Pass4sure NCEES-PE NCEES - PE Civil Engineering exam braindumps

Our NCEES-PE test prep dumps contain practice test as well as genuine NCEES-PE questions. NCEES NCEES-PE Latest Topics that we will give, will offer you NCEES-PE test inquiries with confirmed responses that is a reproduction of a actual test. We at guarantee to have the most recent substance to empower you to breeze through your NCEES-PE test with high scores.

Exam Code: NCEES-PE Practice exam 2023 by team
NCEES-PE NCEES - PE Civil Engineering

The Principles and Practice of Engineering (PE) exam tests for a minimum level of competency in a particular engineering discipline. It is designed for engineers who have gained a minimum of four years post-college work experience in their chosen engineering discipline.

The PE Civil exam is an 8-hour exam with 80 questions. It is administered in pencil-and-paper format twice per year in April and October. See the exam schedule for specific dates.

Reviewing the PE exam specifications and design standardsReading the reference materials and examinee guideUnderstanding scoring and reportingViewing the most up-to-date PE exam pass rates

I. Project Planning

A. Quantity take-off methods

B. Cost estimating

C. Project schedules

D. Activity identification and sequencing

II. Means and Methods

A. Construction loads

B. Construction methods

C. Temporary structures and facilities

III. Soil Mechanics

A. Lateral earth pressure

B. Soil consolidation

C. Effective and total stresses

D. Bearing capacity

E. Foundation settlement

F. Slope stability

Civil Breadth exam Specifications Continued

IV. Structural Mechanics

A. Dead and live loads

B. Trusses

C. Bending (e.g., moments and stresses)

D. Shear (e.g., forces and stresses)

E. Axial (e.g., forces and stresses)

F. Combined stresses

G. Deflection

H. Beams

I. Columns

J. Slabs

K. Footings

L. Retaining walls

V. Hydraulics and Hydrology

A. Open-channel flow

B. Stormwater collection and drainage (e.g., culvert, stormwater inlets, gutter flow, street flow, storm sewer pipes)

C. Storm characteristics (e.g., storm frequency, rainfall measurement and distribution)

D. Runoff analysis (e.g., Rational and SCS/NRCS methods, hydrographic application, runoff time of concentration)

E. Detention/retention ponds

F. Pressure conduit (e.g., single pipe, force mains, Hazen-Williams, Darcy-Weisbach, major and minor losses)

G. Energy and/or continuity equation (e.g., Bernoulli)

VI. Geometrics

A. Basic circular curve elements (e.g., middle ordinate, length, chord, radius)

B. Basic vertical curve elements

C. Traffic volume (e.g., vehicle mix, flow, and speed)

VII. Materials

A. Soil classification and boring log interpretation

B. Soil properties (e.g., strength, permeability, compressibility, phase relationships)

C. Concrete (e.g., nonreinforced, reinforced)

D. Structural steel

E. Material test methods and specification conformance

F. Compaction

VIII. Site Development

A. Excavation and embankment (e.g., cut and fill)

B. Construction site layout and control

C. Temporary and permanent soil erosion and sediment control (e.g., construction erosion control and permits, sediment transport, channel/outlet protection)

D. Impact of construction on adjacent facilities

E. Safety (e.g., construction, roadside, work zone)


I. Earthwork Construction and Layout

A. Excavation and embankment (e.g., cut and fill)

B. Borrow pit volumes

C. Site layout and control

D. Earthwork mass diagrams and haul distance

E. Site and subsurface investigations

II. Estimating Quantities and Costs

A. Quantity take-off methods

B. Cost estimating

C. Cost analysis for resource selection

D. Work measurement and productivity

III. Construction Operations and Methods

A. Lifting and rigging

B. Crane stability

C. Dewatering and pumping

D. Equipment operations (e.g., selection, production, economics)

E. Deep foundation installation

IV. Scheduling

A. Construction sequencing

B. Activity time analysis

C. Critical path method (CPM) network analysis

D. Resource scheduling and leveling

E. Time-cost trade-off

V. Material Quality Control and Production

A. Material properties and testing (e.g., soils, concrete, asphalt)

B. Weld and bolt installation

C. Quality control process (QA/QC)

D. Concrete proportioning and placement

E. Concrete maturity and early strength evaluation

VI. Temporary Structures

A. Construction loads, codes, and standards

B. Formwork

C. Falsework and scaffolding

D. Shoring and reshoring

E. Bracing and anchorage for stability

F. Temporary support of excavation

VII. Health and Safety

A. OSHA regulations and hazard identification/abatement

B. Safety management and statistics

C. Work zone and public safety

NCEES - PE Civil Engineering
NCEES Engineering history
Killexams : NCEES Engineering history - BingNews Search results Killexams : NCEES Engineering history - BingNews Killexams : Mechanical engineering history timeline

Engineering has been an integral factor throughout history, from the first boats to present day projects to Strengthen renewable energy, and a crucial instrument of change and development.

Timelining the major dates and their associated achievements helps us to plot the progress of society, through invention and technical developments.

Hover to the left of images to go backwards, right to go forwards.

Or find out more about our history, the history of the Institution of Mechanical Engineers.

Mon, 28 Nov 2022 09:43:00 -0600 en text/html
Killexams : History Center: Programs & Projects

Engineering and Technology History Wiki: A wiki-based platform that allows IEEE members and organizational units to collaboratively preserve and share their history. The Engineering and Technology History Wiki (ETHW) hosts the Milestones, Oral Histories, and Archives programs (see below) and also allows IEEE members to contribute to Wikipedia-style subject articles or to preserve their own firsthand histories.

Milestone Program: IEEE established the Electrical Engineering Milestones program in 1983 to honor significant achievements in the history of electrical and electronics engineering.

REACH – Raising Engineering Awareness through the Conduit of History: An IEEE History Center program that is currently in its inception phases. REACH will provide high school social studies teachers with free, dynamic, educational resources that will engage students by bringing to life the history of engineering through the use of multi-media. Each lesson will highlight historical aspects of different technologies and the impact that they have had on humanity. Learn more about REACH by visiting the IEEE Foundation to learn how you can help bring the history of engineering technologies to life and inspire the next generation of engineers. Designation should be classified as IEEE REACH fund. Watch this short video to learn more about IEEE REACH.

Oral Histories: The Center conducts oral histories with leading engineers. There are more than 650 full-transcript histories on the Engineering and Technology History Wiki.

IEEE Archives: The IEEE Archives is a relatively small collection (approximately 15 cubic meters) of documents and other archival material that documents the history of IEEE and its predecessor organizations AIEE and IRE. The strongest areas in its collection document the histories of AIEE and IRE, the merger between the two that created IEEE in 1963, and the history of the overall IEEE between 1963 and 1984. The archives, in general, collect the history of the overall institute and do not seek to collect the history of IEEE's many organizational units, but instead encourage those units to use the ETHW to preserve their material. View the IEEE archives policy. (PDF, 30 KB)

IEEE History Center Publications: As part of its mission to bring the history of technology to a wide audience, the History Center staff write journal articles for many IEEE periodicals such as IEEE Spectrum, Proceedings of the IEEE, Insight, The Institute, and others. The History Center also publishes books on the history of IEEE technologies. These books are available from in hard copy and Kindle editions. If you have a book-length manuscript and would like to be a published author, please see the author submission guidelines.

IEEE History Center events: The History Center sponsors a number of virtual and in-person lectures, events, and talks. 

IEEE Fellowship: The IEEE Life Members' Fellowship in Electrical History supports either one year of full-time graduate work in the history of electrical science and technology at a college or university of recognized standing or up to one year of post-doctoral research.

Pugh Young Scholar in Residence: The Pugh Young Scholar in Residence seeks to provide research experience for graduate students in the history of electrical and computer technologies. It is intended for future historians and is not suitable for engineering students unless there is a strong history component to their studies.

IEEE William And Joyce Middleton Electrical Engineering History Award: The IEEE William and Joyce Middleton Electrical Engineering History Award is awarded to the author of a book in the history of an IEEE-related technology that both exemplifies exceptional scholarship and reaches beyond academic communities toward a broad public audience.

The Bernard S. Finn IEEE History Prize: The prize is awarded annually for the best paper on the history of electrical technology published during the preceding year.

Conferences: Historical conferences under the auspices of the History Center.

Wed, 04 Apr 2018 17:30:00 -0500 en text/html
Killexams : Engineering History

Cover Story

It will take 220 million pounds of steel, 300,000 cubic yards of concrete and more than 5,800 workers to build the New NY Bridge—and seven UB alumni are helping to bring it all together.

Progress continues apace on the new bridge’s main span. Photo: Courtesy of the New York State Thruway Authority

“Every day is an adventure.”

It’s what each UB alumnus said—and, we imagine, what every one of the thousands of men and women working on the New NY Bridge project would say as well. The sheer magnitude of the project (it’s a 3.1-mile twin-span over a deep water channel) and the historic location (where Rockefellers played and the Headless Horseman was “born”) are just two of the factors that make it a fascinating undertaking. Add in the fact that there’s a 328-foot crane floating on the Hudson that can lift 12 Statues of Liberty, and you pretty much know you’re involved with something special.

The $3.98 billion bridge project officially began in 2011, when legislation was enacted and labor agreements signed, but its history goes back even further. The original Tappan Zee Bridge, which spans the Hudson River and touches down in Tarrytown, N.Y., and South Nyack, N.Y., opened in 1955. Built during a materials shortage brought on by the Korean War, it has required hundreds of millions of dollars to be spent over the years in maintenance and repair. Even so, some people are amazed it’s still standing. A government official dubbed it the “hold-your-breath bridge.”

So, in 1999, discussions to replace the bridge began. Those “discussions” carried on for more than a decade. Finally, Gov. Andrew M. Cuomo pushed the project forward, making the new bridge a reality. Ground broke—or more correctly, was dredged—in August 2013. The governor had a checklist for the bridge. It had to 1) be aesthetically pleasing, 2) use the design-build process to keep the project on time and on schedule (see below), and 3) be the most open, transparent project in state history. (As for No. 3, check out You’ll find everything you’d ever want to know about bridge construction.)

The new design is a twin-span, cable-stayed bridge with eight 419-foot towers that soar majestically above the Hudson. The largest bridge project in New York State history, it includes cutting-edge design features (like a monitoring system that can detect when winds are too strong for large trucks), and uses construction equipment that allows the steel and concrete towers to be built right on the river.

We spoke to the seven alumni working on the project about what makes the bridge unique, how they contribute to the team and why they enjoy coming to work every day. Whether they provide steel quality assurance or sign the paychecks, all of them said they are awed by the colossal scale of the project, the talent involved in the design and construction, and the fact that each of them is playing a part in New York State history.

Rebecca Rudell (MA ’95, BA ’91) is a contributing writer for At Buffalo.

Bridge Players

New York State Thruway Authority (NYSTA):
Owner of the Tappan Zee Bridge and the New NY Bridge Project

New York State Department of Transportation (NYSDOT):
State agency providing technical and management support to NYSTA

Tappan Zee Constructors (TZC):
Design-builders; hired by NYSTA to design and construct the New NY Bridge

Owner’s engineer; consulting firm hired by NYSTA to provide technical oversight of the project

Subcontractor hired by HNTB to provide a broad range of services

Subcontractor leading TZC’s independent quality-assurance program

There are hundreds of other companies (more than 680 in New York State alone) working with the NYSTA to complete the New NY Bridge.

Sleek & Strong

The project’s first stay cable is raised to its anchor point. Photo: Courtesy of the New York State Thruway Authority

The New NY Bridge will be the first cable-stayed bridge over the Hudson River and will be one of the longest bridges of its kind in the United States. Bridges stay up because of two forces: compression and tension. The weight of the main span deck is transmitted to the towers—through the cables—and, ultimately, to the bedrock below.

  • There will be 192 stay cables on the main span, with 12 on either side of each tower.
  • Stay cables will range from 190 feet to 625 feet.
  • If laid end to end, the length of the cables would equal 14 miles. The metal strands within the cables would extend 700 miles.

Rise and Climb

Self-climbing jump forms were used to build the iconic main span towers. Photo: Courtesy of the New York State Thruway Authority

Between summer 2015 and winter 2016, eight bright, blue boxes could be seen moving up the bridge’s slender, concrete towers as they were built. Called “jump forms,” the ingenious 650-square-foot workspaces were used to create the towers in situ.

  1. Internal and external steel frames are secured to a tower segment.
  2. The external frame is raised up—or “jumps”—creating a safe space for the bridge crew to work inside.
  3. Crew members install steel reinforcements and the internal frame is moved up.
  4. The internal frame, which serves as a casting mold, is filled with concrete.
  5. Once the concrete has cured, a series of rails is lifted to the next level and the process starts again.

Always on Alert

Everything is designed to be “smart” these days, and the New NY Bridge is no exception. Part of the design includes a structural health monitoring system (SHMS), which will be the most comprehensive system of its kind in the country.

Sensors and other instrumentation will measure and monitor the structural behavior of the bridge as it undergoes daily traffic loads and temperature changes—and even during extreme circumstances like hurricanes and earthquakes. The SHMS also will allow bridge officials to program routine and preventive maintenance activities, and alert them if any damage has occurred.

An ironworker helps connect a seismic isolation bearing to the steel girder assembly. Photo: Courtesy of the New York State Thruway Authority

Bird’s Eye View

“Painters Point” is the third of six belvederes on the pedestrian and bike path. Its design reflects the arts and culture of the Hudson Valley region. Rendering: Courtesy of the New York State Thruway Authority

The New NY Bridge isn’t just for vehicles. Six belvederes, aka overlooks, will be situated at strategic locations across the span for pedestrians and bicyclists to take advantage of. Each belvedere has its own specially designed seating, shade structures and interpretive panels, offering a place to learn a bit about Hudson River history and take in the million-dollar views.

Preparing the Bridge Brigade of the Future

IBE students visit the old Tappan Zee Bridge. Photo: Courtesy of IBE

America’s infrastructure is old. Really old. In major cities across the country, we still depend on pre-Civil War water mains and railway tracks. And many bridges built in the 1950s during the construction of the interstate highway system, like the Tappan Zee, no longer meet today’s needs.

Aside from the fact that hundreds of projects languish on a lengthy backlog awaiting government approval, and that the cost of repair or replacement runs into the billions, there’s another issue holding back progress: The professional workforce needed to manage these jobs is retiring. Yes, talented civil engineers are graduating into the workforce every year, but many lack the professional skills needed to lead these complex endeavors.

In 2013, after years of meetings and gathering input from fellow engineers, George Lee, SUNY Distinguished Professor Emeritus, established UB’s Institute of Bridge Engineering (IBE) to help address this dire situation.

While other universities offer courses in bridge engineering, UB’s IBE is the only program of its kind in the country, where students can earn a Master of Science degree that focuses specifically on bridge engineering. And it’s already making a difference. “We are earning a reputation for putting out well-qualified students who can jump into the role of bridge engineer right out of school,” says Jerome O’Connor, the IBE’s executive director.

The IBE program has three focus areas: education, research and professional engagement. Students take core technical courses, like steel bridge design and earthquake engineering, but they also perform studies of genuine bridges, for example, using analytical software to determine whether standing bridges require repair or need to be strengthened to support higher traffic loads.

Students also benefit from interaction with practicing engineers, like Dan D’Angelo and Tim Kaiser. D’Angelo has served on the advisory board of the IBE since 2014, contributing to the curriculum, mentoring students and evaluating projects. Kaiser, an alumnus of the program who joined the board a few months ago, describes the IBE as a community, not just for students to interact with engineers, but also for pros to come together and expand their knowledge.

Indeed, in addition to granting degrees to students, the program offers online courses and seminars as continuing education for professionals—crucial in a field where technologies are being developed all the time. “The IBE,” Kaiser says, “provides an opportunity for experts to share their experiences and really accelerate the profession.”

Old vs. New

Cantilever truss design: a combination of cantilever spans (horizontal structures supported at one end) and truss spans (steel lattice frameworks) Cable-stayed design (see Sleek & Strong, above)
Opened in 1955 and has required significant maintenance and repair in latest decades Set to open in 2018 and designed to last 100 years before any major structural maintenance is required
Seven lanes that are narrower than the required 12 feet. Center lane carries traffic east or west, depending on peak traffic Eight 12-foot traffic lanes + disabled vehicle lane/shoulder + emergency access + room for express bus lane + shared-use path + belvederes (overlooks)
Designed to support 100,000 vehicles a day. Current traffic exceeds 140,000 a day Two separate spans built to handle future traffic growth
Horizontal struts above bridge (these, unfortunately, collect ice in winter, which then drops down on vehicles) Angled towers and stay cables
Superstructure approximately 280 feet tall at its highest point Eight towers, each 419 feet tall

Bridge Facts

More than 300,000 cubic yards of concrete will be used for the new bridge. That’s enough concrete to build a sidewalk from the project site to Key West, Fla.

The project’s educational outreach team has spoken to more than 50,000 students.

Artist Jeff Koons, famous for his balloon-animal sculptures, was on the design panel, prompting people to joke that they would be building a balloon bridge.

More than 5,800 people have worked on the bridge to date.

Dianne S. Wheatley

Fantastic and informative article on this bridge. Like new format for newsletter.

Fri, 14 Apr 2017 07:55:00 -0500 en text/html
Killexams : History of IEEE

IEEE, an organization dedicated to advancing innovation and technological excellence for the benefit of humanity, is the world's largest technical professional society. It is designed to serve professionals involved in all aspects of the electrical, electronic, and computing fields and related areas of science and technology that underlie modern civilization.

IEEE's roots go back to 1884 when electricity began to become a major influence in society. There was one major established electrical industry, the telegraph, which since the 1840s had come to connect the world with a data communications system faster than the speed of transportation. The telephone and electric power and light industries had just gotten underway.

Sun, 26 Jun 2022 21:42:00 -0500 en text/html
Killexams : Program History
1979: First Engineering class offered

Engineering courses have been offered at Hope College since 1979. Initial offerings were instituted by the Department of Physics in response to academic interests of students who were majoring in physics but whose career goals were in engineering. At that time, two faculty members, with interests and training in engineering, began offering a limited number of courses in basic mechanical and electrical engineering topics. During the decade of the 1980s, these courses included Solid Mechanics, Electronics, Thermodynamics, Fluid Mechanics, Material Science and Vibrations. This curriculum was designed and intended to prepare students for graduate study in engineering.

Another option for engineering students was the Hope College Engineering 3-2 Program, in which students combined three years of study at Hope College with two years at a traditional engineering school. Upon successful completion of this program, students received a Bachelor of Science degree from Hope College and a Bachelor of Engineering degree from the engineering school.

1989: A major in Engineering Physics offered

During the mid to late 1980s, the Department of Physics recognized that the current engineering offerings were not providing enough depth of coverage to ensure student success in graduate engineering studies. For this reason, a Bachelor of Science degree with a major in Engineering Physics was established in 1989. The objective of this degree program was to Strengthen the preparation of physics students for continuing on in engineering graduate school. In order to meet the requirements of this new major, the curriculum was modified to offer engineering courses on an alternate year basis. This arrangement allowed efficient use of the existing engineering faculty to provide students with a course pattern which more closely resembled that of a traditional four-year engineering school.

As a result of these improvements to the engineering curriculum, the popularity of the 3-2 Program diminished as a majority of engineering students decided to remain at Hope College for four years to pursue a major in Engineering Physics. Most of these students continued their studies in engineering graduate school, although a fair number of students began pursuing employment in industry directly from Hope College.

1992: Lab courses were added to better prepare students for engineering graduate school
In order to provide the students with an introductory engineering laboratory experience in strength of materials, mechanical testing laboratory equipment was purchased. A laboratory component to the solid mechanics and materials courses was added in 1992.
1994:  Number of engineering faculty doubled to four

In 1994, the engineering faculty increased to four members through the addition of two new hires. This growth was partially supported by a grant from the Fund for the Improvement of Post-Secondary Education (FIPSE, administered by the Department of Education), which was granted to the college to develop a model for engineering programs at liberal arts colleges. The educational objectives of this expansion were to implement a capstone engineering design experience, provide core engineering classes on an every-year basis, and to increase the number of engineering subjects courses offered. These objectives were successfully achieved with the implementation of several changes, including:

  • The development of a two-course capstone sequence in engineering design (ENGS 451, 452)
  • The switch of core engineering classes (ENGS 345, 346) from alternate year to every year basis
  • The development of a freshmen engineering course (ENGS 100)
  • The offering of subjects courses in engineering (Finite Element Analysis, Multi-body Dynamics, Advanced CAD/CAE)
1994-1997: External reviewers encouraged pursuit of accredited program 

From 1994 to 1997, as part of the FIPSE-sponsored study of the Engineering Program, a number of external reviewers from both small and large engineering colleges served as external advisors to the Engineering Program. Reviewers completed campus visits in order to assess the Engineering Program. Based partly on the largely positive reviews of the Engineering Program, the department requested permission from the administration of Hope College to pursue an accredited engineering degree. The motivation for pursuing accreditation was to further Strengthen the quality of engineering education at Hope College by formally implementing a system of continuous improvement via both internal and external review and assessment.

The Administration of Hope College approved the pursuit of an accredited engineering program in 1997, and the department established a new degree designation: the Bachelor of Science with a Major in Engineering. This new engineering major was designed and intended to fulfill the degree requirements as specified by the ABET 2000 criteria.

It was decided to retain the less rigorous engineering degree (which is not accredited and for which no accreditation is sought) the Bachelor of Science with a major in Engineering Science. This degree provides engineering education for students who have other interests, such as a second major in another degree program, that preclude their ability to complete the engineering major requirements within their time at Hope College.

Also in 1997, a fifth engineering faculty member was hired to continue building ties with local industry, to increase offerings in engineering subjects courses (heat transfer and a thermofluids laboratory) and to provide necessary support for implementing assessment and outcomes instruments as required by ABET 2000 criteria. In 1998, the Hope College Curriculum Committee officially approved the new engineering major, and the Department of Physics changed its name to the Department of Physics and Engineering.

2000: Hope Engineering degree received ABET accredidation
The engineering program completed and submitted a self-study and underwent an accreditation visit and review during the fall 1999 semester. In 2000, the Bachelor of Science with a major in Engineering was accredited by the Engineering Commission of ABET (111 Market Place, Suite 1050, Baltimore, MD 21202-4012; telephone: (410) 347-7700).
Mid-2000s:  Additional faculty added and Engineering department separated from Physics
As more faculty were hired and a wider ranger of courses were offered, the engineering department started to work toward forming their own department.  In 2006, engineering and physics offically split, with Dr. John Krupczak serving as the first chair of the Department of Engineering. This added visibility likely contributed to the steady rise in majors over the next several years.
2016: Largest senior class ever  
In the fall of 2012, Hope College enrolled its largest ever freshmen class, which corresponded to a large increase in the number of engineering majors. The growth of interest in engineering allowed the department to increase the number of faculty, and to offer more concentrations to deliver students more career choices.
Fri, 16 Jun 2017 03:22:00 -0500 en text/html
Killexams : Latest In Prompt Engineering Urges You To Welcome And Harness Vagueness In Generative AI, Rather Than Shunning Its Perceived Woes

Vagueness seems to be on the outs these days, particularly when it comes to prompt engineering and generative AI.

A lot of sage advice to newbies and novices that are using generative AI such as ChatGPT or Bard almost always includes the stern warning to avoid being vague in your prompts. Be specific, goes the bellowing mantra. Pick your words carefully and make sure to get to the meat of the matter. Do not be vague, you are forewarned with a nagging wagging finger pointed at you.

One supposes that this nearly overt hatred for vagueness comes partially from a long history of disdain for being vague. We aren’t just now on the alert for vagueness. It has been around for ages. For example, C.S. Lewis, the noteworthy British writer had said this about the dangers of being vague: “Always prefer the plain direct word to the long vague one.” The same qualm about vagueness extends into all arenas, such as this bashing of being vague as expressed by the legendary banker J.P. Morgan: “No problem can be solved until it is reduced to some simple form. The changing of a vague difficulty into a specific, concrete form is a very essential element in thinking.”

Okay, we get the message, loud and clear.

Vagueness is bad. And, by logical extension, being vague when using generative AI is clearly bad too. You can’t seem to beat that deductive reasoning. Any reasonably good prompting and any reasonably good prompt engineering strategy have to make sure to appropriately and dutifully alert you about the unsavoriness of vagueness when using generative AI.

Period, end of story.

Wait for a second, hold the presses!

Maybe there is a scintilla of redeeming value underlying the capacities of vagueness. Do we really need to toss the baby out with the bathwater? Perhaps vagueness can be harnessed and shaped to our advantage. Prompt engineering might be wiser to embrace the hidden gem of leveraging vagueness, doing so when suitable and when done with prompt-wise acumen.

In today’s column, I am continuing my ongoing special series on the latest advances in prompt engineering and will be covering the somewhat surprising revelation that vagueness definitely has an important place in your shrewd prompt engineering techniques and skills. I will be explaining what vagueness consists of and how it compares to the normative proclamation of always landing on the side of specificity. They are the yin and yang of prompting. Sometimes you ought to be using specificity, but not necessarily all of the time. Likewise, sometimes you ought to be using vagueness, but not necessarily all of the time.

Your aim with vagueness is to strive toward the Goldilocks goal, namely you don’t want to be too hot and not too cold. The proper balance of when and how to use vagueness in your prompts is the place you want to be. This is a mindful consideration. I say this because, with all the negative press about being vague, anyone that has gotten used to using generative AI might very well have formed a habit by now of eschewing vagueness like the plague. Well, if so, deliver vagueness a second chance. You’ll be glad you did.

Here's how we will proceed herein.

First, I’ll discuss the nature of vagueness and what we as humans don’t like about it overall. The dislike of vagueness will next be tempered by showcasing the strength and importance of being vague. I will then dive into the role of vagueness within prompts and generative AI all told. If all goes well, the wheels will get spinning in your mind as to leveraging vagueness on a daily basis when you are using generative AI.

In defense of vagueness, there are some famous quotes that provide a happy-face perspective on being vague. John Tukey, the famous mathematician, said this uplighting remark about vagueness: “Far better an approximate answer to the right question, which is often vague, than the exact answer to the wrong question, which can always be made precise”.

Keep in mind too that one of the most powerful elements of being vague is that it can be a boon to creativity, as well stated by the renowned painter Pablo Picasso: “You have an idea of what you are going to do, but it should be a vague idea.” Let’s not rigidly bash vagueness and instead see what it is in a fuller picture for all that it portends, opening our eyes wide to see both the bad and the good at hand.

Before I dive into the crux of leveraging vagueness as an indispensable tool of prompt engineering, let’s make sure we are all on the same page when it comes to the keystones of prompt engineering and generative AI.

Prompt Engineering Is A Cornerstone For Generative AI

As a quick backgrounder, prompt engineering or also referred to as prompt design is a rapidly evolving realm and is vital to effectively and efficiently using generative AI or the use of large language models (LLMs). Anyone using generative AI such as the widely and wildly popular ChatGPT by AI maker OpenAI, or akin AI such as GPT-4 (OpenAI), Bard (Google), Claude 2 (Anthropic), etc. ought to be paying close attention to the latest innovations for crafting viable and pragmatic prompts.

For those of you interested in prompt engineering or prompt design, I’ve been doing an ongoing series of insightful looks at the latest in this expanding and evolving realm, including this coverage:

Anyone stridently interested in prompt engineering and improving their results when using generative AI ought to be familiar with those notable techniques.

Moving on, here’s a bold statement that pretty much has become a veritable golden rule these days:

If you provide a prompt that is poorly composed, the odds are that the generative AI will wander all over the map and you won’t get anything demonstrative related to your inquiry. Being demonstrably specific can be advantageous, but even that can confound or otherwise fail to get you the results you are seeking. A wide variety of cheat sheets and training courses for suitable ways to compose and utilize prompts has been rapidly entering the marketplace to try and help people leverage generative AI soundly. In addition, add-ons to generative AI have been devised to aid you when trying to come up with prudent prompts, see my coverage at the link here.

AI Ethics and AI Law also stridently enter into the prompt engineering domain. For example, whatever prompt you opt to compose can directly or inadvertently elicit or foster the potential of generative AI to produce essays and interactions that imbue untoward biases, errors, falsehoods, glitches, and even so-called AI hallucinations (I do not favor the catchphrase of AI hallucinations, though it has admittedly tremendous stickiness in the media; here’s my take on AI hallucinations at the link here).

There is also a marked chance that we will ultimately see lawmakers come to the fore on these matters, possibly devising and putting in place new laws or regulations to try and scope and curtail misuses of generative AI. Regarding prompt engineering, there are likely going to be heated debates over putting boundaries around the kinds of prompts you can use. This might include requiring AI makers to filter and prevent certain presumed inappropriate or unsuitable prompts, a cringe-worthy issue for some that borders on free speech considerations. For my ongoing coverage of these types of AI Ethics and AI Law issues, see the link here and the link here, just to name a few.

With the above as an overarching perspective, we are ready to jump into today’s discussion.

Foundations Of Vagueness And The Value To Be Derived

We will ease our way into the role of vagueness, doing so by first examining how humans convey and perceive the nature of being vague. After we cover that aspect, we can then consider how humans interacting with generative AI are likely to act and react related to how the AI does or does not express vagueness.

It is useful to first explore how humans express and react to vagueness when interacting with fellow humans. Here’s why. When we use generative AI, we tend to carry over our preexisting assumptions and habits about vagueness that have been dutifully learned or naturally acquired throughout our lives on a human-to-human interaction basis.

I cover the matter in this way with a bit of erstwhile caution because I don’t want anyone to be led down the path of anthropomorphizing AI. In current times, AI is not sentient and should not be equated to the sentience of humans. I will do my best to make that same alert when we get into certain aspects of the generative AI details that might seem overly sentient-like.

Thanks for keeping a level head on these weighty matters.

Let’s start with the meaning of vagueness.

In a research article entitled “Vague Language and Its Social Role” by Mubarak Alkhatnai, published in the Theory and Practice in Language Studies, February 2017, these keystones about vagueness are laid out:

The gist here is that vagueness in our everyday lives is always present. It is all around us. We are immersed in a world of great vagueness. The trouble arises when you are expecting specificity but get mismatched with vagueness.

Suppose that someone tells you that a person is tall. What do you think the genuine height of the person consists of?

It is quite hard to discern from the vague wording entailing tallness. If children tell you that someone is tall, you likely would suspect that the tallness is not much of any notable magnitude since the kids are likely comparing their height to the height of the person being described. On the other hand, if you knew that the person was a professional basketball player, you would undoubtedly reasonably guess that they are indeed relatively tall (online reported stats suggest that such pros are around 6 ½ feet tall, surpassing the average non-player by about 8 inches).

This showcases that some words are inherently vague. We often find ourselves having to cope with vague words and discern what they mean or how to best interpret them. Another facet is the context of the vagueness makes a big difference too.

Imagine that I tell you that someone is 6.2 feet tall. By all appearances, the fractional portion suggests that this is a very specific accounting for the height of someone. We would assume that 6.2 is rather precise.

But that might not really be the case. Pretend that we have a room full of people and they are all perchance 6.2 feet in height (perhaps it is an annual convention of people that are that specific height). They decide to have a contest to see which of them is the tallest. One person yells out that they are 6.2 feet tall. So does the next person, and so on. All of them are that height.

In a sense, 6.2 feet is vague in this context. It is not fully specific. We might measure each person and reach a greater specificity. One person is 6.24 feet tall. Another one is 6.28 in height. This gets us away from the vagueness of 6.2 and toward a more specific accounting.

I bring this all up due to the important consideration that vagueness is often in the eye of the beholder. A given word or phrase is not axiomatically specific and ergo non-vague. Things depend upon what the context consists of and a variety of other mitigating factors that come to play.

One of the most famous examples of this nebulousness about vagueness-versus-specificity comes in the classic paradox-of-the-heap or also known as the sorites paradox (side note, the word “sorites” comes from the original Greek word for a heap).

Here’s how this paradox goes.

You are told that a heap of sand is defined as 1,000,000 grains of sand. This seems plain and simple to understand. Go ahead and assume that the definition is valid, and we all agree that a heap is one million grains of sand.

Another premise or precept that you are told is that taking one grain from a heap of sand is considered relatively inconsequential and therefore you still have a heap of sand. This seems sensible. If we had 1,000,000 grains of sand and took out one grain, the resulting 999,999 grains of sand are certainly going to appear to be like the same heap we had a moment ago. Unless you have some miraculous and uncanny ability to discern the omission of one grain of sand, the heap sure looks still like a heap.

Now the mental trap is set.

Another grain of sand is removed. Does the heap remain as a heap? I suppose you would still believe that a mound of 999,998 grains of sand is pretty much still a heap. You might shrug your shoulders and say that yes, we still have a heap.

Take away another grain. And another grain. All along, you are still seemingly going to say that this is still a heap of sand. Aha, this removal of a grain at a time keeps taking its toll. Eventually, we get down to one grain of sand left.

Would you say that one grain of sand is a heap of sand?

Most people would say that this is not a heap of sand. It seems rather ridiculous to suggest otherwise. But the logic of our approach appears to box us in and we must acknowledge that the one grain of sand is a heap. We step by step kept saying that we still had a heap.

The paradox has us over a barrel. Part of the reason that we are in a pickle is that we might have originally presumed that the definition of a heap was particularly specific. We were told a heap has a million grains of sand. We didn’t start by saying that a heap is a bunch of sand or a collection of sand. The specifics seemed to shine brightly by telling us explicitly that it was a million grains of sand.

The additional rule that said we could take away a single grain and yet still have a heap also seemed quite specific. Unfortunately, this rule has landed us in trouble. We have no specificity as to how many single grains of sand being removed will ultimately toss us out of the heap category. If the rule had said that once you remove say 10,000 grains of sand you no longer have a heap, we would have something more specific to work with.

I trust that you enjoyed that enlightening paradox.

What if we required everyone to be exactingly specific in everything they utter?

Would this fix our daily problem of dealing with vagueness?

Not quite.

You could find yourself exhaustingly having to prattle on and on to try and overcome the vagueness involved. The amount of specificity could almost be endlessly pursued. There is also the likelihood that the specificity isn’t especially helpful to the situation.

Going back to the notion of someone being tall, their tallness might not be an important element and thus trying to nail down the specifics might be a waste of time and effort. For example, if you are told that a person is tall and running away from an angry dog, you might not care about the tallness and be more interested in the running and the matter of the growling dog that is in pursuit. If the tightening of the phrasing about tallness is needed, perhaps this can be dealt with later on.

You see, we can reasonably assert and compellingly proclaim that vagueness does in fact have redeeming qualities.

Our communications with other people can be more efficiently stated by avoiding the grinding delineation of specifics. Using vagueness is bound to (usually) be a faster way to convey something. There is also the possibility that the specificity is unknown and thus the vagueness is used as a placeholder. Maybe we don’t know exactly how tall the person is, but we generally perceive them as tall. This is useful as a placeholder in lieu of knowing the precise height.

There are occasions where you purposefully use vagueness and knowingly do so. I’d bet that you’ve done this many times. You might have been telling a story about an encounter with a tall person but the tallness wasn’t a vital factor. The assertion they were tall is sufficient and no further specificity is required.

One would suppose that there are also occasions where you inadvertently use vagueness and didn’t do so with an intention in mind. You are telling the story about the tall person, and your friend listening to the story stops you. What do you mean by being tall? You realize that you probably should have been more specific. It was not something top of mind.

The same rules apply to being specific. There are occasions where you purposefully use specificity and knowingly do so. Your telling of the story about the tall person might be that you say they are 6 feet 7 inches tall. You want to convey the magnitude or degree of tallness. This impresses your friend that is listening to the story.

On other occasions, you might use specificity and do so without an intention in mind. During the telling of the story about tallness, you mention that the person was 6 feet 7 inches tall. Your friend stops you. How do you know they are exactly that height? This raises a host of questions. You realize that this is a distraction from what you were trying to say. Oops, you probably should have been vaguer and said they were simply tall.

It is safe to say that vagueness has a tradeoff and requires a kind of mental calculation as to the benefits and costs associated with being vague (likewise the same for being specific). There is an ROI (return on investment) underlying the use of vagueness.

We need to be aware of vagueness and specificity. We ought to leverage either one to the circumstances at hand. To say that either one is outrightly bad or wrong to use is a false proposition. They are both tools. Use the right tool in the right way at the right time and place.

With all of that now under our belt, we are ready to see how this applies to prompts, prompt engineering, and the use of generative AI.

Prompt Engineering And The Use Of Vagueness

When you enter a prompt into generative AI, you are oftentimes wanting to get a somewhat focused response generated by the AI. One issue with generative AI is that it is like a box of chocolates, whereby you never know exactly what the generative AI will respond with. You can ask quite a specific question and get a vague answer. You can even get an answer that seems far afield from your question, especially if the generative AI has a so-called AI hallucination (as mentioned earlier and as covered in my column at the link here).

The rule of thumb is that you should be as specific as you can in your prompt. The hope is that the more specificity there is will guide or spur the generative AI to be specific in return. This is a good practice. If your prompt is meandering or confusing, the generative AI is going to have a tough time pattern-matching what you want to find out about.

You can even urge the generative AI to explicitly be specific in its response. If you have a prompt that asks generative AI to identify how to wash a car, you can include in your prompt an indication that you want a listing of five steps that are undertaken when washing a car. The odds are pretty high that the generative AI will respond with five steps.

The problem though is that you might be cornering or constraining the generative AI due to your use of specificity in your prompt.

Suppose that the generative AI would have come up with seven steps regarding how to wash a car. But you told the generative AI to specifically come up with five steps. Your specificity might have shot your own foot. Perhaps there really are seven steps and you’ve now gotten the generative AI to curtail or cut off the seven by reducing the steps to five.

The five steps might be fine and upon comparing to the seven steps you don’t see anything significant that was left out. On the other hand, it could be that the generative AI dropped out two crucial steps and landed on five that aren’t as crucial. This is all going to be context-dependent.

If you had been vague and merely said tell me the steps involved in washing a car, you are generally allowing the generative AI to choose the number of steps. Doing so could be very handy for you. Upon the steps being presented to you, you could subsequently tell the generative AI to make the steps into just five (assume that seven were presented).

Which is better, should you be vague in your prompt or be specific?

There isn’t any singular right answer since the context and what you are trying to do will determine the choice of being vague versus being specific.

For those that are just starting with generative AI, the guidance of being specific is a good one. These novices or newbies will likely be more fulfilled by the responses of the generative AI as a result of being specific in their prompts. This is reassuring.

Akin to riding a bike, instructions about how to ride a bicycle often simplify the world when you first get underway. Once you’ve gotten comfortable riding a bike, you begin to find shortcuts or other ways to ride. More advanced riders also might share their insights about how to further boost their bike riding prowess.

The same goes for vagueness and specificity.

Your best bet when starting with generative AI would be to lean into specificity. Once you have your sea legs under you, you can branch out and leverage vagueness. Anyone of advanced prowess in prompt engineering should purposefully be choosing either vagueness or specificity as befits the situation at hand.

Here are four useful ways to think about this:

The first three of the above recommended practices are perhaps self-evident and I have explained them in my example about washing a car. The fourth one might be a bit puzzling to you. You might be wondering when you would ever start with specificity and then drive the generative AI toward being vague.

We can use the car washing example again. You tell generative AI to deliver you five steps. It does so. You might at that juncture be satisfied and move on. There might though be a small concern in your mind that maybe the five steps are insufficient. You were the one that constrained the generative AI. You don’t know whether this was a suitable number of steps or not.

With that suspicion in hand, you opt to do a follow-up prompt and tell the generative AI to not be constrained with just the stated five steps. This new prompt pushes the generative AI to more freely provide an answer, a vaguer answer.

Upon inspecting the vaguer answer, perhaps this reveals that the generative AI came up with seven steps and reduced things down to five. There is another possibility that the generative AI does an overreach and comes up with seven steps even though only five are sufficient. Sometimes generative AI will respond to a prompt by seemingly attempting to fulfill a request despite there not being a need per se to do so. Rarely will you get generative AI retorting that there are five and only five steps. If you seem to hint or suggest that some other number is viable, this might be contrived or concocted for you by the pattern-matching of the generative AI.

Thinking Bigger About Vagueness When Prompting

I’ve been discussing the use of vagueness in your prompting strategy. Turns out that is one side of the coin. The other side is whether the response by generative AI is vague or not.

We have these four notable conditions arising from two overarching principles:

Let’s briefly walk through those.

The first point of the four listed points, namely #1a, further emphasizes my above discussion about the value of intentionally using vagueness in your prompts. Vagueness is a tool. Use the tool wisely.

The second point, labeled as #1b, refers to unintentionally using vagueness in prompts. I would guess that many users of generative AI are blissfully unaware of the idea that being specific or vague in their prompt might make a difference in how the AI will respond. Thus, they by and large are at times vague even though they aren’t intentionally doing so. It just happens. To them, I wish them luck but also urge that they cognitively consider the wording of their prompts and leverage vagueness and specificity when suitable to do so.

The third point, #2a, is something I have alluded to earlier, but we can now cover it directly at this juncture. You can consider stating in your prompt whether you want generative AI to respond with a specific answer or a vague answer. I suppose asking or telling AI to be vague seems counterintuitive to you. The thing is, as noted about car washing, you might lead the AI down the path of being specific when the better answer might be a vaguer one. It is usually best to distinctively ask for what you want.

The fourth point, #2b, is that if you don’t say anything either way in your prompt about how you want the generative AI to respond (regarding vagueness or specificity), the AI might go either way. Sometimes it might be specific, sometimes it might be vague. A roll of the dice is underway.

My recommendation is that you seek to:

Try to wrap those recommendations into your prompt engineering mindset. The goal is to comfortably use those pieces of salient advice. They should become second nature.

As an overall indication of why you might want vagueness in your prompt, I offer these possibilities:

And here’s an overall indication of why you might want vagueness in the generated response from generative AI:

Those tips and remarks might inspire you to leverage vagueness from time to time.


I’ve got a final twist for you on this.

The characterization of vagueness-versus-specificity tends to suggest that they are exclusively used on a one-at-a-time basis. That is a misnomer.

You can use them both at the same time.

When you compose a prompt, there are undoubtedly some aspects that you prefer to have vaguely stated and other aspects that it makes more sense to distinctly specify. The same goes for the response that you want from the generative AI. You might readily want the generated response to contain some aspects that are vague and other aspects that are very specific.

I am pleased to report that you can at times have your cake and eat it too.

For those portions of your prompt that you believe are best conveyed vaguely, do so. Other portions of your prompt might contain a great deal of specificity. Mix and match as warranted.

For the generated response by AI, you might tell the AI which aspects should be vaguely stated and which should be stated with specificity. You can ask for both too. This could consist of telling the AI to identify how to do a car wash in five steps, and simultaneously having it in the same response list any number of steps that might be suitable to do so. I ought to forewarn you that this simultaneous duality of vagueness and specificity can impact what the generative AI produces in the sense that asking for five could steer the AI in a direction that a vaguer indication would not have. Please keep that caveat in mind.

Let’s end this discussion with an insight into where vagueness can take you. Bertrand Russell, eminent mathematician and grand philosopher said this about being vague: “Everything is vague to a degree you do not realize till you have tried to make it precise.”

That’s the beauty of leveraging vagueness when using generative AI. Your effort to consider the vagueness versus specificity facets is likely to get you thinking more deeply and clearly about what you are wanting to use generative AI for. You will need to confront the at times paradoxical ramifications of what is vague and what is specific.

While you ponder those hefty thoughts and begin to explicitly carry on with these prompting techniques associated with vagueness, I’ll be counting grains of sand. I’ll let you know once I run out of a heap.

Sun, 20 Aug 2023 23:00:00 -0500 Lance Eliot en text/html Killexams : Professional Engineering Exam

As a licensed Professional Engineer, or PE, you can expect many more benefits when compared to other engineers; most employers offer higher salaries and greater opportunities for advancement to PE's. Only PE's can consult in private practice, and seal company documents to be sent to the government. PEs also have more credibility as expert witnesses in court than most engineers.

Steps in obtaining a PE license:

  • Pass the Fundamentals of Engineering (FE) Exam.
  • Graduate with a bachelor's degree from an ABET accredited engineering curriculum (all Engineering curricula at Michigan Tech except Robotics Engineering).
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During your senior year you should take the Fundamentals of Engineering (FE) exam, which is required prior to sitting for the Professional Engineers (PE) Exam. Some requirements vary by state.

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Killexams : History of the Francis College of Engineering

Under the guidance of founder James T. Smith, Lowell Textile opened its doors in February of 1897. Thirty-two students started classes in three rented rooms on Middle Street in downtown Lowell, paying $100 annual tuition for three-year courses that earned them diplomas in cotton or wool manufacture, design, or textile chemistry and dyeing.

In 1903, the school moved to permanent quarters in Southwick Hall, giving the growing student body, state-of-the-art classrooms and laboratories, and vast workrooms necessary for hands-on experience in textiles. 

Ten years later, the school granted its first bachelor’s degrees in textile dyeing and textile engineering. By 1929, Lowell’s expanded curriculum, larger faculty, and livelier extracurricular program warranted a name change that reflected its evolution from a trade school to a technical college, and it became the Lowell Textile Institute. 

In 1953, President Martin Lydon expanded the curriculum to include programs in plastics, leather, paper, and electronics technology, increased the liberal arts, and renamed the school the Lowell Technological Institute. He moved the Institute decisively toward general engineering, setting up a bachelor’s program in 1956. The textile program was closed in 1971.

During World War I, the school became a training camp for prospective U.S. Army recruits (Kitson/Southwick Hall)

Lowell Tech and Lowell State College merged in 1975 to form the University of Lowell. The evolution continued and in 1991, Lowell became part of the five-campus system of the University of Massachusetts. The college adopted the name of James B. Francis, the English hydraulic engineer who in 1834 began his brilliant career in Lowell.

Today, the Francis College of Engineering proudly continues the century-long tradition of hands-on education closely linked to regional industry.  The college currently enrolls over 3,500 undergraduate, graduate, and doctoral students in six academic departments. Ties to industry are maintained through interdisciplinary research centers, an industrial advisory board, and growing co-op internship programs.

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Killexams : A History of the World

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