Artificial Intelligence - Climate Change Crisis And AI.

 

Artificial intelligence has a double-edged sword when it comes to climate change and the environment.

Artificial intelligence is being used by scientists to detect, adapt, and react to ecological concerns.

Civilization is becoming exposed to new environmental hazards and vulnerabilities as a result of the same technologies.

Much has been written on the importance of information technology in green economy solutions.

Data from natural and urban ecosystems is collected and analyzed using intelligent sensing systems and environmental information systems.

Machine learning is being applied in the development of sustainable infrastructure, citizen detection of environmental perturbations and deterioration, contamination detection and remediation, and the redefining of consumption habits and resource recycling.

Planet hacking is a term used to describe such operations.

Precision farming is one example of planet hacking.

Artificial intelligence is used in precision farming to diagnose plant illnesses and pests, as well as detect soil nutrition issues.

Agricultural yields are increased while water, fertilizer, and chemical pesticides are used more efficiently thanks to sensor technology directed by AI.

Controlled farming approaches offer more environmentally friendly land management and (perhaps) biodiversity conservation.

Another example is IBM Research's collaboration with the Chinese government to minimize pollution in the nation via the Green Horizons program.

Green Horizons is a ten-year effort that began in July 2014 with the goal of improving air quality, promoting renewable energy integration, and promoting industrial energy efficiency.

To provide air quality reports and track pollution back to its source, IBM is using cognitive computing, decision support technologies, and sophisticated sensors.

Green Horizons has grown to include global initiatives such as collaborations with Delhi, India, to link traffic congestion patterns with air pollution; Johannesburg, South Africa, to fulfill air quality objectives; and British wind farms, to estimate turbine performance and electricity output.

According to the National Renewable Energy Laboratory at the University of Maryland, AI-enabled automobiles and trucks are predicted to save a significant amount of gasoline, maybe in the region of 15% less use.

Smart cars eliminate inefficient combustion caused by stop-and-go and speed-up and slow-down driving behavior, resulting in increased fuel efficiency (Brown et al.2014).

Intelligent driver input is merely the first step toward a more environmentally friendly automobile.

According to the Society of Automotive Engineers and the National Renewable Energy Laboratory, linked automobiles equipped with vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication might save up to 30% on gasoline (Gonder et al.

2012).

Smart trucks and robotic taxis will be grouped together to conserve fuel and minimize carbon emissions.

Environmental robots (ecobots) are projected to make significant advancements in risk monitoring, management, and mitigation.

At nuclear power plants, service robots are in use.

Two iRobot PackBots were sent to Japan's Fukushima nuclear power plant to measure radioactivity.

Treebot is a dexterous tree-climbing robot that is meant to monitor arboreal environments that are too difficult for people to access.

The Guardian, a robot created by the same person who invented the Roomba, is being developed to hunt down and remove invasive lionfish that endanger coral reefs.

A similar service is being provided by the COTSbot, which employs visual recognition technology to wipe away crown-of-thorn starfish.

Artificial intelligence is assisting in the discovery of a wide range of human civilization's effects on the natural environment.

Cornell University's highly multidisciplinary Institute for Computer Sustainability brings together professional scientists and citizens to apply new computing techniques to large-scale environmental, social, and economic issues.

Birders are partnering with the Cornell Lab of Ornithology to submit millions of observations of bird species throughout North America, to provide just one example.

An app named eBird is used to record the observations.

To monitor migratory patterns and anticipate bird population levels across time and space, computational sustainability approaches are applied.

Wildbook, iNaturalist, Cicada Hunt, and iBats are some of the other crowdsourced nature observation apps.

Several applications are linked to open-access databases and big data initiatives, such as the Global Biodiversity Information Facility, which will include 1.4 billion searchable entries by 2020.

By modeling future climate change, artificial intelligence is also being utilized to assist human populations understand and begin dealing with environmental issues.

A multidisciplinary team from the Montreal Institute for Learning Algorithms, Microsoft Research, and ConscientAI Labs is using street view imagery of extreme weather events and generative adversarial networks—in which two neural networks are pitted against one another—to create realistic images depicting the effects of bushfires and sea level rise on actual neighborhoods.

Human behavior and lifestyle changes may be influenced by emotional reactions to photos.

Virtual reality simulations of contaminated ocean ecosystems are being developed by Stanford's Virtual Human Interaction Lab in order to increase human empathy and modify behavior in coastal communities.

Information technology and artificial intelligence, on the other hand, play a role in the climate catastrophe.

The pollution created by the production of electronic equipment and software is one of the most pressing concerns.

These are often seen as clean industries, however they often use harsh chemicals and hazardous materials.

With twenty-three active Superfund sites, California's Silicon Valley is one of the most contaminated areas in the country.

Many of these hazardous waste dumps were developed by computer component makers.

Trichloroethylene, a solvent used in semiconductor cleaning, is one of the most common soil pollutants.

Information technology uses a lot of energy and contributes a lot of greenhouse gas emissions.

Solar-powered data centers and battery storage are increasingly being used to power cloud computing data centers.

In recent years, a number of cloud computing facilities have been developed around the Arctic Circle to take use of the inherent cooling capabilities of the cold air and ocean.

The so-called Node Pole, situated in Sweden's northernmost county, is a favored location for such building.

In 2020, a data center project in Reykjavik, Iceland, will run entirely on renewable geo thermal and hydroelectric energy.

Recycling is also a huge concern, since life cycle engineering is just now starting to address the challenges of producing environmentally friendly computers.

Toxic electronic trash is difficult to dispose of in the United States, thus a considerable portion of all e-waste is sent to Asia and Africa.

Every year, some 50 million tons of e-waste are produced throughout the globe (United Nations 2019).

Jack Ma of the international e-commerce company Alibaba claimed at the World Economic Forum annual gathering in Davos, Switzerland, that artificial intelligence and big data were making the world unstable and endangering human life.

Artificial intelligence research's carbon impact is just now being quantified with any accuracy.

While Microsoft and Pricewaterhouse Coopers reported that artificial intelligence could reduce carbon dioxide emissions by 2.4 gigatonnes by 2030 (the combined emissions of Japan, Canada, and Australia), researchers at the University of Massachusetts, Amherst discovered that training a model for natural language processing can emit the equivalent of 626,000 pounds of greenhouse gases.

This is over five times the carbon emissions produced by a typical automobile throughout the course of its lifespan, including original production.

Artificial intelligence has a massive influence on energy usage and carbon emissions right now, especially when models are tweaked via a technique called neural architecture search (Strubell et al. 2019).

It's unclear if next-generation technologies like quantum artificial intelligence, chipset designs, and unique machine intelligence processors (such as neuromorphic circuits) would lessen AI's environmental effect.

Artificial intelligence is also being utilized to extract additional oil and gas from beneath, but more effectively.

Oilfield services are becoming more automated, and businesses like Google and Microsoft are opening offices and divisions to cater to them.

Since the 1990s, Total S.A., a French multinational oil firm, has used artificial intelligence to enhance production and understand subsurface data.

Total partnered up with Google Cloud Advanced Solutions Lab professionals in 2018 to use modern machine learning techniques to technical data analysis difficulties in the exploration and production of fossil fuels.

Every geoscience engineer at the oil company will have access to an AI intelligent assistant, according to Google.

With artificial intelligence, Google is also assisting Anadarko Petroleum (bought by Occidental Petroleum in 2019) in analyzing seismic data to discover oil deposits, enhance production, and improve efficiency.

Working in the emerging subject of evolutionary robotics, computer scientists Joel Lehman and Risto Miikkulainen claim that in the case of a future extinction catastrophe, superintelligent robots and artificial life may swiftly breed and push out humans.

In other words, robots may enter the continuing war between plants and animals.

To investigate evolvability in artificial and biological populations, Lehman and Miikkulainen created computer models to replicate extinction events.

The study is mostly theoretical, but it may assist engineers comprehend how extinction events could impact their work; how the rules of variation apply to evolutionary algorithms, artificial neural networks, and virtual organisms; and how coevolution and evolvability function in ecosystems.

As a result of such conjecture, Emerj Artificial Intelligence Research's Daniel Faggella notably questioned if the "environment matter[s] after the Singularity" (Faggella 2019).

Ian McDonald's River of Gods (2004) is a notable science fiction novel about climate change and artificial intelligence.

The book's events take place in 2047 in the Indian subcontinent.

A.I.Artificial Intelligence (2001) by Steven Spielberg is set in a twenty-second-century planet plagued by global warming and rising sea levels.

Humanoid robots are seen as important to the economy since they do not deplete limited resources.

Transcendence, a 2014 science fiction film starring Johnny Depp as an artificial intelligence researcher, portrays the cataclysmic danger of sentient computers as well as its unclear environmental effects.

~ Jai Krishna Ponnappan

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

See also: 

Chatbots and Loebner Prize; Gender and AI; Mobile Recommendation Assistants; Natural Language Processing and Speech Understanding.

Further Reading

Bort, Julie. 2017. “The 43 Most Powerful Female Engineers of 2017.” Business Insider. https://www.businessinsider.com/most-powerful-female-engineers-of-2017-2017-2.

Chan, Sharon Pian. 2011. “Tech-Savvy Dreamer Runs Microsoft’s Social-Media Lab.” Seattle Times. https://www.seattletimes.com/business/tech-savvy-dreamer-runs-microsofts-social-media-lab.

Cheng, Lili. 2018. “Why You Shouldn’t Be Afraid of Artificial Intelligence.” Time. http://time.com/5087385/why-you-shouldnt-be-afraid-of-artificial-intelligence.

Cheng, Lili, Shelly Farnham, and Linda Stone. 2002. “Lessons Learned: Building and Deploying Shared Virtual Environments.” In The Social Life of Avatars: Com￾puter Supported Cooperative Work, edited by Ralph Schroeder, 90–111. London: Springer.

Davis, Jeffrey. 2018. “In Chatbots She Trusts: An Interview with Microsoft AI Leader Lili Cheng.” Workflow. https://workflow.servicenow.com/customer-experience/lili-chang-ai-chatbot-interview.

Artificial Intelligence - What Are Mobile Recommendation Assistants?

 

Mobile Recommendation Assistants, also known as Virtual Assistants, Intelligent Agents, or Virtual Personal Assistants, are a collection of software features that combine a conversational user interface with artificial intelligence to act on behalf of a user.

They may deliver what seems to a user as an agent when they work together.

In this sense, an agent differs from a tool in that it has the ability to act autonomously and make choices with some degree of autonomy.

Many qualities may be included into the design of mobile suggestion helpers to improve the user's impression of agency.

Using visual avatars to represent technology, incorporating features of personality such as humor or informal/colloquial language, giving a voice and a legitimate name, constructing a consistent method of behaving, and so on are examples of such tactics.

A human user can use a mobile recommendation assistant to help them with a wide range of tasks, such as opening software applications, answering questions, performing tasks (operating other software/hardware), or engaging in conversational commerce or entertainment (telling stories, telling jokes, playing games, etc.).

Apple's Siri, Baidu's Xiaodu, Amazon's Alexa, Microsoft's Cortana, Google's Google Assistant, and Xiaomi's Xiao AI are among the mobile voice assistants now in development, each designed for certain companies, use cases, and user experiences.

A range of user interface modali ties are used by mobile recommendation aides.

Some are completely text-based, and they are referred regarded as chatbots.

Business to consumer (B2C) communication is the most common use case for a chatbot, and notable applications include online retail communication, insurance, banking, transportation, and restaurants.

Chatbots are increasingly being employed in medical and psychological applications, such as assisting users with behavior modification.

Similar apps are becoming more popular in educational settings to help students with language learning, studying, and exam preparation.

Facebook Messenger is a prominent example of a chatbot on social media.

While not all mobile recommendation assistants need voice-enabled interaction as an input modality (some, such web site chatbots, may depend entirely on text input), many contemporary examples do.

A Mobile Recommendation Assistant uses a number similar predecessor technologies, including a voice-enabled user interface.

Early voice-enabled user interfaces were made feasible by a command syntax that was hand-coded as a collection of rules or heuristics in advance.

These rule-based systems allowed users to operate devices without using their hands by delivering voice directions.

IBM produced the first voice recognition program, which was exhibited during the 1962 World's Fair in Seattle.

The IBM Shoebox has a limited vocabulary of sixteen words and nine numbers.

By the 1990s, IBM and Microsoft's personal computers and software had basic speech recognition; Apple's Siri, which debuted on the iPhone 4s in 2011, was the first mobile application of a mobile assistant.

These early voice recognition systems were disadvantaged in comparison to conversational mobile agents in terms of user experience since they required a user to learn and adhere to a preset command language.

The consequence of rule-based voice interaction might seem mechanical when it comes to contributing to real humanlike conversation with computers, which is a feature of current mobile recommendation assistants.

Instead, natural language processing (NLP) uses machine learning and statistical inference to learn rules from enormous amounts of linguistic data (corpora).

Decision trees and statistical modeling are used in natural language processing machine learning to understand requests made in a variety of ways that are typical of how people regularly communicate with one another.

Advanced agents may have the capacity to infer a user's purpose in light of explicit preferences expressed via settings or other inputs, such as calendar entries.

Google's Voice Assistant uses a mix of probabilistic reasoning and natural language processing to construct a natural-sounding dialogue, which includes conversational components such as paralanguage ("uh", "uh-huh", "ummm").

To convey knowledge and attention, modern digital assistants use multimodal communication.

Paralanguage refers to communication components that don't have semantic content but are nonetheless important for conveying meaning in context.

These may be used to show purpose, collaboration in a dialogue, or emotion.

The aspects of paralanguage utilized in Google's voice assistant employing Duplex technology are termed vocal segre gates or speech disfluencies; they are intended to not only make the assistant appear more human, but also to help the dialogue "flow" by filling gaps or making the listener feel heard.

Another key aspect of engagement is kinesics, which makes an assistant feel more like an engaged conversation partner.

Kinesics is the use of gestures, movements, facial expressions, and emotion to aid in the flow of communication.

The car firm NIO's virtual robot helper, Nome, is one recent example of the application of face expression.

Nome is a digital voice assistant that sits above the central dashboard of NIO's ES8 in a spherical shell with an LCD screen.

It can swivel its "head" automatically to attend to various speakers and display emotions using facial expressions.

Another example is Dr. Cynthia Breazeal's commercial Jibo home robot from MIT, which uses anthropomorphism using paralinguistic approaches.

Motion graphics or lighting animations are used to communicate states of communication such as listening, thinking, speaking, or waiting in less anthropomorphic uses of kinesics, such as the graphical user interface elements on Apple's Siri or illumination arrays on Amazon Alexa's physical interface Echo or in Xiami's Xiao AI.

The rising intelligence and anthropomorphism (or, in some circumstances, zoomorphism or mechanomorphism) that comes with it might pose some ethical issues about user experience.

The need for more anthropomorphic systems derives from the positive user experience of humanlike agentic systems whose communicative behaviors are more closely aligned with familiar interactions like conversation, which are made feasible by natural language and paralinguistics.

Natural conversation systems have the fundamental advantage of not requiring the user to learn new syntax or semantics in order to successfully convey orders and wants.

These more humanistic human machine interfaces may employ a user's familiar mental model of communication, which they gained through interacting with other people.

Transparency and security become difficulties when a user's judgments about a machine's behavior are influenced by human-to-human communication as machine systems become closer to human-to-human contact.

The establishment of comfort and rapport may obscure the differences between virtual assistant cognition and assumed motivation.

Many systems may be outfitted with motion sensors, proximity sensors, cameras, tiny phones, and other devices that resemble, replicate, or even surpass human capabilities in terms of cognition (the assistant's intellect and perceptive capacity).

While these can be used to facilitate some humanlike interaction by improving perception of the environment, they can also be used to record, document, analyze, and share information that is opaque to a user when their mental model and the machine's interface do not communicate the machine's operation at a functional level.

After a user interaction, a digital assistant visual avatar may shut his eyes or vanish, but there is no need to associate such behavior with the microphone's and camera's capabilities to continue recording.

As digital assistants become more incorporated into human users' daily lives, data privacy issues are becoming more prominent.

Transparency becomes a significant problem to solve when specifications, manufacturer data collecting aims, and machine actions are potentially mismatched with user expectations.

Finally, when it comes to data storage, personal information, and sharing methods, security becomes a concern, as hacking, disinformation, and other types of abuse threaten to undermine faith in technology systems and organizations.

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

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

See also: 

Chatbots and Loebner Prize; Mobile Recommendation Assistants; Natural Language Processing and Speech Understanding.

References & Further Reading:

Lee, Gary G., Hong Kook Kim, Minwoo Jeong, and Ji-Hwan Kim, eds. 2015. Natural Language Dialog Systems and Intelligent Assistants. Berlin: Springer.

Leviathan, Yaniv, and Yossi Matias. 2018. “Google Duplex: An AI System for Accomplishing Real-world Tasks Over the Phone.” Google AI Blog. https://ai.googleblog.com/2018/05/duplex-ai-system-for-natural-conversation.html.

Viken, Alexander. 2009. “The History of Personal Digital Assistants, 1980–2000.” Agile Mobility, April 10, 2009.

Waddell, Kaveh. 2016. “The Privacy Problem with Digital Assistants.” The Atlantic, May 24, 2016. https://www.theatlantic.com/technology/archive/2016/05/the-privacy-problem-with-digital-assistants/483950/.

Cyber Security - Information Management System Challenges.

 

In the beginning, IMSs were primarily used to manage and store commercial data in enterprises and public organizations such as government agencies and universities. 

IMSs' emphasis and capabilities have shifted as software and hardware technologies have evolved. 

Computer and cyber security are two terms that are often used interchangeably. 

These systems now offer services that are used not just by businesses or government agencies, but also by individuals wherever and at any time. 

IMSs should now evolve to: Gather and handle large volumes of heterogeneous information. 

Evaluate and protect the privacy of sensitive pieces of information. 

Consider independent administrators distributed across different organizations. 

Manage diverse components with different requirements and locations in order to provide these services to this wide range of users. 

These facts have added to the complexity of information management procedures, necessitating further study into novel management methods that take into account the prior requirements. 

These techniques should be as automated as feasible to enable for dynamic identification of occurrences that need management process reconfiguration. 

Furthermore, automation procedures assist to decrease the complexity of managing dispersed heterogeneous components by avoiding delays in management operations caused by human errors or misconfigurations. 

 

What the Future Holds For Context & Location Aware Systems.

The complexity of the information management processes done by IMSs has expanded as technology has progressed. 

IMSs now in use handle enormous amounts of heterogeneous data, secure the privacy of sensitive data, enable many administrators to manage resources, and take into account dispersed circumstances. 

The bulk of IMSs are now consumed by individuals, businesses, and government agencies at any time and from any location. 

As a result of this fact, the location of users has become a highly significant piece of information for providing services near to the users. 

With the inclusion of location, the ubiquitous and context-aware paradigms have added additional bits of information about the environment or context in which users are, such as places, activities, identities, time, emotional states, or any other environmental data. 

The complexity of prior management procedures has risen as a result of this new heterogeneous information, influencing the birth of new automated management methods. 

Controlling the behavior of system resources, as well as managing and securing users' information in IMSs that take into account contextual data, are still unresolved concerns that need to be addressed. 

Administrators of IMSs systems should be able to take contextual information into account throughout management operations in order to make judgments about how system resources should behave. 

Furthermore, IMS users should determine and manage what information they wish to expose, as well as where, when, and with whom that information will be shared. 

In context-aware systems, semantic web approaches provide a potential solution to handle and safeguard contextual and personal information. 

This technology enables the formal modeling of data, the exchange of data across independent systems, the definition of privacy regulations to secure data, and the inference of new knowledge based on the data and policies. 

In this regard, the state-of-the-art context-aware solutions that allow for the protection of sensitive data as well as the management of system resource behavior have been discussed in this chapter. 

Following that, we used semantic web approaches to examine location-based and context-aware systems in charge of transmitting and preserving users' information in intra- and inter-context situations. 

Finally, we looked at location-based and context-aware systems for managing network resources securely, taking into account factors like QoS, energy economy, and performance. 

When administrators manage system resources, it is necessary to consider the privacy of users' information and circumstances as future work. 

Allowing users to specify the level of granularity at which they wish to divulge their position to network administrators while they are operating the network infrastructure while taking into consideration the distance and location of devices is an example of this reality. 

In terms of network administration, combining technologies such as SDN and Network Functions Virtualization (NFV) may make it easier to manage network infrastructure and services. 

In this way, the Network Slicing approach may integrate the preceding technologies to manage network resources and services based on the needs of contemporary networks. 

These slices, as well as their resources, should be handled automatically, taking into account the context.

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

You may also want to read and learn more Technology and Engineering here.

You may also want to read and learn more Cyber Security Systems here.

References & Further Reading:

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5. B. Moore. Policy Core Information Model (PCIM) Extensions. IETF Request for Comments 3460, January 2003.

6. S. Godik, and T. Moses. OASIS EXtensible Access Control Markup Language (XACML). OASIS Committee Specification, 2002.

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