text:
2018 American Athletic Conference Men's Basketball Tournament
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2018 American Athletic Conference Men's Basketball Tournament
Classification
Division I
Season
2017–18
Teams
12
Site
Amway Center
Orlando, Florida
Champions
Cincinnati (1st title)
Winning coach
Mick Cronin (1st title)
MVP
Gary Clark (Cincinnati)
Attendance
50,259
Television
CBS, ESPNU, ESPN2
American Athletic Conference Men's Basketball Tournaments
← 2017
2019 →
2017–18 American Athletic Conference men's basketball standings
v
t
e
Conf
Overall
Team
W
L
PCT
W
L
PCT
No. 6 Cincinnati† 16 – 2 .889 31 – 5 .861
No. 21 Houston 14 – 4 .778 27 – 8 .771
No. 16 Wichita State 14 – 4 .778 25 – 8 .758
Tulsa 12 – 6 .667 19 – 12 .613
Memphis 10 – 8 .556 21 – 13 .618
UCF 9 – 9 .500 19 – 13 .594
Temple 8 – 10 .444 17 – 16 .515
Connecticut 7 – 11 .389 14 – 18 .438
SMU 6 – 12 .333 17 – 16 .515
Tulane 5 – 13 .278 14 – 17 .452
East Carolina 4 – 14 .222 10 – 20 .333
South Florida 3 – 15 .167 10 – 22 .313
† 2018 AAC Tournament winner
Rankings from AP Poll
The 2018 American Athletic Conference Men's Basketball Tournament ended the 2017–18 season of the American Athletic Conference. It was held from March 8 through March 11, 2018 at the Amway Center in Orlando, Florida.[1] Regular-season champion Cincinnati won the tournament and with it the league's automatic bid to the NCAA Tournament.
Contents
[hide]
1 Seeds
2 Schedule
3 Bracket
4 References
Seeds[edit]
All 12 conference teams participated in the conference tournament. The top four teams received a bye into the quarterfinals. Teams were seeded by record within the conference, with a tiebreaker system to seed teams with identical conference records. Tiebreakers: win/loss record, head-to-head record, record against the highest ranked team outside of the tied teams, record against the second highest ranked team outside of the tied teams, etc.
Seed
School
Conference
Tiebreaker
1 Cincinnati 16–2
2 Wichita State 14–4 1–0 vs. Memphis
3 Houston 14–4 0–1 vs. Memphis
4 Tulsa 12–6
5 Memphis 10–8
6 UCF 9–9
7 Temple 8–10
8 Connecticut 7–11
9 SMU 6–12
10 Tulane 5–13 1–1 vs. Houston
11 East Carolina 5–13 0–2 vs. Houston
12 South Florida 3–15
Schedule[edit]
Game
Time
Matchup
Score
Television
Attendance
First round – Thursday, March 8
1 Noon No. 8 Connecticut vs No. 9 SMU 73–80 ESPNU 7,856
2 2:30 pm No. 5 Memphis vs No. 12 South Florida 79–77
3 7:00 pm No. 7 Temple vs No. 10 Tulane 82–77 7,918
4 9:30 pm No. 6 UCF vs No. 11 East Carolina 66–52
Quarterfinals – Friday, March 9
5 Noon No. 1 Cincinnati vs No. 9 SMU 61–51 ESPN2 8,491
6 2:30 pm No. 4 Tulsa vs No. 5 Memphis 64–67
7 7:00 pm No. 2 Wichita State vs No. 7 Temple 89–81 ESPNU 8,680
8 9:30 pm No. 3 Houston vs No. 6 UCF 84–56
Semifinals – Saturday, March 10
9 1:00 pm No. 1 Cincinnati vs. No. 5 Memphis 70–60 CBS 8,644
10 3:30 pm No. 2 Wichita State vs. No. 3 Houston 74-77
Championship – Sunday, March 11
11 3:00 pm No. 1 Cincinnati vs. No. 3 Houston 56–55 CBS 8,670
Game times in ET. Rankings denote tournament seeding.
Bracket[edit]
[2]
First round
Thursday, March 8 Quarterfinals
Friday, March 9 Semifinals
Saturday, March 10 Championship
Sunday, March 11
1 Cincinnati 61
9 SMU 51
8 Connecticut 73
9 SMU 80
1 Cincinnati 70
5 Memphis 60
4 Tulsa 64
5 Memphis 67
5 Memphis 79
12 South Florida 77
1 Cincinnati 56
3 Houston 55
2 Wichita State 89
7 Temple 81
7 Temple 82
10 Tulane 77
2 Wichita State 74
3 Houston 77
3 Houston 84
6 UCF 56
6 UCF 66
11 East Carolina 52
References[edit]
College basketball portal
Jump up ^ "American Men's Basketball Semifinals, Championship to be Broadcast on CBS in 2018".
Jump up ^ http://sidearm.sites.s3.amazonaws.com/theamerican.sidearmsports.com/documents/2018/3/4/2018_MBB_Championship_Bracket_Final_.pdf?id=1650
[show]
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2018 NCAA Division I men's basketball tournaments
Conference
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[show]
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American Athletic Conference Men's Basketball Tournament
2014
2015
2016
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2018
Broadcasters
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Applications of artificial intelligence
From Wikipedia, the free encyclopedia
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This article needs additional citations for verification. Please help improve this article by adding citations to reliable sources. Unsourced material may be challenged and removed. (March 2016) (Learn how and when to remove this template message)
Artificial intelligence
Major goals
Knowledge reasoning
Planning
Machine learning
Natural language processing
Computer vision
Robotics
Artificial general intelligence
Approaches
Symbolic
Deep learning
Bayesian networks
Evolutionary algorithms
Philosophy
Ethics
Existential risk
Turing test
Chinese room
Friendly AI
History
Timeline
Progress
AI winter
Technology
Applications
Projects
Programming languages
Glossary
Glossary
Artificial intelligence, defined as intelligence exhibited by machines, has many applications in today's society. More specifically, it is Weak AI, the form of A.I. where programs are developed to perform specific tasks, that is being utilized for a wide range of activities including medical diagnosis, electronic trading, robot control, and remote sensing. AI has been used to develop and advance numerous fields and industries, including finance, healthcare, education, transportation, and more.
Contents
1 AI for Good
2 Aviation
3 Computer science
4 Education
5 Finance
5.1 Algorithmic trading
5.2 Market analysis and data mining
5.3 Personal finance
5.4 Portfolio management
5.5 Underwriting
6 Heavy industry
7 Hospitals and medicine
8 Human resources and recruiting
9 Marketing
10 Media
11 Music
12 News, publishing and writing
13 Online and telephone customer service
14 Telecommunications maintenance
15 Toys and games
16 Transportation
17 Other
18 List of applications
19 See also
20 Notes
21 References
22 External links
AI for Good[edit]
AI for Good is a movement in which institutions are employing AI to tackle some of the world's greatest economic and social challenges. For example, the University of Southern California launched the Center for Artificial Intelligence in Society, with the goal of using AI to address socially relevant problems such as homelessness. At Stanford, researchers are using AI to analyze satellite images to identify which areas have the highest poverty levels.[1]
Aviation[edit]
This section needs additional citations for verification. Please help improve this article by adding citations to reliable sources. Unsourced material may be challenged and removed. (November 2016) (Learn how and when to remove this template message)
The Air Operations Division (AOD) uses AI for the rule based expert systems. The AOD has use for artificial intelligence for surrogate operators for combat and training simulators, mission management aids, support systems for tactical decision making, and post processing of the simulator data into symbolic summaries.[2]
The use of artificial intelligence in simulators is proving to be very useful for the AOD. Airplane simulators are using artificial intelligence in order to process the data taken from simulated flights. Other than simulated flying, there is also simulated aircraft warfare. The computers are able to come up with the best success scenarios in these situations. The computers can also create strategies based on the placement, size, speed and strength of the forces and counter forces. Pilots may be given assistance in the air during combat by computers. The artificial intelligent programs can sort the information and provide the pilot with the best possible maneuvers, not to mention getting rid of certain maneuvers that would be impossible for a human being to perform. Multiple aircraft are needed to get good approximations for some calculations so computer simulated pilots are used to gather data.[3] These computer simulated pilots are also used to train future air traffic controllers.
The system used by the AOD in order to measure performance was the Interactive Fault Diagnosis and Isolation System, or IFDIS. It is a rule based expert system put together by collecting information from TF-30 documents and the expert advice from mechanics that work on the TF-30. This system was designed to be used for the development of the TF-30 for the RAAF F-111C. The performance system was also used to replace specialized workers. The system allowed the regular workers to communicate with the system and avoid mistakes, miscalculations, or having to speak to one of the specialized workers.
The AOD also uses artificial intelligence in speech recognition software. The air traffic controllers are giving directions to the artificial pilots and the AOD wants to the pilots to respond to the ATC's with simple responses. The programs that incorporate the speech software must be trained, which means they use neural networks. The program used, the Verbex 7000, is still a very early program that has plenty of room for improvement. The improvements are imperative because ATCs use very specific dialog and the software needs to be able to communicate correctly and promptly every time.
The Artificial Intelligence supported Design of Aircraft,[4] or AIDA, is used to help designers in the process of creating conceptual designs of aircraft. This program allows the designers to focus more on the design itself and less on the design process. The software also allows the user to focus less on the software tools. The AIDA uses rule based systems to compute its data. This is a diagram of the arrangement of the AIDA modules. Although simple, the program is proving effective.
In 2003, NASA's Dryden Flight Research Center, and many other companies, created software that could enable a damaged aircraft to continue flight until a safe landing zone can be reached.[5] The software compensates for all the damaged components by relying on the undamaged components. The neural network used in the software proved to be effective and marked a triumph for artificial intelligence.
The Integrated Vehicle Health Management system, also used by NASA, on board an aircraft must process and interpret data taken from the various sensors on the aircraft. The system needs to be able to determine the structural integrity of the aircraft. The system also needs to implement protocols in case of any damage taken the vehicle.[6]
Haitham Baomar and Peter Bentley are leading a team from the University College of London to develop an artificial intelligence based Intelligent Autopilot System (IAS) designed to teach an autopilot system to behave like a highly experienced pilot who is faced with an emergency situation such as severe weather, turbulence, or system failure.[7] Educating the autopilot relies on the concept of supervised machine learning “which treats the young autopilot as a human apprentice going to a flying school”.[7] The autopilot records the actions of the human pilot generating learning models using artificial neural networks.[7] The autopilot is then given full control and observed by the pilot as it executes the training exercise.[7]
The Intelligent Autopilot System combines the principles of Apprenticeship Learning and Behavioral Cloning whereby the autopilot observes the low-level actions required to maneuver the airplane and the high-level strategy used to apply those actions.[8] IAS implementation employs three phases; pilot data collection, training, and autonomous control.[8] Baomar and Bentley’s goal is to create a more autonomous autopilot to assist pilots in responding to emergency situations.[8]
Computer science[edit]
AI researchers have created many tools to solve the most difficult problems in computer science. Many of their inventions have been adopted by mainstream computer science and are no longer considered a part of AI. (See AI effect.) According to Russell & Norvig (2003, p. 15), all of the following were originally developed in AI laboratories: time sharing, interactive interpreters, graphical user interfaces and the computer mouse, rapid development environments, the linked list data structure, automatic storage management, symbolic programming, functional programming, dynamic programming and object-oriented programming.
AI can be used to potentially determine the developer of anonymous binaries.[citation needed]
AI can be used to create other AI. For example, around November 2017, Google's AutoML project to evolve new neural net topologies created NASNet, a system optimized for ImageNet and COCO. According to Google, NASNet's performance exceeded all previously published ImageNet performance.[9]
Education[edit]
There are a number of companies that create robots to teach subjects to children ranging from biology to computer science, though such tools have not become widespread yet. There have also been a rise of intelligent tutoring systems, or ITS, in higher education. For example, an ITS called SHERLOCK teaches Air Force technicians to diagnose electrical systems problems in aircraft. Another example is DARPA, Defense Advanced Research Projects Agency, which used AI to develop a digital tutor to train its Navy recruits in technical skills in a shorter amount of time.[1] Universities have been slow in adopting AI technologies due to either a lack of funding or skepticism of the effectiveness of these tools, but in the coming years more classrooms will be utilizing technologies such as ITS to complement teachers.
Advancements in natural language processing, combined with machine learning, have also enabled automatic grading of assignments as well as a data-driven understanding of individual students’ learning needs. This led to an explosion in popularity of MOOCs, or Massive Open Online Courses, which allows students from around the world to take classes online. Data sets collected from these large scale online learning systems have also enabled learning analytics, which will be used to improve the quality of learning at scale. Examples of how learning analytics can be used to improve the quality of learning include predicting which students are at risk of failure and analyzing student engagement.[10]
Finance[edit]
Algorithmic trading[edit]
Algorithmic trading involves the use of complex AI systems to make trading decisions at speeds several orders of magnitudes greater than any human is capable of, often making millions of trades in a day without any human intervention. Automated trading systems are typically used by large institutional investors.[11]
Market analysis and data mining[edit]
Several large financial institutions have invested in AI engines to assist with their investment practices. BlackRock’s AI engine, Aladdin, is used both within the company and to clients to help with investment decisions. Its wide range of functionalities includes the use of natural language processing to read text such as news, broker reports, and social media feeds. It then gauges the sentiment on the companies mentioned and assigns a score. Banks such as UBS and Deutsche Bank use an AI engine called Sqreem (Sequential Quantum Reduction and Extraction Model) which can mine data to develop consumer profiles and match them with the wealth management products they’d most likely want.[12] Goldman Sachs uses Kensho, a market analytics platform that combines statistical computing with big data and natural language processing. Its machine learning systems mine through hoards of data on the web and assess correlations between world events and their impact on asset prices.[13] Information Extraction, part of artificial intelligence, is used to extract information from live news feed and to assist with investment decisions.[14]
Personal finance[edit]
Several products are emerging that utilize AI to assist people with their personal finances. For example, Digit is an app powered by artificial intelligence that automatically helps consumers optimize their spending and savings based on their own personal habits and goals. The app can analyze factors such as monthly income, current balance, and spending habits, then make its own decisions and transfer money to the savings account.[15] Wallet.AI, an upcoming startup in San Francisco, builds agents that analyze data that a consumer would leave behind, from Smartphone check-ins to tweets, to inform the consumer about their spending behavior.[16]
Portfolio management[edit]
Robo-advisors are becoming more widely used in the investment management industry. Robo-advisors provide financial advice and portfolio management with minimal human intervention. This class of financial advisers work based on algorithms built to automatically develop a financial portfolio according to the investment goals and risk tolerance of the clients. It can adjust to real-time changes in the market and accordingly calibrate the portfolio.[17]
Underwriting[edit]
An online lender, Upstart, analyze vast amounts of consumer data and utilizes machine learning algorithms to develop credit risk models that predict a consumer’s likelihood of default. Their technology will be licensed to banks for them to leverage for their underwriting processes as well.[18]
ZestFinance developed their Zest Automated Machine Learning (ZAML) Platform specifically for credit underwriting as well. This platform utilizes machine learning to analyze tens of thousands traditional and nontraditional variables (from purchase transactions to how a customer fills out a form) used in the credit industry to score borrowers. The platform is particularly useful to assign credit scores to those with limited credit histories, such as millennials.[19]
Heavy industry[edit]
Robots have become common in many industries and are often given jobs that are considered dangerous to humans. Robots have proven effective in jobs that are very repetitive which may lead to mistakes or accidents due to a lapse in concentration and other jobs which humans may find degrading.
In 2014, China, Japan, the United States, the Republic of Korea and Germany together amounted to 70% of the total sales volume of robots. In the automotive industry, a sector with particularly high degree of automation, Japan had the highest density of industrial robots in the world: 1,414 per 10,000 employees.[20]
Hospitals and medicine[edit]
X-ray of a hand, with automatic calculation of bone age by a computer software.
Main article: Artificial intelligence in healthcare
Artificial neural networks are used as clinical decision support systems for medical diagnosis, such as in Concept Processing technology in EMR software.
Other tasks in medicine that can potentially be performed by artificial intelligence and are beginning to be developed include:
Computer-aided interpretation of medical images. Such systems help scan digital images, e.g. from computed tomography, for typical appearances and to highlight conspicuous sections, such as possible diseases. A typical application is the detection of a tumor.
Heart sound analysis[21]
Companion robots for the care of the elderly[22]
Mining medical records to provide more useful information.
Design treatment plans.
Assist in repetitive jobs including medication management.
Provide consultations.
Drug creation[23]
Using avatars in place of patients for clinical training[24]
Predict the likelihood of death from surgical procedures
Predict HIV progression
Currently, there are over 90 AI startups in the health industry working in these fields.[25]
IDx's first solution, IDx-DR, is the first autonomous, AI-based diagnostic system authorized for commercialization by the FDA.
Human resources and recruiting[edit]
Another application of AI is in the human resources and recruiting space. There are three ways AI is being used by human resources and recruiting professionals. AI is used to screen resumes and rank candidates according to their level of qualification. Ai is also used to predict candidate success in given roles through job matching platforms. And now, AI is rolling out recruiting chat bots that can automate repetitive communication tasks.
Typically, resume screening involves a recruiter or other HR professional scanning through a database of resumes. Now startups like Pomato, are creating machine learning algorithms to automate resume screening processes. Pomato’s resume screening AI focuses on automating validating technical applicants for technical staffing firms. Pomato’ s AI performs over 200,000 computations on each resume in seconds then designs a custom technical interview based on the mined skills.
From 2016 to 2017, consumer goods company Unilever used artificial intelligence to screen all entry level employees. Unilever’s AI used neuroscience based games, recorded interviews, and facial/speech analysis to predict hiring success. Unilever partnered with Pymetrics and HireVue to enable its novel AI based screening and increased their applicants from 15,000 to 30,000 in a single year. Recruiting with AI also produced Unililever’s “most diverse class to date.’ Unilever also decreased time to hire from 4 months to 4 weeks and saved over 50,000 hours of recruiter time.
From resume screening to neuroscience, speech recognition, and facial analysis...it’s clear AI is having a massive impact on the human resources field. Yet another development in AI is in recruiting chatbots. TextRecruit, a Bay Area startup, released Ari (automated recruiting interface.) Ari is a recruiting chatbot that is designed to hold two-way text message conversations with candidates. Ari automates posting jobs, advertising openings, screening candidates, scheduling interviews, and nurturing candidate relationships with updates as they progress along the hiring funnel. Ari is currently offered as part of TextRecruit’s candidate engagement platform.
Marketing[edit]
Main article: Marketing and artificial intelligence
Media[edit]
Some AI applications are geared towards the analysis of audiovisual media content such as movies, TV programs, advertisement videos or user-generated content. The solutions often involve computer vision, which is a major application area of AI.
Typical use case scenarios include the analysis of images using object recognition or face recognition techniques, or the analysis of video for recognizing relevant scenes, objects or faces. The motivation for using AI-based media analysis can be — among other things — the facilitation of media search, the creation of a set of descriptive keywords for a media item, media content policy monitoring (such as verifying the suitability of content for a particular TV viewing time), speech to text for archival or other purposes, and the detection of logos, products or celebrity faces for the placement of relevant advertisements.
Media analysis AI companies often provide their services over a REST API that enables machine-based automatic access to the technology and allows machine-reading of the results. For example, IBM, Microsoft, Amazon and the video AI company Valossa[26] allow access to their media recognition technology by using RESTful APIs.
Music[edit]
Main article: Music and artificial intelligence
While the evolution of music has always been affected by technology, artificial intelligence has enabled, through scientific advances, to emulate, at some extent, human-like composition.
Among notable early efforts, David Cope created an AI called Emily Howell that managed to become well known in the field of Algorithmic Computer Music.[27] The algorithm behind Emily Howell is registered as a US patent.[28]
The AI Iamus created 2012 the first complete classical album fully composed by a computer.
Other endeavours, like AIVA (Artificial Intelligence Virtual Artist), focus on composing symphonic music, mainly classical music for film scores.[29] It achieved a world first by becoming the first virtual composer to be recognized by a musical professional association.[30]
Artificial intelligences can even produce music usable in a medical setting, with Melomics’s effort to use computer-generated music for stress and pain relief.[31]
Moreover, initiatives such as Google Magenta, conducted by the Google Brain team, want to find out if an artificial intelligence can be capable of creating compelling art.[32]
At Sony CSL Research Laboratory, their Flow Machines software has created pop songs by learning music styles from a huge database of songs. By analyzing unique combinations of styles and optimizing techniques, it can compose in any style.
Another artificial intelligence musical composition project, The Watson Beat, written by IBM Research, doesn't need a huge database of music like the Google Magenta and Flow Machines projects, since it uses Reinforcement Learning and Deep Belief Networks to compose music on a simple seed input melody and a select style. Since the software has been open sourced[33] musicians, such as Taryn Southern[34] have been collaborating with the project to create music.
News, publishing and writing[edit]
The company Narrative Science makes computer generated news and reports commercially available, including summarizing team sporting events based on statistical data from the game in English. It also creates financial reports and real estate analyses.[35] Similarly, the company Automated Insights generates personalized recaps and previews for Yahoo Sports Fantasy Football.[36] The company is projected to generate one billion stories in 2014, up from 350 million in 2013.[37]
Echobox is a software company that helps publishers increase traffic by 'intelligently' posting articles on social media platforms such as Facebook and Twitter.[38] By analysing large amounts of data, it learns how specific audiences respond to different articles at different times of the day. It then chooses the best stories to post and the best times to post them. It uses both historical and real-time data to understand to what has worked well in the past as well as what is currently trending on the web.[39]
Another company, called Yseop, uses artificial intelligence to turn structured data into intelligent comments and recommendations in natural language. Yseop is able to write financial reports, executive summaries, personalized sales or marketing documents and more at a speed of thousands of pages per second and in multiple languages including English, Spanish, French & German.[40]
Boomtrain’s is another example of AI that is designed to learn how to best engage each individual reader with the exact articles — sent through the right channel at the right time — that will be most relevant to the reader. It’s like hiring a personal editor for each individual reader to curate the perfect reading experience.
There is also the possibility that AI will write work in the future. In 2016, a Japanese AI co-wrote a short story and almost won a literary prize.[41]
Online and telephone customer service[edit]
An automated online assistant providing customer service on a web page.
Artificial intelligence is implemented in automated online assistants that can be seen as avatars on web pages.[42] It can avail for enterprises to reduce their operation and training cost.[42] A major underlying technology to such systems is natural language processing.[42] Pypestream uses automated customer service for its mobile application designed to streamline communication with customers.[43]
Currently, major companies are investing in AI to handle difficult customer in the future. Google's most recent development analyzes language and converts speech into text. The platform can identify angry customers through their language and respond appropriately.[44]
Companies have been working on different aspects of customer service to improve this aspect of a company.
Digital Genius, an AI start-up, researches the database of information (from past conversations and frequently asked questions) more efficiently and provide prompts to agents to help them resolve queries more efficiently.
IPSoft is creating technology with emotional intelligence to adapt the customer's interaction. The response is linked to the customer's tone, with the objective of being able to show empathy. Another element IPSoft is developing is the ability to adapt to different tones or languages.
Inbenta’s is focused on developing natural language. In other words, on understanding the meaning behind what someone is asking and not just looking at the words used, using context and natural language processing. One customer service element Ibenta has already achieved is its ability to respond in bulk to email queries.
Telecommunications maintenance[edit]
Many telecommunications companies make use of heuristic search in the management of their workforces, for example BT Group has deployed heuristic search[45] in a scheduling application that provides the work schedules of 20,000 engineers.
Toys and games[edit]
The 1990s saw some of the first attempts to mass-produce domestically aimed types of basic Artificial Intelligence for education, or leisure. This prospered greatly with the Digital Revolution, and helped introduce people, especially children, to a life of dealing with various types of Artificial Intelligence, specifically in the form of Tamagotchis and Giga Pets, iPod Touch, the Internet, and the first widely released robot, Furby. A mere year later an improved type of domestic robot was released in the form of Aibo, a robotic dog with intelligent features and autonomy.
Companies like Mattel have been creating an assortment of AI-enabled toys for kids as young as age three. Using proprietary AI engines and speech recognition tools, they are able to understand conversations, give intelligent responses and learn quickly.[46]
AI has also been applied to video games, for example video game bots, which are designed to stand in as opponents where humans aren't available or desired.
Transportation[edit]
Fuzzy logic controllers have been developed for automatic gearboxes in automobiles. For example, the 2006 Audi TT, VW Touareg[citation needed] and VW Caravell feature the DSP transmission which utilizes Fuzzy Logic. A number of Škoda variants (Škoda Fabia) also currently include a Fuzzy Logic-based controller.
Today's cars now have AI-based driver assist features such as self-parking and advanced cruise controls. AI has been used to optimize traffic management applications, which in turn reduces wait times, energy use, and emissions by as much as 25 percent.[1] In the future, fully autonomous cars will be developed. AI in transportation is expected to provide safe, efficient, and reliable transportation while minimizing the impact on the environment and communities. The major challenge to developing this AI is the fact that transportation systems are inherently complex systems involving a very large number of components and different parties, each having different and often conflicting objectives.[47]
Other[edit]
Various tools of artificial intelligence are also being widely deployed in homeland security, speech and text recognition, data mining, and e-mail spam filtering. Applications are also being developed for gesture recognition (understanding of sign language by machines), individual voice recognition, global voice recognition (from a variety of people in a noisy room), facial expression recognition for interpretation of emotion and non verbal cues. Other applications are robot navigation, obstacle avoidance, and object recognition.[citation needed]
List of applications[edit]
Typical problems to which AI methods are applied
Optical character recognition
Handwriting recognition
Speech recognition
Face recognition
Artificial creativity
Computer vision, Virtual reality, and Image processing
Photo and Video manipulation
Diagnosis (artificial intelligence)
Game theory and Strategic planning
Game artificial intelligence and Computer game bot
Natural language processing, Translation and Chatterbots
Nonlinear control and Robotics
Other fields in which AI methods are implemented
Artificial life
Automated reasoning
Automation
Biologically inspired computing
Concept mining
Data mining
Knowledge representation
Semantic Web
E-mail spam filtering
Robotics
Behavior-based robotics
Cognitive
Cybernetics
Developmental robotics (Epigenetic)
Evolutionary robotics
Hybrid intelligent system
Intelligent agent
Intelligent control
Litigation
See also[edit]
Applications of artificial intelligence to legal informatics
Applications of deep learning
Applications of machine learning
List of artificial intelligence projects
Progress in artificial intelligence
Notes[edit]
^ Jump up to: a b c United States, National Science and Technology Council – Committee on Technology. Executive Office of the President. (2016). Preparing for the future of artificial intelligence.
Jump up ^ "AI bests Air Force combat tactics experts in simulated dogfights". Ars Technica. Retrieved 2016-11-18.
Jump up ^ Jones, Randolph M.; Laird, John E.; Nielsen, Paul E.; Coulter, Karen J.; Kenny, Patrick; Koss, Frank V. (1999-03-15). "Automated Intelligent Pilots for Combat Flight Simulation". AI Magazine. 20 (1): 27. ISSN 0738-4602.
Jump up ^ AIDA Homepage. Kbs.twi.tudelft.nl (April 17, 1997). Retrieved on 2013-07-21.
Jump up ^ The Story of Self-Repairing Flight Control Systems. NASA Dryden. (April 2003). Retrieved on 2016-08-25.
Jump up ^ "Flight Demonstration Of X-33 Vehicle Health Management System Components On The F/A-18 Systems Research Aircraft" (PDF).
^ Jump up to: a b c d Adams, Eric (March 28, 2017). "AI Wields the Power to Make Flying Safer—and Maybe Even Pleasant". Wired.com. Retrieved October 7, 2017.
^ Jump up to: a b c Baomar, Haitham and Bentley, Peter J. (2016). "An Intelligent Autopilot System that learns flight emergency procedures by imitating human pilots". Computational Intelligence (SSCI) 2016 IEEE Symposium Series: 1–9 – via IEEE.org. CS1 maint: Multiple names: authors list (link)
Jump up ^ "Google AI creates its own 'child' bot". The Independent. 5 December 2017. Retrieved 5 February 2018.
Jump up ^ "Education | One Hundred Year Study on Artificial Intelligence (AI100)". ai100.stanford.edu. Retrieved 2016-11-18. [dead link]
Jump up ^ "Algorithmic Trading". Investopedia.
Jump up ^ "Beyond Robo-Advisers: How AI Could Rewire Wealth Management".
Jump up ^ "Kensho's AI For Investors Just Got Valued At Over $500 Million In Funding Round From Wall Street".
Jump up ^ Marco Costantino, Paolo Coletti, Information Extraction in Finance, Wit Press, 2008. ISBN 978-1-84564-146-7
Jump up ^ "Five Best AI-Powered Chatbot Apps".
Jump up ^ "Is Artificial Intelligence the Way Forward for Personal Finance?".
Jump up ^ "Machine learning in finance applications".
Jump up ^ "Machine Learning Is the Future of Underwriting, But Startups Won't be Driving It".
Jump up ^ "ZestFinance Introduces Machine Learning Platform to Underwrite Millennials and Other Consumers with Limited Credit History".
Jump up ^ "World Robotics 2015 Industrial Robots". International Federation of Robotics. Archived from the original on March 27, 2016. Retrieved 27 March 2016. CS1 maint: Unfit url (link)
Jump up ^ Reed, T. R.; Reed, N. E.; Fritzson, P. (2004). "Heart sound analysis for symptom detection and computer-aided diagnosis". Simulation Modelling Practice and Theory. 12 (2): 129. doi:10.1016/j.simpat.2003.11.005.
Jump up ^ Yorita, A.; Kubota, N. (2011-03-01). "Cognitive Development in Partner Robots for Information Support to Elderly People". IEEE Transactions on Autonomous Mental Development. 3 (1): 64–73. doi:10.1109/TAMD.2011.2105868. ISSN 1943-0604.
Jump up ^ "Artificial Intelligence Will Redesign Healthcare – The Medical Futurist". The Medical Futurist. 2016-08-04. Retrieved 2016-11-18.
Jump up ^ Luxton, David D. (2014). "Artificial Intelligence in Psychological Practice: Current and Future Applications and Implication". Professional Psychology: Research and Practice. 45 (5): 332. doi:10.1037/a0034559.
Jump up ^ "From Virtual Nurses To Drug Discovery: 90+ Artificial Intelligence Startups In Healthcare". CB Insights – Blog. 2016-08-31. Retrieved 2016-11-18.
Jump up ^ "Video Recognition API of Valossa". Retrieved 2018-02-08.
Jump up ^ Cheng, Jacqui (30 September 2009). "Virtual composer makes beautiful music—and stirs controversy". Ars Technica.
Jump up ^ US Patent #7696426 https://www.google.com/patents/US7696426
Jump up ^ Hick, Thierry (11 October 2016). "La musique classique recomposée". Luxemburger Wort.
Jump up ^ SACEM Database, https://repertoire.sacem.fr/resultats?filters=parties&query=aiva&nbWorks=20)
Jump up ^ Requena, G; Sanchez, C; Corzo-Higueras, JL; Reyes-Alvarado, S; Rivas-Ruiz, F; Vico, F; Raglio, A (2014). "Melomics music medicine (M3) to lessen pain perception during pediatric prick test procedure". Pediatric Allergy and Immunology. 25 (7): 721. doi:10.1111/pai.12263. PMID 25115240.
Jump up ^ Souppouris, Aaron (23 May 2016). "Google's 'Magenta' project will see if AIs can truly make art". Engadget.
Jump up ^ "Watson Beat on GitHub".
Jump up ^ "Songs in the Key of AI". Wired. 17 May 2018.
Jump up ^ business intelligence solutions Archived November 3, 2011, at the Wayback Machine.. Narrative Science. Retrieved on 2013-07-21.
Jump up ^ Eule, Alexander. "Big Data and Yahoo's Quest for Mass Personalization". Barron's.
Jump up ^ Kirkland, Sam. "'Robot' to write 1 billion stories in 2014 — but will you know it when you see it?". Poynter.
Jump up ^ Williams, Henry (July 4, 2016). "AI online publishing service Echobox closes $3.4m in funding". Startups.co.uk. Retrieved July 21, 2016.
Jump up ^ Smith, Mark (July 22, 2016). "So you think you chose to read this article?". BBC. Retrieved July 27, 2016.
Jump up ^ "Archived copy". Archived from the original on 2013-04-12. Retrieved 2013-03-11.
Jump up ^ "A Japanese AI program just wrote a short novel, and it almost won a literary prize". Digital Trends. 2016-03-23. Retrieved 2016-11-18.
^ Jump up to: a b c Implementing an online help desk system based on conversational agent Authors: Alisa Kongthon, Chatchawal Sangkeettrakarn, Sarawoot Kongyoung and Choochart Haruechaiyasak. Published by ACM 2009 Article, Bibliometrics Data Bibliometrics. Published in: Proceeding, MEDES '09 Proceedings of the International Conference on Management of Emergent Digital EcoSystems, ACM New York, NY, USA. ISBN 978-1-60558-829-2, doi:10.1145/1643823.1643908
Jump up ^ Sara Ashley O'Brien (January 12, 2016). "Is this app the call center of the future?". CNN. Retrieved September 26, 2016.
Jump up ^ jackclarkSF, Jack Clark (2016-07-20). "New Google AI Brings Automation to Customer Service". Bloomberg.com. Retrieved 2016-11-18.
Jump up ^ Success Stories Archived October 4, 2011, at the Wayback Machine..
Jump up ^ "How artificial intelligence is moving from the lab to your kid's playroom". Washington Post. Retrieved 2016-11-18.
Jump up ^ Meyer, Michael D. (January 2007). "Artificial Intelligence in Transportation Information for Application" (PDF). Transportation Research Circular.
References[edit]
Russell, Stuart J.; Norvig, Peter (2003). Artificial Intelligence: A Modern Approach (2nd ed.). Upper Saddle River, New Jersey: Prentice Hall. ISBN 0-13-790395-2.
Kurzweil, Ray (2005). The Singularity is Near: When Humans Transcend Biology. New York: Viking. ISBN 978-0-670-03384-3.
National Research Council (1999). "Developments in Artificial Intelligence". Funding a Revolution: Government Support for Computing Research. National Academy Press. ISBN 0-309-06278-0. OCLC 246584055.
Moghaddam, M. J., M. R. Soleymani, and M. A. Farsi. "Sequence planning for stamping operations in progressive dies." Journal of Intelligent Manufacturing(2013): 1-11.
United States, National Science and Technology Council – Committee on Technology. Executive Office of the President. (2016). Preparing for the future of artificial intelligence.
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Mole (animal)
From Wikipedia, the free encyclopedia
Jump to: navigation, search
This article is about mammals called "moles". For other uses, see Mole (disambiguation).
Not to be confused with Vole.
Moles
European mole
Scientific classification
Kingdom: Animalia
Phylum: Chordata
Class: Mammalia
Infraclass: Eutheria
Order: Eulipotyphla
Family: Talpidae
in part
Genera
12 genera, see text
Moles are small mammals adapted to a subterranean lifestyle (i.e., fossorial). They have cylindrical bodies, velvety fur, very small, inconspicuous ears and eyes,[1] reduced hindlimbs and short, powerful forelimbs with large paws adapted for digging. The term "mole" is especially and most properly used for "true moles" of the Talpidae family in the order Eulipotyphla found in most parts of North America,[2] Asia, and Europe; although it may also refer to other completely unrelated mammals of Australia and southern Africa that have also evolved the mole body plan; it is not commonly used for some talpids, such as desmans and shrew-moles, which do not quite fit the common definition of "mole".
Contents
[hide]
1 Terminology
2 Characteristics
2.1 Underground breathing
2.2 Extra thumbs
3 Diet
4 Breeding
5 Social structure
6 Classification
7 Other "moles"
7.1 Golden moles
7.2 Marsupial moles
8 Interaction with humans
8.1 Pelts
8.2 Pest status
8.3 Meat
9 See also
10 References
11 External links
Terminology[edit]
In Middle English, moles were known as moldwarp. The expression "do not make a mountain out of a mole hill" – exaggerating problems – was first recorded in Tudor times.[3] By the era of Early Modern English, the mole was also known in English as mouldywarp, a word having cognates in other Germanic languages such as German (Maulwurf),[4] and Danish, Norwegian, Swedish and Icelandic (muldvarp, mullvad, moldvarpa), where the muld/mull/mold part of the word means soil and the varp/vad/varpa part means throw, hence "one who throws soil" or "dirt tosser".
Male moles are called "boars", females are called "sows". A group of moles is called a "labour".[5]
Characteristics[edit]
Underground breathing[edit]
Moles have been found to tolerate higher levels of carbon dioxide than other mammals, because their blood cells have a special form of hemoglobin, that has a higher affinity to oxygen than other forms. In addition to this, moles utilize oxygen more effectively by reusing the exhaled air, and as a result, are able to survive in low-oxygen environments such as underground burrows.[6]
Extra thumbs[edit]
Mole paw
Moles have polydactyl forepaws; each has an extra thumb (also known as a prepollex) next to the regular thumb. While the mole's other digits have multiple joints, the prepollex has a single, sickle-shaped bone that develops later and differently from the other fingers during embryogenesis from a transformed sesamoid bone in the wrist, independently evolved but similar to the giant panda thumb. This supernumerary digit is species-specific, as it is not present in shrews, the mole's closest relatives. Androgenic steroids are known to affect the growth and formation of bones, and a connection is possible between this species-specific trait and the "male" genitals apparatus in female moles of many mole species (gonads with testicular and ovary tissues).[7]
Diet[edit]
A mole's diet primarily consists of earthworms and other small invertebrates found in the soil, and a variety of nuts. The mole runs are in reality "worm traps", the mole sensing when a worm falls into the tunnel and quickly running along to kill and eat it.[8] Because their saliva contains a toxin that can paralyze earthworms, moles are able to store their still-living prey for later consumption. They construct special underground "larders" for just this purpose; researchers have discovered such larders with over a thousand earthworms in them. Before eating earthworms, moles pull them between their squeezed paws to force the collected earth and dirt out of the worm's gut.[9]
The star-nosed mole can detect, catch and eat food faster than the human eye can follow.[10]
Breeding[edit]
Breeding season for a mole depends on species but is generally February through May. Males search for females by letting out high-pitched squeals and tunneling through foreign areas.
The gestation period of the Eastern (North America) mole (Scalopus aquaticus) is approximately 42 days. Three to five young are born, mainly in March and early April.[11]
Townsend moles mate in February and March, and the 2–4 young are born in March and April after a gestation period of about 1 month.[12] The Townsend mole is endangered in the United States and Canada.[13]
Coast moles produce a litter of 2–5 pups between March and April.[13]
Pups leave the nest 30–45 days after birth to find territories of their own.
Social structure[edit]
Moles are solitary creatures, coming together only to mate. Territories may overlap, but moles avoid each other and males may fight fiercely if they meet.
Classification[edit]
Uropsilus
Broad-footed mole
Eastern mole
The family Talpidae contains all the true moles and some of their close relatives. Desmans, which are Talpidae but are not normally called "moles", are not shown below, but belong to the subfamily Talpinae (note the slightly different name). Those species called "shrew moles" represent an intermediate form between the moles and their shrew ancestors, and as such may not be fully described by the article.
On the other hand, there is no monophyletic relation between the mole and the hedgehog, both of which were previously placed in the now abandoned order Insectivora. As a result, Soricomorpha ("shrew-like animals" including moles), previously within Insectivora, has been elevated to the level of an order.[14]
Subfamily Scalopinae: New World moles
Tribe Condylurini Star-nosed mole (North America)
Genus Condylura: Star-nosed mole (sole species)
Tribe Scalopini New World moles
Genus Parascalops: Hairy-tailed mole (northeastern North America)
Genus Scalopus: Eastern mole (North America)
Genus Scapanulus: Gansu mole (China)
Genus Scapanus: Western North American moles (three species)
Subfamily Talpinae Old World moles, desmans (not shown), and shrew moles
Tribe Talpini: Old World moles
Genus Euroscaptor: Six Asian species
Genus Mogera Nine species from Japan, Korea, and Eastern China
Genus Parascaptor: White-tailed mole, southern Asia
Genus Scaptochirus: Short-faced mole, China
Genus Talpa Ten species, Europe and western Asia
Tribe Scaptonychini Long-tailed mole
Genus Scaptonyx: Long-tailed mole (China and Myanmar)
Tribe Urotrichini: Japanese shrew moles
Genus Dymecodon: True’s shrew mole
Genus Urotrichus: Japanese shrew mole
Tribe Neurotrichini New World shrew moles
Genus Neurotrichus: Shrew mole (American shrew mole, Pacific northwest USA, southwest British Columbia)
Subfamily Uropsilinae: Asian shrew-like moles, (Chinese shrew moles)
Genus Uropsilus Four species in China, Bhutan, and Myanmar
Other "moles"[edit]
While many groups of burrowing animals (pink fairy armadillos, tuco-tucos, mole rats, mole crickets, and mole crabs) have developed close physical similarities with moles due to convergent evolution, two of these are so similar to true moles, they are commonly called and thought of as "moles" in common English, although they are completely unrelated to true moles or to each other. These are the golden moles of southern Africa and the marsupial moles of Australia. While difficult to distinguish from each other, they are most easily distinguished from true moles by shovel-like patches on their noses, which they use in tandem with their abbreviated forepaws to swim through sandy soils.
Golden moles[edit]
A golden mole
The golden moles belong to the same branch on the tree of life as the tenrecs, called Tenrecomorpha, which in turn stem from a main branch of placental mammals called the Afrosoricida. This means that they share a closer common ancestor with such existing afrosoricids as elephants, manatees, and aardvarks than they do with other placental mammals, such as true Talpidae moles.
ORDER AFROSORICIDA
Suborder Tenrecomorpha
Family Tenrecidae: tenrecs, 34 species in 10 genera
Suborder Chrysochloridea
Family Chrysochloridae
Subfamily Chrysochlorinae
Genus Carpitalpa
Arends' golden mole (Carpitalpa arendsi)
Genus Chlorotalpa
Duthie's golden mole (Chlorotalpa duthieae)
Sclater's golden mole (Chlorotalpa sclateri)
Genus Chrysochloris
Subgenus Chrysochloris
Cape golden mole (Chrysochloris asiatica)
Visagie's golden mole (Chrysochloris visagiei)
Subgenus Kilimatalpa
Stuhlmann's golden mole (Chrysochloris stuhlmanni)
Genus Chrysospalax
Giant golden mole (Chrysospalax trevelyani)
Rough-haired golden mole (Chrysospalax villosus)
Genus Cryptochloris
De Winton's golden mole (Cryptochloris wintoni)
Van Zyl's golden mole (Cryptochloris zyli)
Genus Eremitalpa
Grant's golden mole (Eremitalpa granti)
Subfamily Amblysominae
Genus Amblysomus
Fynbos golden mole (Amblysomus corriae)
Hottentot golden mole (Amblysomus hottentotus)
Marley's golden mole (Amblysomus marleyi)
Robust golden mole (Amblysomus robustus)
Highveld golden mole (Amblysomus septentrionalis)
Genus Calcochloris
Subgenus Huetia
Congo golden mole (Calcochloris leucorhinus)
Subgenus Calcochloris
Yellow golden mole (Calcochloris obtusirostris)
Subgenus incertae sedis
Somali golden mole (Calcochloris tytonis)
Genus Neamblysomus
Juliana's golden mole (Neamblysomus julianae)
Gunning's golden mole (Neamblysomus gunningi)
Marsupial moles[edit]
A marsupial mole
As marsupials, these moles are even more distantly related to true Talpidae moles than golden moles, both of which belong to the eutheria, or placental mammals. This means that they are more closely related to such existing Australian marsupials as kangaroos or koalas, and even to a lesser extent to American marsupials, such as opossums, than they are to placental mammals, such as golden or Talpidae moles.
Class Mammalia
Subclass Prototheria: monotremes: echidnas and the platypus
Subclass Theriiformes: live-bearing mammals and their prehistoric relatives
Infraclass Holotheria: modern live-bearing mammals and their prehistoric relatives
Supercohort Theria: live-bearing mammals
Cohort Marsupialia: marsupials
Magnorder Ameridelphia: New World marsupials
Order Didelphimorphia (opossums)
Order Paucituberculata (shrew opossums)
Superorder Australidelphia Australian marsupials
Order Dasyuromorphia Tasmanian devils, numbats
Order Peramelemorphia Bilbies and bandicoots
Order Diprotodontia Koalas, wombats, diprotodons, possums, cuscuses, sugar gliders, kangaroos, and many more
Order Notoryctemorphia Marsupial moles and closely related extinct families of marsupials
Family Notoryctidae Living marsupial genera and extinct marsupial mole genera
Genus Notoryctes Only genus of marsupial moles with existent species
Species Notoryctes typhlops, the southern marsupial mole
Species Notoryctes caurinus, the northern marsupial mole
Interaction with humans[edit]
Pelts[edit]
Advertisement in Illustrated Sporting and Dramatic News, 1921
Moles' pelts have a velvety texture not found in surface animals. Surface-dwelling animals tend to have longer fur with a natural tendency for the nap to lie in a particular direction, but to facilitate their burrowing lifestyle, mole pelts are short and very dense and have no particular direction to the nap. This makes it easy for moles to move backwards underground, as their fur is not "brushed the wrong way". The leather is extremely soft and supple. Queen Alexandra, the wife of Edward VII of the United Kingdom, ordered a mole-fur garment to start a fashion that would create a demand for mole fur, thereby turning what had been a serious pest problem in Scotland into a lucrative industry for the country. Hundreds of pelts are cut into rectangles and sewn together to make a coat. The natural color is taupe, (derived from the French noun taupe meaning mole) but it is readily dyed any color.[15]
Pest status[edit]
Molehills in eastern Bohemia
Moles are considered agricultural pests in some countries, while in others, such as Germany, they are a protected species, but may be killed with a permit. Problems cited as caused by moles include contamination of silage with soil particles, making it unpalatable to livestock, the covering of pasture with fresh soil reducing its size and yield, damage to agricultural machinery by the exposure of stones, damage to young plants through disturbance of the soil, weed invasion of pasture through exposure of freshly tilled soil, and damage to drainage systems and watercourses. Other species such as weasels and voles may use mole tunnels to gain access to enclosed areas or plant roots.
Moles burrow and raise molehills, killing parts of lawns. They can undermine plant roots, indirectly causing damage or death. Moles do not eat plant roots.[16]
A mole trap
Moles are controlled with traps such as mole-catchers, smoke bombs, and poisons such as calcium carbide. Strychnine was also used for this purpose in the past. The most common method now is Phostoxin or Talunex tablets. They contain aluminium phosphide and are inserted in the mole tunnels, where they turn into phosphine gas (not be confused with phosgene gas). More recently, high-grade nitrogen gas has proven effective at killing moles, with the added advantage of having no polluting effect to the environment.[16]
Other common defensive measures include cat litter and blood meal, to repel the mole, or smoking its burrow. Devices are also sold to trap the mole in its burrow, when one sees the "mole hill" moving and therefore knows where the animal is, and then stabbing it. Humane traps that capture the mole alive so it may be transported elsewhere are also options.[16]
However, in many gardens, the damage caused by moles to lawns is mostly visual, and it is also possible to simply remove the earth of the molehills as they appear, leaving their permanent galleries for the moles to continue their existence underground.[16] However, when the tunnels are near the surface, they collapse when the ground is soft after heavy rain and leave unsightly furrows in the lawn.
Meat[edit]
William Buckland, known for eating every animal he could, opined that mole meat tastes vile.[17]
See also[edit]
Molehill
Moleskin
Molecatcher
References[edit]
Jump up ^ Kott, Ondřej; Sumbera, Radim; Nemec, Pavel (2010). Iwaniuk, Andrew, ed. "Light Perception in Two Strictly Subterranean Rodents: Life in the Dark or Blue?". PLoS ONE. 5 (7): e11810. PMC 2911378  . PMID 20676369. doi:10.1371/journal.pone.0011810.
Jump up ^ Campbell, Kevin. "Mole Distribution Maps". University of Manitoba. Retrieved 11 March 2010.
Jump up ^ Roper, William (1557) Life of Sir Thomas More.
Jump up ^ Rackham, Oliver, The Illustrated History Of The Countryside page 130 (quoting J. Seddon, The boke of surveying and improvments – [sic]) ISBN 0-297-84335-4
Jump up ^ "Moles". animalcorner.co.uk. Archived from the original on 12 November 2010.
Jump up ^ "Secret of how moles breathe underground revealed". The Telegraph. 20 July 2010. Archived from the original on 23 July 2010. Retrieved 19 August 2016.
Jump up ^ "How the mole got its twelve fingers". University of Zurich. 12 July 2011.
Jump up ^ "Moles". Pest-management.co.uk. Archived from the original on 1 May 2009. Retrieved 19 August 2016.
Jump up ^ Attenborough, David (2002) The Life of Mammals. Discovery Channel
Jump up ^ Salisbury, David F. (2 February 2005). "Marsh-dwelling mole gives new meaning to the term 'fast food'". EurekAlert. Retrieved 19 August 2016.
Jump up ^ "Moles their biology and control". Icwdm.org.
Jump up ^ Scapanus townsendii. California Department of Fish and Game
^ Jump up to: a b "Coast Mole Control and Trapping". Animalcontrolsolutions.com.
Jump up ^ Mouchaty, Suzette K.; Gullberg, Anette; Janke, Axel; Arnason, Ulfur (2000). "The Phylogenetic Position of the Talpidae Within Eutheria Based on Analysis of Complete Mitochondrial Sequences". Mol Biol Evol. 17 (1): 60–67. PMID 10666706. doi:10.1093/oxfordjournals.molbev.a026238.
Jump up ^ "Furs types in brief". furcommission.com. Archived from the original on 21 July 2011.
^ Jump up to: a b c d "How to get rid of moles". Extermine.com. 2004.
Jump up ^ Howard, Martin (1 April 2010). "Why we need eccentricity". The Guardian. London. Retrieved 13 May 2010.
External links[edit]
Wikimedia Commons has media related to Talpa europaea.
Wikispecies has information related to: Talpa europaea
UK Government DEFRA paper on control the European mole
British Traditional Molecatchers Register
Retrieved from "https://en.wikipedia.org/w/index.php?title=Mole_(animal)&oldid=796093371"
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