Nanoengineering – Wikipedia

Nanoengineering is the practice of engineering on the nanoscale. It derives its name from the nanometre, a unit of measurement equalling one billionth of a meter.

Nanoengineering is largely a synonym for nanotechnology, but emphasizes the engineering rather than the pure science aspects of the field.

The first nanoengineering program was started at the University of Toronto within the Engineering Science program as one of the options of study in the final years. In 2003, the Lund Institute of Technology started a program in Nanoengineering. In 2004, the College of Nanoscale Science and Engineering at SUNY Polytechnic Institute was established on the campus of the University at Albany. In 2005, the University of Waterloo established a unique program which offers a full degree in Nanotechnology Engineering. [2] Louisiana Tech University started the first program in the U.S. in 2005. In 2006 the University of Duisburg-Essen started a Bachelor and a Master program NanoEngineering. [3] Unlike early NanoEngineering programs, the first Nanoengineering Department in the world, offering both undergraduate and graduate degrees, was established by the University of California, San Diego in 2007.In 2009, the University of Toronto began offering all Options of study in Engineering Science as degrees, bringing the second nanoengineering degree to Canada. Rice University established in 2016 a Department of Materials Science and NanoEngineering (MSNE).DTU Nanotech – the Department of Micro- and Nanotechnology – is a department at the Technical University of Denmark established in 1990.

In 2013, Wayne State University began offering a Nanoengineering Undergraduate Certificate Program, which is funded by a Nanoengineering Undergraduate Education (NUE) grant from the National Science Foundation. The primary goal is to offer specialized undergraduate training in nanotechnology. Other goals are: 1) to teach emerging technologies at the undergraduate level, 2) to train a new adaptive workforce, and 3) to retrain working engineers and professionals.[4]

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Nanoengineering – Wikipedia

UC San Diego NanoEngineering Department

The NanoEngineering program has received accreditation by the Accreditation Commission of ABET, the global accreditor of college and university programs in applied and natural science, computing, engineering and engineering technology. UC San Diego’s NanoEngineering program is the first of its kind in the nation to receive this accreditation. Our NanoEngineering students can feel confident that their education meets global standards and that they will be prepared to enter the workforce worldwide.

ABET accreditation assures that programs meet standards to produce graduates ready to enter critical technical fields that are leading the way in innovation and emerging technologies, and anticipating the welfare and safety needs of the public. Please visit the ABET website for more information on why accreditation matters.

Congratulations to the NanoEngineering department and students!

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UC San Diego NanoEngineering Department

NanoEngineering | NanoEngineering

The Department of NanoEngineering (NE) now offers the M.S. and Ph.D. degree in NanoEngineering with a new, unique curriculum centered on our strong research position in nano-biomedical engineering and nanomaterials synthesis and characterization activities. The NanoEngineering Graduate Program provides a course of study for both the M.S. and Ph.D. degrees, with a focus on underlying scientific, technical and engineering challenges for advancing nanotechnology in the controlled synthesis of nanostructured materials, especially for biomedical, energy, and environmentally-related technologies. Our graduate degree program is uniquely designed to educate students with a highly interdisciplinary curriculum, focusing on core scientific fundamentals, but extending the application of that fundamental understanding to complex problems requiring the ability to integrate across traditional science and engineering boundaries. Specific courses in our core cluster address both the fundamental science and the integration of this science into engineering problem solving. Three main educational paths within the single degree title NanoEngineering are proposed:

The new NE curriculum has the following objectives:

In NanoEngineering, we design and manufacture devices and systems that exploit the unique properties of nanoscale materials to create entirely new functionality and capabilities. Due to the scale of engineering involved, the field of NanoEngineering is inherently interdisciplinary that often utilizes biochemical processes to create nanoscale materials designed to interact with synthetic inorganic materials. The curriculum is built to address the educational needs of this new engineering field.

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NanoEngineering | NanoEngineering

NanoEngineering Detailed Description | NanoEngineering

The NanoEngineering graduate degree program prepares students to enter the Nanotechnology workforce, as well as prepare students to enter a wider variety of engineering, science and/or medical career paths. It is clear that Nanotechnology-based industries will play a major role in the future economy. Our proposed curriculum is specifically intended to develop graduate students to be team leaders and innovators in corporations that have nanotechnology-centric applications, where our graduates will play the critical role to integrate across the varied disciplines involved, and help overcome the inherent challenges of engineering at the nanoscale. Their unique training in NanoEngineering will enable them to naturally become these leaders.

Linkage between fundamental science and engineering disciplines and research focus areas for the NanoEngineering department.

As with all of the graduate engineering degrees in the Jacobs School of Engineering, a common set of educational principles and expectations will exist for our graduate students:

In addition, the new NE curriculum will have the following course-specific outcomes:

All graduate students in NanoEngineering are required to take each of five core classes that have been carefully crafted to provide an in-depth understanding of the chemistry, physics, materials, and interface science germane to the nanoscale [courses NANO-201, 202, and 204]. In addition, NANO-203 focuses on the complex and innovative new technologies in place and being developed for the tailored synthesis of controlled, functional nanostructures and directed self-assembly of complex nanostructures and nanosystems. NANO-205 specifically addresses the challenge of nanoscale systems integration, focusing on making connections of scientific principles across physical boundaries between diverse materials to achieve new, unique, nanoscale functionality.

The additional courses required for completion of each graduate degree, beyond the 5 core classes, will come from a series of NE elective courses, sub-divided into the three research focus areas: Biomedical Nanotechnology, Molecular and Nanomaterials, or Nanotechnologies for Energy and the Environment. Additional courses needed to develop team engineering, technology leadership, and entrepreneur skills will be made available to our graduate students through the new series of Engineering-wide courses [ENG-100, 101, and 101L] developed in collaboration with the UCSD Rady Management school, the UCSD von Liebig foundation, and engineering faculty. None of the required courses that comprise the M.S. or Ph.D. programs in NanoEngineering rely upon teaching from faculty in any other department on campus. However, at times, particularly with regard to the NE Affiliate Faculty, courses are offered by faculty in other departments of interest to NE graduate students, and these students may enroll in these courses as electives, upon consent of their advisor.

There are 3 different degree paths in the NanoEngineering Graduate Degree program:

Students wishing to pursue aMaster of Science (M.S.) in NanoEngineeringdegree can be admitted into the program for either the M.S.-only route (a terminal Masters degree) or the M.S. route, where the student intends to pursue a Ph.D. degree after completing the M.S. degree. Irrespective of whether the student chooses the M.S.-only route or the M.S. route, the student has two other options for the pursuit of their M.S. degree: aThesis Routeand anExamination Route. Both routes require the completion of the same 5 core classes, with theThesis Routerequiring 1 additional elective course and theExamination Routerequiring 4 additional elective courses. Both routes require a total of thirty-six (36) units.

Additional details of the M.S. degree requirements are shown here:

ADoctor of Philosophy (Ph.D.) in NanoEngineeringrequires the selection of a specific focus [Biomedical Nanotechnology, Molecular and Nanomaterials, or Nanotechnologies for Energy and the Environment], and consists of the successful completion of 12 courses — the 5 required core courses, 4 electives from the students selected focus, and 3 electives from any of the two remaining focuses, the ENG-10X courses (for team engineering, leadership, and entrepreneur skills) or from a variety of electives from other departments across campus, withadvisors consent. The non-NanoEngineering elective courses are all open for enrollment by our graduate students. The additional degree details for the Ph.D. in NanoEngineering are discussed below.

Ph.D.: M.S. comprehensive examination used as Ph.D. entrance exam (passing grade of 70% required), literature review examination, senate (candidacy) exam.

Master of Science {Thesis Route requires the completion of a Thesis document and presentation of the thesis to a faculty thesis committee}

Doctor of Philosophy (Ph.D.) requires the completion of a Dissertation and presentation of the research contained in the dissertation (See Final Examination below).

Graduate students will defend their thesis or dissertation in a final oral examination. The exam will consist of a) a presentation of the thesis or dissertation by the graduate student, b) questioning by the general audience, and c) closed door questioning by the thesis or dissertation committee. The student will be informed of the exam result at the completion of the entire oral examination. The final report of the doctoral committee will be signed by all members of the committee and the final version of the dissertation will conform to the procedures outlined in the publication, Instructions for the Preparation and Submission of Doctoral and Masters Theses.

Both programs utilize the same 5 Core Courses

All students will take 5 core courses and start a research project their first year.

Spring of 2nd year Qualifying ExaminationSpring of 3rd year Advance to CandidacyEnd of 5th year Ph.D.

Normative time is defined as that period of time in which students under normal circumstances are expected to complete their doctoral program. Normative time for a Ph.D. in NanoEngineering is five years. The maximum length of time that a student may remain a pre-candidate for the Ph.D. degree is three years.

Graduate student academic progress and policies are monitored by the NanoEngineering Department at UCSD and ensures that students make timely progress towards completion of their degree. The policies include spring evaluations and annual substantive progress reviews as directed by the Graduate Council. The NanoEngineering Graduate Affairs Committee Chair, in coordination with the Office of Graduate Studies OGS and the NanoEngineering Dept. Chair, will implement these policies for the program.

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NanoEngineering Detailed Description | NanoEngineering

NanoEngineering (NANO) Courses – University of California …

[ undergraduate program | graduate program | faculty ]

All courses, faculty listings, and curricular and degree requirements described herein are subject to change or deletion without notice.

For course descriptions not found in the UC San Diego General Catalog 201819, please contact the department for more information.

The department website is http://nanoengineering.ucsd.edu/undergrad-programs

All students enrolled in NanoEngineering courses or admitted to the NanoEngineering major are expected to meet prerequisite and performance standards, i.e., students may not enroll in any NanoEngineering courses or courses in another department that are required for the major prior to having satisfied prerequisite courses with a C or better. (The department does not consider D or F grades as adequate preparation for subsequent material.) Additional details are given under the program outline, course descriptions, and admission procedures for the Jacobs School of Engineering in this catalog.

NANO 1. NanoEngineering Seminar (1)

Overview of NanoEngineering. Presentations and discussions of basic knowledge and career opportunities in nanotechnology for professional development. Introduction to campus library resources. P/NP grades only.

NANO 4. ExperienceNanoEngineering(1)

Introduction to NanoEngineering lab-based skills. Hands-on training and experimentation with nanofabrication techniques, integration, and analytical tools. This class is for NANO majors who are incoming freshmen, to be taken their first year.This class is for NanoEngineering majors who are incoming freshmen, to be taken their first year. P/NP grades only. Prerequisites: department approval required.

NANO 15. Engineering Computation Using Matlab (4)

Introduction to the solution of engineering problems using computational methods. Formulating problem statements, selecting algorithms, writing computer programs, and analyzing output using Matlab. Computational problems from NanoEngineering, chemical engineering, and materials science are introduced. The course requires no prior programming skills. Cross-listed with CENG 15.

NANO 15R. Engineering Computation Using Matlab Online

Introduction to solution of engineering problems using computational methods. Formulating problem statements, selecting algorithms, writing computer programs, and analyzing output using Matlab. Computational problems from NanoEngineering, chemical engineering, and materials science are introduced. This is a fully online, self-paced course that utilizes multi-platform instructional techniques (video, text, and instructional coding environments). The course requires no prior programming skills. Students may not receive credit for both CENG 15 and NANO 15. Cross-listed with CENG 15R. Students may only receive credit for one of the following: NANO 15R, NANO 15, CENG 15R, or CENG 15.

NANO 100L. Physical Properties of Materials Lab (4)

Experimental investigation of physical properties of materials such as: thermal expansion coefficient, thermal conductivity, glass transitions in polymers, resonant vibrational response, longitudinal and shear acoustic wave speeds, Curie temperatures, UV-VIS absorption and reflection. Prerequisites: NANO 108.

NANO 101. Introduction to NanoEngineering (4)

Introduction to NanoEngineering; nanoscale fabrication: nanolithography and self-assembly; characterization tools; nanomaterials and nanostructures: nanotubes, nanowires, nanoparticles, and nanocomposites; nanoscale and molecular electronics; nanotechnology in magnetic systems; nanotechnology in integrative systems; nanoscale optoelectronics; nanobiotechnology: biomimetic systems, nanomotors, nanofluidics, and nanomedicine. Priority enrollment given to NanoEngineering majors. Prerequisites: NANO 1 or NANO 4, Chem 6B, Phys 2B, Math 20C, and CENG 15 or CENG 15R or NANO 15 or NANO 15R or MAE 8. Department approval required.

NANO 102. Foundations in NanoEngineering: Chemical Principles (4)

Chemical principles involved in synthesis, assembly, and performance of nanostructured materials and devices. Chemical interactions, classical and statistical thermodynamics of small systems, diffusion, carbon-based nanomaterials, supramolecular chemistry, liquid crystals, colloid and polymer chemistry, lipid vesicles, surface modification, surface functionalization, catalysis. Priority enrollment given to NanoEngineering majors. Prerequisites: Chem 6C, Math 20D, NANO 101, PHYS 2D, and NANO 106. Restricted to NanoEngineering majors or by department approval.

NANO 103. Foundations in NanoEngineering: Biochemical Principles (4)

Principles of biochemistry tailored to nanotechnologies. The structure and function of biomolecules and their specific roles in molecular interactions and signal pathways. Detection methods at the micro and nano scales. Priority enrollment will be given to NanoEngineering majors. Prerequisites: BILD 1, Chem 6C, NANO 101, and NANO 102. Department approval required.

NANO 104. Foundations in NanoEngineering: Physical Principles (4)

Introduction to quantum mechanics and nanoelectronics. Wave mechanics, the Schroedinger equation, free and confined electrons, band theory of solids. Nanosolids in 0D, 1D, and 2D. Application to nanoelectronic devices. Priority enrollment given to NanoEngineering majors Prerequisites: Math 20D, NANO 101. Department approval required.

NANO 106. Crystallography of Materials (4)

Fundamentals of crystallography, and practice of methods to study material structure and symmetry. Curie symmetries. Tensors as mathematical description of material properties and symmetry restrictions. Introduction to diffraction methods, including X-ray, neutron, and electron diffraction. Close-packed and other common structures of real-world materials. Derivative and superlattice structures. Prerequisites: Math 20F.

NANO 107.Electronic Devices and Circuits for Nanoengineers (4)

Overview of electrical devices and CMOS integrated circuits emphasizing fabrication processes, and scaling behavior. Design, and simulation of submicron CMOS circuits including amplifiers active filters digital logic, and memory circuits. Limitations of current technologies and possible impact of nanoelectronic technologies.Prerequisites: NANO 15, NANO 101, Math 20B or Math 20D, and Phys 2B.

NANO 108. Materials Science and Engineering (4)

Structure and control of materials: metals, ceramics, glasses, semiconductors, polymers to produce useful properties. Atomic structures. Defects in materials, phase diagrams, micro structural control. Mechanical, rheological, electrical, optical and magnetic properties discussed. Time temperature transformation diagrams. Diffusion. Scale dependent material properties. Prerequisites: upper-division standing.

NANO 110. Molecular Modeling of Nanoscale Systems (4)

Principles and applications of molecular modeling and simulations toward NanoEngineering. Topics covered include molecular mechanics, energy minimization, statistical mechanics, molecular dynamics simulations, and Monte Carlo simulations. Students will get hands-on training in running simulations and analyzing simulation results. Prerequisites: Math 20F, NANO 102, NANO 104, and NANO 15 or CENG 15 or MAE 8. Restricted to NanoEngineering majors or by department approval.

NANO 111. Characterization of NanoEngineering Systems (4)

Fundamentals and practice of methods to image, measure, and analyze materials and devices that are structured at the nanometer scale. Optical and electron microscopy; scanning probe methods; photon-, ion-, electron-probe methods, spectroscopic, magnetic, electrochemical, and thermal methods. Prerequisites: NANO 102.

NANO 112. Synthesis and Fabrication of NanoEngineering Systems (4)

Introduction to methods for fabricating materials and devices in NanoEngineering. Nano-particle, -vesicle, -tube, and -wire synthesis. Top-down methods including chemical vapor deposition, conventional and advanced lithography, doping, and etching. Bottom-up methods including self-assembly. Integration of heterogeneous structures into functioning devices. Prerequisites: NANO 102, NANO 104, NANO 111.

NANO 114. Probability and Statistical Methods for Engineers (4)

Probability theory, conditional probability, Bayes theorem, discrete random variables, continuous random variables, expectation and variance, central limit theorem, graphical and numerical presentation of data, least squares estimation and regression, confidence intervals, testing hypotheses. Cross-listed with CENG 114. Students may not receive credit for both NANO 114 and CENG 114. Prerequisites: Math 20F and NANO 15 or CENG 15 or MAE 8.

NANO 120A. NanoEngineering System Design I (4)

Principles of product design and the design process. Application and integration of technologies in the design and production of nanoscale components. Engineering economics. Initiation of team design projects to be completed in NANO 120B. Prerequisites: NANO 110.

NANO 120B. NanoEngineering System Design II (4)

Principles of product quality assurance in design and production. Professional ethics. Safety and design for the environment. Culmination of team design projects initiated in NANO 120A with a working prototype designed for a real engineering application. Prerequisites: NANO 120A.

NANO 134. Polymeric Materials (4)

Foundations of polymeric materials. Topics: structure of polymers; mechanisms of polymer synthesis; characterization methods using calorimetric, mechanical, rheological, and X-ray-based techniques; and electronic, mechanical, and thermodynamic properties. Special classes of polymers: engineering plastics, semiconducting polymers,photoresists, and polymers for medicine. Cross-listed with CENG 134.Students may not receive credit for bothCENG134 andNANO134. Prerequisites:Chem 6Cand Phys2C.

NANO 141A. Engineering Mechanics I: Analysis of Equilibrium (4)

Newtons laws. Concepts of force and moment vector. Free body diagrams. Internal and external forces. Equilibrium of concurrent, coplanar, and three-dimensional system of forces. Equilibrium analysis of structural systems, including beams, trusses, and frames. Equilibrium problems with friction. Prerequisites:Math 20C and Phys 2A.

NANO 141B.Engineering Mechanics II: Analysis of Motion (4)

Newtons laws of motion. Kinematic and kinetic description of particle motion. Angular momentum. Energy and work principles. Motion of the system of interconnected particles.Mass center. Degrees of freedom. Equations of planar motion of rigid bodies. Energy methods. Lagranges equations of motion. Introduction to vibration. Free and forced vibrations of a single degree of freedom system. Undamped and damped vibrations. Application to NanoEngineering problems.Prerequisites:Math 20D and NANO 141A.

NANO 146. Nanoscale Optical Microscopy and Spectroscopy (4)

Fundamentals in optical imaging and spectroscopy at the nanometer scale. Diffraction-limited techniques, near-field methods, multi-photon imaging and spectroscopy, Raman techniques, Plasmon-enhanced methods, scan-probe techniques, novel sub-diffraction-limit imaging techniques, and energy transfer methods. Prerequisites: NANO 103 and 104.

NANO 148. Thermodynamics of Materials (4)

Fundamental laws of thermodynamics for simple substances; application to flow processes and to non-reacting mixtures; statistical thermodynamics of ideal gases and crystalline solids; chemical and materials thermodynamics; multiphase and multicomponent equilibria in reacting systems; electrochemistry. Prerequisites: upper-division standing.

NANO 150. Mechanics of Nanomaterials (4)

Introduction to mechanics of rigid and deformable bodies. Continuum and atomistic models, interatomic forces and intermolecular interactions. Nanomechanics, material defects, elasticity, plasticity, creep, and fracture. Composite materials, nanomaterials, biological materials. Prerequisites: NANO 108.

NANO 156. Nanomaterials (4)

Basic principles of synthesis techniques, processing, microstructural control, and unique physical properties of materials in nanodimensions. Nanowires, quantum dots, thin films, electrical transport, optical behavior, mechanical behavior, and technical applications of nanomaterials. Cross-listed with MAE 166. Prerequisites: upper-division standing.

NANO 158. Phase Transformations and Kinetics (4)

Materials and microstructures changes. Understanding of diffusion to enable changes in the chemical distribution and microstructure of materials, rates of diffusion. Phase transformations, effects of temperature and driving force on transformations and microstructure. Prerequisites: NANO 108 and NANO 148.

NANO 158L.Materials Processing Laboratory(4)

Metal casting processes, solidification, deformation processing, thermal processing: solutionizing, aging, and tempering, joining processes such as welding and brazing. The effect of processing route on microstructure and its effect on mechanical and physical properties will be explored.NanoEngineering majors have priority enrollment. Prerequisites:NANO 158.

NANO 161. Material Selection in Engineering (4)

Selection of materials for engineering systems, based on constitutive analyses of functional requirements and material properties. The role and implications of processing on material selection. Optimizing material selection in a quantitative methodology. NanoEngineering majors receive priority enrollment. Prerequisites: NANO 108. Department approval required. Restricted to major code NA25.

NANO 164. Advanced Micro- and Nano-materials for Energy Storage and Conversion (4)

Materials for energy storage and conversion in existing and future power systems, including fuel cells and batteries, photovoltaic cells, thermoelectric cells, and hybrids. Prerequisites: NANO 101, NANO 102, NANO 148.

NANO 168. Electrical, Dielectric, and Magnetic Properties of Engineering Materials (4)

Introduction to physical principles of electrical, dielectric, and magnetic properties. Semiconductors, control of defects, thin film, and nanocrystal growth, electronic and optoelectronic devices. Processing-microstructure-property relations of dielectric materials, including piezoelectric, pyroelectric and ferroelectric, and magnetic materials. Prerequisites: NANO 102 and NANO 104.

NANO 174. Mechanical Behavior of Materials (4)

Microscopic and macroscopic aspects of the mechanical behavior of engineering materials, with emphasis on recent development in materials characterization by mechanical methods. The fundamental aspects of plasticity in engineering materials, strengthening mechanisms, and mechanical failure modes of materials systems. Prerequisites: NANO 108.

NANO 174L. Mechanical Behavior Laboratory (4)

Experimental investigation of mechanical behavior of engineering materials. Laboratory exercises emphasize the fundamental relationship between microstructure and mechanical properties, and the evolution of the microstructure as a consequence of rate process. Prerequisites: NANO 174.

NANO 199. Independent Study for Undergraduates (4)

Independent reading or research on a problem by special arrangement with a faculty member. P/NP grades only. Prerequisites: upper division and department stamp.

NANO 200. Graduate Seminar in Chemical Engineering (1)

Each graduate student in NANO is expected to attend three seminars per quarter, of his or her choice, dealing with current topics in chemical engineering. Topics will vary. Cross-listed with CENG 205. S/U grades only. May be taken for credit four times. Prerequisites: graduate standing.

NANO 201. Introduction to NanoEngineering (4)

Understanding nanotechnology, broad implications, miniaturization: scaling laws; nanoscale physics; types and properties of nanomaterials; nanomechanical oscillators, nano(bio)electronics, nanoscale heat transfer; fluids at the nanoscale; machinery cell; applications of nanotechnology and nanobiotechnology. Students may not receive credit for both NANO 201 and CENG 211. Prerequisites: graduate standing.

NANO 202. Intermolecular and Surface Forces (4)

Development of quantitative understanding of the different intermolecular forces between atoms and molecules and how these forces give rise to interesting phenomena at the nanoscale, such as flocculation, wetting, self-assembly in biological (natural) and synthetic systems. Cross-listed with CENG 212. Students may not receive credit for both NANO 202 and CENG 212. Prerequisites: consent of instructor.

NANO 203. Nanoscale Characterization (4)

Examination of nanoscale characterization approaches including imaging, scattering, and spectroscopic techniques and their physical operating mechanisms. Microscopy (optical and electron: SEM, TEM); scattering and diffraction; spectroscopies (EDX, SIMS, mass spec, Raman, XPS, XAS); scanning probe microscopes (SPM, AFM); particle size analysis.

NANO 204. Nanoscale Physics and Modeling (4)

This course will introduce students to analytical and numerical methods such as statistical mechanisms, molecular simulations, and finite differences and finite element modeling through their application to NanoEngineering problems involving polymer and colloiod self-assembly, absorption, phase separation, and diffusion. Cross-listed with CENG 214. Students may not receive credit for both NANO 204 and CENG 214. Prerequisites: NANO 202 or consent ofinstructor.

NANO 205. Nanosystems Integration (4)

Scaling issues and hierarchical assembly of nanoscale components into higher order structures which retain desired properties at microscale and macroscale levels. Novel ways to combine top-down and bottom-up processes for integration of heterogeneous components into higher order structures. Cross-listed with CENG 215. Students may not receive credit for both NANO 205 and CENG 215. Prerequisites: consent of instructor.

NANO 208. Nanofabrication (4)

Basic engineering principles of nanofabrication. Topics include: photo-electronbeam and nanoimprint lithography, block copolymers and self-assembled monolayers, colloidal assembly, biological nanofabrication. Cross-listed with CENG 208. Students may not receive credit for both NANO 208 and CENG 208. Prerequisites: consent of instructor.

NANO 210. Molecular Modeling and Simulations of Nanoscale Systems (4)

Molecular and modeling and simulation techniques like molecular dynamics, Monte Carlo, and Brownian dynamics to model nanoscale systems and phenomena like molecular motors, self-assembly, protein-ligand binding, RNA, folding. Valuable hands-on experience with different simulators.Prerequisites: consent of instructor.

NANO 212. Computational Modeling of Nanosystems (4)

Various modeling techniques like finite elements, finite differences, and simulation techniques like molecular dynamics and Monte Carlo to model fluid flow, mechanical properties, self-assembly at the nanoscale, and protein, RNA and DNA folding.Prerequisites: consent of instructor.

NANO 227. Structure and Analysis of Solids (4)

Key concepts in the atomic structure and bonding of solids such as metals, ceramics, and semiconductors. Symmetry operations, point groups, lattice types, space groups, simple and complex inorganic compounds, structure/property comparisons, structure determination with X-ray diffraction. Ionic, covalent, metallic bonding compared with physical properties. Atomic and molecular orbitals, bands verses bonds, free electron theory. Cross-listed with MATS 227, MAE 251 and Chem 222.Prerequisites: consent of instructor.

NANO 230. Synchrotron Characterization of Nanomaterials (4)

Advanced topics in characterizing nanomaterials using synchrotron X-ray sources. Introduction to synchrotron sources, X-ray interaction with matter, spectroscopic determination of electronic properties of nanomagnetic, structural determination using scattering techniques and X-ray imaging techniques. Cross-listed with CENG 230. Students may not receive credit for both NANO 230 and CENG 230. Prerequisites: consent of instructor.

NANO 234. Advanced Nanoscale Fabrication (4)

Engineering principles of nanofabrication. Topics include: photo-, electron beam, and nanoimprint lithography, block copolymers and self-assembled monolayers, colloidal assembly, biological nanofabrication. Relevance to applications in energy, electronics, and medicine will be discussed.Prerequisites: consent of instructor.

NANO 238. Scanning Probe Microscopy (4)

Scanning electron microscopy (SEM) detectors, imaging, image interpretation, and artifacts, introduction to lenses, electron beam-specimen interactions. Operating principles and capabilities for atomic force microscopy and scanning tunneling microscopy, scanning optical microscopy and scanning transmission electron microscopy.Prerequisites: consent of instructor.

NANO 239. Nanomanufacturing (4)

Fundamental nanomanufacturing science and engineering, top-down nanomanufacturing processes, bottom-up nanomanufacturing processes, integrated top-down and bottom-up nanofabrication processes, three-dimensional nanomanufacturing, nanomanufacturing systems, nanometrology, nanomanufactured devices for medicine, life sciences, energy, and defense applications.Prerequisites: department approval required.

NANO 241. Organic Nanomaterials (4)

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NanoEngineering (NANO) Courses – University of California …

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The Nano School

Nanotechnology is often referred to as convergent technology because it utilizes knowledge from a diverse array of disciplines including biology, chemistry, physics, engineering, and technology. JSNN has six research focus areasnanobioscience, nanometrology, nanomaterials (with special emphasis on nanocomposite materials), nanobioelectronics, nanoenergy, and computational nanotechnology.

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JSNN Limited Only By Your Imagination

Electrochemical Nanoengineering Group

TheElectrochemical Nanoengineering Group is part of the Mechanical Engineering Department at the University of Hong Kong. Ourresearch focuses on the electrochemical fabrication of nanostructured materials and their applications in photo-/thermo- electrochemical energy conversion and storage. Our work is interdisciplinary and combines mechanical engineering, chemical engineering, electrical engineering, and materials science.

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Electrochemical Nanoengineering Group

Home | Nano | University of Pittsburgh

The NFCF supports the fabrication and characterization of nanoscale materials and structures, and integration of devices at all length scales. The facility houses advanced equipment with core nano-level (20 nm or below) capability for fabrication and characterization, including electron-beam lithography system, dual-beam system, plasma etching, thin film deposition, TEM, multifunctional scanning probe station, modular XRD, and more (see Facilities and Equipment).

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NanoEngineering for Medicine and Biology (NEMB)

Applications of Nanoengineering for medicine and biology are having a dramatic impact on a myriad of healthcare needs, product development and biomedical research. NEMB brings together the relevant players and key stakeholders to discuss the integration of engineering, materials science and Nanotechnology in addressing fundamental problems in biology and medicine.

Conference ChairAbraham Lee, University of California Irvine

Program ChairBumsoo Han, Purdue University

Executive CommitteeJohn Bischof, University of MinnesotaGuy Genin, Washington University St LouisGang Bao, Rice University

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NanoEngineering for Medicine and Biology (NEMB)

Electrochemical Nanoengineering Group

TheElectrochemical Nanoengineering Group is part of the Mechanical Engineering Department at the University of Hong Kong. Ourresearch focuses on the electrochemical fabrication of nanostructured materials and their applications in photo-/thermo- electrochemical energy conversion and storage. Our work is interdisciplinary and combines mechanical engineering, chemical engineering, electrical engineering, and materials science.

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Electrochemical Nanoengineering Group

Home | Nano | University of Pittsburgh

The NFCF supports the fabrication and characterization of nanoscale materials and structures, and integration of devices at all length scales. The facility houses advanced equipment with core nano-level (20 nm or below) capability for fabrication and characterization, including electron-beam lithography system, dual-beam system, plasma etching, thin film deposition, TEM, multifunctional scanning probe station, modular XRD, and more (see Facilities and Equipment).

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Home | Nano | University of Pittsburgh

NanoEngineering for Medicine and Biology (NEMB)

Applications of Nanoengineering for medicine and biology are having a dramatic impact on a myriad of healthcare needs, product development and biomedical research. NEMB brings together the relevant players and key stakeholders to discuss the integration of engineering, materials science and Nanotechnology in addressing fundamental problems in biology and medicine.

Conference ChairAbraham Lee, University of California Irvine

Program ChairBumsoo Han, Purdue University

Executive CommitteeJohn Bischof, University of MinnesotaGuy Genin, Washington University St LouisGang Bao, Rice University

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NanoEngineering for Medicine and Biology (NEMB)

JSNN Limited Only By Your Imagination

The Nano School

Nanotechnology is often referred to as convergent technology because it utilizes knowledge from a diverse array of disciplines including biology, chemistry, physics, engineering, and technology. JSNN has six research focus areasnanobioscience, nanometrology, nanomaterials (with special emphasis on nanocomposite materials), nanobioelectronics, nanoenergy, and computational nanotechnology.

See the original post here:

JSNN Limited Only By Your Imagination

Electrochemical Nanoengineering Group

TheElectrochemical Nanoengineering Group is part of the Mechanical Engineering Department at the University of Hong Kong. Ourresearch focuses on the electrochemical fabrication of nanostructured materials and their applications in photo-/thermo- electrochemical energy conversion and storage. Our work is interdisciplinary and combines mechanical engineering, chemical engineering, electrical engineering, and materials science.

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Electrochemical Nanoengineering Group

Home | Nano | University of Pittsburgh

The NFCF supports the fabrication and characterization of nanoscale materials and structures, and integration of devices at all length scales. The facility houses advanced equipment with core nano-level (20 nm or below) capability for fabrication and characterization, including electron-beam lithography system, dual-beam system, plasma etching, thin film deposition, TEM, multifunctional scanning probe station, modular XRD, and more (see Facilities and Equipment).

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Artificial intelligence – Wikipedia

Intelligence demonstrated by machines

In computer science, artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. Computer science defines AI research as the study of “intelligent agents”: any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.[1] More in detail, Kaplan and Haenlein define AI as a systems ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation.[2] Colloquially, the term “artificial intelligence” is applied when a machine mimics “cognitive” functions that humans associate with other human minds, such as “learning” and “problem solving”.

The scope of AI is disputed: as machines become increasingly capable, tasks considered as requiring “intelligence” are often removed from the definition, a phenomenon known as the AI effect, leading to the quip in Tesler’s Theorem, “AI is whatever hasn’t been done yet.”[4] For instance, optical character recognition is frequently excluded from “artificial intelligence”, having become a routine technology.[5] Modern machine capabilities generally classified as AI include successfully understanding human speech, competing at the highest level in strategic game systems (such as chess and Go),[7] autonomously operating cars, and intelligent routing in content delivery networks and military simulations.

Borrowing from the management literature, Kaplan and Haenlein classify artificial intelligence into three different types of AI systems: analytical, human-inspired, and humanized artificial intelligence.[8] Analytical AI has only characteristics consistent with cognitive intelligence generating cognitive representation of the world and using learning based on past experience to inform future decisions. Human-inspired AI has elements from cognitive as well as emotional intelligence, understanding, in addition to cognitive elements, also human emotions considering them in their decision making. Humanized AI shows characteristics of all types of competencies (i.e., cognitive, emotional, and social intelligence), able to be self-conscious and self-aware in interactions with others.

Artificial intelligence was founded as an academic discipline in 1956, and in the years since has experienced several waves of optimism,[9][10] followed by disappointment and the loss of funding (known as an “AI winter”),[11][12] followed by new approaches, success and renewed funding.[10][13] For most of its history, AI research has been divided into subfields that often fail to communicate with each other.[14] These sub-fields are based on technical considerations, such as particular goals (e.g. “robotics” or “machine learning”),[15] the use of particular tools (“logic” or artificial neural networks), or deep philosophical differences.[16][17][18] Subfields have also been based on social factors (particular institutions or the work of particular researchers).[14]

The traditional problems (or goals) of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception and the ability to move and manipulate objects.[15] General intelligence is among the field’s long-term goals.[19] Approaches include statistical methods, computational intelligence, and traditional symbolic AI. Many tools are used in AI, including versions of search and mathematical optimization, artificial neural networks, and methods based on statistics, probability and economics. The AI field draws upon computer science, information engineering, mathematics, psychology, linguistics, philosophy, and many others.

The field was founded on the claim that human intelligence “can be so precisely described that a machine can be made to simulate it”.[20] This raises philosophical arguments about the nature of the mind and the ethics of creating artificial beings endowed with human-like intelligence which are issues that have been explored by myth, fiction and philosophy since antiquity.[21] Some people also consider AI to be a danger to humanity if it progresses unabated.[22] Others believe that AI, unlike previous technological revolutions, will create a risk of mass unemployment.[23]

In the twenty-first century, AI techniques have experienced a resurgence following concurrent advances in computer power, large amounts of data, and theoretical understanding; and AI techniques have become an essential part of the technology industry, helping to solve many challenging problems in computer science, software engineering and operations research.[24][13]

Thought-capable artificial beings appeared as storytelling devices in antiquity,[25] and have been common in fiction, as in Mary Shelley’s Frankenstein or Karel apek’s R.U.R. (Rossum’s Universal Robots).[26] These characters and their fates raised many of the same issues now discussed in the ethics of artificial intelligence.[21]

The study of mechanical or “formal” reasoning began with philosophers and mathematicians in antiquity. The study of mathematical logic led directly to Alan Turing’s theory of computation, which suggested that a machine, by shuffling symbols as simple as “0” and “1”, could simulate any conceivable act of mathematical deduction. This insight, that digital computers can simulate any process of formal reasoning, is known as the ChurchTuring thesis.[27] Along with concurrent discoveries in neurobiology, information theory and cybernetics, this led researchers to consider the possibility of building an electronic brain. Turing proposed that “if a human could not distinguish between responses from a machine and a human, the machine could be considered “intelligent”.[28] The first work that is now generally recognized as AI was McCullouch and Pitts’ 1943 formal design for Turing-complete “artificial neurons”.

The field of AI research was born at a workshop at Dartmouth College in 1956.[30] Attendees Allen Newell (CMU), Herbert Simon (CMU), John McCarthy (MIT), Marvin Minsky (MIT) and Arthur Samuel (IBM) became the founders and leaders of AI research.[31] They and their students produced programs that the press described as “astonishing”: computers were learning checkers strategies (c. 1954)[33] (and by 1959 were reportedly playing better than the average human),[34] solving word problems in algebra, proving logical theorems (Logic Theorist, first run c. 1956) and speaking English.[35] By the middle of the 1960s, research in the U.S. was heavily funded by the Department of Defense[36] and laboratories had been established around the world.[37] AI’s founders were optimistic about the future: Herbert Simon predicted, “machines will be capable, within twenty years, of doing any work a man can do”. Marvin Minsky agreed, writing, “within a generation… the problem of creating ‘artificial intelligence’ will substantially be solved”.[9]

They failed to recognize the difficulty of some of the remaining tasks. Progress slowed and in 1974, in response to the criticism of Sir James Lighthill and ongoing pressure from the US Congress to fund more productive projects, both the U.S. and British governments cut off exploratory research in AI. The next few years would later be called an “AI winter”,[11] a period when obtaining funding for AI projects was difficult.

In the early 1980s, AI research was revived by the commercial success of expert systems,[39] a form of AI program that simulated the knowledge and analytical skills of human experts. By 1985, the market for AI had reached over a billion dollars. At the same time, Japan’s fifth generation computer project inspired the U.S and British governments to restore funding for academic research.[10] However, beginning with the collapse of the Lisp Machine market in 1987, AI once again fell into disrepute, and a second, longer-lasting hiatus began.[12]

In the late 1990s and early 21st century, AI began to be used for logistics, data mining, medical diagnosis and other areas.[24] The success was due to increasing computational power (see Moore’s law), greater emphasis on solving specific problems, new ties between AI and other fields (such as statistics, economics and mathematics), and a commitment by researchers to mathematical methods and scientific standards.[40] Deep Blue became the first computer chess-playing system to beat a reigning world chess champion, Garry Kasparov, on 11 May 1997.

In 2011, a Jeopardy! quiz show exhibition match, IBM’s question answering system, Watson, defeated the two greatest Jeopardy! champions, Brad Rutter and Ken Jennings, by a significant margin. Faster computers, algorithmic improvements, and access to large amounts of data enabled advances in machine learning and perception; data-hungry deep learning methods started to dominate accuracy benchmarks around 2012.[43] The Kinect, which provides a 3D bodymotion interface for the Xbox 360 and the Xbox One, uses algorithms that emerged from lengthy AI research[44] as do intelligent personal assistants in smartphones.[45] In March 2016, AlphaGo won 4 out of 5 games of Go in a match with Go champion Lee Sedol, becoming the first computer Go-playing system to beat a professional Go player without handicaps.[7][46] In the 2017 Future of Go Summit, AlphaGo won a three-game match with Ke Jie,[47] who at the time continuously held the world No. 1 ranking for two years.[48][49] This marked the completion of a significant milestone in the development of Artificial Intelligence as Go is an extremely complex game, more so than Chess.

According to Bloomberg’s Jack Clark, 2015 was a landmark year for artificial intelligence, with the number of software projects that use AI within Google increased from a “sporadic usage” in 2012 to more than 2,700 projects. Clark also presents factual data indicating that error rates in image processing tasks have fallen significantly since 2011.[50] He attributes this to an increase in affordable neural networks, due to a rise in cloud computing infrastructure and to an increase in research tools and datasets.[13] Other cited examples include Microsoft’s development of a Skype system that can automatically translate from one language to another and Facebook’s system that can describe images to blind people.[50] In a 2017 survey, one in five companies reported they had “incorporated AI in some offerings or processes”.[51][52] Around 2016, China greatly accelerated its government funding; given its large supply of data and its rapidly increasing research output, some observers believe it may be on track to becoming an “AI superpower”.[53][54]

A typical AI perceives its environment and takes actions that maximize its chance of successfully achieving its goals.[1] An AI’s intended goal function can be simple (“1 if the AI wins a game of Go, 0 otherwise”) or complex (“Do actions mathematically similar to the actions that got you rewards in the past”). Goals can be explicitly defined, or can be induced. If the AI is programmed for “reinforcement learning”, goals can be implicitly induced by rewarding some types of behavior and punishing others.[a] Alternatively, an evolutionary system can induce goals by using a “fitness function” to mutate and preferentially replicate high-scoring AI systems; this is similar to how animals evolved to innately desire certain goals such as finding food, or how dogs can be bred via artificial selection to possess desired traits. Some AI systems, such as nearest-neighbor, instead reason by analogy; these systems are not generally given goals, except to the degree that goals are somehow implicit in their training data. Such systems can still be benchmarked if the non-goal system is framed as a system whose “goal” is to successfully accomplish its narrow classification task.[57]

AI often revolves around the use of algorithms. An algorithm is a set of unambiguous instructions that a mechanical computer can execute.[b] A complex algorithm is often built on top of other, simpler, algorithms. A simple example of an algorithm is the following recipe for optimal play at tic-tac-toe:

Many AI algorithms are capable of learning from data; they can enhance themselves by learning new heuristics (strategies, or “rules of thumb”, that have worked well in the past), or can themselves write other algorithms. Some of the “learners” described below, including Bayesian networks, decision trees, and nearest-neighbor, could theoretically, if given infinite data, time, and memory, learn to approximate any function, including whatever combination of mathematical functions would best describe the entire world. These learners could therefore, in theory, derive all possible knowledge, by considering every possible hypothesis and matching it against the data. In practice, it is almost never possible to consider every possibility, because of the phenomenon of “combinatorial explosion”, where the amount of time needed to solve a problem grows exponentially. Much of AI research involves figuring out how to identify and avoid considering broad swaths of possibilities that are unlikely to be fruitful.[59] For example, when viewing a map and looking for the shortest driving route from Denver to New York in the East, one can in most cases skip looking at any path through San Francisco or other areas far to the West; thus, an AI wielding an pathfinding algorithm like A* can avoid the combinatorial explosion that would ensue if every possible route had to be ponderously considered in turn.[61]

The earliest (and easiest to understand) approach to AI was symbolism (such as formal logic): “If an otherwise healthy adult has a fever, then they may have influenza”. A second, more general, approach is Bayesian inference: “If the current patient has a fever, adjust the probability they have influenza in such-and-such way”. The third major approach, extremely popular in routine business AI applications, are analogizers such as SVM and nearest-neighbor: “After examining the records of known past patients whose temperature, symptoms, age, and other factors mostly match the current patient, X% of those patients turned out to have influenza”. A fourth approach is harder to intuitively understand, but is inspired by how the brain’s machinery works: the artificial neural network approach uses artificial “neurons” that can learn by comparing itself to the desired output and altering the strengths of the connections between its internal neurons to “reinforce” connections that seemed to be useful. These four main approaches can overlap with each other and with evolutionary systems; for example, neural nets can learn to make inferences, to generalize, and to make analogies. Some systems implicitly or explicitly use multiple of these approaches, alongside many other AI and non-AI algorithms;[62] the best approach is often different depending on the problem.[64]

Learning algorithms work on the basis that strategies, algorithms, and inferences that worked well in the past are likely to continue working well in the future. These inferences can be obvious, such as “since the sun rose every morning for the last 10,000 days, it will probably rise tomorrow morning as well”. They can be nuanced, such as “X% of families have geographically separate species with color variants, so there is an Y% chance that undiscovered black swans exist”. Learners also work on the basis of “Occam’s razor”: The simplest theory that explains the data is the likeliest. Therefore, to be successful, a learner must be designed such that it prefers simpler theories to complex theories, except in cases where the complex theory is proven substantially better. Settling on a bad, overly complex theory gerrymandered to fit all the past training data is known as overfitting. Many systems attempt to reduce overfitting by rewarding a theory in accordance with how well it fits the data, but penalizing the theory in accordance with how complex the theory is. Besides classic overfitting, learners can also disappoint by “learning the wrong lesson”. A toy example is that an image classifier trained only on pictures of brown horses and black cats might conclude that all brown patches are likely to be horses. A real-world example is that, unlike humans, current image classifiers don’t determine the spatial relationship between components of the picture; instead, they learn abstract patterns of pixels that humans are oblivious to, but that linearly correlate with images of certain types of real objects. Faintly superimposing such a pattern on a legitimate image results in an “adversarial” image that the system misclassifies.[c][67][68][69]

Compared with humans, existing AI lacks several features of human “commonsense reasoning”; most notably, humans have powerful mechanisms for reasoning about “nave physics” such as space, time, and physical interactions. This enables even young children to easily make inferences like “If I roll this pen off a table, it will fall on the floor”. Humans also have a powerful mechanism of “folk psychology” that helps them to interpret natural-language sentences such as “The city councilmen refused the demonstrators a permit because they advocated violence”. (A generic AI has difficulty inferring whether the councilmen or the demonstrators are the ones alleged to be advocating violence.)[72][73][74] This lack of “common knowledge” means that AI often makes different mistakes than humans make, in ways that can seem incomprehensible. For example, existing self-driving cars cannot reason about the location nor the intentions of pedestrians in the exact way that humans do, and instead must use non-human modes of reasoning to avoid accidents.[75][76][77]

The overall research goal of artificial intelligence is to create technology that allows computers and machines to function in an intelligent manner. The general problem of simulating (or creating) intelligence has been broken down into sub-problems. These consist of particular traits or capabilities that researchers expect an intelligent system to display. The traits described below have received the most attention.[15]

Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions.[78] By the late 1980s and 1990s, AI research had developed methods for dealing with uncertain or incomplete information, employing concepts from probability and economics.[79]

These algorithms proved to be insufficient for solving large reasoning problems, because they experienced a “combinatorial explosion”: they became exponentially slower as the problems grew larger.[59] In fact, even humans rarely use the step-by-step deduction that early AI research was able to model. They solve most of their problems using fast, intuitive judgements.[80]

Knowledge representation[81] and knowledge engineering[82] are central to classical AI research. Some “expert systems” attempt to gather together explicit knowledge possessed by experts in some narrow domain. In addition, some projects attempt to gather the “commonsense knowledge” known to the average person into a database containing extensive knowledge about the world. Among the things a comprehensive commonsense knowledge base would contain are: objects, properties, categories and relations between objects;[83] situations, events, states and time;[84] causes and effects;[85] knowledge about knowledge (what we know about what other people know);[86] and many other, less well researched domains. A representation of “what exists” is an ontology: the set of objects, relations, concepts, and properties formally described so that software agents can interpret them. The semantics of these are captured as description logic concepts, roles, and individuals, and typically implemented as classes, properties, and individuals in the Web Ontology Language.[87] The most general ontologies are called upper ontologies, which attempt to provide a foundation for all other knowledge[88] by acting as mediators between domain ontologies that cover specific knowledge about a particular knowledge domain (field of interest or area of concern). Such formal knowledge representations can be used in content-based indexing and retrieval,[89] scene interpretation,[90] clinical decision support,[91] knowledge discovery (mining “interesting” and actionable inferences from large databases),[92] and other areas.[93]

Among the most difficult problems in knowledge representation are:

Intelligent agents must be able to set goals and achieve them.[100] They need a way to visualize the futurea representation of the state of the world and be able to make predictions about how their actions will change itand be able to make choices that maximize the utility (or “value”) of available choices.[101]

In classical planning problems, the agent can assume that it is the only system acting in the world, allowing the agent to be certain of the consequences of its actions.[102] However, if the agent is not the only actor, then it requires that the agent can reason under uncertainty. This calls for an agent that can not only assess its environment and make predictions, but also evaluate its predictions and adapt based on its assessment.[103]

Multi-agent planning uses the cooperation and competition of many agents to achieve a given goal. Emergent behavior such as this is used by evolutionary algorithms and swarm intelligence.[104]

Machine learning, a fundamental concept of AI research since the field’s inception,[105] is the study of computer algorithms that improve automatically through experience.[106][107]

Unsupervised learning is the ability to find patterns in a stream of input, without requiring a human to label the inputs first.[108] Supervised learning includes both classification and numerical regression, which requires a human to label the input data first. Classification is used to determine what category something belongs in, after seeing a number of examples of things from several categories. Regression is the attempt to produce a function that describes the relationship between inputs and outputs and predicts how the outputs should change as the inputs change.[107] Both classifiers and regression learners can be viewed as “function approximators” trying to learn an unknown (possibly implicit) function; for example, a spam classifier can be viewed as learning a function that maps from the text of an email to one of two categories, “spam” or “not spam”. Computational learning theory can assess learners by computational complexity, by sample complexity (how much data is required), or by other notions of optimization.[109] In reinforcement learning[110] the agent is rewarded for good responses and punished for bad ones. The agent uses this sequence of rewards and punishments to form a strategy for operating in its problem space.

Natural language processing[111] (NLP) gives machines the ability to read and understand human language. A sufficiently powerful natural language processing system would enable natural-language user interfaces and the acquisition of knowledge directly from human-written sources, such as newswire texts. Some straightforward applications of natural language processing include information retrieval, text mining, question answering[112] and machine translation.[113] Many current approaches use word co-occurrence frequencies to construct syntactic representations of text. “Keyword spotting” strategies for search are popular and scalable but dumb; a search query for “dog” might only match documents with the literal word “dog” and miss a document with the word “poodle”. “Lexical affinity” strategies use the occurrence of words such as “accident” to assess the sentiment of a document. Modern statistical NLP approaches can combine all these strategies as well as others, and often achieve acceptable accuracy at the page or paragraph level, but continue to lack the semantic understanding required to classify isolated sentences well. Besides the usual difficulties with encoding semantic commonsense knowledge, existing semantic NLP sometimes scales too poorly to be viable in business applications. Beyond semantic NLP, the ultimate goal of “narrative” NLP is to embody a full understanding of commonsense reasoning.[114]

Machine perception[115] is the ability to use input from sensors (such as cameras (visible spectrum or infrared), microphones, wireless signals, and active lidar, sonar, radar, and tactile sensors) to deduce aspects of the world. Applications include speech recognition,[116] facial recognition, and object recognition.[117] Computer vision is the ability to analyze visual input. Such input is usually ambiguous; a giant, fifty-meter-tall pedestrian far away may produce exactly the same pixels as a nearby normal-sized pedestrian, requiring the AI to judge the relative likelihood and reasonableness of different interpretations, for example by using its “object model” to assess that fifty-meter pedestrians do not exist.[118]

AI is heavily used in robotics.[119] Advanced robotic arms and other industrial robots, widely used in modern factories, can learn from experience how to move efficiently despite the presence of friction and gear slippage.[120] A modern mobile robot, when given a small, static, and visible environment, can easily determine its location and map its environment; however, dynamic environments, such as (in endoscopy) the interior of a patient’s breathing body, pose a greater challenge. Motion planning is the process of breaking down a movement task into “primitives” such as individual joint movements. Such movement often involves compliant motion, a process where movement requires maintaining physical contact with an object.[122][123] Moravec’s paradox generalizes that low-level sensorimotor skills that humans take for granted are, counterintuitively, difficult to program into a robot; the paradox is named after Hans Moravec, who stated in 1988 that “it is comparatively easy to make computers exhibit adult level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility”.[124][125] This is attributed to the fact that, unlike checkers, physical dexterity has been a direct target of natural selection for millions of years.[126]

Moravec’s paradox can be extended to many forms of social intelligence.[128][129] Distributed multi-agent coordination of autonomous vehicles remains a difficult problem.[130] Affective computing is an interdisciplinary umbrella that comprises systems which recognize, interpret, process, or simulate human affects. Moderate successes related to affective computing include textual sentiment analysis and, more recently, multimodal affect analysis (see multimodal sentiment analysis), wherein AI classifies the affects displayed by a videotaped subject.[134]

In the long run, social skills and an understanding of human emotion and game theory would be valuable to a social agent. Being able to predict the actions of others by understanding their motives and emotional states would allow an agent to make better decisions. Some computer systems mimic human emotion and expressions to appear more sensitive to the emotional dynamics of human interaction, or to otherwise facilitate humancomputer interaction.[135] Similarly, some virtual assistants are programmed to speak conversationally or even to banter humorously; this tends to give nave users an unrealistic conception of how intelligent existing computer agents actually are.[136]

Historically, projects such as the Cyc knowledge base (1984) and the massive Japanese Fifth Generation Computer Systems initiative (19821992) attempted to cover the breadth of human cognition. These early projects failed to escape the limitations of non-quantitative symbolic logic models and, in retrospect, greatly underestimated the difficulty of cross-domain AI. Nowadays, the vast majority of current AI researchers work instead on tractable “narrow AI” applications (such as medical diagnosis or automobile navigation).[137] Many researchers predict that such “narrow AI” work in different individual domains will eventually be incorporated into a machine with artificial general intelligence (AGI), combining most of the narrow skills mentioned in this article and at some point even exceeding human ability in most or all these areas.[19][138] Many advances have general, cross-domain significance. One high-profile example is that DeepMind in the 2010s developed a “generalized artificial intelligence” that could learn many diverse Atari games on its own, and later developed a variant of the system which succeeds at sequential learning.[139][140][141] Besides transfer learning,[142] hypothetical AGI breakthroughs could include the development of reflective architectures that can engage in decision-theoretic metareasoning, and figuring out how to “slurp up” a comprehensive knowledge base from the entire unstructured Web. Some argue that some kind of (currently-undiscovered) conceptually straightforward, but mathematically difficult, “Master Algorithm” could lead to AGI. Finally, a few “emergent” approaches look to simulating human intelligence extremely closely, and believe that anthropomorphic features like an artificial brain or simulated child development may someday reach a critical point where general intelligence emerges.[144][145]

Many of the problems in this article may also require general intelligence, if machines are to solve the problems as well as people do. For example, even specific straightforward tasks, like machine translation, require that a machine read and write in both languages (NLP), follow the author’s argument (reason), know what is being talked about (knowledge), and faithfully reproduce the author’s original intent (social intelligence). A problem like machine translation is considered “AI-complete”, because all of these problems need to be solved simultaneously in order to reach human-level machine performance.

There is no established unifying theory or paradigm that guides AI research. Researchers disagree about many issues.[146] A few of the most long standing questions that have remained unanswered are these: should artificial intelligence simulate natural intelligence by studying psychology or neurobiology? Or is human biology as irrelevant to AI research as bird biology is to aeronautical engineering?[16]Can intelligent behavior be described using simple, elegant principles (such as logic or optimization)? Or does it necessarily require solving a large number of completely unrelated problems?[17]

In the 1940s and 1950s, a number of researchers explored the connection between neurobiology, information theory, and cybernetics. Some of them built machines that used electronic networks to exhibit rudimentary intelligence, such as W. Grey Walter’s turtles and the Johns Hopkins Beast. Many of these researchers gathered for meetings of the Teleological Society at Princeton University and the Ratio Club in England.[147] By 1960, this approach was largely abandoned, although elements of it would be revived in the 1980s.

When access to digital computers became possible in the middle 1950s, AI research began to explore the possibility that human intelligence could be reduced to symbol manipulation. The research was centered in three institutions: Carnegie Mellon University, Stanford and MIT, and as described below, each one developed its own style of research. John Haugeland named these symbolic approaches to AI “good old fashioned AI” or “GOFAI”.[148] During the 1960s, symbolic approaches had achieved great success at simulating high-level thinking in small demonstration programs. Approaches based on cybernetics or artificial neural networks were abandoned or pushed into the background.[149]Researchers in the 1960s and the 1970s were convinced that symbolic approaches would eventually succeed in creating a machine with artificial general intelligence and considered this the goal of their field.

Economist Herbert Simon and Allen Newell studied human problem-solving skills and attempted to formalize them, and their work laid the foundations of the field of artificial intelligence, as well as cognitive science, operations research and management science. Their research team used the results of psychological experiments to develop programs that simulated the techniques that people used to solve problems. This tradition, centered at Carnegie Mellon University would eventually culminate in the development of the Soar architecture in the middle 1980s.[150][151]

Unlike Simon and Newell, John McCarthy felt that machines did not need to simulate human thought, but should instead try to find the essence of abstract reasoning and problem-solving, regardless of whether people used the same algorithms.[16] His laboratory at Stanford (SAIL) focused on using formal logic to solve a wide variety of problems, including knowledge representation, planning and learning.[152] Logic was also the focus of the work at the University of Edinburgh and elsewhere in Europe which led to the development of the programming language Prolog and the science of logic programming.[153]

Researchers at MIT (such as Marvin Minsky and Seymour Papert)[154] found that solving difficult problems in vision and natural language processing required ad-hoc solutionsthey argued that there was no simple and general principle (like logic) that would capture all the aspects of intelligent behavior. Roger Schank described their “anti-logic” approaches as “scruffy” (as opposed to the “neat” paradigms at CMU and Stanford).[17] Commonsense knowledge bases (such as Doug Lenat’s Cyc) are an example of “scruffy” AI, since they must be built by hand, one complicated concept at a time.[155]

When computers with large memories became available around 1970, researchers from all three traditions began to build knowledge into AI applications.[156] This “knowledge revolution” led to the development and deployment of expert systems (introduced by Edward Feigenbaum), the first truly successful form of AI software.[39] A key component of the system architecture for all expert systems is the knowledge base, which stores facts and rules that illustrate AI.[157] The knowledge revolution was also driven by the realization that enormous amounts of knowledge would be required by many simple AI applications.

By the 1980s, progress in symbolic AI seemed to stall and many believed that symbolic systems would never be able to imitate all the processes of human cognition, especially perception, robotics, learning and pattern recognition. A number of researchers began to look into “sub-symbolic” approaches to specific AI problems.[18] Sub-symbolic methods manage to approach intelligence without specific representations of knowledge.

This includes embodied, situated, behavior-based, and nouvelle AI. Researchers from the related field of robotics, such as Rodney Brooks, rejected symbolic AI and focused on the basic engineering problems that would allow robots to move and survive.[158] Their work revived the non-symbolic viewpoint of the early cybernetics researchers of the 1950s and reintroduced the use of control theory in AI. This coincided with the development of the embodied mind thesis in the related field of cognitive science: the idea that aspects of the body (such as movement, perception and visualization) are required for higher intelligence.

Within developmental robotics, developmental learning approaches are elaborated upon to allow robots to accumulate repertoires of novel skills through autonomous self-exploration, social interaction with human teachers, and the use of guidance mechanisms (active learning, maturation, motor synergies, etc.).[159][160]

Interest in neural networks and “connectionism” was revived by David Rumelhart and others in the middle of the 1980s.[163] Artificial neural networks are an example of soft computingthey are solutions to problems which cannot be solved with complete logical certainty, and where an approximate solution is often sufficient. Other soft computing approaches to AI include fuzzy systems, evolutionary computation and many statistical tools. The application of soft computing to AI is studied collectively by the emerging discipline of computational intelligence.[164]

Much of traditional GOFAI got bogged down on ad hoc patches to symbolic computation that worked on their own toy models but failed to generalize to real-world results. However, around the 1990s, AI researchers adopted sophisticated mathematical tools, such as hidden Markov models (HMM), information theory, and normative Bayesian decision theory to compare or to unify competing architectures. The shared mathematical language permitted a high level of collaboration with more established fields (like mathematics, economics or operations research).[d] Compared with GOFAI, new “statistical learning” techniques such as HMM and neural networks were gaining higher levels of accuracy in many practical domains such as data mining, without necessarily acquiring semantic understanding of the datasets. The increased successes with real-world data led to increasing emphasis on comparing different approaches against shared test data to see which approach performed best in a broader context than that provided by idiosyncratic toy models; AI research was becoming more scientific. Nowadays results of experiments are often rigorously measurable, and are sometimes (with difficulty) reproducible.[40][165] Different statistical learning techniques have different limitations; for example, basic HMM cannot model the infinite possible combinations of natural language. Critics note that the shift from GOFAI to statistical learning is often also a shift away from Explainable AI. In AGI research, some scholars caution against over-reliance on statistical learning, and argue that continuing research into GOFAI will still be necessary to attain general intelligence.

AI has developed a large number of tools to solve the most difficult problems in computer science. A few of the most general of these methods are discussed below.

Many problems in AI can be solved in theory by intelligently searching through many possible solutions:[174] Reasoning can be reduced to performing a search. For example, logical proof can be viewed as searching for a path that leads from premises to conclusions, where each step is the application of an inference rule.[175] Planning algorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process called means-ends analysis.[176] Robotics algorithms for moving limbs and grasping objects use local searches in configuration space.[120] Many learning algorithms use search algorithms based on optimization.

Simple exhaustive searches[177] are rarely sufficient for most real-world problems: the search space (the number of places to search) quickly grows to astronomical numbers. The result is a search that is too slow or never completes. The solution, for many problems, is to use “heuristics” or “rules of thumb” that prioritize choices in favor of those that are more likely to reach a goal and to do so in a shorter number of steps. In some search methodologies heuristics can also serve to entirely eliminate some choices that are unlikely to lead to a goal (called “pruning the search tree”). Heuristics supply the program with a “best guess” for the path on which the solution lies.[178] Heuristics limit the search for solutions into a smaller sample size.

A very different kind of search came to prominence in the 1990s, based on the mathematical theory of optimization. For many problems, it is possible to begin the search with some form of a guess and then refine the guess incrementally until no more refinements can be made. These algorithms can be visualized as blind hill climbing: we begin the search at a random point on the landscape, and then, by jumps or steps, we keep moving our guess uphill, until we reach the top. Other optimization algorithms are simulated annealing, beam search and random optimization.[179]

Evolutionary computation uses a form of optimization search. For example, they may begin with a population of organisms (the guesses) and then allow them to mutate and recombine, selecting only the fittest to survive each generation (refining the guesses). Classic evolutionary algorithms include genetic algorithms, gene expression programming, and genetic programming.[180] Alternatively, distributed search processes can coordinate via swarm intelligence algorithms. Two popular swarm algorithms used in search are particle swarm optimization (inspired by bird flocking) and ant colony optimization (inspired by ant trails).[181][182]

Logic[183] is used for knowledge representation and problem solving, but it can be applied to other problems as well. For example, the satplan algorithm uses logic for planning[184] and inductive logic programming is a method for learning.[185]

Several different forms of logic are used in AI research. Propositional logic[186] involves truth functions such as “or” and “not”. First-order logic[187] adds quantifiers and predicates, and can express facts about objects, their properties, and their relations with each other. Fuzzy set theory assigns a “degree of truth” (between 0 and 1) to vague statements such as “Alice is old” (or rich, or tall, or hungry) that are too linguistically imprecise to be completely true or false. Fuzzy logic is successfully used in control systems to allow experts to contribute vague rules such as “if you are close to the destination station and moving fast, increase the train’s brake pressure”; these vague rules can then be numerically refined within the system. Fuzzy logic fails to scale well in knowledge bases; many AI researchers question the validity of chaining fuzzy-logic inferences.[e][189][190]

Default logics, non-monotonic logics and circumscription[95] are forms of logic designed to help with default reasoning and the qualification problem. Several extensions of logic have been designed to handle specific domains of knowledge, such as: description logics;[83] situation calculus, event calculus and fluent calculus (for representing events and time);[84] causal calculus;[85] belief calculus;[191] and modal logics.[86]

Overall, qualitiative symbolic logic is brittle and scales poorly in the presence of noise or other uncertainty. Exceptions to rules are numerous, and it is difficult for logical systems to function in the presence of contradictory rules.[193]

Many problems in AI (in reasoning, planning, learning, perception, and robotics) require the agent to operate with incomplete or uncertain information. AI researchers have devised a number of powerful tools to solve these problems using methods from probability theory and economics.[194]

Bayesian networks[195] are a very general tool that can be used for a large number of problems: reasoning (using the Bayesian inference algorithm),[196] learning (using the expectation-maximization algorithm),[f][198] planning (using decision networks)[199] and perception (using dynamic Bayesian networks).[200] Probabilistic algorithms can also be used for filtering, prediction, smoothing and finding explanations for streams of data, helping perception systems to analyze processes that occur over time (e.g., hidden Markov models or Kalman filters).[200] Compared with symbolic logic, formal Bayesian inference is computationally expensive. For inference to be tractable, most observations must be conditionally independent of one another. Complicated graphs with diamonds or other “loops” (undirected cycles) can require a sophisticated method such as Markov Chain Monte Carlo, which spreads an ensemble of random walkers throughout the Bayesian network and attempts to converge to an assessment of the conditional probabilities. Bayesian networks are used on Xbox Live to rate and match players; wins and losses are “evidence” of how good a player is. AdSense uses a Bayesian network with over 300 million edges to learn which ads to serve.

A key concept from the science of economics is “utility”: a measure of how valuable something is to an intelligent agent. Precise mathematical tools have been developed that analyze how an agent can make choices and plan, using decision theory, decision analysis,[201] and information value theory.[101] These tools include models such as Markov decision processes,[202] dynamic decision networks,[200] game theory and mechanism design.[203]

The simplest AI applications can be divided into two types: classifiers (“if shiny then diamond”) and controllers (“if shiny then pick up”). Controllers do, however, also classify conditions before inferring actions, and therefore classification forms a central part of many AI systems. Classifiers are functions that use pattern matching to determine a closest match. They can be tuned according to examples, making them very attractive for use in AI. These examples are known as observations or patterns. In supervised learning, each pattern belongs to a certain predefined class. A class can be seen as a decision that has to be made. All the observations combined with their class labels are known as a data set. When a new observation is received, that observation is classified based on previous experience.[204]

A classifier can be trained in various ways; there are many statistical and machine learning approaches. The decision tree[205] is perhaps the most widely used machine learning algorithm. Other widely used classifiers are the neural network,[207]k-nearest neighbor algorithm,[g][209]kernel methods such as the support vector machine (SVM),[h][211]Gaussian mixture model,[212] and the extremely popular naive Bayes classifier.[i][214] Classifier performance depends greatly on the characteristics of the data to be classified, such as the dataset size, distribution of samples across classes, the dimensionality, and the level of noise. Model-based classifiers perform well if the assumed model is an extremely good fit for the actual data. Otherwise, if no matching model is available, and if accuracy (rather than speed or scalability) is the sole concern, conventional wisdom is that discriminative classifiers (especially SVM) tend to be more accurate than model-based classifiers such as “naive Bayes” on most practical data sets.[215]

Neural networks, or neural nets, were inspired by the architecture of neurons in the human brain. A simple “neuron” N accepts input from multiple other neurons, each of which, when activated (or “fired”), cast a weighted “vote” for or against whether neuron N should itself activate. Learning requires an algorithm to adjust these weights based on the training data; one simple algorithm (dubbed “fire together, wire together”) is to increase the weight between two connected neurons when the activation of one triggers the successful activation of another. The net forms “concepts” that are distributed among a subnetwork of shared[j] neurons that tend to fire together; a concept meaning “leg” might be coupled with a subnetwork meaning “foot” that includes the sound for “foot”. Neurons have a continuous spectrum of activation; in addition, neurons can process inputs in a nonlinear way rather than weighing straightforward votes. Modern neural nets can learn both continuous functions and, surprisingly, digital logical operations. Neural networks’ early successes included predicting the stock market and (in 1995) a mostly self-driving car.[k] In the 2010s, advances in neural networks using deep learning thrust AI into widespread public consciousness and contributed to an enormous upshift in corporate AI spending; for example, AI-related M&A in 2017 was over 25 times as large as in 2015.[218][219]

The study of non-learning artificial neural networks[207] began in the decade before the field of AI research was founded, in the work of Walter Pitts and Warren McCullouch. Frank Rosenblatt invented the perceptron, a learning network with a single layer, similar to the old concept of linear regression. Early pioneers also include Alexey Grigorevich Ivakhnenko, Teuvo Kohonen, Stephen Grossberg, Kunihiko Fukushima, Christoph von der Malsburg, David Willshaw, Shun-Ichi Amari, Bernard Widrow, John Hopfield, Eduardo R. Caianiello, and others.

The main categories of networks are acyclic or feedforward neural networks (where the signal passes in only one direction) and recurrent neural networks (which allow feedback and short-term memories of previous input events). Among the most popular feedforward networks are perceptrons, multi-layer perceptrons and radial basis networks.[220] Neural networks can be applied to the problem of intelligent control (for robotics) or learning, using such techniques as Hebbian learning (“fire together, wire together”), GMDH or competitive learning.[221]

Today, neural networks are often trained by the backpropagation algorithm, which had been around since 1970 as the reverse mode of automatic differentiation published by Seppo Linnainmaa,[222][223] and was introduced to neural networks by Paul Werbos.[224][225][226]

Hierarchical temporal memory is an approach that models some of the structural and algorithmic properties of the neocortex.[227]

To summarize, most neural networks use some form of gradient descent on a hand-created neural topology. However, some research groups, such as Uber, argue that simple neuroevolution to mutate new neural network topologies and weights may be competitive with sophisticated gradient descent approaches. One advantage of neuroevolution is that it may be less prone to get caught in “dead ends”.[228]

Deep learning is any artificial neural network that can learn a long chain of causal links. For example, a feedforward network with six hidden layers can learn a seven-link causal chain (six hidden layers + output layer) and has a “credit assignment path” (CAP) depth of seven. Many deep learning systems need to be able to learn chains ten or more causal links in length.[229] Deep learning has transformed many important subfields of artificial intelligence, including computer vision, speech recognition, natural language processing and others.[230][231][229]

According to one overview,[232] the expression “Deep Learning” was introduced to the Machine Learning community by Rina Dechter in 1986[233] and gained traction afterIgor Aizenberg and colleagues introduced it to Artificial Neural Networks in 2000.[234] The first functional Deep Learning networks were published by Alexey Grigorevich Ivakhnenko and V. G. Lapa in 1965.[235][pageneeded] These networks are trained one layer at a time. Ivakhnenko’s 1971 paper[236] describes the learning of a deep feedforward multilayer perceptron with eight layers, already much deeper than many later networks. In 2006, a publication by Geoffrey Hinton and Ruslan Salakhutdinov introduced another way of pre-training many-layered feedforward neural networks (FNNs) one layer at a time, treating each layer in turn as an unsupervised restricted Boltzmann machine, then using supervised backpropagation for fine-tuning. Similar to shallow artificial neural networks, deep neural networks can model complex non-linear relationships. Over the last few years, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks that contain many layers of non-linear hidden units and a very large output layer.[238]

Deep learning often uses convolutional neural networks (CNNs), whose origins can be traced back to the Neocognitron introduced by Kunihiko Fukushima in 1980.[239] In 1989, Yann LeCun and colleagues applied backpropagation to such an architecture. In the early 2000s, in an industrial application CNNs already processed an estimated 10% to 20% of all the checks written in the US.[240]Since 2011, fast implementations of CNNs on GPUs havewon many visual pattern recognition competitions.[229]

CNNs with 12 convolutional layers were used in conjunction with reinforcement learning by Deepmind’s “AlphaGo Lee”, the program that beat a top Go champion in 2016.[241]

Early on, deep learning was also applied to sequence learning with recurrent neural networks (RNNs)[242] which are in theory Turing complete[243] and can run arbitrary programs to process arbitrary sequences of inputs. The depth of an RNN is unlimited and depends on the length of its input sequence; thus, an RNN is an example of deep learning.[229] RNNs can be trained by gradient descent[244][245][246] but suffer from the vanishing gradient problem.[230][247] In 1992, it was shown that unsupervised pre-training of a stack of recurrent neural networks can speed up subsequent supervised learning of deep sequential problems.[248]

Numerous researchers now use variants of a deep learning recurrent NN called the long short-term memory (LSTM) network published by Hochreiter & Schmidhuber in 1997.[249] LSTM is often trained by Connectionist Temporal Classification (CTC).[250] At Google, Microsoft and Baidu this approach has revolutionised speech recognition.[251][252][253] For example, in 2015, Google’s speech recognition experienced a dramatic performance jump of 49% through CTC-trained LSTM, which is now available through Google Voice to billions of smartphone users.[254] Google also used LSTM to improve machine translation,[255] Language Modeling[256] and Multilingual Language Processing.[257] LSTM combined with CNNs also improved automatic image captioning[258] and a plethora of other applications.

AI, like electricity or the steam engine, is a general purpose technology. There is no consensus on how to characterize which tasks AI tends to excel at.[259] While projects such as AlphaZero have succeeded in generating their own knowledge from scratch, many other machine learning projects require large training datasets.[260][261] Researcher Andrew Ng has suggested, as a “highly imperfect rule of thumb”, that “almost anything a typical human can do with less than one second of mental thought, we can probably now or in the near future automate using AI.”[262] Moravec’s paradox suggests that AI lags humans at many tasks that the human brain has specifically evolved to perform well.[126]

Games provide a well-publicized benchmark for assessing rates of progress. AlphaGo around 2016 brought the era of classical board-game benchmarks to a close. Games of imperfect knowledge provide new challenges to AI in the area of game theory.[263][264] E-sports such as StarCraft continue to provide additional public benchmarks.[265][266] There are many competitions and prizes, such as the Imagenet Challenge, to promote research in artificial intelligence. The most common areas of competition include general machine intelligence, conversational behavior, data-mining, robotic cars, and robot soccer as well as conventional games.[267]

The “imitation game” (an interpretation of the 1950 Turing test that assesses whether a computer can imitate a human) is nowadays considered too exploitable to be a meaningful benchmark.[268] A derivative of the Turing test is the Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA). As the name implies, this helps to determine that a user is an actual person and not a computer posing as a human. In contrast to the standard Turing test, CAPTCHA is administered by a machine and targeted to a human as opposed to being administered by a human and targeted to a machine. A computer asks a user to complete a simple test then generates a grade for that test. Computers are unable to solve the problem, so correct solutions are deemed to be the result of a person taking the test. A common type of CAPTCHA is the test that requires the typing of distorted letters, numbers or symbols that appear in an image undecipherable by a computer.

Proposed “universal intelligence” tests aim to compare how well machines, humans, and even non-human animals perform on problem sets that are generic as possible. At an extreme, the test suite can contain every possible problem, weighted by Kolmogorov complexity; unfortunately, these problem sets tend to be dominated by impoverished pattern-matching exercises where a tuned AI can easily exceed human performance levels.[270][271]

AI is relevant to any intellectual task. Modern artificial intelligence techniques are pervasive and are too numerous to list here. Frequently, when a technique reaches mainstream use, it is no longer considered artificial intelligence; this phenomenon is described as the AI effect.

High-profile examples of AI include autonomous vehicles (such as drones and self-driving cars), medical diagnosis, creating art (such as poetry), proving mathematical theorems, playing games (such as Chess or Go), search engines (such as Google search), online assistants (such as Siri), image recognition in photographs, spam filtering, predicting flight delays,[274] prediction of judicial decisions[275] and targeting online advertisements.[276][277]

With social media sites overtaking TV as a source for news for young people and news organisations increasingly reliant on social media platforms for generating distribution,[278] major publishers now use artificial intelligence (AI) technology to post stories more effectively and generate higher volumes of traffic.[279]

AI is being applied to the high cost problem of dosage issueswhere findings suggested that AI could save $16 billion. In 2016, a ground breaking study in California found that a mathematical formula developed with the help of AI correctly determined the accurate dose of immunosuppressant drugs to give to organ patients.[280]

Artificial intelligence is breaking into the healthcare industry by assisting doctors. According to Bloomberg Technology, Microsoft has developed AI to help doctors find the right treatments for cancer.[281] There is a great amount of research and drugs developed relating to cancer. In detail, there are more than 800 medicines and vaccines to treat cancer. This negatively affects the doctors, because there are too many options to choose from, making it more difficult to choose the right drugs for the patients. Microsoft is working on a project to develop a machine called “Hanover”. Its goal is to memorize all the papers necessary to cancer and help predict which combinations of drugs will be most effective for each patient. One project that is being worked on at the moment is fighting myeloid leukemia, a fatal cancer where the treatment has not improved in decades. Another study was reported to have found that artificial intelligence was as good as trained doctors in identifying skin cancers.[282] Another study is using artificial intelligence to try and monitor multiple high-risk patients, and this is done by asking each patient numerous questions based on data acquired from live doctor to patient interactions.[283] One study was done with transfer learning, the machine performed a diagnosis similarly to a well-trained ophthalmologist, and could generate a decision within 30 seconds on whether or not the patient should be referred for treatment, with more than 95% percent accuracy.[284]

According to CNN, a recent study by surgeons at the Children’s National Medical Center in Washington successfully demonstrated surgery with an autonomous robot. The team supervised the robot while it performed soft-tissue surgery, stitching together a pig’s bowel during open surgery, and doing so better than a human surgeon, the team claimed.[285] IBM has created its own artificial intelligence computer, the IBM Watson, which has beaten human intelligence (at some levels). Watson not only won at the game show Jeopardy! against former champions,[286] but was declared a hero after successfully diagnosing a woman who was suffering from leukemia.[287]

Advancements in AI have contributed to the growth of the automotive industry through the creation and evolution of self-driving vehicles. As of 2016[update], there are over 30 companies utilizing AI into the creation of driverless cars. A few companies involved with AI include Tesla, Google, and Apple.[288]

Many components contribute to the functioning of self-driving cars. These vehicles incorporate systems such as braking, lane changing, collision prevention, navigation and mapping. Together, these systems, as well as high performance computers, are integrated into one complex vehicle.[289]

Recent developments in autonomous automobiles have made the innovation of self-driving trucks possible, though they are still in the testing phase. The UK government has passed legislation to begin testing of self-driving truck platoons in 2018.[290] Self-driving truck platoons are a fleet of self-driving trucks following the lead of one non-self-driving truck, so the truck platoons aren’t entirely autonomous yet. Meanwhile, the Daimler, a German automobile corporation, is testing the Freightliner Inspiration which is a semi-autonomous truck that will only be used on the highway.[291]

One main factor that influences the ability for a driver-less automobile to function is mapping. In general, the vehicle would be pre-programmed with a map of the area being driven. This map would include data on the approximations of street light and curb heights in order for the vehicle to be aware of its surroundings. However, Google has been working on an algorithm with the purpose of eliminating the need for pre-programmed maps and instead, creating a device that would be able to adjust to a variety of new surroundings.[292] Some self-driving cars are not equipped with steering wheels or brake pedals, so there has also been research focused on creating an algorithm that is capable of maintaining a safe environment for the passengers in the vehicle through awareness of speed and driving conditions.[293]

Another factor that is influencing the ability for a driver-less automobile is the safety of the passenger. To make a driver-less automobile, engineers must program it to handle high-risk situations. These situations could include a head-on collision with pedestrians. The car’s main goal should be to make a decision that would avoid hitting the pedestrians and saving the passengers in the car. But there is a possibility the car would need to make a decision that would put someone in danger. In other words, the car would need to decide to save the pedestrians or the passengers.[294] The programming of the car in these situations is crucial to a successful driver-less automobile.

Financial institutions have long used artificial neural network systems to detect charges or claims outside of the norm, flagging these for human investigation. The use of AI in banking can be traced back to 1987 when Security Pacific National Bank in US set-up a Fraud Prevention Task force to counter the unauthorised use of debit cards. Programs like Kasisto and Moneystream are using AI in financial services.

Banks use artificial intelligence systems today to organize operations, maintain book-keeping, invest in stocks, and manage properties. AI can react to changes overnight or when business is not taking place.[295] In August 2001, robots beat humans in a simulated financial trading competition.[296] AI has also reduced fraud and financial crimes by monitoring behavioral patterns of users for any abnormal changes or anomalies.[297]

The use of AI machines in the market in applications such as online trading and decision making has changed major economic theories.[298] For example, AI based buying and selling platforms have changed the law of supply and demand in that it is now possible to easily estimate individualized demand and supply curves and thus individualized pricing. Furthermore, AI machines reduce information asymmetry in the market and thus making markets more efficient while reducing the volume of trades. Furthermore, AI in the markets limits the consequences of behavior in the markets again making markets more efficient. Other theories where AI has had impact include in rational choice, rational expectations, game theory, Lewis turning point, portfolio optimization and counterfactual thinking.

In video games, artificial intelligence is routinely used to generate dynamic purposeful behavior in non-player characters (NPCs). In addition, well-understood AI techniques are routinely used for pathfinding. Some researchers consider NPC AI in games to be a “solved problem” for most production tasks. Games with more atypical AI include the AI director of Left 4 Dead (2008) and the neuroevolutionary training of platoons in Supreme Commander 2 (2010).[299][300]

Worldwide annual military spending on robotics rose from US$5.1 billion in 2010 to US$7.5 billion in 2015.[301][302] Military drones capable of autonomous action are widely considered a useful asset. In 2017, Vladimir Putin stated that “Whoever becomes the leader in (artificial intelligence) will become the ruler of the world”.[303][304] Many artificial intelligence researchers seek to distance themselves from military applications of AI.[305]

For financial statements audit, AI makes continuous audit possible. AI tools could analyze many sets of different information immediately. The potential benefit would be the overall audit risk will be reduced, the level of assurance will be increased and the time duration of audit will be reduced.[306]

It is possible to use AI to predict or generalize the behavior of customers from their digital footprints in order to target them with personalized promotions or build customer personas automatically.[307] A documented case reports that online gambling companies were using AI to improve customer targeting.[308]

Moreover, the application of Personality computing AI models can help reducing the cost of advertising campaigns by adding psychological targeting to more traditional sociodemographic or behavioral targeting.[309]

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Artificial intelligence – Wikipedia

Artificial Intelligence – Journal – Elsevier

This journal has partnered with Heliyon, an open access journal from Elsevier publishing quality peer reviewed research across all disciplines. Heliyons team of experts provides editorial excellence, fast publication, and high visibility for your paper. Authors can quickly and easily transfer their research from a Partner Journal to Heliyon without the need to edit, reformat or resubmit.>Learn more at Heliyon.com

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Artificial Intelligence – Journal – Elsevier

Benefits & Risks of Artificial Intelligence – Future of Life …

Many AI researchers roll their eyes when seeing this headline:Stephen Hawking warns that rise of robots may be disastrous for mankind. And as many havelost count of how many similar articles theyveseen.Typically, these articles are accompanied by an evil-looking robot carrying a weapon, and they suggest we should worry about robots rising up and killing us because theyve become conscious and/or evil.On a lighter note, such articles are actually rather impressive, because they succinctly summarize the scenario that AI researchers dontworry about. That scenario combines as many as three separate misconceptions: concern about consciousness, evil, androbots.

If you drive down the road, you have a subjective experience of colors, sounds, etc. But does a self-driving car have a subjective experience? Does it feel like anything at all to be a self-driving car?Although this mystery of consciousness is interesting in its own right, its irrelevant to AI risk. If you get struck by a driverless car, it makes no difference to you whether it subjectively feels conscious. In the same way, what will affect us humans is what superintelligent AIdoes, not how it subjectively feels.

The fear of machines turning evil is another red herring. The real worry isnt malevolence, but competence. A superintelligent AI is by definition very good at attaining its goals, whatever they may be, so we need to ensure that its goals are aligned with ours. Humans dont generally hate ants, but were more intelligent than they are so if we want to build a hydroelectric dam and theres an anthill there, too bad for the ants. The beneficial-AI movement wants to avoid placing humanity in the position of those ants.

The consciousness misconception is related to the myth that machines cant have goals.Machines can obviously have goals in the narrow sense of exhibiting goal-oriented behavior: the behavior of a heat-seeking missile is most economically explained as a goal to hit a target.If you feel threatened by a machine whose goals are misaligned with yours, then it is precisely its goals in this narrow sense that troubles you, not whether the machine is conscious and experiences a sense of purpose.If that heat-seeking missile were chasing you, you probably wouldnt exclaim: Im not worried, because machines cant have goals!

I sympathize with Rodney Brooks and other robotics pioneers who feel unfairly demonized by scaremongering tabloids,because some journalists seem obsessively fixated on robots and adorn many of their articles with evil-looking metal monsters with red shiny eyes. In fact, the main concern of the beneficial-AI movement isnt with robots but with intelligence itself: specifically, intelligence whose goals are misaligned with ours. To cause us trouble, such misaligned superhuman intelligence needs no robotic body, merely an internet connection this may enable outsmarting financial markets, out-inventing human researchers, out-manipulating human leaders, and developing weapons we cannot even understand. Even if building robots were physically impossible, a super-intelligent and super-wealthy AI could easily pay or manipulate many humans to unwittingly do its bidding.

The robot misconception is related to the myth that machines cant control humans. Intelligence enables control: humans control tigers not because we are stronger, but because we are smarter. This means that if we cede our position as smartest on our planet, its possible that we might also cede control.

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Benefits & Risks of Artificial Intelligence – Future of Life …

What is Artificial Intelligence (AI)? – Definition from …

Artificial intelligence is a branch of computer science that aims to create intelligent machines. It has become an essential part of the technology industry.

Research associated with artificial intelligence is highly technical and specialized. The core problems of artificial intelligence include programming computers for certain traits such as:

Knowledge engineering is a core part of AI research. Machines can often act and react like humans only if they have abundant information relating to the world. Artificial intelligence must have access to objects, categories, properties and relations between all of them to implement knowledge engineering. Initiating common sense, reasoning and problem-solving power in machines is a difficult and tedious task.

Machine learning is also a core part of AI. Learning without any kind of supervision requires an ability to identify patterns in streams of inputs, whereas learning with adequate supervision involves classification and numerical regressions. Classification determines the category an object belongs to and regression deals with obtaining a set of numerical input or output examples, thereby discovering functions enabling the generation of suitable outputs from respective inputs. Mathematical analysis of machine learning algorithms and their performance is a well-defined branch of theoretical computer science often referred to as computational learning theory.

Machine perception deals with the capability to use sensory inputs to deduce the different aspects of the world, while computer vision is the power to analyze visual inputs with a few sub-problems such as facial, object and gesture recognition.

Robotics is also a major field related to AI. Robots require intelligence to handle tasks such as object manipulation and navigation, along with sub-problems of localization, motion planning and mapping.

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What is Artificial Intelligence (AI)? – Definition from …

Artificial Intelligence: The Robots Are Now Hiring – WSJ

Sept. 20, 2018 5:30 a.m. ET

Some Fortune 500 companies are using tools that deploy artificial intelligence to weed out job applicants. But is this practice fair? In this episode of Moving Upstream, WSJ’s Jason Bellini investigates.

Some Fortune 500 companies are using tools that deploy artificial intelligence to weed out job applicants. But is this practice fair? In this episode of Moving Upstream, WSJ’s Jason Bellini investigates.

Hiring is undergoing a profound revolution.

Nearly all Fortune 500 companies now use some form of automation — from robot avatars interviewing job candidates to computers weeding out potential employees by scanning keywords in resumes. And more and more companies are using artificial intelligence and machine learning tools to assess possible employees.

DeepSense, based in San Francisco and India, helps hiring managers scan peoples social media accounts to surface underlying personality traits. The company says it uses a scientifically based personality test, and it can be done with or without a potential candidates knowledge.

The practice is part of a general trend of some hiring companies to move away from assessing candidates based on their resumes and skills, towards making hiring decisions based on peoples personalities.

The Robot Revolution: An inside look at how humanoid robots are evolving.

WSJS Jason Bellini explores breakthrough technologies that are reshaping our world and beginning to impact human happiness, health and productivity. Catch the latest episode by signing up here.

Cornell sociology and law professor Ifeoma Ajunwa said shes concerned about these tools potential for bias. Given the large scale of these automatic assessments, she believes potentially faulty algorithms could do more damage than one biased human manager. And she wants scientists to test if the algorithms are fair, transparent and accurate.

In the episode of Moving Upstream above, correspondent Jason Bellini visits South Jordan, Utah-based HireVue, which is delivering AI-based assessments of digital interviews to over 50 companies. HireVue says its algorithm compares candidates tone of voice, word clusters and micro facial expressionsCC with people who have previously been identified as high performers on the job.

Write to Jason Bellini at jason.bellini@wsj.com and Hilke Schellmann at hilke.schellmann@wsj.com

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Artificial Intelligence: The Robots Are Now Hiring – WSJ