Showing posts with label Workplace Automation. Show all posts
Showing posts with label Workplace Automation. Show all posts

Artificial Intelligence - Who Is Hans Moravec?

 




Hans Moravec(1948–) is well-known in the computer science community as the long-time head of Carnegie Mellon University's Robotics Institute and an unashamed techno logical optimist.

For the last twenty-five years, he has studied and produced artificially intelligent robots at the CMU lab, where he is still an adjunct faculty member.

Moravec spent almost 10 years as a research assistant at Stanford University's groundbreaking Artificial Intelligence Lab before coming to Carnegie Mellon.

Moravec is also noted for his paradox, which states that, contrary to popular belief, it is simple to program high-level thinking skills into robots—as with chess or Jeopardy!—but difficult to transmit sensorimo tor agility.

Human sensory and motor abilities have developed over millions of years and seem to be easy, despite their complexity.

Higher-order cognitive abilities, on the other hand, are the result of more recent cultural development.

Geometry, stock market research, and petroleum engineering are examples of disciplines that are difficult for people to learn but easier for robots to learn.

"The basic lesson of thirty-five years of AI research is that the hard issues are simple, and the easy ones are hard," writes Steven Pinker of Moravec's scientific career.

Moravec built his first toy robot out of scrap metal when he was eleven years old, and his light-following electronic turtle and a robot operated by punched paper tape earned him two high school science fair honors.

He proposed a Ship of Theseus-like analogy for the viability of artificial brains while still in high school.

Consider replacing a person's human neurons one by one with precisely manufactured equivalents, he said.

When do you think human awareness will vanish? Is anybody going to notice? Is it possible to establish that the person is no longer human? Later in his career, Moravec would suggest that human knowledge and training might be broken down in the same manner, into subtasks that machine intelligences could take over.

Moravec's master's thesis focused on the development of a computer language for artificial intelligence, while his PhD research focused on the development of a robot that could navigate obstacle courses utilizing spatial representation methods.

The area of interest (ROI) in a scene was identified by these robot vision systems.

Moravec's early computer vision robots were extremely sluggish by today's standards, taking around five hours to go from one half of the facility to the other.

To measure distance and develop an internal picture of physical impediments in the room, a remote computer carefully analysed continuous video-camera images recorded by the robot from various angles.

Moravec finally developed 3D occupancy grid technology, which allowed a robot to create an awareness of a cluttered area in a matter of seconds.

Moravec's lab took on a new challenge by converting a Pontiac TransSport minivan into one of the world's first road-ready autonomous cars.

The self-driving minivan reached speeds of up to 60 miles per hour.

DANTE II, a robot capable of going inside the crater of an active volcano on Mount Spurr in Alaska, was also constructed by the CMU Robotics Institute.

While DANTE II's immediate aim was to sample harmful fumarole gases, a job too perilous for humans, it was also planned to demonstrate technologies for robotic expeditions to distant worlds.

The volcanic explorer robot used artificial intelligence to navigate the perilous, boulder-strewn terrain on its own.

Because such rovers produced so much visual and other sensory data that had to be analyzed and managed, Moravec believes that experience with mobile robots spurred the development of powerful artificial intelligence and computer vision methods.

For the National Aeronautics and Space Administration (NASA), Moravec's team built fractal branching ultra-dexterous robots ("Bush robots") in the 1990s.

These robots, which were proposed but never produced due to the lack of necessary manufacturing technologies, comprised of a branching hierarchy of dynamic articulated limbs, starting with a main trunk and splitting down into smaller branches.

As a result, the Bush robot would have "hands" at all scales, from macroscopic to tiny.

The tiniest fingers would be nanoscale in size, allowing them to grip very tiny objects.

Moravec said the robot would need autonomy and depend on artificial intelligence agents scattered throughout the robot's limbs and branches because to the intricacy of manipulating millions of fingers in real time.

He believed that the robots may be made entirely of carbon nanotube material, employing the quick prototyping technology known as 3D printers.

Moravec believes that artificial intelligence will have a significant influence on human civilization.

To stress the role of AI in change, he coined the concept of the "landscape of human capability," which physicist Max Tegmark has later converted into a graphic depiction.

Moravec's picture depicts a three-dimensional environment in which greater altitudes reflect more challenging jobs in terms of human difficulty.

The point where the swelling waters meet the shore reflects the line where robots and humans both struggle with the same duties.

Art, science, and literature are now beyond of grasp for an AI, but the sea has already defeated mathematics, chess, and the game Go.

Language translation, autonomous driving, and financial investment are all on the horizon.

More controversially, in two popular books, Mind Children (1988) and Robot: Mere Machine to Transcendent Mind (1989), Moravec engaged in future conjecture based on what he understood of developments in artificial intelligence research (1999).

In 2040, he said, human intellect will be surpassed by machine intelligence, and the human species would go extinct.

Moravec evaluated the functional equivalence between 50,000 million instructions per second (50,000 MIPS) of computer power and a gram of brain tissue and came up with this figure.

He calculated that home computers in the early 2000s equaled only an insect's nervous system, but that if processing power doubled every eighteen months, 350 million years of human intellect development could be reduced to just 35 years of artificial intelligence advancement.

He estimated that a hundred million MIPS would be required to create human-like universal robots.

Moravec refers to these sophisticated robots as our "mind children" in the year 2040.

Humans, he claims, will devise techniques to delay biological civilization's final demise.

Moravec, for example, was the first to anticipate what is now known as universal basic income, which is delivered by benign artificial superintelligences.

In a completely automated society, a basic income system would provide monthly cash payments to all individuals without any type of employment requirement.

Moravec is more concerned about the idea of a renegade automated corporation breaking its programming and refusing to pay taxes into the human cradle-to-grave social security system than he is about technological unemployment.

Nonetheless, he predicts that these "wild" intelligences will eventually control the universe.

Moravec has said that his books Mind Children and Robot may have had a direct impact on the last third of Stanley Kubrick's original screenplay for A.I. Artificial Intelligence (later filmed by Steven Spielberg).

Moravecs, on the other hand, are self-replicating devices in the science fiction books Ilium and Olympos.

Moravec defended the same physical fundamentalism he expressed in his high school thoughts throughout his life.

He contends in his most transhumanist publications that the only way for humans to stay up with machine intelligences is to merge with them by replacing sluggish human cerebral tissue with artificial neural networks controlled by super-fast algorithms.

In his publications, Moravec has blended the ideas of artificial intelligence with virtual reality simulation.


He's come up with four scenarios for the development of consciousness.

(1) human brains in the physical world, 

(2) a programmed AI implanted in a physical robot, 

(3) a human brain immersed in a virtual reality simulation, and 

(4) an AI functioning inside the boundaries of virtual reality All of them are equally credible depictions of reality, and they are as "real" as we believe them to be.


Moravec is the creator and chief scientist of the Pittsburgh-based Seegrid Corporation, which makes autonomous Robotic Industrial Trucks that can navigate warehouses and factories without the usage of automated guided vehicle systems.

A human trainer physically pushes Seegrid's vehicles through a new facility once.

The robot conducts the rest of the job, determining the most efficient and safe pathways for future journeys, while the trainer stops at the appropriate spots for the truck to be loaded and unloaded.

Seegrid VGVs have transported over two million production miles and eight billion pounds of merchandise for DHL, Whirlpool, and Amazon.

Moravec was born in the Austrian town of Kautzen.

During World War II, his father was a Czech engineer who sold electrical products.

When the Russians invaded Czechoslovakia in 1944, the family moved to Austria.

In 1953, his family relocated to Canada, where he now resides.

Moravec earned a bachelor's degree in mathematics from Acadia University in Nova Scotia, a master's degree in computer science from the University of Western Ontario, and a doctorate from Stanford University, where he worked with John McCarthy and Tom Binford on his thesis.

The Office of Naval Study, the Defense Advanced Research Projects Agency, and NASA have all supported his research.

Elon Musk (1971–) is an American businessman and inventor.

Elon Musk is an engineer, entrepreneur, and inventor who was born in South Africa.

He is a dual citizen of South Africa, Canada, and the United States, and resides in California.

Musk is widely regarded as one of the most prominent inventors and engineers of the twenty-first century, as well as an important influencer and contributor to the development of artificial intelligence.

Despite his controversial personality, Musk is widely regarded as one of the most prominent inventors and engineers of the twenty-first century and an important influencer and contributor to the development of artificial intelligence.

Musk's business instincts and remarkable technological talent were evident from an early age.

By the age of 10, he had self-taught himself how program computers, and by the age of twelve, he had produced a video game and sold the source code to a computer maga zine.

Musk has included allusions to some of his favorite novels in SpaceX's Falcon Heavy rocket launch and Tesla's software since he was a youngster.

Musk's official schooling was centered on economics and physics rather than engineering, interests that are mirrored in his subsequent work, such as his efforts in renewable energy and space exploration.

He began his education at Queen's University in Canada, but later transferred to the University of Pennsylvania, where he earned bachelor's degrees in Economics and Physics.

Musk barely stayed at Stanford University for two days to seek a PhD in energy physics before departing to start his first firm, Zip2, with his brother Kimbal Musk.

Musk has started or cofounded many firms, including three different billion-dollar enterprises: SpaceX, Tesla, and PayPal, all driven by his diverse interests and goals.

• Zip2 was a web software business that was eventually purchased by Compaq.

• X.com: an online bank that merged with PayPal to become the online payments corporation PayPal.

• Tesla, Inc.: an electric car and solar panel maker • SpaceX: a commercial aircraft manufacturer and space transportation services provider (via its subsidiarity SolarCity) • Neuralink: a neurotechnology startup focusing on brain-computer connections • The Boring Business: an infrastructure and tunnel construction corporation • OpenAI: a nonprofit AI research company focused on the promotion and development of friendly AI Musk is a supporter of environmentally friendly energy and consumption.

Concerns over the planet's future habitability prompted him to investigate the potential of establishing a self-sustaining human colony on Mars.

Other projects include the Hyperloop, a high-speed transportation system, and the Musk electric jet, a jet-powered supersonic electric aircraft.

Musk sat on President Donald Trump's Strategy and Policy Forum and Manufacturing Jobs Initiative for a short time before stepping out when the US withdrew from the Paris Climate Agreement.

Musk launched the Musk Foundation in 2002, which funds and supports research and activism in the domains of renewable energy, human space exploration, pediatric research, and science and engineering education.

Musk's effect on AI is significant, despite his best-known work with Tesla and SpaceX, as well as his contentious social media pronouncements.

In 2015, Musk cofounded the charity OpenAI with the objective of creating and supporting "friendly AI," or AI that is created, deployed, and utilized in a manner that benefits mankind as a whole.

OpenAI's objective is to make AI open and accessible to the general public, reducing the risks of AI being controlled by a few privileged people.

OpenAI is especially concerned about the possibility of Artificial General Intelligence (AGI), which is broadly defined as AI capable of human-level (or greater) performance on any intellectual task, and ensuring that any such AGI is developed responsibly, transparently, and distributed evenly and openly.

OpenAI has had its own successes in taking AI to new levels while staying true to its goals of keeping AI friendly and open.

In June of 2018, a team of OpenAI-built robots defeated a human team in the video game Dota 2, a feat that could only be accomplished through robot teamwork and collaboration.

Bill Gates, a cofounder of Microsoft, praised the achievement on Twitter, calling it "a huge milestone in advancing artificial intelligence" (@BillGates, June 26, 2018).

Musk resigned away from the OpenAI board in February 2018 to prevent any conflicts of interest while Tesla advanced its AI work for autonomous driving.

Musk became the CEO of Tesla in 2008 after cofounding the company in 2003 as an investor.

Musk was the chairman of Tesla's board of directors until 2018, when he stepped down as part of a deal with the US Securities and Exchange Commission over Musk's false claims about taking the company private.

Tesla produces electric automobiles with self-driving capabilities.

Tesla Grohmann Automation and Solar City, two of its subsidiaries, offer relevant automotive technology and manufacturing services and solar energy services, respectively.

Tesla, according to Musk, will reach Level 5 autonomous driving capabilities in 2019, as defined by the National Highway Traffic Safety Administration's (NHTSA) five levels of autonomous driving.

Tes la's aggressive development with autonomous driving has influenced conventional car makers' attitudes toward electric cars and autonomous driving, and prompted a congressional assessment of how and when the technology should be regulated.

Musk is widely credited as a key influencer in moving the automotive industry toward autonomous driving, highlighting the benefits of autonomous vehicles (including reduced fatalities in vehicle crashes, increased worker productivity, increased transportation efficiency, and job creation) and demonstrating that the technology is achievable in the near term.

Tesla's autonomous driving code has been created and enhanced under the guidance of Musk and Tesla's Director of AI, Andrej Karpathy (Autopilot).

The computer vision analysis used by Tesla, which includes an array of cameras on each car and real-time image processing, enables the system to make real-time observations and predictions.

The cameras, as well as other exterior and internal sensors, capture a large quantity of data, which is evaluated and utilized to improve Autopilot programming.

Tesla is the only autonomous car maker that is opposed to the LIDAR laser sensor (an acronym for light detection and ranging).

Tesla uses cameras, radar, and ultrasonic sensors instead.

Though academics and manufacturers disagree on whether LIDAR is required for fully autonomous driving, the high cost of LIDAR has limited Tesla's rivals' ability to produce and sell vehicles at a pricing range that allows a large number of cars on the road to gather data.

Tesla is creating its own AI hardware in addition to its AI programming.

Musk stated in late 2017 that Tesla is building its own silicon for artificial-intelligence calculations, allowing the company to construct its own AI processors rather than depending on third-party sources like Nvidia.

Tesla's AI progress in autonomous driving has been marred by setbacks.

Tesla has consistently missed self-imposed deadlines, and serious accidents have been blamed on flaws in the vehicle's Autopilot mode, including a non-injury accident in 2018, in which the vehicle failed to detect a parked firetruck on a California freeway, and a fatal accident in 2018, in which the vehicle failed to detect a pedestrian outside a crosswalk.

Neuralink was established by Musk in 2016.

With the stated objective of helping humans to keep up with AI breakthroughs, Neuralink is focused on creating devices that can be implanted into the human brain to better facilitate communication between the brain and software.

Musk has characterized the gadgets as a more efficient interface with computer equipment, while people now operate things with their fingertips and voice commands, directives would instead come straight from the brain.

Though Musk has made major advances to AI, his pronouncements regarding the risks linked with AI have been apocalyptic.

Musk has called AI "humanity's greatest existential danger" and "the greatest peril we face as a civilisation" (McFarland 2014).

(Morris 2017).

He cautions against the perils of power concentration, a lack of independent control, and a competitive rush to acceptance without appropriate analysis of the repercussions.

While Musk has used colorful terminology such as "summoning the devil" (McFarland 2014) and depictions of cyborg overlords, he has also warned of more immediate and realistic concerns such as job losses and AI-driven misinformation campaigns.

Though Musk's statements might come out as alarmist, many important and well-respected figures, including as Microsoft cofounder Bill Gates, Swedish-American scientist Max Tegmark, and the late theoretical physicist Stephen Hawking, share his concern.

Furthermore, Musk does not call for the cessation of AI research.

Instead, Musk supports for responsible AI development and regulation, including the formation of a Congressional committee to spend years studying AI with the goal of better understanding the technology and its hazards before establishing suitable legal limits.


~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram


You may also want to read more about Artificial Intelligence here.



See also: 


Superintelligence; Technological Singularity; Workplace Automation.



References & Further Reading:


Moravec, Hans. 1988. Mind Children: The Future of Robot and Human Intelligence. Cambridge, MA: Harvard University Press.

Moravec, Hans. 1999. Robot: Mere Machine to Transcendent Mind. Oxford, UK: Oxford University Press.

Moravec, Hans. 2003. “Robots, After All.” Communications of the ACM 46, no. 10 (October): 90–97.

Pinker, Steven. 2007. The Language Instinct: How the Mind Creates Language. New York: Harper.




Artificial Intelligence - Intelligent Tutoring Systems.

  



Intelligent tutoring systems are artificial intelligence-based instructional systems that adapt instruction based on a variety of learner variables, such as dynamic measures of students' ongoing knowledge growth, personal interest, motivation to learn, affective states, and aspects of how they self-regulate their learning.

For a variety of problem areas, such as STEM, computer programming, language, and culture, intelligent tutoring systems have been created.

Complex problem-solving activities, collaborative learning activities, inquiry learning or other open-ended learning activities, learning through conversations, game-based learning, and working with simulations or virtual reality environments are among the many types of instructional activities they support.

Intelligent tutoring systems arose from a field of study known as AI in Education (AIED).

MATHia® (previously Cognitive Tutor), SQL-Tutor, ALEKS, and Rea soning Mind's Genie system are among the commercially successful and widely used intelligent tutoring systems.

Intelligent tutoring systems are frequently more successful than conventional kinds of training, according to six comprehensive meta-analyses.

This efficiency might be due to a number of things.

First, intelligent tutoring systems give adaptive help inside issues, allowing classroom instructors to scale one-on-one tutoring beyond what they could do without it.

Second, they allow adaptive problem selection based on the understanding of particular pupils.

Third, cognitive task analysis, cognitive theory, and learning sciences ideas are often used in intelligent tutoring systems.

Fourth, the employment of intelligent tutoring tools in so-called blended classrooms may result in favorable cultural adjustments by allowing teachers to spend more time working one-on-one with pupils.

Fifth, more sophisticated tutoring systems are repeatedly developed using new approaches from the area of educational data mining, based on data.

Finally, Open Learner Models (OLMs), which are visual representations of the system's internal student model, are often used in intelligent tutoring systems.

OLMs have the potential to assist learners in productively reflecting on their current level of learning.

Model-tracing tutors, constraint-based tutors, example-tracing tutors, and ASSISTments are some of the most common intelligent tutoring system paradigms.

These paradigms vary in how they are created, as well as in tutoring behaviors and underlying representations of domain knowledge, student knowledge, and pedagogical knowledge.

For domain reasoning (e.g., producing future steps in a problem given a student's partial answer), assessing student solutions and partial solutions, and student modeling, intelligent tutoring systems use a number of AI approaches (i.e., dynamically estimating and maintaining a range of learner vari ables).

To increase systems' student modeling skills, a range of data mining approaches (including Bayesian models, hidden Markov models, and logistic regression models) are increasingly being applied.

Machine learning approaches, such as reinforcement learning, are utilized to build instructional policies to a lesser extent.

Researchers are looking at concepts for the smart classroom of the future that go beyond the capabilities of present intelligent tutoring technologies.

AI systems, in their visions, typically collaborate with instructors and students to provide excellent learning experiences for all pupils.

Recent research suggests that rather than designing intelligent tutoring systems to handle all aspects of adaptation, such as providing teachers with real-time analytics from an intelligent tutoring system to draw their attention to learners who may need additional support, promising approaches that adaptively share regulation of learning processes across students, teachers, and AI systems—rather than designing intelligent tutoring systems to handle all aspects of adaptation, for example—by providing teachers with real-time analytics from an intelligent tutoring system to draw their attention to learners who may need additional support.



Jai Krishna Ponnappan


You may also want to read more about Artificial Intelligence here.



See also: 


Natural Language Processing and Speech Understanding; Workplace Automation.



Further Reading:




Aleven, Vincent, Bruce M. McLaren, Jonathan Sewall, Martin van Velsen, Octav Popescu, Sandra Demi, Michael Ringenberg, and Kenneth R. Koedinger. 2016. “Example-Tracing Tutors: Intelligent Tutor Development for Non-Programmers.” International Journal of Artificial Intelligence in Education 26, no. 1 (March): 224–69.

Aleven, Vincent, Elizabeth A. McLaughlin, R. Amos Glenn, and Kenneth R. Koedinger. 2017. “Instruction Based on Adaptive Learning Technologies.” In Handbook of Research on Learning and Instruction, Second edition, edited by Richard E. Mayer and Patricia Alexander, 522–60. New York: Routledge.

du Boulay, Benedict. 2016. “Recent Meta-Reviews and Meta-Analyses of AIED Systems.” International Journal of Artificial Intelligence in Education 26, no. 1: 536–37.

du Boulay, Benedict. 2019. “Escape from the Skinner Box: The Case for Contemporary Intelligent Learning Environments.” British Journal of Educational Technology, 50, no. 6: 2902–19.

Heffernan, Neil T., and Cristina Lindquist Heffernan. 2014. “The ASSISTments Ecosystem: Building a Platform that Brings Scientists and Teachers Together for Minimally Invasive Research on Human Learning and Teaching.” International Journal of Artificial Intelligence in Education 24, no. 4: 470–97.

Koedinger, Kenneth R., and Albert T. Corbett. 2006. “Cognitive Tutors: Technology Bringing Learning Sciences to the Classroom.” In The Cambridge Handbook of the Learning Sciences, edited by Robert K. Sawyer, 61–78. New York: Cambridge University Press.

Mitrovic, Antonija. 2012. “Fifteen Years of Constraint-Based Tutors: What We Have Achieved and Where We Are Going.” User Modeling and User-Adapted Interaction 22, no. 1–2: 39–72.

Nye, Benjamin D., Arthur C. Graesser, and Xiangen Hu. 2014. “AutoTutor and Family: A Review of 17 Years of Natural Language Tutoring.” International Journal of Artificial Intelligence in Education 24, no. 4: 427–69.

Pane, John F., Beth Ann Griffin, Daniel F. McCaffrey, and Rita Karam. 2014. “Effectiveness of Cognitive Tutor Algebra I at Scale.” Educational Evaluation and Policy Analysis 36, no. 2: 127–44.

Schofield, Janet W., Rebecca Eurich-Fulcer, and Chen L. Britt. 1994. “Teachers, Computer Tutors, and Teaching: The Artificially Intelligent Tutor as an Agent for Classroom Change.” American Educational Research Journal 31, no. 3: 579–607.

VanLehn, Kurt. 2016. “Regulative Loops, Step Loops, and Task Loops.” International Journal of Artificial Intelligence in Education 26, no. 1: 107–12.


Artificial Intelligence - Agriculture Using Intelligent Sensing.

  



From Neolithic tools that helped humans transition from hunter gatherers to farmers to the British Agricultural Revolution, which harnessed the power of the Industrial Revolution to increase yields (Noll 2015), technological innovation has always driven food production.

Today, agriculture is highly technical, as scientific discoveries continue to be integrated into production systems.

Intelligent Sensing Agriculture is one of the newest additions to a long history of integrating cutting-edge technology to the production, processing, and distribution of food.

These technological gadgets are generally used to achieve the dual aim of boosting crop yields while lowering agricultural system environmental effects.

Intelligent sensors are devices that, as part of their stated duty, may execute a variety of complicated operations.

These sensors should not be confused with "smart" sensors or instrument packages that can collect data from the physical environment (Cleaveland 2006).

Intelligent sensors are unique in that they not only detect but also react to varied circumstances in nuanced ways depending on the information they collect.

"In general, sensors are devices that measure a physical quantity and turn the result into a signal that can be read by an observer or instrument; however, intelligent sensors may analyze measured data" (Bialas 2010, 822).

Their capacity to govern their own processes in response to environmental stimuli is what distinguishes them as "intelligent." They collect fundamental elements from various factors (such as light, temperature, and humidity) and then develop intermediate responses to these aspects (Yamasaki 1996).

The capacity to do sophisticated learning, information processing, and adaptation all in one integrated package is required for this feature.

These sensor packages are employed in a broad variety of applications, from aerospace to health care, and their scope is growing.

While all of these applications are novel, the use of intelligent sensors in agriculture might have a broad variety of social advantages owing to the technology.

There is a pressing need to boost the productivity of existing productive agricultural fields.

In 2017, the world's population approached 7.6 billion people, according to the United Nations (2017).

The majority of the world's arable land, on the other hand, is already being used for food.

Currently, over half of the land in the United States is used to generate agricultural goods, whereas 40% of the land in the United Kingdom is utilized to create agricultural products (Thompson 2010).

Due to a scarcity of undeveloped land, agricultural production must skyrocket within the next 10 years, yet environmental effects must be avoided in order to boost overall sustainability and long-term productivity.

Intelligent sensors aid in maximizing the use of all available resources, lowering agricultural expenses, and limiting the use of hazardous inputs (Pajares 2011).

"When nutrients in the soil, humidity, solar radiation, weed density, and a wide range of other factors and data affecting production are known," Pajares says, "the situation improves, and the use of chemical products such as fertilizers, herbicides, and other pollutants can be significantly reduced" (Pajares 2011, 8930).

The majority of intelligent sensor applications in this context may be classified as "precise agriculture," which is described as "information-intensive crop management that use technology to watch, react, and quantify crucial factors." When combined with computer networks, this data enables for field administration from afar.

Combinations of several kinds of sensors (such as temperature and image-based devices) enable for monitoring and control regardless of distance.

Intelligent sensors gather in-field data to aid agricultural production management in a variety of ways.

The following are some examples of specialized applications: Unmanned Aerial Vehicles (UAVs) with a suite of sensors detect fires (Pajares 2011); LIDAR sensors paired with GPS identify trees and estimate forest biomass; and capacitance probes measure soil moisture while reflectometers determine crop moisture content.

Other sensor types may identify weeds, evaluate soil pH, quantify carbon metabolism in peatlands, regulate irrigation systems, monitor temperatures, and even operate machinery like sprayers and tractors.

When equipped with sophisticated sensors, robotic devices might be utilized to undertake many of the tasks presently performed by farmers.

Modern farming is being revolutionized by intelligent sensors, and as technology progresses, chores will become more automated.

Agricultural technology, on the other hand, have a long history of public criticism.

One criticism of the use of intelligent sensors in agriculture is that it might have negative societal consequences.

While these devices improve agricultural systems' efficiency and decrease environmental problems, they may have a detrimental influence on rural populations.

Technological advancements have revolutionized the way farmers manage their crops and livestock since the invention of the first plow.

Intelligent sensors may allow tractors, harvesters, and other equipment to operate without the need for human involvement, potentially altering the way food is produced.

This might lower the number of people required in the agricultural industry, and consequently the number of jobs available in rural regions, where agricultural production is mostly conducted.

Furthermore, this technology may be too costly for farmers, increasing the likelihood of small farms failing.

The so-called "technology treadmill" is often blamed for such failures.

This term describes a situation in which a small number of farmers adopt a new technology and profit because their production costs are lower than their competitors'.

Increased earnings are no longer possible when more producers embrace this technology and prices decline.

It becomes important to use this new technology in order to compete in a market where others are doing so.

Farmers who do not implement the technology are eventually forced out of business, while those who do thrive.

The use of clever sensors may help to keep the technological treadmill going.

Regard less, the sensors have a broad variety of social, economic, and ethical effects that will need to be examined, as the technology advances.

 


Jai Krishna Ponnappan


You may also want to read more about Artificial Intelligence here.



See also: 


Workplace Automation.



Further Reading:



Bialas, Andrzej. 2010. “Intelligent Sensors Security.” Sensors 10, no. 1: 822–59.

Cleaveland, Peter. 2006. “What Is a Smart Sensor?” Control Engineering, January 1, 2006. https://www.controleng.com/articles/what-is-a-smart-sensor/.

Noll, Samantha. 2015. “Agricultural Science.” In A Companion to the History of American Science, edited by Mark Largent and Georgina Montgomery. New York: Wiley-Blackwell.

Pajares, Gonzalo. 2011. “Advances in Sensors Applied to Agriculture and Forestry.” Sensors 11, no. 9: 8930–32.

Thompson, Paul B. 2009. “Philosophy of Agricultural Technology.” In Philosophy of Technology and Engineering Sciences, edited by Anthonie Meijers, 1257–73. Handbook of the Philosophy of Science. Amsterdam: North-Holland.

Thompson, Paul B. 2010. The Agrarian Vision: Sustainability and Environmental Ethics. Lexington: University Press of Kentucky.

United Nations, Department of Economic and Social Affairs. 2017. World Population Prospects: The 2017 Revision. New York: United Nations.

Yamasaki, Hiro. 1996. “What Are the Intelligent Sensors.” In Handbook of Sensors and Actuators, vol. 3, edited by Hiro Yamasaki, 1–17. Amsterdam: Elsevier Science B.V.



Artificial Intelligence - Who Is Martin Ford?


 


Martin Ford (active from 2009 until the present) is a futurist and author who focuses on artificial intelligence, automation, and the future of employment.


Rise of the Robots, his 2015 book, was named the Financial Times and McKinsey Business Book of the Year, as well as a New York Times bestseller.



Artificial intelligence, according to Ford, is the "next killer app" in the American economy.


Ford highlights in his writings that most economic sectors in the United States are becoming more mechanized.


  • The transportation business is being turned upside down by self-driving vehicles and trucks.
  • Self-checkout is transforming the retail industry.
  • The hotel business is being transformed by food preparation robots.


According to him, each of these developments will have a significant influence on the American workforce.



Not only will robots disrupt blue-collar labor, but they will also pose a danger to white-collar employees and professionals in fields such as medicine, media, and finance.


  • According to Ford, the majority of this job is similarly regular and can be automated.
  • Under particular, middle management is in jeopardy.
  • According to Ford, there will be no link between human education and training and automation vulnerability in the future, just as worker productivity and remuneration have become unrelated phenomena.

Artificial intelligence will alter knowledge and information work as sophisticated algorithms, machine-learning tools, and clever virtual assistants are incorporated into operating systems, business software, and databases.


Ford’s viewpoint has been strengthened by a 2013 research by Carl Benedikt Frey and Michael Osborne of the Oxford University Martin Program on the Impacts of Future Technology and the Oxford University Engineering Sciences Department.

Frey and Osborne’s study, done with the assistance of machine-learning algorithms, indicated that over half of 702 various types of American employment may be automated in the next 10 to twenty years.



Ford points out that when automation precipitates primary job losses in areas susceptible to computerization, it will also cause a secondary wave of job destruction in sectors that are sustained by them, even if they are themselves automation resistant.


  • Ford suggests that capitalism will not go away in the process, but it will need to adapt if it is to survive.
  • Job losses will not be immediately staunched by new technology jobs in the highly automated future.

Ford has advocated a universal basic income—or “citizens dividend”—as one way to help American workers transition to the economy of the future.


  • Without consumers making wages, he asserts, there simply won’t be markets for the abundant goods and services that robots will produce.
  • And those displaced workers would no longer have access to home owner ship or a college education.
  • A universal basic income could be guaranteed by placing value added taxes on automated industries.
  • The wealthy owners in these industries would agree to this tax out of necessity and survival.



Further financial incentives, he argues, should be targeted at individuals who are working to enhance human culture, values, and wisdom, engaged in earning new credentials or innovating outside the mainstream automated economy.


  • Political and sociocultural changes will be necessary as well.
  • Automation and artificial intelligence, he says, have exacerbated economic inequality and given extraordinary power to special interest groups in places like the Silicon Valley.
  • He also suggests that Americans will need to rethink the purpose of employment as they are automated out of jobs.



Work, Ford believes, will not primarily be about earning a living, but rather about finding purpose and meaning and community.


  • Education will also need to change.
  • As the number of high-skill jobs is depleted, fewer and fewer highly educated students will find work after graduation.



Ford has been criticized for assuming that hardly any job will remain untouched by computerization and robotics.


  • It may be that some occupational categories are particularly resistant to automation, for instance, the visual and performing arts, counseling psychology, politics and governance, and teaching.
  • It may also be the case that human energies currently focused on manufacture and service will be replaced by work pursuits related to entrepreneurship, creativity, research, and innovation.



Ford speculates that it will not be possible for all of the employed Americans in the manufacturing and service economy to retool and move to what is likely to be a smaller, shallower pool of jobs.



In The Lights in the Tunnel: Automation, Accelerating Technology, and the Economy of the Future (2009), Ford introduced the metaphor of “lights in a tunnel” to describe consumer purchasing power in the mass market.


A billion individual consumers are represented as points of light that vary in intensity corresponding to purchasing power.

An overwhelming number of lights are of middle intensity, corresponding to the middle classes around the world.

  • Companies form the tunnel. Five billion other people, mostly poor, exist outside the tunnel.
  • In Ford’s view, automation technologies threaten to dim the lights and collapse the tunnel.
  • Automation poses dangers to markets, manufacturing, capitalist economics, and national security.



In Rise of the Robots: Technology and the Threat of a Jobless Future (2015), Ford focused on the differences between the current wave of automation and prior waves.


  • He also commented on disruptive effects of information technology in higher education, white-collar jobs, and the health-care industry.
  • He made a case for a new economic paradigm grounded in the basic income, incentive structures for risk-taking, and environmental sensitivity, and he described scenarios where inaction might lead to economic catastrophe or techno-feudalism.


Ford’s book Architects of Intelligence: The Truth about AI from the People Building It (2018) includes interviews and conversations with two dozen leading artificial intelligence researchers and entrepreneurs.


  • The focus of the book is the future of artificial general intelligence and predictions about how and when human-level machine intelligence will be achieved.



Ford holds an undergraduate degree in Computer Engineering from the University of Michigan.

He earned an MBA from the UCLA Anderson School of Management.

He is the founder and chief executive officer of the software development company Solution-Soft located in Santa Clara, California.



Jai Krishna Ponnappan


You may also want to read more about Artificial Intelligence here.



See also: 


Brynjolfsson, Erik; Workplace Automation.


Further Reading:


Ford, Martin. 2009. The Lights in the Tunnel: Automation, Accelerating Technology, and the Economy of the Future. Charleston, SC: Acculant.

Ford, Martin. 2013. “Could Artificial Intelligence Create an Unemployment Crisis?” Communications of the ACM 56 7 (July): 37–39.

Ford, Martin. 2016. Rise of the Robots: Technology and the Threat of a Jobless Future. New York: Basic Books.

Ford, Martin. 2018. Architects of Intelligence: The Truth about AI from the People Build￾ing It. Birmingham, UK: Packt Publishing



Artificial Intelligence - Who Is Erik Brynjolfsson?

 



The Massachusetts Institute of Technology's Initiative on the Digital Economy is directed by Erik Brynjolfsson (1962–).

He is also a Research Associate at the National Bureau of Economic Research and a Schussel Family Professor at the MIT Sloan School (NBER).

Brynjolfsson's research and writing focuses on the relationship between information technology productivity and labor and innovation.

Brynjolfsson's work has long been at the focus of debates concerning how technology affects economic relationships.

His early research focused on the link between information technology and productivity, particularly the "productivity conundrum." Brynjolfsson discovered "large negative associations between economywide productivity and information worker productivity," according to his findings (Brynjolfs son 1993, 67).

He proposed that the paradox may be explained by effect mismeasurement, a lag between initial cost and final benefits, private benefits accumulating at the expense of the collective benefit, or blatant mismanagement.

However, multiple empirical studies by Brynjolfsson and associates demonstrate that investing in information technology has increased productivity significantly—at least since 1991.

Information technology, notably electronic communication networks, enhances multitasking, according to Brynjolfsson.

Multitasking, in turn, boosts productivity, knowledge network growth, and worker performance.

More than a simple causal connection, the relationship between IT and productivity constitutes a "virtuous cycle": as performance improves, users are motivated to embrace knowledge networks that boost productivity and operational performance.

In the era of artificial intelligence, the productivity paradox has resurfaced as a topic of discussion.

The digital economy faces a new set of difficulties as the battle between human and artificial labor heats up.

Brynjolfsson discusses the phenomenon of frictionless commerce, a trait brought about by internet activities such as smart shopbots' rapid pricing comparison.

Retailers like Amazon have redesigned their supply chains and distribution tactics to reflect how online marketplaces function in the age of AI.

This restructuring of internet commerce has changed the way we think about efficiency.

Price and quality comparisons may be made by covert human consumers in the brick-and-mortar economy.

This procedure may be time-consuming and expensive.

Consumers (and web-scraping bots) may now effortlessly navigate from one website to another, thereby lowering the cost of obtaining various types of internet information to zero.

Brynjolfsson and coauthor Andrew McAfee discuss the impact of technology on employment, the economy, and productivity development in their best-selling book Race Against the Machine (2011).

They're particularly interested in the process of creative destruction, which economist Joseph Schumpeter popularized in his book Capitalism, Socialism, and Democracy (1942).

While technology is a beneficial asset for the economy as a whole, Brynjolfsson and McAfee illustrate that it does not always benefit everyone in society.

In reality, the advantages of technical advancements may be uneven, benefiting small groups of innovators and investors who control digital marketplaces.

The key conclusion reached by Brynjolfsson and McAfee is that humans should collaborate with machines rather than compete with them.

When people learn skills to participate in the new age of smart machines, innovation and human capital improve.

Brynjolfsson and McAfee expanded on this topic in The Second Machine Age (2014), evaluating the significance of data in the digital economy and the growing prominence of artificial intelligence.

Data-driven intelligent devices, according to the authors, are a key component of online business.

Artificial intelligence brings us a world of new possibilities in terms of services and features.

They suggest that these changes have an impact on productivity indices as well as our understanding of what it means to participate in capitalist business.

Brynjolfsson and McAfee both have a lot to say on the disruptive effects of a widening gap between internet billionaires and regular people.

The authors are particularly concerned about the effects of artificial intelligence and smart robots on employment.

Brynjolfsson and McAfee reaffirm in Second Machine Age that there should be no race against technology, but rather purposeful cohabitation with it in order to develop a better global economy and society.

Brynjolfsson and McAfee argue in Machine, Platform, Crowd (2017) that the human mind will have to learn to cohabit with clever computers in the future.

The big difficulty is figuring out how society will utilize technology and how to nurture the beneficial features of data-driven innovation and artificial intelligence while weeding out the undesirable aspects.

Brynjolfsson and McAfee envision a future in which labor is not only suppressed by efficient machines and the disruptive effects of platforms, but also in which new matchmaking businesses govern intricate economic structures and large enthusiastic online crowds, and vast amounts of human knowledge and expertise are used to strengthen supply chains and economic processes.

Machines, platforms, and the crowd, according to Brynjolfsson and McAfee, may be employed in a variety of ways, either to concentrate power or to disperse decision-making and wealth.

They come to the conclusion that individuals do not have to be passively reliant on previous technological trends; instead, they may modify technology to make it more productive and socially good.

Brynjolfsson's current research interests include productivity, inequality, labor, and welfare, and he continues to work on artificial intelligence and the digital economy.

He graduated from Harvard University with degrees in Applied Mathematics and Decision Sciences.

In 1991, he received his doctorate in Managerial Economics from the MIT Sloan School.

"Information Technology and the Reorganization of Work: Theory and Evidence," was the title of his dissertation.


~ Jai Krishna Ponnappan

You may also want to read more about Artificial Intelligence here.



See also: 

Ford, Martin; Workplace Automation.



Further Reading

Aral, Sinan, Erik Brynjolfsson, and Marshall Van Alstyne. 2012. “Information, Technology, and Information Worker Productivity.” Information Systems Research 23, no. 3, pt. 2 (September): 849–67.

Brynjolfsson, Erik. 1993. “The Productivity Paradox of Information Technology.” Com￾munications of the ACM 36, no. 12 (December): 67–77.

Brynjolfsson, Erik, Yu Hu, and Duncan Simester. 2011. “Goodbye Pareto Principle, Hello Long Tail: The Effect of Search Costs on the Concentration of Product Sales.” Management Science 57, no. 8 (August): 1373–86.

Brynjolfsson, Erik, and Andrew McAfee. 2012. Race Against the Machine: How the Digital Revolution Is Accelerating Innovation, Driving Productivity, and Irreversibly Transforming Employment and the Economy. Lexington, MA: Digital Frontier Press.

Brynjolfsson, Erik, and Andrew McAfee. 2016. The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. New York: W. W. Norton.

Brynjolfsson, Erik, and Adam Saunders. 2013. Wired for Innovation: How Information Technology Is Reshaping the Economy. Cambridge, MA: MIT Press.

McAfee, Andrew, and Erik Brynjolfsson. 2017. Machine, Platform, Crowd: Harnessing Our Digital Future. New York: W. W. Norton.


What Is Artificial General Intelligence?

Artificial General Intelligence (AGI) is defined as the software representation of generalized human cognitive capacities that enables the ...