Showing posts with label Gender and AI. Show all posts
Showing posts with label Gender and AI. Show all posts

Artificial Intelligence - Machine Learning Regressions.


"Machine learning," a phrase originated by Arthur Samuel in 1959, is a kind of artificial intelligence that produces results without requiring explicit programming.

Instead, the system learns from a database on its own and improves over time.

Machine learning techniques have a wide range of applications (e.g., computer vision, natural language processing, autonomous gaming agents, classification, and regressions) and are used in practically every sector due to their resilience and simplicity of implementation (e.g., tech, finance, research, education, gaming, and navigation).

Machine learning algorithms may be generically classified into three learning types: supervised, unsupervised, and reinforcement, notwithstanding their vast range of applications.

Supervised learning is exemplified by machine learning regressions.

They use algorithms that have been trained on data with labeled continuous numerical outputs.

The quantity of training data or validation criteria required once the regression algorithm has been suitably trained and verified will depend on the issues being addressed.

For data with comparable input structures, the newly developed predictive models give inferred outputs.

These aren't static models.

They may be updated on a regular basis with new training data or by displaying the actual right outputs on previously unlabeled inputs.

Despite machine learning methods' generalizability, there is no one program that is optimal for all regression issues.

When choosing the best machine learning regression method for the present situation, there are a lot of things to think about (e.g., programming languages, available libraries, algorithm types, data size, and data structure).

There are machine learning programs that employ single or multivariable linear regression approaches, much like other classic statistical methods.

These models represent the connections between a single or several independent feature variables and a dependent target variable.

The models provide linear representations of the combined input variables as their output.

These models are only applicable to noncomplex and small data sets.

Polynomial regressions may be used with nonlinear data.

This necessitates the programmers' knowledge of the data structure, which is often the goal of utilizing machine learning models in the first place.

These methods are unlikely to be appropriate for most real-world data, but they give a basic starting point and might provide users with models that are straightforward to understand.

Decision trees, as the name implies, are tree-like structures that map the input features/attributes of programs to determine the eventual output goal.

The answer to the condition of that node splits into edges in a decision tree algorithm, which starts with the root node (i.e., an input variable).

A leaf is defined as an edge that no longer divides; an internal edge is defined as one that continues to split.

For example, age, weight, and family diabetic history might be used as input factors in a dataset of diabetic and nondiabetic patients to estimate the likelihood of a new patient developing diabetes.

The age variable might be used as the root node (e.g., age 40), with the dataset being divided into those who are more than or equal to 40 and those who are 39 and younger.

The model provides that leaf as the final output if the following internal node after picking more than or equal to 40 is whether or not a parent has/had diabetes, and the leaf estimates the affirmative responses to have a 60% likelihood of this patient acquiring diabetes.

This is a very basic decision tree that demonstrates the decision-making process.

Thousands of nodes may readily be found in a decision tree.

Random forest algorithms are just decision tree mashups.

They are made up of hundreds of decision trees, the ultimate outputs of which are the averaged outputs of the individual trees.

Although decision trees and random forests are excellent at learning very complex data structures, they are prone to overfitting.

With adequate pruning (e.g., establishing the n values limits for splitting and leaves) and big enough random forests, overfitting may be reduced.

Machine learning techniques inspired by the neural connections of the human brain are known as neural networks.

Neurons are the basic unit of neural network algorithms, much as they are in the human brain, and they are organized into numerous layers.

The input layer contains the input variables, the hidden layers include the layers of neurons (there may be numerous hidden levels), and the output layer contains the final neuron.

A single neuron in a feedforward process 

(a) takes the input feature variables, 

(b) multiplies the feature values by a weight, 

(c) adds the resultant feature products, together with a bias variable, and 

(d) passes the sums through an activation function, most often a sigmoid function.

The partial derivative computations of the previous neurons and neural layers are used to alter the weights and biases of each neuron.

Backpropagation is the term for this practice.

The output of the activation function of a single neuron is distributed to all neurons in the next hidden layer or final output layer.

As a result, the projected value is the last neuron's output.

Because neural networks are exceptionally adept at learning exceedingly complicated variable associations, programmers may spend less time reconstructing their data.

Neural network models, on the other hand, are difficult to interpret due to their complexity, and the intervariable relationships are largely hidden.

When used on extremely big datasets, neural networks operate best.

They need meticulous hyper-tuning and considerable processing capacity.

For data scientists attempting to comprehend massive datasets, machine learning has become the standard technique.

Machine learning systems are always being improved in terms of accuracy and usability by researchers.

Machine learning algorithms, on the other hand, are only as useful as the data used to train the model.

Poor data produces dramatically erroneous outcomes, while biased data combined with a lack of knowledge deepens societal disparities.


Jai Krishna Ponnappan

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

See also: 

Algorithmic Bias and Error; Automated Machine Learning; Deep Learning; Explainable AI; Gender and AI.

Further Reading:

Garcia, Megan. 2016. “Racist in the Machine: The Disturbing Implications of Algorithmic Bias.” World Policy Journal 33, no. 4 (Winter): 111–17.

Géron, Aurelien. 2019. Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. Sebastopol, CA: O’Reilly.

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.


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.

Chan, Sharon Pian. 2011. “Tech-Savvy Dreamer Runs Microsoft’s Social-Media Lab.” Seattle Times.

Cheng, Lili. 2018. “Why You Shouldn’t Be Afraid of Artificial Intelligence.” Time.

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.

Artificial Intelligence - What Is Algorithmic Error and Bias?


Bias in algorithmic systems has emerged as one of the most pressing issues surrounding artificial intelligence ethics.

Algorithmic bias refers to a computer system's recurrent and systemic flaws that discriminate against certain groups or people.

It's crucial to remember that bias isn't necessarily a bad thing: it may be included into a system in order to fix an unjust system or reality.

Bias causes problems when it leads to an unjust or discriminating conclusion that affects people's lives and chances.

Individuals and communities that are already weak in society are often at danger from algorithmic prejudice and inaccuracy.

As a result, algorithmic prejudice may exacerbate social inequality by restricting people's access to services and goods.

Algorithms are increasingly being utilized to guide government decision-making, notably in the criminal justice sector for sentencing and bail, as well as in migration management using biometric technology like face and gait recognition.

When a government's algorithms are shown to be biased, individuals may lose faith in the AI system as well as its usage by institutions, whether they be government agencies or private businesses.

There have been several incidents of algorithmic prejudice during the past few years.

A high-profile example is Facebook's targeted advertising, which is based on algorithms that identify which demographic groups a given advertisement should be viewed by.

Indeed, according to one research, job advertising for janitors and related occupations on Facebook are often aimed towards lower-income groups and minorities, while ads for nurses or secretaries are focused at women (Ali et al. 2019).

This involves successfully profiling persons in protected classifications, such as race, gender, and economic bracket, in order to maximize the effectiveness and profitability of advertising.

Another well-known example is Amazon's algorithm for sorting and evaluating resumes in order to increase efficiency and ostensibly impartiality in the recruiting process.

Amazon's algorithm was trained using data from the company's previous recruiting practices.

However, once the algorithm was implemented, it became evident that it was prejudiced against women, with résumés that contained the terms "women" or "gender" or indicated that the candidate had attended a women's institution receiving worse rankings.

Little could be done to address the algorithm's prejudices since it was trained on Amazon's prior recruiting practices.

While the algorithm was plainly prejudiced, this example demonstrates how such biases may mirror social prejudices, including, in this instance, Amazon's deeply established biases against employing women.

Indeed, bias in an algorithmic system may develop in a variety of ways.

Algorithmic bias occurs when a group of people and their lived experiences are not taken into consideration while the algorithm is being designed.

This can happen at any point during the algorithm development process, from collecting data that isn't representative of all demographic groups to labeling data in ways that reproduce discriminatory profiling to the rollout of an algorithm that ignores the differential impact it may have on a specific group.

In recent years, there has been a proliferation of policy documents addressing the ethical responsibilities of state and non-state bodies using algorithmic processing—to ensure against unfair bias and other negative effects of algorithmic processing—partly in response to significant publicity of algorithmic biases (Jobin et al.2019).

The European Union's "Ethics Guidelines for Trustworthy AI," issued in 2018, is one of the most important rules in this area.

The EU statement lays forth seven principles for fair and ethical AI and algorithmic processing regulation.

Furthermore, with the adoption of the General Data Protection Regulation (GDPR) in 2018, the European Union has been in the forefront of legislative responses to algorithmic processing.

A corporation may be penalized up to 4% of its annual worldwide turnover if it uses an algorithm that is found to be prejudiced on the basis of race, gender, or another protected category, according to the GDPR, which applies in the first instance to the processing of all personal information inside the EU.

The difficulty of determining where a bias occurred and what dataset caused prejudice is a persisting challenge for algorithmic processing regulation.

This is sometimes referred to as the algorithmic black box problem: an algorithm's deep data processing layers are so intricate and many that a human cannot comprehend them.

Different data is fed into the algorithm to observe where the unequal results emerge, based on the right to an explanation when, subject to an automated decision under the GDPR, one of the replies has been to identify where the bias occurred via counterfactual explanations (Wachter et al.2018).

Technical solutions to the issue included building synthetic datasets that seek to repair naturally existing biases in datasets or provide an unbiased and representative dataset, in addition to legal and legislative instruments for tackling algorithmic bias.

While such channels for redress are vital, one of the most comprehensive solutions to the issue is to have far more varied human teams developing, producing, using, and monitoring the effect of algorithms.

A mix of life experiences within diverse teams makes it more likely that prejudices will be discovered and corrected sooner.

~ Jai Krishna Ponnappan

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

See also: Biometric Technology; Explainable AI; Gender and AI.

Further Reading

Ali, Muhammed, Piotr Sapiezynski, Miranda Bogen, Aleksandra Korolova, Alan Mislove, and Aaron Rieke. 2019. “Discrimination through Optimization: How Facebook’s Ad Delivery Can Lead to Skewed Outcomes.” In Proceedings of the ACM on Human-Computer Interaction, vol. 3, CSCW, Article 199 (November). New York: Association for Computing Machinery.

European Union. 2018. “General Data Protection Regulation (GDPR).”

European Union. 2019. “Ethics Guidelines for Trustworthy AI.”

Jobin, Anna, Marcello Ienca, and Effy Vayena. 2019. “The Global Landscape of AI Ethics Guidelines.” Nature Machine Intelligence 1 (September): 389–99.

Noble, Safiya Umoja. 2018. Algorithms of Oppression: How Search Engines Reinforce Racism. New York: New York University Press.

Pasquale, Frank. 2016. The Black Box Society: The Secret Algorithms that Control Money and Information. Cambridge, MA: Harvard University Press.

Wachter, Sandra, Brent Mittelstadt, and Chris Russell. 2018. “Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR.” Harvard Journal of Law & Technology 31, no. 2 (Spring): 841–87.

Zuboff, Shoshana. 2018. The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. London: Profile Books.

What Is Artificial General Intelligence?

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