of intervention that complement model ensemble techniques.

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Quick Study: Artificial Intelligence Ethics and Bias - InformationWeek