AI - Smart Homes And Smart Cities.

 

Projects to develop the infrastructure for smart cities and houses are involving public authorities, professionals, businessmen, and residents all around the world.

These smart cities and houses make use of information and communication technology (ICT) to enhance quality of life, local and regional economies, urban planning and transportation, and government.

Urban informatics is a new area that gathers data, analyzes patterns and trends, and utilizes the information to implement new ICT in smart cities.

Data may be gathered from a number of different sources.

Surveillance cameras, smart cards, internet of things sensor networks, smart phones, RFID tags, and smart meters are just a few examples.

In real time, any kind of data may be captured.

Passenger occupancy and flow may be used to obtain data on mass transit utilization.

Road sensors can count cars on the road or in parking lots.

They may also use urban machine vision technologies to determine individual wait times for local government services.

From public thoroughfares and sidewalks, license plate numbers and people's faces may be identified and documented.

Tickets may be issued, and statistics on crime can be gathered.

The information gathered in this manner may be compared to other big datasets on neighborhood income, racial and ethnic mix, utility reliability statistics, and air and water quality indices.

Artificial intelligence (AI) may be used to build or improve city infrastructure.

Stop signal frequencies at crossings are adjusted and optimized based on data acquired regarding traffic movements.

This is known as intelligent traffic signaling, and it has been found to cut travel and wait times, as well as fuel consumption, significantly.

Smart parking structures assist cars in quickly locating available parking spaces.

Law enforcement is using license plate identification and face recognition technologies to locate suspects and witnesses at crime scenes.

Shotspotter, a business that triangulates the position of gunshots using a sensor network placed in special streetlights, tracked and informed police agencies to over 75,000 bullets fired in 2018.

Information on traffic and pedestrian deaths is also being mined via big data initiatives.

Vision Zero is a global highway safety initiative that aspires to decrease road fatalities to zero.

Data analysis using algorithms has resulted in road safety efforts as well as road redesign that has saved lives.

Cities have also been able to respond more swiftly to severe weather occurrences because to ubiquitous sensor technology.

In Seattle, for example, conventional radar data is combined with RainWatch, a network of rain gauges.

Residents get warnings from the system, and maintenance staff are alerted to possible problem places.

Transport interconnection enabling completely autonomous autos is one long-term aim for smart cities.

At best, today's autonomous cars can monitor their surroundings to make judgments and avoid crashes with other vehicles and numerous road hazards.

However, cars that connect with one another in several directions are likely to create fully autonomous driving systems.

Collisions are not only averted, but also prevented in these systems.

Smart cities are often mentioned in conjunction with smart economy initiatives and foreign investment development by planners.

Data-driven entrepreneurial innovation, as well as productivity analyses and evaluation, might be indicators of sensible economic initiatives.

Some smart towns want to emulate Silicon Valley's success.

Neom, Saudi Arabia, is one such project.

It is a proposed megacity city that is expected to cost half a trillion dollars to build.

Artificial intelligence is seen as the new oil in the city's ambitions, despite sponsorship by Saudi Aramco, the state-owned petroleum giant.

Everything will be controlled by interconnected computer equipment and future artificial intelligence decision-making, from home technology to transportation networks and electronic medical record distribution.

One of Saudi Arabia's most significant cultural activities—monitoring the density and pace of pilgrims around the Kaaba in Mecca—has already been entrusted to AI vision technologies.

The AI is intended to avert a disaster on the scale of the 2015 Mina Stampede, which claimed the lives of 2,000 pilgrims.

The use of highly data-driven and targeted public services is another trademark of smart city programs.

Information-driven agencies are frequently referred to as "smart" or "e-government" when they work together.

Open data projects to encourage openness and shared engagement in local decision-making might be part of smart governance.

Local governments will collaborate with contractors to develop smart utility networks for the provision of electricity, telecommunications, and the internet.

Waste bins are linked to the global positioning system and cloud servers, alerting vehicles when garbage is ready for pickup, allowing for smart waste management and recycling initiatives in Barcelona.

Lamp poles have been converted into community wi-fi hotspots or mesh networks in certain areas to provide pedestrians with dynamic lighting safety.

Forest City in Malaysia, Eko Atlantic in Nigeria, Hope City in Ghana, Kigamboni New City in Tanzania, and Diamniadio Lake City in Senegal are among the high-tech centres proposed or under development.

Artificial intelligence is predicted to be the brain of the smart city in the future.

Artificial intelligence will personalize city experiences to match the demands of specific inhabitants or tourists.

Through customized glasses or heads-up displays, augmented systems may give virtual signs or navigational information.

Based on previous use and location data, intelligent smartphone agents are already capable of predicting user movements.

Artificial intelligence technologies are used in smart homes in a similar way.

Google Home and other smart hubs now integrate with over 5,000 different types of smart gadgets sold by 400 firms to create intelligent environments in people's homes.

Amazon Echo is Google Home's main rival.

These kinds of technologies can regulate heating, ventilation, and air conditioning, as well as lighting and security, as well as household products like smart pet feeders.

In the early 2000s, game-changing developments in home robotics led to widespread consumer acceptance of iRobot's Roomba vacuum cleaner.

Obsolescence, proprietary protocols, fragmented platforms and interoperability issues, and unequal technological standards have all plagued such systems in the past.

Machine learning is being pushed forward by smart houses.

Smart technology' analytical and predictive capabilities are generally regarded as the backbone of one of the most rapidly developing and disruptive commercial sectors: home automation.

To function properly, the smarter connected home of the future needs collect fresh data on a regular basis in order to develop.

Smart houses continually monitor the interior environment and use aggregated past data to establish settings and functionalities in buildings with smart components installed.

Smart houses may one day anticipate their owners' requirements, such as automatically changing blinds as the sun and clouds move across the sky.

A smart house may produce a cup of coffee at precisely the correct time, order Chinese takeout, or play music based on the resident's mood as detected by emotion detectors.

Pervasive, sophisticated technologies are used in smart city and household AI systems.

The benefits of smart cities are many.

Smart cities pique people's curiosity because of its promise for increased efficiency and convenience.

It's enticing to live in a city that anticipates and easily fulfills personal wants.

Smart cities, however, are not without their detractors.

Smart havens, if left uncontrolled, have the ability to cause major privacy invasion via continuous video recording and microphones.

Google contractors might listen to recordings of exchanges with users of its famous Google Assistant artificial intelligence system, according to reports in 2019.

The influence of smart cities and households on the environment is yet unknown.

Biodiversity considerations are often ignored in smart city ideas.

Critical habitat is routinely destroyed in order to create space for the new cities that tech entrepreneurs and government officials desire.

Conventional fossil-fuel transportation methods continue to reign supreme in smart cities.

The future viability of smart homes is likewise up in the air.

A recent research in Finland found that improved metering and consumption monitoring did not successfully cut smart home power use.

In reality, numerous smart cities that were built from the ground up are now almost completely empty.

Many years after their initial construction, China's so-called ghost cities, such as Ordos Kangbashi, have attained occupancy levels of one-third of all housing units.

Despite direct, automated vacuum waste collection tubes in individual apartments and building elevators timed to the arrival of residents' automobiles, Songdo, Korea, an early "city in a box," has not lived up to promises.

Smart cities are often portrayed as impersonal, elitist, and costly, which is the polar opposite of what the creators intended.

Songdo exemplifies the smart city trend in many aspects, with its underpinning structure of ubiquitous computing technologies that power everything from transportation systems to social networking channels.

The unrivaled integration and synchronization of services is made possible by the coordination of all devices.

As a result, by turning the city into an electronic panopticon or surveillance state for observing and controlling residents, the city simultaneously weakens the protective advantages of anonymity in public settings.

Authorities studying smart city infrastructures are now fully aware of the computational biases of proactive and predictive policing.

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

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

See also: 

Biometric Privacy and Security; Biometric Technology; Driverless Cars and Trucks; Intelligent Transportation; Smart Hotel Rooms.

References & Further Reading:

Albino, Vito, Umberto Berardi, and Rosa Maria Dangelico. 2015. “Smart Cities: Definitions, Dimensions, Performance, and Initiatives.” Journal of Urban Technology 22, no. 1: 3–21.

Batty, Michael, et al. 2012. “Smart Cities of the Future.” European Physical Journal Special Topics 214, no. 1: 481–518.

Friedman, Avi. 2018. Smart Homes and Communities. Mulgrave, Victoria, Australia: Images Publishing.

Miller, Michael. 2015. The Internet of Things: How Smart TVs, Smart Cars, Smart Homes, and Smart Cities Are Changing the World. Indianapolis: Que.

Shepard, Mark. 2011. Sentient City: Ubiquitous Computing, Architecture, and the Future of Urban Space. New York: Architectural League of New York.

Townsend, Antony. 2013. Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia. New York: W. W. Norton & Company.

Artificial Intelligence - Person of Interest(2011–2016), The CBS Sci-Fi Series

 

Between 2011 through 2016, the fictitious television program Person of Interest ran on CBS for five seasons.

Although the show's early episodes resembled a serial crime drama, the tale developed into a science fiction genre that probed ethical questions around artificial intelligence development.

The show's central concept revolves upon a monitoring system known as "The Machine," which was developed for the United States by millionaire Harold Finch, portrayed by Michael Emerson.

This technology was created largely to avoid terrorist acts, but it has evolved to the point where it can anticipate crimes before they happen.

However, owing to its architecture, it only discloses the "person of interest's" social security number, which might be either the victim or the offender.

Normally, each episode is centered on a single person of interest number that has been produced.

Although the ensemble increases in size over the seasons, Finch first employs ex-CIA agent John Reese, portrayed by Jim Caviezel, to assist him in investigating and preventing these atrocities.

Person of Interest is renowned for emphasizing and dramatizing ethical issues surrounding both the invention and deployment of artificial intelligence.

Season four, for example, delves deeply into how Finch constructed The Machine in the first place.

Finch took enormous pains to ensure that The Machine had the correct set of values before exposing it to actual data, as shown by flashbacks.

As Finch strove to get the settings just correct, viewers were able to see exactly what might go wrong.

In one flashback, The Machine altered its own programming before lying about it.

When these failures arise, Finch deletes the incorrect code, noting that The Machine will have unrivaled capabilities.

The Machine quickly responds by overriding its own deletion procedures and even attempting to murder Finch.

"I taught it how to think," Finch says as he reflects on the process.

All I have to do now is educate it how to be concerned." Finally, Finch is able to program The Machine successfully with the proper set of ideals, which includes the preservation of human life.

The interaction of numerous AI beings is a second key ethical subject that runs through seasons three through five.

In season three, Samaritan, a competing AI surveillance software, is built.

This system does not care about human life in the same way as The Machine does, and as a result, it causes enormous harm and turmoil in order to achieve its goals, which include sustaining the United States' national security and its own survival.

As a result of their differences, Samaritan and The Machine find themselves at odds.

The Machine finally beats Samaritan, despite the fact that the program implies that Samaritan is more powerful owing to the employment of newer technology.

This program was mainly a critical success; nevertheless, declining ratings led to its cancellation after just thirteen episodes in its fifth season.

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

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

See also: 

Biometric Privacy and Security; Biometric Technology; Predictive Policing.

References & Further Reading:

McFarland, Melanie. 2016. “Person of Interest Comes to an End, but the Technology Central to the Story Will Keep Evolving.” Geek Wire, June 20, 2016. https://www.geekwire.com/2016/person-of-interest/.

Newitz, Annalee. 2016. “Person of Interest Remains One of the Smartest Shows about AI on Television.” Ars Technica, May 3, 2016. https://arstechnica.com/gaming/2016/05/person-of-interest-remains-one-of-the-smartest-shows-about-ai-on-television/.

Artificial Intelligence - Who Is Helen Nissenbaum?

 

In her research, Helen Nissenbaum (1954–), a PhD in philosophy, looks at the ethical and political consequences of information technology.

She's worked at Stanford University, Princeton University, New York University, and Cornell Tech, among other places.

Nissenbaum has also worked as the primary investigator on grants from the National Security Agency, the National Science Foundation, the Air Force Office of Scientific Research, the United States Department of Health and Human Services, and the William and Flora Hewlett Foundation, among others.

Big data, machine learning, algorithms, and models, according to Nissenbaum, lead to output outcomes.

Her primary issue, which runs across all of these themes, is privacy.

Nissenbaum explores these problems in her 2010 book, Privacy in Context: Technology, Policy, and the Integrity of Social Life, by using the concept of contextual integrity, which views privacy in terms of acceptable information flows rather than merely prohibiting all information flows.

In other words, she's interested in establishing an ethical framework within which data may be obtained and utilized responsibly.

The challenge with developing such a framework, however, is that when many data sources are combined, or aggregated, it becomes possible to understand more about the people from whose the data was obtained than it would be feasible to accomplish with each individual source of data.

Such aggregated data is used to profile consumers, allowing credit and insurance businesses to make judgments based on the information.

Outdated data regulation regimes hamper such activities even more.

One big issue is that the distinction between monitoring users to construct profiles and targeting adverts to those profiles is blurry.

To make things worse, adverts are often supplied by third-party websites other than the one the user is currently on.

This leads to the ethical dilemma of many hands, a quandary in which numerous parties are involved and it is unclear who is ultimately accountable for a certain issue, such as maintaining users' privacy in this situation.

Furthermore, because so many organizations may receive this information and use it for a variety of tracking and targeting purposes, it is impossible to adequately inform users about how their data will be used and allow them to consent or opt out.

In addition to these issues, the AI systems that use this data are biased itself.

This prejudice, on the other hand, is a social issue rather than a computational one, since much of the scholarly effort focused on resolving computational bias has been misplaced.

As an illustration of this prejudice, Nissenbaum cites Google's Behavioral Advertising system.

When a search contains a name that is traditionally African American, the Google Behavioral Advertising algorithm will show advertising for background checks more often.

This sort of racism isn't encoded into the coding; rather, it develops through social contact with adverts, since those looking for traditionally African-American names are more likely to click on background check links.

Correcting these bias-related issues, according to Nissenbaum, would need considerable regulatory reforms connected to the ownership and usage of big data.

In light of this, and with few data-related legislative changes on the horizon, Nissenbaum has worked to devise measures that can be implemented right now.

Obfuscation, which comprises purposely adding superfluous information that might interfere with data gathering and monitoring procedures, is the major framework she has utilized to construct these tactics.

She claims that this is justified by the uneven power dynamics that have resulted in near-total monitoring.

Nissenbaum and her partners have created a number of useful internet browser plug-ins based on this obfuscation technology.

TrackMeNot was the first of these obfuscating browser add-ons.

This pluinator makes random queries to a number of search engines in attempt to contaminate the stream of data obtained and prevent search businesses from constructing an aggregated profile based on the user's genuine searches.

This plug-in is designed for people who are dissatisfied with existing data rules and want to take quick action against companies and governments who are aggressively collecting information.

This approach adheres to the obfuscation theory since, rather than concealing the original search phrases, it just hides them with other search terms, which Nissenbaum refers to as "ghosts." Adnostic is a Firefox web browser prototype plugin aimed at addressing the privacy issues related with online behavioral advertising tactics.

Currently, online behavioral advertising is accomplished by recording a user's activity across numerous websites and then placing the most relevant adverts at those sites.

Multiple websites gather, aggregate, and keep this behavioral data forever.

Adnostic provides a technology that enables profiling and targeting to take place exclusively on the user's computer, with no data exchanged with third-party websites.

Although the user continues to get targeted advertisements, third-party websites do not gather or keep behavioral data.

AdNauseam is yet another obfuscation-based plugin.

This program, which runs in the background, clicks all of the adverts on the website.

The declared goal of this activity is to contaminate the data stream, making targeting and monitoring ineffective.

Advertisers' expenses will very certainly rise as a result of this.

This project proved controversial, and in 2017, it was removed from the Chrome Web Store.

Although workarounds exist to enable users to continue installing the plugin, its loss of availability in the store makes it less accessible to the broader public.

Nissenbaum's book goes into great length into the ethical challenges surrounding big data and the AI systems that are developed on top of it.

Nissenbaum has built realistic obfuscation tools that may be accessed and utilized by anybody interested, in addition to offering specific legislative recommendations to solve troublesome privacy issues.

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

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

See also: 

Biometric Privacy and Security; Biometric Technology; Robot Ethics.

References & Further Reading:

Barocas, Solon, and Helen Nissenbaum. 2009. “On Notice: The Trouble with Notice and Consent.” In Proceedings of the Engaging Data Forum: The First International Forum on the Application and Management of Personal Electronic Information, n.p. Cambridge, MA: Massachusetts Institute of Technology.

Barocas, Solon, and Helen Nissenbaum. 2014. “Big Data’s End Run around Consent and Anonymity.” In Privacy, Big Data, and the Public Good, edited by Julia Lane, Victoria Stodden, Stefan Bender, and Helen Nissenbaum, 44–75. Cambridge, UK: Cambridge University Press.

Brunton, Finn, and Helen Nissenbaum. 2015. Obfuscation: A User’s Guide for Privacy and Protest. Cambridge, MA: MIT Press.

Lane, Julia, Victoria Stodden, Stefan Bender, and Helen Nissenbaum, eds. 2014. Privacy, Big Data, and the Public Good. New York: Cambridge University Press.

Nissenbaum, Helen. 2010. Privacy in Context: Technology, Policy, and the Integrity of Social Life. Stanford, CA: Stanford University Press.

Artificial Intelligence - What Is Biometric Technology?

 

The measuring of a human attribute is referred to as a biometric.

It might be physiological, like fingerprint or face identification, or behavioral, like keystroke pattern dynamics or walking stride length.

Biometric characteristics are defined by the White House National Science and Technology Council's Subcommittee on Biometrics as "measurable biological (anatomical and physiological) and behavioral traits that may be employed for automated recognition" (White House, National Science and Technology Council 2006, 4).

Biometric technologies are "technologies that automatically confirm the identity of people by comparing patterns of physical or behavioral characteristics in real time against enrolled computer records of those patterns," according to the International Biometrics and Identification Association (IBIA) (International Biometrics and Identification Association 2019).

Many different biometric technologies are either in use or being developed.

Previously used to access personal smartphones, pay for goods and services, and verify identities for various online accounts and physical facilities, fingerprints are now used to access personal smartphones, pay for goods and services, and verify identities for various online accounts and physical facilities.

The most well-known biometric technology is finger print recognition.

Ultrasound, thermal, optical, and capacitive sensors may all be used to acquire fingerprint image collections.

In order to find matches, AI software applications often use minutia-based matching or pattern matching.

By lighting up the palm, sensors capture pictures of human veins, and vascular pattern identification is now feasible.

Other common biometrics are based on facial, iris, or voice characteristics.

Recognizing people by their faces Individual identification, verification, detection, and characterization may all be possible with AI technology.

Detection and characterization processes rarely involve determining an individual's identity.

Although current systems have great accuracy rates, privacy problems arise since a face might be gathered passively, that is, without the subject's awareness.

Iris identification makes use of near-infrared light to extract the iris's distinct structural characteristics.

The retinal blood vessels are examined using retinal technology, which employs a strong light.

The scanned eyeball is compared to the stored picture to evaluate recognition.

Voice recognition is a more advanced technology than voice activation, which identifies speech content.

Each individual user must be able to be identified via voice recognition.

To present, technology has not been sufficiently precise to allow for trustworthy identification in many situations.

For security and law enforcement applications, biometric technology has long been accessible.

However, in the private sector, these systems are increasingly being employed as a verification mechanism for authentication that formerly needed a password.

The introduction of Apple's iPhone fingerprint scanner in 2013 raised public awareness.

The company's newer models have shifted to face recognition access, which further normalizes the notion.

Financial services, transportation, health care, facility access, and voting are just a few of the industries where biometric technology is being used.

~ Jai Krishna Ponnappan

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

See also: 

Biometric Privacy and Security.

Further Reading

International Biometrics and Identity Association. 2019. “The Technologies.” https://www.ibia.org/biometrics/technologies/.

White House. National Science and Technology Council. 2006. Privacy and Biometrics: Building a Conceptual Foundation. Washington, DC: National Science and Technology Council. Committee on Technology. Committee on Homeland and National Security. Subcommittee on Biometrics.