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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.

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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

Sophia. Source: https://www.hansonrobotics.com/press/

Humans are the most advanced form of Artificial Intelligence (AI), with an ability to reproduce.

Artificial Intelligence (AI) is no longer a theory but is part of our everyday life. Services like TikTok, Netflix, YouTube, Uber, Google Home Mini, and Amazon Echo are just a few instances of AI in our daily life.

This field of knowledge always attracted me in strange ways. I have been an avid reader and I read a variety of subjects of non-fiction nature. I love to watch movies not particularly sci-fi, but I liked Innerspace, Flubber, Robocop, Terminator, Avatar, Ex Machina, and Chappie.

When I think of Artificial Intelligence, I see it from a lay perspective. I do not have an IT background. I am a researcher and a communicator; and, I consider myself a happy person who loves to learn and solve problems through simple and creative ideas. My thoughts on AI may sound different, but Im happy to discuss them.

Humans are the most advanced form of AI that we may know to exit. My understanding is that the only thing that differentiates humans and Artificial Intelligence is the capability to reproduce. While humans have this ability to multiply through male and female union and transfer their abilities through tiny cells, machines lack that function. Transfer of cells to a newborn is no different from the transfer of data to a machine. Its breathtaking that how a tiny cell in a human body has all the necessary information of not only that particular individual but also their ancestry.

Allow me to give an introduction to the recorded history of AI. Before that, I would like to take a moment to share with you my recent achievement that I feel proud to have accomplished. I finished a course in AI from Algebra University in Croatia in July. I could attend this course through a generous initiative and bursary from Humber College (Toronto). Such initiatives help intellectually curious minds like me to learn. I would also like to express that the views expressed are my own understanding and judgment.

What is AI?

AI is a branch of computer science that is based on computer programming like several other coding programs. What differentiates Artificial Intelligence, however, is its aim that is to mimic human behavior. And this is where things become fascinating as we develop artificial beings.

Origins

I have divided the origins of AI into three phases so that I can explain it better and you dont miss on the sequence of incidents that led to the step by step development of AI.

Phase 1

AI is not a recent concept. Scientists were already brainstorming about it and discussing the thinking capabilities of machines even before the term Artificial Intelligence was coined.

I would like to start from 1950 with Alan Turing, a British intellectual who brought WW II to an end by decoding German messages. Turing released a paper in the October of 1950 Computing Machinery and Intelligence that can be considered as among the first hints to thinking machines. Turing starts the paper thus: I propose to consider the question, Can machines think?. Turings work was also the beginning of Natural Language Processing (NLP). The 21st-century mortals can relate it with the invention of Apples Siri. The A.M. Turing Award is considered the Nobel of computing. The life and death of Turing are unusual in their own way. I will leave it at that but if you are interested in delving deeper, here is one article by The New York Times.

Five years later, in 1955, John McCarthy, an Assistant Professor of Mathematics at Dartmouth College, and his team proposed a research project in which they used the term Artificial Intelligence, for the first time.

McCarthy explained the proposal saying, The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. He continued, An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.

It started with a few simple logical thoughts that germinated into a whole new branch of computer science in the coming decades. AI can also be related to the concept of Associationism that is traced back to Aristotle from 300 BC. But, discussing that in detail will be outside the scope of this article.

It was in 1958 that we saw the first model replicating the brains neuron system. This was the year when psychologist Frank Rosenblatt developed a program called Perceptron. Rosenblatt wrote in his article, Stories about the creation of machines having human qualities have long been fascinating province in the realm of science fiction. Yet we are now about to witness the birth of such a machine a machine capable of perceiving, recognizing, and identifying its surroundings without any human training or control.

A New York Times article published in 1958 introduced the invention to the general public saying, The Navy revealed the embryo of an electronic computer today that it expects will be able to walk, talk, see, write, reproduce itself and be conscious of its existence.

My investigation in one of the papers of Rosenblatt hints that even in the 1940s scientists talked about artificial neurons. Notice in the Reference section of Rosenblatts paper published in 1958. It lists Warren S. McCulloch and Walter H. Pitts paper of 1943. If you are interested in more details, I would suggest an article published in Medium.

The first AI conference took place in 1959. However, by this time, the leads in Artificial Intelligence had already exhausted the computing capabilities of the time. It is, therefore, no surprise that not much could be achieved in AI in the next decade.

Thankfully, the IT industry was catching up quickly and preparing the ground for stronger computers. Gordon Moore, the co-founder of Intel, made a few predictions in his article in 1965. Moore predicted a huge growth of integrated circuits, more components per chip, and reduced costs. Integrated circuits will lead to such wonders as home computers or at least terminals connected to a central computerautomatic controls for automobiles, and personal portable communications equipment, Moore predicted. Although scientists had been toiling hard to launch the Internet, it was not until the late 1960s that the invention started showing some promises. On October 29, 1969, ARPAnet delivered its first message: a node-to-node communication from one computer to another, notes History.com.

With the Internet in the public domain, computer companies had a reason to accelerate their own developments. In 1971, Intel introduced its first chip. It was a huge breakthrough. Intel impressively compared the size and computing abilities of the new hardware saying, This revolutionary microprocessor, the size of a little fingernail, delivered the same computing power as the first electronic computer built in 1946, which filled an entire room.

Around the 1970s more popular versions of languages came in use, for instance, C and SQL. I mention these two as I remember when I did my Diploma in Network-Centered Computing in 2002, the advanced versions of these languages were still alive and kicking. Britannica has a list of computer programming languages if you care to read more on when the different languages came into being.

These advancements created a perfect amalgamation of resources to trigger the next phase in AI.

Phase 2

In the late 1970s, we see another AI enthusiast coming in the scene with several research papers on AI. Geoffrey Hinton, a Canadian researcher, had confidence in Rosenblatts work on Perceptron. He resolved an inherent problem with Rosenblatts model that was made up of a single layer perceptron. To be fair to Rosenblatt, he was well aware of the limitations of this approach he just didnt know how to learn multiple layers of features efficiently, Hinton noted in his paper in 2006.

This multi-layer approach can be referred to as a Deep Neural Network.

Another scientist, Yann LeCun, who studied under Hinton and worked with him, was making strides in AI, especially Deep Learning (DL, explained later in the article) and Backpropagation Learning (BL). BL can be referred to as machines learning from their mistakes or learning from trial and error.

Similar to Phase 1, the developments of Phase 2 end here due to very limited computing power and insufficient data. This was around the late 1990s. As the Internet was fairly recent, there was not much data available to feed the machines.

Phase 3

In the early 21st-century, the computer processing speed entered a new level. In 2011, IBMs Watson defeated its human competitors in the game of Jeopardy. Watson was quite impressive in its performance. On September 30, 2012, Hinton and his team released the object recognition program called Alexnet and tested it on Imagenet. The success rate was above 75 percent, which was not achieved by any such machine before. This object recognition sent ripples across the industry. By 2018, image recognition programming became 97% accurate! In other words, computers were recognizing objects more accurately than humans.

In 2015, Tesla introduced its self-driving AI car. The company boasts its autopilot technology on its web site saying, All new Tesla cars come standard with advanced hardware capable of providing Autopilot features today, and full self-driving capabilities in the futurethrough software updates designed to improve functionality over time.

Go enthusiasts will also remember the 2016 incident when Google-owned DeepMinds AlphaGo defeated the human Go world-champion Lee Se-dol. This incident came at least a decade too soon. We know that Go is considered one of the most complex games in human history. And, AI could learn it in just 3 days, to a level to beat a world champion who, I would assume must have spent decades to achieve that proficiency!

The next phase shall be to work on Singularity. Singularity can be understood as machines building better machines, all by themselves. In 1993, scientist Vernor Vinge published an essay in which he wrote, Within thirty years, we will have the technological means to create superhuman intelligence. Shortly after, the human era will be ended. Scientists are already working on the concept of technological singularity. If these achievements can be used in a controlled way, these can help several industries, for instance, healthcare, automobile, and oil exploration.

I would also like to add here that Canadian universities are contributing significantly to developments in Artificial Intelligence. Along with Hinton and LeCun, I would like to mention Richard Sutton. Sutton, Professor at the University of Alberta, is of the view that advancements in the singularity can be expected around 2040. This makes me feel that when AI will no longer need human help, it will be a kind of specie in and of itself.

To get to the next phase, however, we would need more computer power to achieve the goals of tomorrow.

Now that we have some background on the genesis of AI and some information on the experts who nourished this advancement all these years, it is time to understand a few key terms of AI. By the way, if you ask me, every scientist who is behind these developments is a new topic in themselves. I have tried to put a good number of researched sources in the article to generate your interest and support your knowledge in AI.

Big Data

With the Internet of Things (IoT), we are saving tons of data every second from every corner of the world. Consider, for instance, Google. It seems that it starts tracking our intentions as soon as we type the first alphabet on our keyboard. Now think for a second how much data is generated from all the internet users from all over the World. Its already making predictions of our likes, dislikes, actionseverything.

The concept of big data is important as that makes the memory of Artificial Intelligence. Its like a parent sharing their experience with their child. If the child can learn from that experience, they develop cognizant abilities and venture into making their own judgments and decisions. Similarly, big data is the human experience that is shared with machines and they develop on that experience. This can be supervised as well as unsupervised learning.

Symbolic Reasoning and Machine Learning

The basics of all processes are some mathematical patterns. I think that this is because math is something that is certain and easy to understand for all humans. 2 + 2 will always be 4 unless there is something we havent figured out in the equation.

Symbolic reasoning is the traditional method of getting work done through machines. According to Pathmind, to build a symbolic reasoning system, first humans must learn the rules by which two phenomena relate, and then hard-code those relationships into a static program. Symbolic reasoning in AI is also known as the Good Old Fashioned AI (GOFAI).

Machine Learning (ML) refers to the activity where we feed big data to machines and they identify patterns and understand the data by themselves. The outcomes are not as predicted as here machines are not programmed to specific outcomes. Its like a human brain where we are free to develop our own thoughts. A video by ColdFusion explains ML thus: ML systems analyze vast amounts of data and learn from their past mistakes. The result is an algorithm that completes its task effectively. ML works well with supervised learning.

Here I would like to make a quick tangent for all those creative individuals who need some motivation. I feel that all inventions were born out of creativity. Of course, creativity comes with some basic understanding and knowledge. Out of more than 7 billion brains, somewhere someone is thinking out of the box, verifying their thoughts, and trying to communicate their ideas. Creativity is vital for success. This may also explain why some of the most important inventions took place in a garage (Google and Microsoft). Take, for instance, a small creative tool like a pizza cutter. Someone must have thought about it. Every time I use it, I marvel how convenient and efficient it is to slice a pizza without disturbing the toppings with that running cutter. Always stay creative and avoid preconceived ideas and stereotypes.

Alright, back to the topic!

Deep Learning

Deep Learning (DL) is a subset of ML. This technology attempts to mimic the activity of neurons in our brain using matrix mathematics, explains ColdFusion. I found this article that describes DL well. With better computers and big data, it is now possible to venture into DL. Better computers provide the muscle and the big data provides the experience to a neuron network. Together, they help a machine think and execute tasks just like a human would do. I would suggest reading this paper titled Deep Leaning by LeCun, Bengio, and Hinton (2015) for a deeper perspective on DL.

The ability of DL makes it a perfect companion for unsupervised learning. As big data is mostly unlabelled, DL processes it to identify patterns and make predictions. This not only saves a lot of time but also generates results that are completely new to a human brain. DL offers another benefit it can work offline; meaning, for instance, a self-driving car. It can take instantaneous decisions while on the road.

What next?

I think that the most important future development will be AI coding AI to perfection, all by itself.

Neural nets designing neural nets have already started. Early signs of self-production are in vision. Google has already created programs that can produce its own codes. This is called Automatic Machine Learning or AutoML. Sundar Pichai, CEO of Google and Alphabet, shared the experiment in his blog. Today, designing neural nets is extremely time intensive, and requires an expertise that limits its use to a smaller community of scientists and engineers. Thats why weve created an approach called AutoML, showing that its possible for neural nets to design neural nets, said Pichai (2017).

Full AI capabilities will also trigger several other programs like fully-automated self-driving cars, full-service assistance in sectors like health care and hospitality.

Among the several useful programs of AI, ColdFusion has identified the five most impressive ones in terms of image outputs. These are AI generating an image from a text (Plug and Play Generative Networks: Conditional Iterative Generation of Images in Latent Space), AI reading lip movements from a video with 95% accuracy (LipNet), Artificial Intelligence creating new images from just a few inputs (Pix2Pix), AI improving the pixels of an image (Google Brains Pixel Recursive Super Resolution), and AI adding color to b/w photos and videos (Let There Be Color). In the future, these technologies can be used for more advanced functions like law enforcement et cetera.

AI can already generate images of non-existing humans and add sound and body movements to the videos of individuals! In the coming years, these tools can be used for gaming purposes, or maybe fully capable multi-dimensional assistance like the one we see in the movie Iron Man. Of course, all these developments would require new AI laws to avoid misuse; however, that is a topic for another discussion.

Humans are advanced AI

Artificial Intelligence is getting so good at mimicking humans that it seems that humans themselves are some sort of AI. The way Artificial Intelligence learns from data, retains information, and then develops analytical, problem solving, and judgment capabilities are no different from a parent nurturing their child with their experience (data) and then the child remembering the knowledge and using their own judgments to make decisions.

We may want to remember here that there are a lot of things that even humans have not figured out with all their technology. A lot of things are still hidden from us in plain sight. For instance, we still dont know about all the living species in the Amazon rain forest. Astrology and astronomy are two other fields where, I think, very little is known. Air, water, land, and celestial bodies control human behavior, and science has evidence for this. All this hints that we as humans are not in total control of ourselves. This feels similar to AI, which so far requires external intervention, like from humans, to develop it.

I think that our past has answers to a lot of questions that may unravel our future. Take for example the Great Pyramid at Giza, Egypt, which we still marvel for its mathematical accuracy and alignment with the earths equator as well as the movements of celestial bodies. By the way, we could compare the measurements only because we have already reached a level to know the numbers relating to the equator.

Also, think of Indias knowledge of astrology. It has so many diagrams of planetary movements that are believed to impact human behavior. These sketches have survived several thousand years. One of Indias languages, Vedic, is considered more than 4,000 years old, perhaps one of the oldest in human history. This was actually a question asked from IBM Watson during the 2011 Jeopardy competition. Understanding the literature in this language might unlock a wealth of information.

I feel that with the kind of technology we have in AI, we should put some of it at work to unearth our wisdom from the past. It is a possibility that if we overlook it, we may waste resources by reinventing the wheel.

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The world of Artificial... - The American Bazaar

AUGUST 2, 2020

I.

THERE IS THE idea of a physical human to human encounter.

As a being together.

The image is one of a shared experience of time a time constituted by the act of committing to one another, to an encounter.

Of inhabiting, together, a space where bodies meet, where talking and laughing and crying is a haptic experience. Where one breathes the same air, smells the same smells.

An experience the body can remember sensorially long after.

Reaching out and touching. Shared surfaces. Breathing, talking, anything really.[1]

Can this kind of encounter happen through machines or machine interfaces? Zoom, Facebook, Google, Twitter, LinkedIn, Skype, Microsoft Teams, and many more.

Can it happen with a machine?

Traditionally, the answer to both questions is no.

No, it cannot really happen by way of a machine interface because too much is lost.

And no, one cannot have a true encounter with machines.

II.

In times of COVID-19, we spend more of our life online in networks than ever before.

What is the effect of this life lived on screen on what it is to be human?

We have more Zoom meetings, surf longer on Instagram, spend more time on Facebook and Twitter than ever before.

What is the transformation of the human brought about by life lived on screens and how to bring this transformation into focus?

As a site of philosophical change and as an opportunity for philosophers, artists, and technologists to come together and give shape?

What are the philosophical and poetic and political stakes and opportunities of this, of our moment in time?

III.

The migration of human activity to technological platforms began long before COVID-19.

The reference, here, is particularly to the emergence in the early 2000s of interactive, often user-generated content, and the emergence of network companies.

The classic examples here are companies like Google (Google mastered microtargeting), Facebook, Twitter, Amazon, Google, Microsoft (Skype and Team), and now also Zoom.

This matters for two reasons.

The first is that the material infrastructural conditions of possibility for how we now spend much of our time has been laid long before the present: satellites, high-speed fiber-optic cables between cities and underneath the ocean, file sharing systems in massive computer farms that host servers, AI algorithms that work through enormous amounts of data quickly to find patterns and calculate preferences, etc.

The second reason is that the material infrastructure that makes life lived on screen possible is inseparably related to platform capitalism. Platform capitalism consists (mostly but not exclusively) of companies that make money by offering free services such as search or posting images or messaging but that collect and harvest user data in order to either sell it to other platform companies or, more often, to sell it to advertising companies (who then devise microtargeting strategies, that is, they deliver ads to specific audiences).

In order to generate data, these companies have been busy finding ways long before COVID-19 to migrate human activity online.

Or, perhaps more accurately: They have been busy creating new forms of human activity suited to life online surfing, search, texting, sexting, browsing, FaceTiming, YouTubing, binge watching, etc.

AR and VR especially via Facebook and Oculus may soon be an additional element of life on screen.

And COVID-19?

Well, for most platform companies, the spread of SARS-CoV-2 and the shelter-at-home orders have been a massive boost: screen time has increased dramatically and so has their capacity to generate and mine data.

That is, COVID-19 has been a consolidation and even an expansion event for platform capitalism.

The contrast to older forms of capitalism, especially to industrial manufacturing, couldnt be sharper.

The question thus emerges whether or not we are currently seeing a powerful acceleration of a shift from earlier forms of capitalism toward a new, still-nascent form called platform capitalism.

A shift from a mode of production focused on the industrial production of goods by labor to another one that is about users, data, and AI?

What are the philosophical, poetic, and political dimension of this shift?

IV.

In my observation, platform companies have made dominant a form of relationality networks that runs diagonal to the usual, place-based socialities of the nation (usually framed in terms of belonging and non-belonging, inclusion and exclusion of a people imagined in territorial and ethnic or racial terms).

In fact, I think it is no exaggeration to argue that networks have given rise to a new structure and experience of reality that is radically different from and even incommensurable with the structure and experience of reality that defined societies.

I offer a simple juxtaposition to illustrate my point.

Societies, usually, have three main features.

First, they are organized hierarchically. That is, they typically have a few powerful individuals at the top, while the vast majority of individuals assemble at the bottom.

Second, they are organized vertically, by which I mean that they accommodate an often vast diversity of opinions and points of views.

Third, societies are usually held together by a national sentiment and, most importantly, by a national communication or media system. The form this media system almost always takes is mass communication, where the few communicate to the many. What they communicate is information information people may vehemently disagree about, but the baseline of this disagreement is that people agree about the things that they disagree about. Mass communication assures that people have a shared sense of reality.

Networks defy all three of those features.

First, if societies are hierarchical and vertical, then networks are flat and horizontal: networks tend to be self-assemblies of people with similar views and inclinations.

Second, while societies are contained by national territories, networks tend to be global and cut across national boundaries: another way of saying this is that while societies are place-specific units, networks are non-place-specific units.

And third, if in society the few communicate with the many and what they communicate is information, then in networks the many communicate directly unfiltered with the many, and what they communicate is not information but affective (emotional) intensity.

It strikes me as uncontroversial that today more and more humans live in networks and that networks, ultimately, defy the logic of society.

Indeed, the rise of networks has created a situation in which, counter to what the moderns thought, society and the social are not timeless ontological categories that define the human.

On the contrary, they are recent and transitory concepts that have no universal validity for all of humanity or all of human history.

Of course, societas is an ancient concept. However, up until the late 18th century, a societas was a legal and not a national or territorial concept; it referred to those who held legal rights vis--vis the monarch.

Things only changed in the years predating the French Revolution when the argument emerged that the people and not the aristocrats and the grand bourgeoisie who held legal rights vis--vis the king should be the society constitutive of the political entity called France.

The early nation-states, which emerged in the context of the first Industrial Revolution and at a time when several cholera epidemics ravaged Europe, found themselves confronted with the need to know their societies, to know how many people lived on their territory, how many were born, how many died, how many got sick and of what; they had to know how many married and how many divorced.

As political existence and the biological vitality of the national society were understood to be connected, states began to conduct massive surveys to understand how they could reform and advance their societies.

Over time between the 1830s and the 1890s this gave rise to what one could call the logic of the social: the idea that the truth about humans is that they are born in societies and that society will shape them and even determine them. The truth about humans is that they are social, in the sense of societal being: tell me in which segment you were born, and I tell you who you are likely to marry, how many kids you we will have, what your job will be, what you are likely to die of.

The social was discovered as the true ontological ground of the human.

To this day, most normative theories of the human call them anthropology: from Marx via the Frankfurt School to Pierre Bourdieu are based on the idea that society is the true ontological ground of the human.

All our modern political institutions are based on society.

If it is true that networks defy the logic of society, then the social sciences, simply because they take the social for granted as the true logic of the human, will fail to bring the human into view.

What we need, then, is a shift from social anthropology (an anthropology that grounds in the concept of the social) to a network anthropology: a multifaceted study of how networks give rise to humans.

V.

The difference between networks and societies which appears to map onto the difference between platform and industrial capitalism is related to the changing relation between humans and machines brought about by recent advances in AI, specifically in machine learning.

One can say that machine learning technologies are beginning to liberate machines from the narrow industrial concept of what a machine is and that this liberation may have far-reaching consequences for what it means to have an encounter.

Traditionally, there were unbridgeable differences between human and machines.

Partly, because humans have intelligence reason while machines are reducible to mechanism.

Partly because machines have no life, no quality of their own. They are reducible to the engineers who invented them and hence mere tools.

The implication, often, is that there is no will, no interference, no freedom, no opening.

But machine learning and neurotechnology make us reconsider these boundaries between organisms and machines, between humans and mechanisms.

First, the success of artificial neural nets or the basic continuity between neural and mechanical processes suggests that the distinction between the natural and the artificial may perhaps matter much less than we thought.

Second, the emergence of deep learning architectures has led to machines with a mind of their own: they have an agency that is not reducible to the intent of or the program written by the engineer.

The exemplary reference here is a 2016 game of Go, played by a deep learning system named AlphaGo (built by DeepMind, a London-based, Google-owned AI company) against Lee Sedol, an 18-time world champion. Toward the end of Game Two in a Best of Five series, AlphaGo opted for a move move 37 that was highly unusual.

DeepMind later announced that AlphaGo had calculated the odds that an expert human player would have made the same move at 1 in 10,000.

It played the move anyway: as if it judged that a nonhuman move would be better in this case.

Fan Hui, the three-time European Go champion, remarked: Its not a human move. So beautiful. So beautiful.

Wired wrote shortly after the game was over: Move 37 showed that AlphaGo wasnt just regurgitating years of programming or cranking through a brute-force predictive algorithm. It was the moment AlphaGo proved it[s] [] ability to play a beautiful game not just like a person but in a way no person could.[2]

Traditionally, a program that doesnt conform to the intentionality of the engineer was considered faulty. However, contemporary machine learning systems are built to defy to exceed the mind of the engineer: it is expected that the machine brings something to a game, a conversation, a question that the engineers did not and could not possibly provide it with (something nonhuman).

These developments one could call them the liberation of machines from the human or at least from the concept of the machine that up until recently defined the human imagination of what a machine could be are related to the rise of networks.

They are related insofar as in networks, relationality once a human to human prerogative may no longer be limited to human to human encounters anymore.

What effects will the liberation of machines which is constitutive of networks as much as of machines have on what it is to be human?

Or on what it is to be in relation?

VI.

As I see it, what is needed now are philosophical investigations of the new technology that is being built.

Not studies in terms of society, as this would ultimately imply holding on to the old concept of the human as social being.

Nor studies in terms of the human, if that means the defense of the human against the machine.

But rather, collaborative studies conducted jointly by philosophers and artists in collaboration with technologists of how networks and machine learning are challenging old and enabling new, yet to be explored concepts of living together.

All by itself, COVID-19 has little to do with these most far-reaching philosophical transformations brought about by networks and by machine learning.

And yet, COVID-19 brings this transformation into view with sharper clarity than ever before and has led to circumstances due to which this new and different world might come faster than we anticipated.

What will it mean to be together with a machine?

To address this question, we may need a whole new vocabulary of encounters and relations.

[1] From Lauren Lee McCarthy, Later Date, 2020, https://vimeo.com/416588466/bb8762077d.

[2] Cade Metz, What the AI Behind AlphaGo Can Teach Us About Being Human, Wired, May 19, 2016, https://www.wired.com/2016/05/google-alpha-go-ai.

Image Credit: Stills from Lauren Lee McCarthy,Later Date, 2020

Tobias Rees isthe founding Director of the Berggruen Institutes Transformations of the Human Program. He also serves as Reid Hoffman Professor of Humanities at the New School for Social Research and is a Fellow of the Canadian Institute for Advanced Research.

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Life Lived On Screen: Philosophical, Poetic, and Political Observations - lareviewofbooks