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Alphago Games - Visual Archive

It is able to do this by using a novel form of reinforcement learning, in which AlphaGo Zero becomes its own teacher. The system starts off with a neural network that knows nothing about the game of Go. It then plays games against itself, by combining this neural network with a powerful search algorithm. As it plays, the neural network is tuned and updated to predict moves, as well as the eventual winner of the games.

This updated neural network is then recombined with the search algorithm to create a new, stronger version of AlphaGo Zero, and the process begins again. In each iteration, the performance of the system improves by a small amount, and the quality of the self-play games increases, leading to more and more accurate neural networks and ever stronger versions of AlphaGo Zero.

This technique is more powerful than previous versions of AlphaGo because it is no longer constrained by the limits of human knowledge. Instead, it is able to learn tabula rasa from the strongest player in the world: AlphaGo itself.

It also differs from previous versions in other notable ways.

All of these differences help improve the performance of the system and make it more general. But it is the algorithmic change that makes the system much more powerful and efficient.

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AlphaGo Zero: Starting from scratch | DeepMind

Like new Alexa Skills on your Amazon Echo, these past couple of decades have seen A.I. gradually gain the ability to best humanity at more and more of our beloved games: Chess with Deep Blue in 1997, Jeopardy with IBM Watson in 2011, Atari games with DeepMind in 2013, Go with AlphaGo in 2016, and so on. To the general public, at least, each instance turns the abstract path of computational progress into a spectator sport. Skynet is getting smarter. How do we know? Because check out the growing number of pastimes it can convincingly beat us at.

With that background, its not too much of a shocker to hear that A.I. can now perform compellingly well at Pictionary, the charades-inspired word guessing game that requires one person to draw an image and others to try to figure out what theyve sketched as quickly as possible.

Thats what researchers from the U.K.s University of Surrey recently carried out with the creation of Pixelor, a competitive sketching A.I. agent. Given a visual concept, Pixelor is able to draw a sketch that is recognizable (both by humans and machines) as its intended subject as quickly or even faster than a human competitor.

Our A.I. agent is able to render a sketch from scratch, Yi-Zhe Song, reader of Computer Vision and Machine Learning at the Center for Vision Speech and Signal Processing at the University of Surrey, Give it a word like face and it will know what to draw. It will draw a different cat, a different dog, a different face, every single time. But always with the knowledge of how to win the Pictionary game.

Being able to reduce a complex real-world image into a sketch is, itself, pretty impressive. It takes a level of abstraction to look at a human face and see it as an oval with two smaller ovals for eyes, a line for a nose, and a half-circle for a mouth. In kids, the ability to perceive an image in this way shows, among other things, a burgeoning cognitive understanding of concepts.

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However, as with many aspects of A.I., often summarized as Moravecs Paradox that the hard problems are easy and the easy problems are hard, its a significant challenge for machine intelligence despite the fact that its a basic, unremarkable skill for the majority of two-year-old children.

Its not an unsolvable challenge, though. In 2016, we wrote about Songs work with a tool called Sketch, a deep-learning neural network that was able to recognize hand-drawn sketches and use them to search for real-life products. That particular network was trained using a dataset consisting of some 30,000 sketch-photo comparisons, allowing it to be able to recognize the way real objects are presented in hand drawing. Pixelor does something similar, but can also generate its own drawings, rather than just recognizing other peoples.

But thats not enough to win at Pictionary. Pictionary is a time-challenged game where the goal isnt just to draw, say, a cat, but to draw a cat in as few strokes as possible. You could be the worlds greatest artist but, if it takes you 12 hours to draw a picture-perfect cat, youre a terrible Pictionary player.

This meant building an A.I. that could study humans to see which strategies they use to play Pictionary well. As Song said, What are the most important bits to draw to enable other human judges to be able to guess? We want our drawing to be guessed as early as possible.

To do this, the researchers took QuickDraw, the largest human sketch dataset available to date. They then built a neural sorting algorithm that prioritizes the order of strokes an artist needs to make; giving a guessable representation of an object in as few lines as possible. This means breaking sketches down into strokes, then shuffling the order of these strokes and testing the results until they establish the precise order in which they need to be laid down on paper.

For example, an artist could start drawing a cat by sketching a circular outline for its head. But a circle could be any number of things, even if you know that it is supposed to represent a head. Draw two pointy ears, however, or two sets of whiskers and the number of potential things that you could be drawing reduces very, very quickly. This information is then used to instruct the sketching agent.

Song said that the team could release a public-facing version of this Pictionary-playing bot so that human players can have their own go at beating a sketching A.I. master. (Who knows? Playing an expert could even help improve your own Pictionary game.)

Theres more to Pixelor than just another trivial game-playing bot, however. Just like a computer system has both a surface-level interface that we interact with and under-the-hood backend code, so, too, does every major A.I. game-playing milestone have an ulterior motive. Unless theyre explicitly making computer games, research labs dont spend countless person-hours building game-playing A.I. agents just to add another entry on the big list of things humans are no longer the best at. The purpose is always to advance some fundamental part of A.I. problem-solving.

In the case of Pixelor, the hidden objective is to make machines that are better able to figure out whats important to a human in a particular scene. When we look at an image, were immediately able to tell what the most salient details are.

Lets say youre driving home from work. While the trees lining the side of the road may be picturesque and the billboard for a new movie could be interesting, neither is as important as the face and body language of the person who may or may not be about to walk out in front of you. Before you have even consciously processed the information, your brain has singled out the most important details. How do you teach a computer to be able to do this? Well, it turns out that one great way to do so is to see how humans prioritize the salient recognizable details in an image when theyre sketching it.

Theres no human knowledge inherently embedded in photos [alone], said Song. What we want is human data which can give us signals on how humans understand an object.

As noted, a good Pictionary player, like a good boxer, will know the absolute minimum they need to do to achieve a certain objective. This, in a macro sense, is what Yi-Zhe Song and his colleagues care about. Its not anything as trivial as getting a computer to play a game; its getting a computer to understand whats important about certain scenes and, hopefully, to be able to better generalize.

As everything from self-driving cars to robots in the workplace become increasingly common, this is an essential task to solve.

A paper describing the work will be presented at SIGGRAPH Asia 2020 in November.

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This bot will destroy you at Pictionary. Its also a huge milestone for A.I. - Yahoo! Voices

Artificial intelligence, or AI, has long been the object of excitement and fear.

In July, the Financial Times Future Forum think-tank convened a panel of experts to discuss the realities of AI what it can and cannot do, and what it may mean for the future.

Entitled The Impact of Artificial Intelligence on Business and Society, the event, hosted by John Thornhill, the innovation editor of the FT, featured Kriti Sharma, founder of AI for Good UK, Michael Wooldridge, professor of computer sciences at Oxford university, and Vivienne Ming, co-founder of Socos Labs.

For the purposes of the discussion, AI was defined as any machine that does things a brain can do. Intelligent machines under that definition still have many limitations: we are a long way from the sophisticated cyborgs depicted in the Terminator films.

Such machines are not yet self-aware and they cannot understand context, especially in language. Operationally, too, they are limited by the historical data from which they learn, and restricted to functioning within set parameters.

Rose Luckin, professor at University College London Knowledge Lab and author of Machine Learning and Human Intelligence, points out that AlphaGo, the computer that beat a professional (human) player of Go, the board game, cannot diagnose cancer or drive a car. A surgeon might be able to do all of those things.

Intelligent machines are, therefore, unlikely to unseat humans in the near future but they will come into their own as a valuable tool. Because of developments in neural technology and data collection, as well as increased computing power, AI will augment and streamline many human activities.

It will take over repetitive manufacturing processes and perform routine tasks involving language and pattern recognition, as well as assist in medical diagnoses and treatment. Used properly, intelligent machines can improve outcomes for products and services.

To stay ahead of the competition, companies must think creatively about how to incorporate AI into their strategy. This report looks at areas where AI can be deployed, some of the issues that may arise and what we should expect to see.

Adoption of AI has been particularly widespread in the financial services sector. Forrester, the research group, notes that about two-thirds of finance firms have implemented or are adding AI in areas from customer insights to IT efficiencies. Data analysis already detects fraud.

Jamie Dimon, chief executive of JPMorgan, noted in 2018 that as well as having the potential to provide about $150m of benefits each year, machine-learning systems allowed for the approval of 1m good customers who might otherwise have been declined, while an equal number of fraudulent applications were turned down.

AI is also useful in stock market analysis. Schroders, the fund manager, says such systems are basically sophisticated pattern-recognition methods yet they can nevertheless add value and improve productivity.

Schroders uses AI in tools that forecast the performance of companies after initial public offerings, monitor directors trades and analyse the language in transcripts of meetings.

Like many other businesses, the company also employs AI to automate low-judgment, repetitive back-office processes.

Interestingly Schroders believes we may already be at peak AI since the technology is difficult to implement in a meaningful way for many of the high-complexity tasks that a typical knowledge worker does as part of their job.

Professor Richard Susskind, author of Online Courts and the Future of Justice and technology adviser to the Lord Chief Justice of England and Wales, observes that professionals invariably see much greater scope for the use of AI in professions other than their own.

Elsewhere in professional services, law firms have applied language recognition to assess contracts, streamline redaction and sift materials for review in litigation cases, as well as to analyse judgments. The London firm Clifford Chance notes, however, that the facilitation of processes does not yet transform the legal approach.

Prof Susskind says: I am in no doubt that much of the work of todays lawyers will be taken on by tomorrows machines. This could have major implications for how lawyers are trained and recruited.

Healthcare is another sector to benefit from AIs rapid development.

Applied to large data sets, AI has identified new drug solutions, enabled the selection of candidates for clinical trials and monitored patients with specific conditions. Roche, for example, uses deep-learning algorithms to gain insights into Parkinsons disease.

In the consumer sector, data and language analysis has been applied to develop translation apps, online moderation and product and content marketing. It has also identified epidemic outbreaks and verified academic papers.

In energy, Iberdrola, the Spanish multinational, has achieved efficiency gains that benefit both the company and the environment. It uses AI to improve the operation and maintenance of its assets through data analytics. Systems developed with machine learning co-ordinate the planning and delivery of maintenance, monitor electricity usage and optimise distribution.

Set against these advances, it should be acknowledged that AI has also worked in less benign ways: it has given criminals the means to commit sophisticated fraud and assisted in the creation and dissemination of fake news.

Chatbots software that can simulate conversation have become the mainstay of many customer service centres and are used to answer questions on topics ranging from product options for online marketplaces to telephone inquiries at utilities and banks.

These digital assistants vary in sophistication and are limited by their command of what is known as natural language processing: the ability to treat words as more than mere inputs and outputs. This makes empathetic responses difficult to simulate, while the inability to comprehend context means that AI cannot distinguish a joke from a slur. Advances in this area could be transformational to the range of possible applications, as well as to acceptance by consumers.

Elsewhere AI developed by Huawei has been deployed by Rainforest Connection to fight illegal logging and poaching.

Facial recognition is perhaps the best-known use of image analysis. From its application in identity verification to unlock mobile phones to its more sinister deployment by surveillance states in Xinjiang province in China, for instance its adoption is increasingly widespread.

There remain significant drawbacks to the technology, not least its unreliability in identifying the faces of people of colour just one of the many ethical problems connected to the use of AI.

Less controversially, image analysis is being used in the medical industry. It can help in the identification and diagnosis of diseases such as cancer and its performance in eye scans is at least as accurate as that of human specialists.

In 2018 the US Food and Drug Administration approved a retinal scan algorithm designed by IDx, an Iowa start-up, that can diagnose diabetic retinopathy without the need for an eyecare specialist. The implications for healthcare could be far-reaching, both in terms of changes in the skills needed as well as improved access to care.

Image recognition has also been put to use in environmental conservation. A platform called Ewa Guard, jointly developed by Lenovo and Bytelake, remotely counts trees and monitors the health of forests. Lenovo, which is based in Beijing, has joined North Carolina State University in the US to apply deep-learning algorithms to identify farmland and monitor soil and crops to optimise water management.

A further possible application is in waste management, where image identification may assist robots to extract recyclable items based on logo or component recognition.

Personalisation of products and marketing is an area of rapid development which could greatly benefit manufacturers and retailers. A 2018 report from PwC, the Big Four accounting firm, estimated that the value derived from the effect of AI on consumer behaviour, for instance through product personalisation and an increase in free time, could be as much as $9.1tn by 2030.

Among the sophisticated algorithms to personalise internet content is that used by TikTok, the app that allows users to upload short videos. Byte Dance, TikToks owner, revealed in June that its system is based on user interactions, video information and to a lesser extent, device and account settings.

Cosmetics, too, can be personalised by data analysis. Companies such as Kao, a beauty group, use genetic data to tackle wrinkles and dermatological conditions.

Meanwhile the redesign of carmaking processes by Mercedes converting dumb robots on its production line into human-operated, AI-assisted cobots has enabled a previously impossible level of customisation, such that no two cars coming off the production line are the same, according to a report in Harvard Business Review.

So much for the way AI is being deployed in businesses around the world. What are the implications of its widespread adoption?

For a business to adopt AI with any degree of success it must have a coherent and active strategy. Equally critical is that the strategy is controlled centrally rather than executed piecemeal: businesses need to consider the use of AI holistically, so that entire processes are reimagined, along with the redesign of tasks to blend machine and employee skills.

FT panellist Ms Ming cited an example in which her company came up with a tool to eradicate inefficiencies in manufacturing processes. While the technology did what was needed, the companies were not ready to act as their entire workflows would have to change.

This perhaps offers an advantage to companies that operate without the burden of legacy processes, but incremental change is still better than none. Research by Automation Anywhere and Goldsmiths, University of London found that [AI] augmented companies enjoy 28 per cent better performance levels compared with competitors.

Buy-in from employees is also essential and can be made easier by including the workforce in the process of redesigning their roles. Lenovo suggests that in future as teams become more experienced, part of their training will be focused...in identifying which parts of their work are suitable to deploy AI towards. Communication and transparency with employees is critical to engendering trust in the adoption of AI.

IT systems, too, are likely to need a radical overhaul to function in an AI world, and those built from scratch will be more effective than bolt-ons to existing software. Although the cost may be daunting, Clifford Chance argues that the marginal cost of AI systems is relatively low once they are built and offset by the fact that AI can help to significantly reduce the cost of providing legal services.

As well as establishing ownership of AI strategy at board level, companies will also need to consider how to deal with the ethical challenges the technology brings. Coupled with the focus on environmental, social and governance (ESG) goals encouraged by the Covid-19 crisis, is a need for more formalised ethics oversight on boards to ensure that AI implementation conforms with corporate values. Could chief ethics officer be the next boardroom position?

Businesses will have to consider the risk of deploying AI from multiple perspectives, including the legal, regulatory and ethical.

In a global survey of 200 board members, Clifford Chance found that 88 per cent agreed (somewhat or strongly) that their board fully understands the legal, regulatory and ethical implications of their AI use, but that only 36 per cent of the same board members said they had taken preliminary steps to address the risks posed by lack of oversight for AI use.

We are all familiar with blood-curdling predictions that AI could steal our jobs. The consensus among researchers, however, is that rather than put humans out of work, the adoption of AI is more likely to change both the nature of the jobs we do and how we carry them out.

In its Future of Jobs Report 2018 the World Economic Forum cited one set of estimates indicating that while 75m jobs may be displaced, 133m could be created to adapt to the new division of labour between humans, machines and algorithms.

Carl Frey, author of The Technology Trap and director of the Future of Work programme at Oxford Martin School, estimated in 2013 that 47 per cent of US jobs (based on occupation classifications) were at risk of automation, while UK categorisations gave a figure of 35 per cent.

These numbers have been widely debated but Mr Frey observes that they account for those jobs that can be restructured in order to be automated and individuals can be allocated new tasks as long as they acquire fresh skills.

While occupations involving, say, the ability to navigate social relations are to a large extent secure, Mr Frey points out that this is true mainly for more complex interactions. For example, fast-food outlets, where interaction is not integral to the appeal of a product, use more automation technology than fine-dining restaurants.

As businesses reliance on AI increases, it is clear that a redistribution of labour is inevitable. To deal with the shift in skills that this implies, retraining the workforce is critical. The WEF notes that on average about half of the workforce across all sectors will require some retraining to accommodate changes in working patterns brought about by AI.

Prof Luckin points out that businesses have a huge amount of data on their staff that could be invaluable to understanding how to optimise redeployment. The savvy businesses will be really trying to understand their current workforce and what workforce they need, and looking to see how they can retrain on that basis.

Much of that education is likely to go to the higher-skilled segment of the workforce and saving people if not saving jobs will have to be considered. In the first instance, the burden may fall to governments but the threat to low-skilled workers could require businesses to pick up the slack, especially given the additional pressures caused by Covid-19.

So far it appears that the pandemic has accelerated the trend towards automation. The effect is being felt in call centres, part of an outsourcing services industry worth nearly $25bn to the Philippines in 2018. Even before the pandemic, the IT and Business Process Association of the Philippines noted that the increase in headcount in 2017 and 2018 had been just 3.5 per cent, against a forecast of nearly 9 per cent. One of the reasons for this is increased automation.

Call centre operators in countries such as the Philippines and India have suffered further from the requirement to work from home during the pandemic. They have been hampered by poor infrastructure, which ranges from a lack of IT equipment or fast internet to security considerations when dealing with customers financial information.

At the end of April, US-based outsourcer [24]7.ai said demand for some automated products had risen by half since the beginning of the year, well ahead of the call for human services.

Food preparation roles may also be at increasing risk of redundancy because of automation spurred by Covid-19, according to the European Centre for the Development of Vocational Training. The advent of robots such as Flippy, which can cook burgers and french fries and knows when to clean its own tools, shows that such a shift is not out of the question.

One domain in which AI has failed to encroach successfully, says Mr Frey, is the arts: creative output that is original and makes sense to people has not yet been successfully replicated, even if an algorithm could be programmed to produce something that sounds similar to Mozart.

The reason is simply that artists dont just draw upon pre-existing works, they draw upon experiences from all walks of life maybe even a dream and a lot of our experiences are always going to be outside of the training dataset.

Mr Freys point is echoed by Prof Wooldridge, who said people will have to wait a long time for works created by AI that would deeply engage them.

AI affects education many ways. People will need to be taught what AI is and how to use it, as well as the way its inputs and outputs are conceived. Education is also crucial to establishing public trust.

This summers school exam-marking controversy in the UK shows what happens when trust in computer-generated results is eroded. An automated system designed to mark A-levels in line with previous years led to a public outcry. A lack of transparency as to how the algorithms used would work, combined with a lack of confidence in the metrics used, undermined the exercise.

Prof Luckin stresses that if public consent and trust are to be gained, then AI-driven processes should be both transparent and easily explained.

Data literacy will be hugely important, says Prof Luckin, to ensure that people are equipped to assess and refine AI output.

Thats the real problem. It was an algorithm and they took the human out of the loop. It needed much more human intervention with the data. It is just having someone who is contextually aware going hang on a minute, thats not going to work.

Finally, AI can also be used as a pedagogical tool, complementing the work of human teachers. It can assess our ability to learn and advise us on the best way to retain information. For example, Up Learn, a UK company, offers learning powered by AI and neuroscience and promises a refund in the event that customers do not achieve a top grade.

The widespread adoption of AI obviously raises ethical challenges, but numerous organisations have sprung up to monitor and advise on best practice. These include AI for Good, the AI Now Foundation and Partnership on AI.

Governments are also taking steps, with more than 40 countries adopting the OECD Principles on Artificial Intelligence in May 2019 as a global reference point for trustworthy AI. At about the same time, China released its Beijing AI Principles. In July, the European Commission published the results of its white paper consultation canvassing views on regulation and policy.

Despite this there is no globally agreed set of standards: regulation remains piecemeal.

The British A-level controversy drew attention to the problem of historical bias, showing how AI is dependent on data and programming inputs.

Diversity is another problem, both in terms of the poor representation of women among AI professionals but also in how AI is developed. Facial recognition, for instance, works best on white male faces, a technical problem for which, Ms Ming noted, there is limited incentive to fix in the absence of regulatory enforcement.

On the other hand, AI can help to promote diversity through colour-blind recruitment processes. Schroders, for example, uses AI tools when it looks for early-career trainees and graduates. Given that the alternative is people looking at candidates CVs (with ample scope to favour candidates like themselves), the company says, this can be much more fair.

Facial recognition technology raises further ethical concerns in relation to surveillance for instance, of the Uighur population in China.

Abuse of data harvested through facial recognition is not restricted to the state, however. Identity fraud and data privacy are significant problems.

In July, UK and Australian regulators announced a joint investigation of Clearview AI, the facial recognition company whose image-scraping tool has been used by police forces around the world.

Other ethical problems loom. Gartner says that by 2022 one-tenth of personal devices will have emotion AI capabilities, allowing them to recognise and respond to human emotions, which will present opportunities for manipulative marketing. Accenture advises that the groundwork for the ethically responsible use of such technology needs to be laid now.

Businesses and employees alike need to be prepared for what is likely to be widespread and sometimes bewildering change as a result of AI adoption, and the ethical and regulatory challenges that will come with it.

Doubters find it hard to grasp that the pace of technological change is accelerating, not slowing down, says Prof Susskind.

There is no apparent finishing line. Machines will outperform us not by copying us but by harnessing the combination of colossal quantities of data, massive processing power and remarkable algorithms.

This article is part of the FT Future Forum an authoritative space for businesses to share ideas, build relationships and develop solutions to future challenges.

Continued here:
The impact of AI on business and society - Financial Times

New automated harvesters like the Harvest CROO Robotics strawberry robot utilizes AI to capture images of ripe berries ready to pick.Photo by Frank Giles

Artificial intelligence, or AI as it is more commonly called, has become more prominent in conversations about technology these days. But what does it mean? And how might it shape the future of agriculture?

In many ways, AI is already at work in agricultural research and in-field applications, but there is much more to come. Researchers in the field are excited about its potential power to process massive amounts of data and learn from it at a pace that far outstretches the capability of the human mind.

The newly installed University of Florida Vice President of Agriculture and Natural Resources, Scott Angle, sees AI as a unifying element of technology as it advances.

Robotics, visioning, automation, and genetic breakthroughs will need advanced AI to benefit growers, he says. Fortunately, UF recognized this early on and is developing a program to significantly ramp up AI research at the university.

Jim Carroll is a global futurist who specializes in technology and explaining it in a way that non-computer scientists can understand. He says first and foremost, AI is not some out-of-control robot that will terrorize and destroy our way of life like it is often portrayed in the media and popular culture.

This isnt new, Carroll says. I actually found articles in Popular Mechanics magazine in the 1930s that spoke of Giant Robot Brains that would steal all our jobs.

What is AI, really? The best way to think about it is that its an algorithm at heart its a computer that is really good at processing data, whether that be pure data, images, or other information. It has been trained and learns how to recognize patterns, trends, and insights in that information. The more it does it and gets the right scores, the better it gets. Its not really that scary.

John McCarthy is considered one of the founding fathers of AI and is credited with coining the term in 1955. He was joined by Alan Turing, Marvin Minsky, Allen Newell, and Herbert Simon in the early development of the technology.

Back in 1955, AI entered the academic world as a new discipline, and in subsequent years has experienced momentum in fits and starts. The technology went through a phase of frozen funding some called the AI winter. Some of this was because AI research was divided into subfields that didnt communicate with each other. Robotics went down one path while machine learning went down another. How and where would artificial neural networks be applied to practical effect?

But, as computing power has in-creased exponentially over time, AI, as Angle notes, is becoming a unifying technology that can tie all the subfields together. What once could only be imagined is becoming reality.

Dr. Yiannis Ampatzidis, an Assistant Professor who teaches precision agriculture and machine learning at UF/IFAS, says applications are already at work in agriculture including imaging, robotics, and big data analysis.

In precision agriculture, AI is used for detecting plant diseases and pests, plant stress, poor plant nutrition, and poor water management, Ampatzidis says. These detection technologies could be aerial [using drones] or ground based.

The imaging technology used to detect plant stress also could be deployed for precision spraying applications. Currently, John Deere is working to commercialize a weed sprayer from Blue River Technology that detects weeds and applies herbicides only to the weed.

Ampatzidis notes AI is utilized in robotics as well. The technology is used in the blossoming sector of robot harvesters where it is utilized to detect ripe fruit for picking. Floridas Harvest CROO Robotics is one example. Its robot strawberry harvester was used in commercial harvest during the 2019-2020 strawberry season in Florida.

Ampatzidis says AI holds great potential in the analytics of big data. In many ways, it is the key to unlocking the power of the massive amounts of data being generated on farms and in ag research. He and his team at UF/IFAS have developed the AgroView cloud-based technology that uses AI algorithms to process, analyze, and visualize data being collected from aerial- and ground-based platforms.

The amount of these data is huge, and its very difficult for a human brain to process and analyze them, he says. AI algorithms can detect patterns in these data that can help growers make smart decisions. For example, Agroview can detect and count citrus trees, estimate tree height and canopy size, and measure plant nutrient levels.

Carroll adds there is so much data in imagery being collected today.

An AI system can often do a better analysis at a lower cost, he says. Its similar to what we are talking about in the medical field. An AI system can read the information from X-rays and be far more accurate in a diagnosis.

So, are robots and AI coming to steal all our jobs? Thats a complicated question yet to be fully played out as the technology advances. Ampatzidis believes the technology will replace repetitive jobs and ones that agriculture is already struggling to fill with human labor.

It will replace jobs in factories, in agriculture [hand harvesters and some packinghouse jobs], vehicle drivers, bookkeepers, etc., Ampatzidis says. It also will replace many white-collar jobs in the fields of law, healthcare, accounting, hospitality, etc.

Of course, AI also could develop new jobs in the area of computer science, automation, robotics, data analytics, and computer gaming.

Carroll adds people should not fear the potential creative destruction brought on by the technologies enabled by AI. I always tell my audiences, Dont fear the future, he says. I then observe that some people see the future and see a threat. Innovators see the same future and see an opportunity.

Yiannis Ampatzidis, an Assistant Professor who teaches precision agriculture and machine learning at UF/IFAS, says AI applications are already at work in agriculture.Photo by Frank Giles

In July, the University of Florida announced a $70 million public-private partnership with NVIDIA, a multinational technology company, to build the worlds fastest AI supercomputer in academia. The system will be operating in early 2021. UF faculty and staff will have the tools to apply AI in multiple fields, such as dealing with major challenges like rising sea levels, population aging, data security, personalized medicine, urban transportation, and food insecurity. UF expects to educate 30,000 AI-supporting graduates by 2030.

AlphaGo, a 2017 documentary film, probably does about as good a job as any in illustrating the potential power of AI. The film documents a team of scientists who built a supercomputer to master the board game Go that originated in Asia more than 3,000 years ago. It also is considered one of the most complex games known to man. The conventional wisdom was that no computer would be capable of learning the vast number of solutions in the game and the reasoning required to win.

The computer, AlphGo, not only mastered the game in short order it took down human masters and champions of the game.

To learn more about the film, visit AlphaGoMovie.com.

Giles is editor of Florida Grower, a Meister Media Worldwide publication. See all author stories here.

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How Artificial Intelligence Will Guide the Future of Agriculture - Growing Produce