Artificial Intelligence - Gender and Artificial Intelligence.

 

Artificial intelligence and robots are often thought to be sexless and genderless in today's society, but this is not the case.

Humans, on the other hand, encode gender and stereo types into artificial intelligence systems in a similar way that gender is woven into language and culture.

The data used to train artificial intelligences has a gender bias.

Biased data may cause significant discrepancies in computer predictions and conclusions.

These differences would be said to be discriminating in humans.

AIs are only as good as the people who provide the data that machine learning systems capture, and they are only as ethical as the programmers who create and supervise them.

Machines presume gender prejudice is normal (if not acceptable) human behavior when individuals exhibit it.

When utilizing numbers, text, graphics, or voice recordings to teach algorithms, bias might emerge.

Machine learning is the use of statistical models to evaluate and categorize large amounts of data in order to generate predictions.

Deep learning is the use of neural network topologies that are expected to imitate human brainpower.

Data is labeled using classifiers based on previous patterns.

Classifiers have a lot of power.

By studying data from automobiles visible in Google Street View, they can precisely forecast income levels and political leanings of neighborhoods and cities.

The language individuals employ reveals gender prejudice.

This bias may be apparent in the names of items as well as how they are ranked in significance.

Beginning with the frequency with which their respective titles are employed and they are referred to as men and women vs boys and girls, descriptions of men and women are skewed.

The analogies and words employed are skewed as well.

Biased AI may influence whether or not individuals of particular genders or ethnicities are targeted for certain occupations, whether or not medical diagnoses are correct, whether or not they are able to acquire loans, and even how exams are scored.

"Woman" and "girl" are more often associated with the arts than with mathematics in AI systems.

Similar biases have been discovered in Google's AI systems for finding employment prospects.

Facebook and Microsoft's algorithms regularly correlate pictures of cooking and shopping with female activity, whereas sports and hunting are associated with masculine activity.

Researchers have discovered instances when gender prejudices are purposefully included into AI systems.

Men, for example, are more often provided opportunities to apply for highly paid and sought-after positions on job sites than women.

Female-sounding names for digital assistants on smartphones include Siri, Alexa, and Cortana.

According to Alexa's creator, the name came from negotiations with Amazon CEO Jeff Bezos, who desired a virtual assistant with the attitude and gender of the Enterprise starship computer from the Star Trek television program, which is a woman.

Debo rah Harrison, the Cortana project's head, claims that their female voice arose from studies demonstrating that people react better to female voices.

However, when BMW introduced a female voice to its in-car GPS route planner, it experienced instant backlash from males who didn't want their vehicles to tell them what to do.

Female voices should seem empathic and trustworthy, but not authoritative, according to the company.

Affectiva, a startup that specializes in artificial intelligence, utilizes photographs of six million people's faces as training data to attempt to identify their underlying emotional states.

The startup is now collaborating with automakers to utilize real-time footage of drivers to assess whether or not they are weary or furious.

The automobile would advise these drivers to pull over and take a break.

However, the organization has discovered that women seem to "laugh more" than males, which complicates efforts to accurately estimate the emotional states of normal drivers.

In hardware, the same biases might be discovered.

A disproportionate percentage of female robots are created by computer engineers, who are still mostly male.

The NASA Valkyrie robot, which has been deployed on Shuttle flights, has breasts.

Jia, a shockingly human-looking robot created at China's University of Science and Technology, has long wavy black hair, pale complexion, and pink lips and cheeks.

She maintains her eyes and head inclined down when initially spoken to, as though in reverence.

She wears a tight gold gown that is slender and busty.

"Yes, my lord, what can I do for you?" she says as a welcome.

"Don't get too near to me while you're taking a photo," Jia says when asked to snap a picture.

It will make my face seem chubby." In popular culture, there is a strong prejudice against female robots.

Fembots in the 1997 film Austin Powers discharged bullets from their breast cups, weaponizing female sexuality.

The majority of robots in music videos are female robots.

Duran Duran's "Electric Barbarella" was the first song accessible for download on the internet.

Bjork's video "The Girl And The Robot" gave birth to the archetypal white-sheathed robot seen today in so many places.

Marina and the Diamonds' protest that "I Am Not a Robot" is met by Hoodie Allen's fast answer that "You Are Not a Robot." In "The Ghost Inside," by the Broken Bells, a female robot sacrifices plastic body parts to pay tolls and reclaim paradise.

The skin of Lenny Kravitz's "Black Velveteen" is titanium.

Hatsune Miku and Kagamine Rin are anime-inspired holographic vocaloid singers.

Daft Punk is the notable exception, where robot costumes conceal the genuine identity of the male musicians.

Sexy robots are the principal love interests in films like Metropolis (1927), The Stepford Wives (1975), Blade Runner (1982), Ex Machina (2014), and Her (2013), as well as television programs like Battlestar Galactica and Westworld.

Meanwhile, "killer robots," or deadly autonomous weapons systems, are hypermasculine.

Atlas, Helios, and Titan are examples of rugged military robots developed by the Defense Advanced Research Projects Agency (DARPA).

Achilles, Black Knight, Overlord, and Thor PRO are some of the names given to self-driving automobiles.

The HAL 9000 computer implanted in the spacecraft Discovery in 2001: A Space Odyssey (1968), the most renowned autonomous vehicle of all time, is masculine and deadly.

In the field of artificial intelligence, there is a clear gender disparity.

The head of the Stanford Artificial Intelligence Lab, Fei-Fei Li, revealed in 2017 that her team was mostly made up of "men in hoodies" (Hempel 2017).

Women make up just approximately 12% of the researchers who speak at major AI conferences (Simonite 2018b).

In computer and information sciences, women have 19% of bachelor's degrees and 22% of PhD degrees (NCIS 2018).

Women now have a lower proportion of bachelor's degrees in computer science than they did in 1984, when they had a peak of 37 percent (Simonite 2018a).

This is despite the fact that the earliest "computers," as shown in the film Hidden Figures (2016), were women.

There is significant dispute among philosophers over whether un-situated, gender-neutral knowledge may exist in human society.

Users projected gender preferences on Google and Apple's unsexed digital assistants even after they were launched.

White males developed centuries of professional knowledge, which was eventually unleashed into digital realms.

Will machines be able to build and employ rules based on impartial information for hundreds of years to come? In other words, is there a gender to scientific knowledge? Is it masculine or female? Alison Adam is a Science and Technology Studies researcher who is more concerned in the gender of the ideas created by the participants than the gender of the persons engaged.

Sage, a British corporation, recently employed a "conversation manager" entrusted with building a gender-neutral digital assistant, which was eventually dubbed "Pegg." To help its programmers, the organization has also formalized "five key principles" in a "ethics of code" paper.

According to Sage CEO Kriti Sharma, "by 2020, we'll spend more time talking to machines than our own families," thus getting technology right is critical.

Aether, a Microsoft internal ethics panel for AI and Ethics in Engineering and Research, was recently established.

Gender Swap is a project that employs a virtual reality system as a platform for embodiment experience, a kind of neuroscience in which users may sense themselves in a new body.

Human partners utilize the immersive Head Mounted Display Oculus Rift and first-person cameras to generate the brain illusion.

Both users coordinate their motions to generate this illusion.

The embodiment experience will not operate if one does not correlate to the movement of the other.

It implies that every move they make jointly must be agreed upon by both users.

On a regular basis, new causes of algorithmic gender bias are discovered.

Joy Buolamwini, an MIT computer science graduate student, discovered gender and racial prejudice in the way AI detected individuals' looks in 2018.

She discovered, with the help of other researchers, that the dermatologist-approved Fitzpatrick The datasets for Skin Type categorization systems were primarily made up of lighter-skinned people (up to 86 percent).

The researchers developed a skin type system based on a rebalanced dataset and used it to compare three gender categorization systems available off the shelf.

They discovered that darker-skinned girls are the most misclassified in all three commercial systems.

Buolamwini founded the Algorithmic Justice League, a group that fights unfairness in decision-making software.

~ Jai Krishna Ponnappan

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

See also: 

Algorithmic Bias and Error; Explainable AI.

Further Reading:

Buolamwini, Joy and Timnit Gebru. 2018. “Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification.” Proceedings of Machine Learning Research: Conference on Fairness, Accountability, and Transparency 81: 1–15.

Hempel, Jessi. 2017. “Melinda Gates and Fei-Fei Li Want to Liberate AI from ‘Guys With Hoodies.’” Wired, May 4, 2017. https://www.wired.com/2017/05/melinda-gates-and-fei-fei-li-want-to-liberate-ai-from-guys-with-hoodies/.

Leavy, Susan. 2018. “Gender Bias in Artificial Intelligence: The Need for Diversity and Gender Theory in Machine Learning.” In GE ’18: Proceedings of the 1st International Workshop on Gender Equality in Software Engineering, 14–16. New York: Association for Computing Machinery.

National Center for Education Statistics (NCIS). 2018. Digest of Education Statistics. https://nces.ed.gov/programs/digest/d18/tables/dt18_325.35.asp.

Roff, Heather M. 2016. “Gendering a Warbot: Gender, Sex, and the Implications for the Future of War.” International Feminist Journal of Politics 18, no. 1: 1–18.

Simonite, Tom. 2018a. “AI Is the Future—But Where Are the Women?” Wired, August 17, 2018. https://www.wired.com/story/artificial-intelligence-researchers-gender-imbalance/.

Simonite, Tom. 2018b. “AI Researchers Fight Over Four Letters: NIPS.” Wired, October 26, 2018. https://www.wired.com/story/ai-researchers-fight-over-four-letters-nips/.

Søraa, Roger Andre. 2017. “Mechanical Genders: How Do Humans Gender Robots?” Gender, Technology, and Development 21, no. 1–2: 99–115.

Wosk, Julie. 2015. My Fair Ladies: Female Robots, Androids, and Other Artificial Eves. New Brunswick, NJ: Rutgers University Press.

Artificial Intelligence - Who Is Demis Hassabis (1976–)?

Demis Hassabis lives in the United Kingdom and works as a computer game programmer, cognitive scientist, and artificial intelligence specialist.

He is a cofounder of DeepMind, the company that created the AlphaGo deep learning engine.

Hassabis is well-known for being a skilled game player.

His passion for video games paved the way for his career as an artificial intelligence researcher and computer game entrepreneur.

Hassabis' parents noticed his chess prowess at a young age.

At the age of thirteen, he had achieved the status of chess master.

He's also a World Team Champion in the strategic board game Diplomacy, a World Series of Poker Main Event participant, and numerous World Pentamind and World Deca mentathlon Champions in the London Mind Sports Olympiad.

Hassabis began working at Bullfrog Games in Guildford, England, with renowned game designer Peter Molyneux when he was seventeen years old.

Bullfrog was notable for creating a variety of popular computer "god games." A god game is a computer-generated life simulation in which the user has power and influence over semiautonomous people in a diverse world.

Molyneux's Populous, published in 1989, is generally regarded as the first god game.

Has sabis co-designed and coded Theme Park, a simulation management game published by Bullfrog in 1994.

Hassabis dropped out of Bullfrog Games to pursue a degree at Queens' College, Cambridge.

In 1997, he earned a bachelor's degree in computer science.

Following graduation, Hassabis rejoined Molyneux at Lionhead Studios, a new gaming studio.

Hassabis worked on the artificial intelligence for the game Black & White, another god game in which the user reigned over a virtual island inhabited by different tribes, for a short time.

Hassabis departed Lionhead after a year to launch his own video game studio, Elixir Studios.

Hassabis has signed arrangements with major publishers such as Microsoft and Vivendi Universal.

Before closing in 2005, Elixir created a variety of games, including the diplomatic strategy simulation game Republic: The Revolution and the real-time strategy game Evil Genius.

Republic's artificial intelligence is modeled after Elias Canetti's 1960 book People and Authority, which explores problems concerning how and why crowds follow rulers' power (which Hassabis boiled down to force, money, and influence).

Republic required the daily programming efforts of twenty-five programmers over the course of four years.

Hassabis thought that the AI in the game would be valuable to academics.

Hassabis took a break from game creation to pursue additional studies at University College London (UCL).

In 2009, he received his PhD in Cognitive Neuroscience.

In his research of individuals with hippocampal injury, Hassabis revealed links between memory loss and poor imagination.

These findings revealed that the brain's memory systems may splice together recalled fragments of previous experiences to imagine hypothetical futures.

Hassabis continued his academic studies at the Gatsby Computational Neuroscience Unit at UCL and as a Wellcome Trust fellow for another two years.

He was also a visiting researcher at MIT and Harvard University.

Hassabis' cognitive science study influenced subsequent work on unsupervised learning, memory and one-shot learning, and imagination-based planning utilizing generic models in artificial intelligence.

With Shane Legg and Mustafa Suleyman, Hassabis cofounded the London-based AI start-up DeepMind Technologies in 2011.

The organization was focused on interdisciplinary science, bringing together premier academics and concepts from machine learning, neurology, engineering, and mathematics.

The mission of DeepMind was to create scientific breakthroughs in artificial intelligence and develop new artificial general-purpose learning capabilities.

Hassabis has compared the project to the Apollo Program for AI.

DeepMind was tasked with developing a computer capable of defeating human opponents in the abstract strategic board game Go.

Hassabis didn't want to build an expert system, a brute-force computer preprogrammed with Go-specific algorithms and heuristics.

Rather than the chess-playing single-purpose Deep Blue system, he intended to construct a computer that adapted to play ing games in ways comparable to human chess champ Garry Kasparov.

He sought to build a machine that could learn to deal with new issues and have universality, which he defined as the ability to do a variety of jobs.

The reinforcement learning architecture was used by the company's AlphaGo artificial intelligence agent, which was built to compete against Lee Sedol, an eighteen-time world champion Go player.

Agents in the environment (in this example, the Go board) aim to attain a certain objective via reinforcement learning (winning the game).

The agents have perceptual inputs (such as visual input) as well as a statistical model based on environmental data.

The agent creates plans and goes through simulations of actions that will modify the model in order to accomplish the objective while collecting perceptual input and developing a representation of its surroundings.

The agent is always attempting to choose behaviors that will get it closer to its goal.

Hassabis argues that resolving all of the issues of goal-oriented agents in a reinforcement learning framework would be adequate to fulfill artificial general intelligence's promise.

He claims that biological systems work in a similar manner.

The dopamine system in human brains is responsible for implementing a reinforcement learning framework.

To master the game of Go, it usually takes a lifetime of study and practice.

Go includes a significantly broader search area than chess.

On the board, there are more potential Go locations than there are atoms in the cosmos.

It is also thought to be almost hard to develop an evaluation function that covers a significant portion of those places in order to determine where the next stone should be placed on the board.

Each game is essentially unique, and exceptional players describe their decisions as being guided by intuition rather than logic.

AlphaGo addressed these obstacles by leveraging data gathered from thousands of strong amateur games played by human Go players to train a neural network.

After that, AlphaGo played millions of games against itself, predicting how probable each side was to win based on the present board positions.

No specific assessment standards were required in this manner.

In Seoul, South Korea, in 2006, AlphaGo beat Go champion Lee Sedol (four games to one).

The way AlphaGo plays is considered cautious.

It favors diagonal stone placements known as "shoulder hits" to enhance victory while avoiding risk or point spread—thus putting less apparent focus on achieving territorial gains on the board.

In order to play any two-person game, AlphaGo has subsequently been renamed AlphaZero.

Without any human training data or sample games, AlphaZero learns from begin.

It only learns from random play.

After just four hours of training, AlphaZero destroyed Stock fish, one of the best free and open-source chess engines (28 games to 0 with 72 draws).

AlphaZero prefers the mobility of the pieces above their materiality while playing chess, which results in a creative style of play (similar to Go).

Another task the business took on was to develop a versatile, adaptable, and durable AI that could teach itself how to play more than 50 Atari video games just by looking at the pixels and scores on a video screen.

Hassabis introduced deep reinforcement learning, which combines reinforcement learning and deep learning, for this difficulty.

To create a neural network capable of reliable perceptual identification, deep neural networks need an input layer of observations, weighting mechanisms, and backpropagation.

In the instance of the Atari challenge, the network was trained using the 20,000-pixel values that flashed on the videogame screen at any given time.

Under deep learning, reinforcement learning takes the machine from the point where it perceives and recognizes a given input to the point where it can take meaningful action toward a goal.

In the Atari challenge, the computer learnt how to win over hundreds of hours of playtime by doing eighteen distinct exact joystick actions in a certain time-step.

To put it another way, a deep reinforcement learning machine is an end-to-end learning system capable of analyzing perceptual inputs, devising a strategy, and executing the strategy from start.

DeepMind was purchased by Google in 2014.

Hassabis continues to work at Google with DeepMind's deep learning technology.

Optical coherence tomography scans for eye disorders are used in one of these attempts.

By triaging patients and proposing how they should be referred for further treatment, DeepMind's AI system can swiftly and reliably diagnose from eye scans.

AlphaFold is a machine learning, physics, and structural biology system that predicts three-dimensional protein structures simply based on its genetic sequence.

AlphaFold took first place in the 2018 "world championship" for Critical Assessment of Techniques for Protein Structure Prediction, successfully predicting the most accurate structure for 25 of 43 proteins.

AlphaStar is currently mastering the real-time strategy game StarCraft II. 

~ Jai Krishna Ponnappan

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

See also: 

Deep Learning.

Further Reading:

“Demis Hassabis, Ph.D.: Pioneer of Artificial Intelligence.” 2018. Biography and interview. American Academy of Achievement. https://www.achievement.org/achiever/demis-hassabis-ph-d/.

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

Gibney, Elizabeth. 2015. “DeepMind Algorithm Beats People at Classic Video Games.” Nature 518 (February 26): 465–66.

Gibney, Elizabeth. 2016. “Google AI Algorithm Masters Ancient Game of Go.” Nature 529 (January 27): 445–46.

Proudfoot, Kevin, Josh Rosen, Gary Krieg, and Greg Kohs. 2017. AlphaGo. Roco Films.

Quantum Revolution 2.0 Epilogue - In the year 2050

 

 Markus, who was born in the year 2020, is sleeping a little longer today. 

His 30th birthday has arrived. 

His fMRT alarm clock interacts with Markus's subconscious by logging into his dream and allowing it to become lucid (with lucid dreams, the dreamer is aware that he is dreaming). 

Markus emerges from the REM period as fresh as possible, according to the system's long-ago calculation of the optimum wake-up time. 

The nanobots in his body monitor the latest developments on potential inflammations, vascular plaques, or cell alterations just before he wakes up. 

The info appears on Markus' nano-retina as soon as he opens his eyes. 

His breakfast consists of a butter croissant and jam, like it does every morning. 

Nanobots have become active once again. 

All unnecessary sugar and fat molecules have been eliminated, and essential vitamins, trace minerals, and dietary fibre have been added in their stead. 

The fact that the croissants still taste as buttery as they did forty years ago may also be attributed to the nanobots' abilities. 

They use the right neuro-signals to activate Markus' taste buds. 

The kitchen is eerily quiet. 

Appliances and materials for the kitchen are no longer required. 

What was formerly a tiny oven that was ideally suited to the size of the roast has now been transformed into a toaster. 

This is made feasible through the use of nanoparticle-based programmable matter. 

Markus puts the almost fat-free butter on his croissant carefully. 

Markus is immediately linked to the internet through his retina implant and a microchip in his brain, which transmits messages customized to his interests straight into his brain. 

Markus's tastes, ranging from his favorite football team to his political beliefs, are better known to the AI running on quantum computers, which has been taught and tailored for him and his personality. 

Because it has kept track of every detail of his life and is constantly running algorithms to improve his well-being. 

The conversation between Markus and his AI is, of course, bidirectional. 

He expresses his desire to learn more about the Middle East conflict via his ideas. 

He instantly gets the necessary information, which is delivered to the proper neurons in his brain through suitable impulses, allowing him to not only see but also smell, taste, and hear the smoke and gunshots. 

He recognized the rainforest scene on the wall as the one that lulled him to sleep the night before. 

The scent of dampness is still in his nose, or rather, in the relevant neurons in his brain's olfactory bulb. 

He likes a beach this morning, so he makes his wish. 

Immediately, a tropical coral reef appears in front of him, complete with ocean noises and scents. 

Perceptions are produced directly in his brain, or rather within him. 

When he uses Brainchat, the new brain-to-brain program, to communicate with his love Iris, his AI informs him that an unauthorized individual is listening in on his quantum communication channel. 

The program gives you the option of changing the encryption or switching to a different channel. 

The news article that has been playing in his head has altered. 

He's now listening in on a debate on the abolition of money. 

The value of ownership has shifted dramatically in recent years. 

There are no longer any rare products worth spending money on. 

With 3D printers, even the most basic materials can be made into anything. 

All desired emotions and sensations may be generated directly in the brain via appropriate neuro-stimulation. 

Representatives of the new socialist movement urge that all software for printing and converting goods be made freely available. 

Alphabet and Dodax (formed in 2029 following the merging of Facebook and Microsoft), the only surviving software firms from the information era in the first 20 years of the twenty-first century, continue to resist. 

However, their cause has long since been abandoned. 

The free market economy has lost its luster. 

Everything that humans need may be found in the form of software. 

All they have to do now is print things out or load the necessary software into the physical devices. 

Previously, software needed the use of specific devices known as computers. 

They were both costly and rigid. 

But 10 years ago, when the technical issue of decoherence of entangled quantum systems had been addressed, quantum computer software was created and immediately integrated into objects, for virtually any type of matter. 

Quantum computers allowed individual atoms in a material combination to be controlled in such a manner that they could be combined to create any energetically feasible shape. 

All that was required was the right software. 

In parliament, the New Socialists, who evolved from the Social Democratic movement in 2041, currently have a two-thirds majority. 

They want to make free access to all software a fundamental right for all citizens, according to their electoral program. 

Alphabet and Dodax would be extinct. 

However, it might not be such a terrible thing, and this is the current debate's tone.

It would be like to the last dinosaurs becoming extinct. 

Markus returns to his passion of creating new animal and plant species via genetic engineering. 

He hasn't had a paid work in years, and most of his pals have also lost their jobs. 

At the press of a button, he has access to almost everything he needs (and, eventually, almost everything). 

Almost everything is taken care of by AI-enabled devices and nanobots. 

There is no longer any need to work for a living. 

Money as a means of trade has lost its significance, and the next generation will struggle to comprehend why it was once so essential. 

Markus shivers as he recalls previous times when he had to consider if he could afford to purchase the newest model of electric vehicle and struggled to repay his debts. 

As he leans over his little CRISPR gadget, he wonders if his brain chip, which links him to the central AI, was designed to have such a strong dislike for previous eras. 

But then he grins to himself and returns his attention to the orange color of the moss he intends to use to cover his walls.

~ Jai Krishna Ponnappan

You may also want to read more about Quantum Computing here.

Artificial Intelligence - What Is The Advanced Soldier Sensor Information Systems and Technology (ASSIST)?

 

Soldiers are often required to do missions that may take many hours and are quite stressful.

Soldiers are requested to write a report detailing the most significant events that occurred once a mission is completed.

This report is designed to collect information about the environment and local/foreign people in order to better organize future operations.

Soldiers often offer this report primarily based on their memories, still photographs, and GPS data from portable equipment.

There are probably numerous cases when crucial information is missing and not accessible for future mission planning due to the severe stress they face.

Soldiers were equipped with sensors that could be worn directly on their uniforms as part of the ASSIST (Advanced Soldier Sensor Information Systems and Technology) program, which addressed this problem.

Sensors continually recorded what was going on around the troops during the operation.

When the troops returned from their mission, the sensor data was indexed and an electronic record of the events that occurred while the ASSIST system was recording was established.

Soldiers might offer more accurate reports if they had this knowledge instead of depending simply on their memories.

Numerous functions were made possible by AI-based algorithms, including:

• "Capabilities for Image/Video Data Analysis"

• Object Detection/Image Classification—the capacity to detect and identify items (such as automobiles, persons, and license plates) using video, images, and/or other data sources.

• "Audio Data Analysis Capabilities"

• "Arabic Text Translation"—the ability to detect, recognize, and translate written Arabic text (e.g., in imagery data)

• "Change Detection"—the ability to detect changes in related data sources over time (e.g., identify differences in imagery of the same location at different times)

• Sound Recognition/Speech Recognition—the capacity to distinguish speech (e.g., keyword spotting and foreign language recognition) and identify sound events (e.g., explosions, gunfire, and cars) in audio data.

• Shooter Localization/Shooter Classification—the ability to recognize gunshots in the environment (e.g., via audio data processing), as well as the kind of weapon used and the shooter's position.

• "Capabilities for Soldier Activity Data Analysis"

• Soldier State Identification/Soldier Localization—the capacity to recognize a soldier's course of movement in a given area and characterize the soldier's activities (e.g., running, walking, and climbing stairs) To be effective, AI systems like this (also known as autonomous or intelligent systems) must be thoroughly and statistically analyzed to verify that they would work correctly and as intended in a military setting.

The National Institute of Standards and Technology (NIST) was entrusted with assessing these AI systems using three criteria:

1. The precision with which objects, events, and activities are identified and labeled

2. The system's capacity to learn and improve its categorization performance.

3. The system's usefulness in improving operational efficiency To create its performance measurements, NIST devised a two-part test technique.

Metrics 1 and 2 were assessed using component- and system-level technical performance evaluations, respectively, while meter 3 was assessed using system-level utility assessments.

The utility assessments were created to estimate the effect these technologies would have on warfighter performance in a range of missions and job tasks, while the technical performance evaluations were created to ensure the ongoing improvement of ASSIST system technical capabilities.

NIST endeavored to create assessment techniques that would give an appropriate degree of difficulty for system and soldier performance while defining the precise processes for each sort of evaluation.

The ASSIST systems were divided down into components that implemented certain capabilities at the component level.

For example, the system was divided down into an Arabic text identification component, an Arabic text extraction component (to localize individual text characters), and a text translation component to evaluate its Arabic translation capacity.

Each factor was evaluated on its own to see how it affected the system.

Each ASSIST system was assessed as a black box at the system level, with the overall performance of the system being evaluated independently of the individual component performance.

The total system received a single score, which indicated the system's ability to complete the overall job.

A test was also conducted at the system level to determine the system's usefulness in improving operational effectiveness for after-mission reporting.

Because all of the systems reviewed as part of this initiative were in the early phases of development, a formative assessment technique was suitable.

NIST was especially interested in determining the system's value for warfighters.

As a result, we were worried about the influence on their procedures and goods.

User-centered metrics were used to represent this viewpoint.

NIST set out to find measures that may assist answer questions like: What information do infantry troops seek and/or require after completing a field mission? From both the troops' and the S2's (Staff 2—Intelligence Officer) perspectives, how successfully are information demands met? What was ASSIST's contribution to mission reporting in terms of user-stated information requirements? This examination was carried out at the Aberdeen Test Center Military Operations in Urban Terrain (MOUT) location in Aberdeen, Maryland.

The location was selected for a variety of reasons:

• Ground truth—Aberdeen was able to deliver ground truth to within two centimeters of chosen locations.

This provided a strong standard against which the system output could be compared, enabling the assessment team to get a good depiction of what really transpired in the environment.

• Realism—The MOUT location has around twenty structures that were built up to seem like an Iraqi town.

• Testing infrastructure—The facility was outfitted with a number of cameras (both inside and outside) to help us better comprehend the surroundings during testing.

• Soldier availability—For the assessment, the location was able to offer a small squad of active-duty troops.

The MOUT site was enhanced with items, people, and background noises whose location and behavior were programmed to provide a more operationally meaningful test environment.

The goal was to provide an environment in which the various ASSIST systems could test their capabilities by detecting, identifying, and/or capturing various forms of data.

Foreign language speech detection and classification, Arabic text detection and recognition, detection of shots fired and vehicle sounds, classification of soldier states and tracking their locations (both inside and outside of buildings), and identifying objects of interest such as vehicles, buildings, people, and so on were all included in NIST's utility assessments.

Because the tests required the troops to respond according to their training and experience, the soldiers' actions were not scripted as they progressed through each exercise.

~ Jai Krishna Ponnappan

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

See also: Battlefield AI and Robotics; Cybernetics and AI.

Further Reading

Schlenoff, Craig, Brian Weiss, Micky Steves, Ann Virts, Michael Shneier, and Michael Linegang. 2006. “Overview of the First Advanced Technology Evaluations for ASSIST.” In Proceedings of the Performance Metrics for Intelligence Systems Workshop, 125–32. Gaithersburg, MA: National Institute of Standards and Technology.

Steves, Michelle P. 2006. “Utility Assessments of Soldier-Worn Sensor Systems for ASSIST.” In Proceedings of the Performance Metrics for Intelligence Systems Workshop, 165–71. Gaithersburg, MA: National Institute of Standards and Technology.

Washington, Randolph, Christopher Manteuffel, and Christopher White. 2006. “Using an Ontology to Support Evaluation of Soldier-Worn Sensor Systems for ASSIST.” In Proceedings of the Performance Metrics for Intelligence Systems Workshop, 172–78. Gaithersburg, MA: National Institute of Standards and Technology.

Weiss, Brian A., Craig I. Schlenoff, Michael O. Shneier, and Ann Virts. 2006. “Technol￾ogy Evaluations and Performance Metrics for Soldier-Worn Sensors for ASSIST.” In Proceedings of the Performance Metrics for Intelligence Systems Workshop, 157–64. Gaithersburg, MA: National Institute of Standards and Technology.

AI - Symbolic Logic

 

In mathematical and philosophical reasoning, symbolic logic entails the use of symbols to express concepts, relations, and positions.

Symbolic logic varies from (Aristotelian) syllogistic logic in that it employs ideographs or a particular notation to "symbolize exactly the item discussed" (Newman 1956, 1852), and it may be modified according to precise rules.

Traditional logic investigated the truth and falsehood of assertions, as well as their relationships, using terminology derived from natural language.

Unlike nouns and verbs, symbols do not need interpretation.

Because symbol operations are mechanical, they may be delegated to computers.

Symbolic logic eliminates any ambiguity in logical analysis by codifying it entirely inside a defined notational framework.

Gottfried Wilhelm Leibniz (1646–1716) is widely regarded as the founding father of symbolic logic.

Leibniz proposed the use of ideographic symbols instead of natural language in the seventeenth century as part of his goal to revolutionize scientific thinking.

Leibniz hoped that by combining such concise universal symbols (characteristica universalis) with a set of scientific reasoning rules, he could create an alphabet of human thought that would promote the growth and dissemination of scientific knowledge, as well as a corpus containing all human knowledge.

Boolean logic, the logical underpinnings of mathematics, and decision issues are all topics of symbolic logic that may be broken down into subcategories.

George Boole, Alfred North Whitehead, and Bertrand Russell, as well as Kurt Gödel, wrote important contributions in each of these fields.

George Boole published The Mathematical Analysis of Logic (1847) and An Investigation of the Laws of Thought in the mid-nineteenth century (1854).

Boole zoomed down on a calculus of deductive reasoning, which led him to three essential operations in a logical mathematical language known as Boolean algebra: AND, OR, and NOT.

The use of symbols and operators greatly aided the creation of logical formulations.

Claude Shannon (1916–2001) employed electromechanical relay circuits and switches to reproduce Boolean algebra in the twentieth century, laying crucial foundations in the development of electronic digital computing and computer science in general.

Alfred North Whitehead and Bertrand Russell established their seminal work in the subject of symbolic logic in the early twentieth century.

Their Principia Mathematica (1910, 1912, 1913) demonstrated how all of mathematics may be reduced to symbolic logic.

Whitehead and Russell developed a logical system from a handful of logical concepts and a set of postulates derived from those ideas in the first book of their work.

Whitehead and Russell established all mathematical concepts, including number, zero, successor of, addition, and multiplication, using fundamental logical terminology and operational principles like proposition, negation, and either-or in the second book of the Principia.

In the last and third volumes, Whitehead and Russell were able to demonstrate that the nature and reality of all mathematics is built on logical concepts and connections.

The Principia showed how every mathematical postulate might be inferred from previously explained symbolic logical facts.

Only a few decades later, Kurt Gödel's On Formally Undecidable Propositions in the Principia Mathematica and Related Systems (1931) critically analyzed the Principia's strong and deep claims, demonstrating that Whitehead and Russell's axiomatic system could not be consistent and complete at the same time.

Even so, it required another important book in symbolic logic, Ernst Nagel and James Newman's Gödel's Proof (1958), to spread Gödel's message to a larger audience, including some artificial intelligence practitioners.

Each of these seminal works in symbolic logic had a different influence on the development of computing and programming, as well as our understanding of a computer's capabilities as a result.

Boolean logic has made its way into the design of logic circuits.

The Logic Theorist program by Simon and Newell provided logical arguments that matched those found in the Principia Mathematica, and was therefore seen as evidence that a computer could be programmed to do intelligent tasks via symbol manipulation.

Gödel's incompleteness theorem raises intriguing issues regarding how programmed machine intelligence, particularly strong AI, will be realized in the end.

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

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

See also: 

Symbol Manipulation.

References And Further Reading

Boole, George. 1854. Investigation of the Laws of Thought on Which Are Founded the Mathematical Theories of Logic and Probabilities. London: Walton.

Lewis, Clarence Irving. 1932. Symbolic Logic. New York: The Century Co.

Nagel, Ernst, and James R. Newman. 1958. Gödel’s Proof. New York: New York University Press.

Newman, James R., ed. 1956. The World of Mathematics, vol. 3. New York: Simon and Schuster.

Whitehead, Alfred N., and Bertrand Russell. 1910–1913. Principia Mathematica. Cambridge, UK: Cambridge University Press.

Artificial Intelligence - What Are AI Berserkers?

 

Berserkers are intelligent killing robots initially described by science fiction and fantasy novelist Fred Saberhagen (1930–2007) in his 1962 short tale "Without a Thought." Berserkers later emerged as frequent antagonists in many more of Saberhagen's books and novellas.

Berserkers are a sentient, self-replicating race of space-faring robots with the mission of annihilating all life.

They were built as an ultimate doomsday weapon in a long-forgotten interplanetary conflict between two extraterrestrial cultures (i.e., one intended as a threat or deterrent more than actual use).

The facts of how the Berserkers were released are lost to time, since they seem to have killed off their creators as well as their foes and have been ravaging the Milky Way galaxy ever since.

They come in a variety of sizes, from human-scale units to heavily armored planetoids (cf.

Death Star), and are equipped with a variety of weaponry capable of sterilizing worlds.

Any sentient species that fights back, such as humans, is a priority for the Berserkers.

They construct factories in order to duplicate and better themselves, but their basic objective of removing life remains unchanged.

It's uncertain how far they evolve; some individual units end up questioning or even changing their intentions, while others gain strategic brilliance (e.g., Brother Assassin, "Mr.Jester," Rogue Berserker, Shiva in Steel).

While the Berserkers' ultimate purpose of annihilating all life is evident, their tactical activities are uncertain owing to unpredictability in their cores caused by radioactive decay.

Their name is derived from Norse mythology's Berserkers, powerful human warriors who battled in a fury.

Berserkers depict a worst-case scenario for artificial intelligence: murdering robots that think, learn, and reproduce in a wild and emotionless manner.

They demonstrate the deadly arrogance of providing AI with strong weapons, harmful purpose, and unrestrained self-replication in order to transcend its creators' comprehension and control.

If Berserkers are ever developed and released, they may represent an inexhaustible danger to living creatures over enormous swaths of space and time.

They're quite hard to get rid of after they've been unbottled.

This is owing to their superior defenses and weaponry, as well as their widespread distribution, ability to repair and multiply, autonomous functioning (i.e., without centralized control), capacity to learn and adapt, and limitless patience to lay in wait.

The discovery of Berserkers is so horrifying in Saberhagen's books that human civilizations are terrified of constructing their own AI for fear that it may turn against its creators.

Some astute humans, on the other hand, find a fascinating Berserker counter-weapon: Qwib-Qwibs, self-replicating robots designed to eliminate all Berserkers rather than all life ("Itself Surprised" by Roger Zelazny).

Humans have also utilized cyborgs as an anti-Berserker technique, pushing the boundaries of what constitutes biological intelligence (Berserker Man, Ber serker Prime, Berserker Kill).

Berserkers also exemplifies artificial intelligence's potential for inscrutability and strangeness.

Even while Berserkers can communicate with each other, their huge brains are generally unintelligible to sentient organic lifeforms fleeing or battling them, and they are difficult to study owing to their proclivity to self-destruct if caught.

What can be deduced from their reasoning is that they see life as a plague, a material illness that must be eradicated.

In consequence, the Berserkers lack a thorough understanding of biological intellect and have never been able to adequately duplicate organic life, despite several tries.

They do, however, sometimes enlist human defectors (dubbed "goodlife") to aid the Berserkers in their struggle against "badlife" (i.e., any life that resists extermination).

Nonetheless, Berserkers and humans think in almost irreconcilable ways, hindering attempts to reach a common understanding between life and nonlife.

The seeming contrasts between human and machine intellect are at the heart of most of the conflict in the tales (e.g., artistic appreciation, empathy for animals, a sense of humor, a tendency to make mistakes, the use of acronyms for mnemonics, and even fake encyclopedia entries made to detect pla giarism).

Berserkers have been known to be defeated by non-intelligent living forms such as plants and mantis shrimp ("Pressure" and "Smasher").

Berserkers may be seen of as a specific example of the von Neumann probe, which was invented by mathematician and physicist John von Neumann (1903–1957): self-replicating space-faring robots that might be deployed over the galaxy to efficiently investigate it In the Berserker tales, the Turing Test, developed by mathematician and computer scientist Alan Turing (1912–1954), is both investigated and upended.

In "Inhuman Error," human castaways compete with a Berserker to persuade a rescue crew that they are human, while in "Without a Thought," a Berserker tries to figure out whether its game opponent is human.

The Fermi paradox—the concept that if intelligent extraterrestrial civilizations exist, we should have heard from them by now—is also explained by Berserkers.

It's possible that extraterrestrial civilizations haven't contacted Earth because they were destroyed by Berserker-like robots or are hiding from them.

Berserkers, or anything like them, have featured in a number of science fiction books in addition to Saberhagen's (e.g., works by Greg Bear, Gregory Benford, David Brin, Ann Leckie, and Martha Wells; the Terminator series of movies; and the Mass Effect series of video games).

All of these instances demonstrate how the potential for existential risks posed by AI may be investigated in the lab of fiction.

~ Jai Krishna Ponnappan

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

See also: 

de Garis, Hugo; Superintelligence; The Terminator.

Further Reading

Saberhagen, Fred. 2015a. Berserkers: The Early Tales. Albuquerque: JSS Literary Productions.

Saberhagen, Fred. 2015b. Berserkers: The Later Tales. Albuquerque: JSS Literary Productions.

Saberhagen’s Worlds of SF and Fantasy. http://www.berserker.com.

The TAJ: Official Fan site of Fred Saberhagen’s Berserker® Universe. http://www.berserkerfan.org.