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Diesel Engines Move Goods Around the Globe; UVA Engineers Work to Curb Emissions – University of Virginia

As technical remedies to climate change go, taking carbon emissions out of passenger transportation is easy; we can switch from gasoline engines to electric motors fueled by renewable energy sources.

In contrast, decarbonizing heavy freight carried by trucks is hard. No technology today has the power and efficiency to replace diesel engines for moving goods over long distances by land and sea, which is unfortunate because societys increased shipping demands are driving up diesel consumption.

Researchers at the University of Virginias School of Engineering and Applied Science are taking on the challenge to discover which diesel fuels burn cleanest, under what conditions and with the fewest offsetting environmental costs. Their particular focus is minimizing production of nitrous oxide, or N2O.

William S. Epling, professor and chair of chemical engineering, is leading the effort with co-principal investigators Lisa Colosi-Peterson, an associate professor in the Department of Engineering Systems and Environment, and Chloe Dedic, an assistant professor in the Department of Mechanical and Aerospace Engineering.

Also contributing as senior investigators are Robert Davis, William Mynn Thornton Professor of Chemical Engineering, and Chris Paolucci, assistant professor of chemical engineering.

The project is supported by a $1.7 million grant through the Environmental Convergence Opportunities program of the National Science Foundations Division of Chemical, Bioengineering, Environmental and Transport Systems. The program requires that research team members have disparate expertise and perspectives to find imaginative solutions to difficult and pressing societal challenges.

So, for this funding, even before team members can grapple with solving the technical problem, they have to find common ground on the research questions amongst a group of people whose expertise and perspectives are, by design, different from one another.

That was harder than you might imagine it to be, but it was hard on purpose, said Colosi-Peterson, an environmental engineer who joined the team because she recognized that diesel engines arent going away any time soon. I think the NSF is very clever to have such a big incentive, because it took a lot of thinking to come up with something that really leveraged all our individual skills and interests.

Colosi-Peterson is an expert in life cycle assessment, a computer modeling-based area of study that examines the environmental price tag of the products, processes or services people use. Dedic studies combustion and reacting flow systems, and is a rising star in the development of new laser-based techniques for nonintrusive measurements of the complex chemical reactions and fluid dynamics that occur during combustion. Epling is well known in his sphere of catalysis, especially for his work to reduce pollutants emitted from diesel engine exhaust systems.

When fuel whether biomass- or petroleum-based is combusted, not all of it is burned. The exact chemical composition of the leftover hydrocarbons depends upon numerous factors, including fuel type. To meet Environmental Protection Agency vehicle emissions regulations, the catalytic converter in your car and the after-treatment systems used in diesel semi-trailer tractors clean those exhaust gases through chemical reactions after they leave the engine.

What eventually comes out the tailpipe at any given moment also depends on variables such as whether the engine is hot or cold, how effectively the air and fuel mixed in the engine, and the age of the catalyst material, which loses efficacy over time. The team is looking broadly at these emissions, including carbon dioxide, methane and particulate soot, but the primary target is N2O.

Heres the thing: N2O is not made in your engine.

Its made over your catalytic converter, which is supposed to clean up the exhaust gas. What we want to do is understand how a diesel catalytic converter makes N2O, as a function of the fuel type, Epling said. If we know what hydrocarbons lead to the most or least N2O being made, then we can think about what is the right type of fuel that minimizes how much N2O is made.

Why the focus on N2O? The EPA, historically more concerned with air pollution than climate change, only began regulating N2O for diesel exhaust in 2011 after numerous studies showed the gas global warming potential, which scientists call GWP, to be 298 times greater than carbon dioxide. Thats because N2O traps more heat than other greenhouse gases.

Carbon dioxide is still the biggest greenhouse gas contributor from diesel because of how much is exhausted, said Carlos Weiler, a Ph.D. student in Eplings lab who is running the catalysis experiments for the project. The industry also doesnt have a good way of mitigating N2O production.

And so, we have to look at the other products that are formed, Weiler said. Theyre all destructive to the environment in some way, but in terms of the global warming potential of diesel exhaust, N2O is pretty potent.

Weiler will run experiments using different fuel inputs and catalyst combinations working under advisement from Epling and Davis, another widely recognized catalysis expert. Essentially, the unburnt hydrocarbons from combusted fuel will go into a catalytic reactor that simulates a diesel after-treatment system, and Weiler will measure what gases come out. Weiler will feed his results to Sugandha Verma, a graduate student in Paoluccis computational catalysis group, who will use computer simulations of catalytic reactions to predict outcomes and suggest further lab experiments.

But understanding how N2O is made by the catalytic converter, or how much, requires knowing what hydrocarbons leave the engine. Thats where Dedic comes in. Measuring whats left of a fuel after engine combustion is difficult because the compounds are structurally very similar. Traditional spectroscopy, which relies on how light interacts with various molecules, struggles to distinguish one hydrocarbon from the next. There are techniques to count the number of carbon and hydrogen molecules, but that entails removing gas samples from the engine during combustion, potentially changing the chemistry.

The best-case scenario is taking a measurement without interrupting the sample where the reaction is happening, Dedic said. Then youre getting a true measurement of whats occurring within your reactor.

Dedic is approaching these challenges from two angles. First, her team is designing a simplified reactor a burner that can safely combust liquid diesel formulations in the lab to isolate the combustion chemistry from other engine effects, such as fluid dynamics. The second is using ultrafast laser sources to develop new measurement techniques for the project.

We want to probe these molecules on the same time scales that theyre reacting and colliding with one another. We can observe molecular vibrations and rotations in time to provide more information than you get from traditional frequency-resolved spectroscopy, Dedic said.

While Dedics lab identifies diesel products for Eplings team to experiment with, Colosi-Petersons life-cycle assessment group is taking a systems approach, designing models to use the lab data to predict the costs of implementing the fuels in the real world. For example, if biomass-based diesels produce less N2O, how much agricultural or carbon-capturing forest land would be lost to fuel production? How much energy will we expend to grow biofuels, and where will that energy come from? Another important question Colosi-Peterson will examine is whether reducing N2O from diesel emissions even matters, or is it insignificant relative to the effects of other pollutants?

The project sets up a back-and-forth dynamic between the experimentalists and the modelers to better integrate the three research groups, which ordinarily would do their work independent of each other. Its a more proactive approach to improving the environmental performance of new technology in development, Colosi-Peterson said, rather than waiting for the technology to come along, and then assessing its societal cost.

Bill and Chloe might say, Heres a couple of things were thinking of doing, and I put all of that data into my model, and say, Well, heres what would happen if we did that at large scale. Is there any way you could make this part of the process a little bit less emitting?

And so I think that this desire to work more closely together during the bench scale is meant to cut out some of that trial and error and to be more intentional about the kinds of fundamental science that Bill and Chloe do.

The teams research will be exacting in its science, but holistic in its approach, Epling said, referencing the projects title, A holistic effort to decarbonize diesel for heavy duty transportation: Targeted combustion and exhaust catalysis research to improve life-cycle performance. He hopes the collaboration will lead to more nuanced policy-level understanding of to what extent proposed strategies such as a regulation to reduce N2O emissions from diesel engines can deliver meaningful global warming improvements.

One way policy may ultimately be improved is through the cross-training of the students collaborating on the project, who are just beginning their Ph.D. studies, Colosi-Peterson said.

Its really powerful when the student making the catalyst and running reactions in the lab is confronted with questions about the difference their technology will make, or its consequences, she said.

Conversely, Bill or Chloe may challenge some of the values my students are using in their systems-level analyses, in effect ground-truthing the assumptions we make when we build models for policy formation and legislation. I think theres a lot of benefit in students having to think at the bench scale and at the systems level.

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Diesel Engines Move Goods Around the Globe; UVA Engineers Work to Curb Emissions - University of Virginia

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Learning science and engineering gets more effective, accessible, and fun with this portable teaching box! – Yanko Design

STEM subjects have evolved since I was a student. With assistance from projectors and computers, the teaching methods have changed in the process to provide interactive ways to impart learning. In most educational systems, old-school methods are still prevalent, and the first-hand experience in the classroom suggests that students often find it difficult to grasp scientific concepts. This means that there is scope for a method of teaching science and engineering, and this is where the grasp it comes into the scene.

Designed by Augmented Haptics, and brainchild of Greg and Fabian, the rig is a demonstrative method of scientific teaching that classrooms will adopt instantly. The website of the product notes that Dr Gregory Quinn (Gerg) and Fabian Schneider, design engineer and computer scientist respectively, came up with the idea of grasp it with the intention to make learning in engineering and science more effective, accessible and fun.

From how it appears, its a very portable and convenient box of possibilities. The suitcase-style teaching equipment made from wood can be easily carried by teachers into the classroom and opened up to reveal endless possibilities of interactive, haptic and demonstrative learning. Using the grasp it, comprising a set of LEGO-like plastic pegs that can be attached together to form various tangible structures that can be tweaked, twisted and rebuilt depending on usage. These modules can be fastened to the board (attached to the equipment) through the holes built into it.

Interestingly, the grasp it presents a teaching method that keeps both teachers and students active. It is convenient to use and setup and inculcates the power of observation, thinking and reasoning in students. To this end, grasp it creates unlimited pedagogy possibilities using the power of touch and digital augmentation. The product comes with a small drawer that houses a tactile stick and a projector. When the interactive class of engineering demands, the projection can be turned on and the structures created using the plastic pegs can be applied with pressure at various points (using a tool). This can demonstrate the class with torque and force being applied on the creation to help them understand the reliability of a structure per se.

Grasp it is still a work in progress and limited to learning of science and engineering. It is expected to expand into many more STEM subjects including electronics, thermodynamics, computing and more.

Designer: Augmented Haptics

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Learning science and engineering gets more effective, accessible, and fun with this portable teaching box! - Yanko Design

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2021’s news highlights from the Faculty of Science and Engineering – The University of Manchester

Our world-leading science and engineering at The University of Manchester has been the cause of some exciting stories this year. Whether its space, materials, or the climate, our stories have been top news across the country and the world. Heres some of the most popular and interesting news releases from the Faculty of Science and Engineering in 2021. Enjoy!

January

The worlds finest fabric: 2021 started with an award win as a team of University scientists were honoured with theGuinness World Recordfor weaving threads of individual molecules together to create the worlds finest fabric, overtaking finest Egyptian linen.

February

Mysterious gamma-ray source identified: The start of the year continued with a spectacular space discovery as a rapidly rotating neutron star was found to be at the core of a celestial object now known as PSR J2039-5617. The astronomers findings were uniquely boosted by the Einstein@Home project, a network of thousands of civilian volunteers lending their home computing power to the efforts of the Fermi Telescopes work.

March

7bn innovation investment: A major investment boost for the North was announced in March to the tune of 7 billion to support economic growth in the region. The University of Manchester will be joined by leading innovators from business, science, academia and local government in developing the Innovation GM partnership as the basis of a formal collaboration deal with Government, suggesting it could create 100,000 jobs.

April

Solved: The Brazil nut puzzle: April saw researchers finally crack the age-old Brazil nut puzzle. For the first time they captured the complex dynamics of particle movement in granular materials, helping to explain why mixed nuts often see the larger Brazil nuts gather at the top. The findings could have vital impact on industries struggling with the phenomenon, such as pharmaceuticals and mining.

May

Graphene solves concretes big problem: In May, graphene met concrete in another world first which could revolutionise the concrete industry and its impact on the environment. In a joint venture, with Nationwide Engineering the team has laid the floor slab of a new gym with graphene-enhanced 'Concretene', removing 30% of material and all steel reinforcement. Depending on the size of onward projects, it is estimated to provide a 10-20% saving to its customers.

June

Plans for ID Manchester revealed: Summer began with the announcement that The University had found a partner to deliver the ambitious 1.5 billion ID Manchester project. The project will look to re-develop the North Campus to become a globally significant innovation district with specialist infrastructure to commercialise scientific discovery and R&D innovation.

July

New technology to help achieve Net Zero: July saw new efforts to help the world achieve its Net Zero targets with the aim of converting CO2, waste and sustainable biomass into clean and sustainable fuels and products. Catalysts are involved in helping to manufacture an estimated 80% of materials required in modern life, so are integral in manufacturing processes. As a result, up to 35% of the worlds GDP relies on catalysis. To reach net zero, it will be critical to develop new sustainable catalysts and processes.

August

Breakthrough in metal bonding: In summer we reported that scientists managed to successfully make actinide metals form molecular actinide-actinide bonds for the first time, opening up a new field of scientific study in materials research. Reported in the journal Nature, a group of scientists from Manchester and Stuttgart universities successfully prepared and characterised long-sought actinide bonding in an isolable compound.

September

Using astronaut blood to build space houses: September saw blood, sweat, tears and space with a discovery that astronaut blood could be the key to creating affordable housing in space. In their study, published in Materials Today Bio, a protein from human blood, combined with a compound from urine, sweat or tears, could glue together simulated moon or Mars soil to produce a material stronger than ordinary concrete, perfectly suited for construction work in extra-terrestrial environments.

October

New era of physics thanks to neutrino experiment: A two-decade long physics question was explored in October with a discovery that could cause a radical shift in our understanding of the universe. A major new physics experiment used four complementary analyses to show no signs of a theorised fourth kind of neutrino known as the sterile neutrino. Its existence is considered a possible explanation for anomalies seen in previous physics experiments.

November

New study shows link between weather and COVID-19 transmission: It wouldnt be a 2021 news round-up without mention of COVID-19. A new meta-analysis of over 150 research papers published during the early stages of the COVID-19 pandemic demonstrated the link between the weather and the spread of the illness. The research, published in the journal Weather, Climate, and Society, started with 158 studies that were published early in the pandemic using data before November 2020. It was discovered that early data was often inconsistent as they were affected by seasonal cycles and weather conditions impacting on the spread of the virus.

December

Challenging Einstein with stars: Rounding off another unusual year we saw scientists across the globe collaborate to challenge one of Einsteins greatest theories the theory of relativity. Using seven radio telescopes and taking 16 years the team successfully observed a double-pulsar system which demonstrated new relativistic effects that, while expected had never been observed and proved before.

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2021's news highlights from the Faculty of Science and Engineering - The University of Manchester

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Research Engineer job with KHALIFA UNIVERSITY | 275757 – Times Higher Education (THE)

Job Description

(Research Engineer)

Description

Research Engineer

Job Purpose

To provide high quality research support and undertake competitive research and development aimed at reporting to the Executive Affair Authority (EAA) thereby contributing to the academic and research translation mission of the University.

The primary purpose of the role is to manage equipment and pilots related to the Executive Affair Authority (EAA) project led by Khalifa University on airborne water generation (AWG). The platform developed during this project will involve experimental design, pilot operation and maintenance and modelling of data.

The research engineer will engage actively to acquire new and unique skills necessary to advance their career with guidance from the advisor. These skills include, but are not limited to, the ability to present research plans and findings in a convincing style, both in oral and written modes of communication, the ability to understand research group management and supervision of others, the ability to establish contacts and network with colleagues pursuing a similar research agenda, the ability to organize and teach a class or a course if more inclined towards a teaching career (if relevant).

Key Roles & Responsibilities

Strategic Responsibilities

Operational Responsibilities

Supervisory Responsibilities

Qualifications

Qualifications & Experience

Required Qualifications

Required Experience

Should you require further assistance or if you face any issue with the online application, please feel to contact the Recruitment Team (recruitmentteam@ku.ac.ae).

Primary Location: KUK Khalifa UniversityJob: Research EngineerSchedule: RegularShift: StandardJob Type: Full-time

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Research Engineer job with KHALIFA UNIVERSITY | 275757 - Times Higher Education (THE)

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Data Curation and Governance are the Top-Two Data Engineering Challenges for 2022 – Solutions Review

Data curation and governance are the top-two data engineering challenges for 2022, according to a report by Gradient Flow and Immuta.

Data curation and governance are the top-two data engineering challenges for 2022, according to a new report commissioned by Gradient Flow and Immuta. The 2022 State of Data Engineering survey examined the changing landscape of data engineering and operations challenges, tools, and opportunities. The data engineering challenges that data professionals worry about most come after data has been extracted, loaded, and transformed. Data for the report was gathered from a global audience of 372 respondents, more than half of which were data engineers or data architects, over 61 days.

The main data engineering challenges cited by those polled include validation, data monitoring and auditing for compliance, data masking and anonymization, as well as data discovery. Nearly two-thirds of respondents (65 percent) said their company is either 100 percent cloud-based or will be in the next 12-to-24 months. In the same way, 62 percent of respondents signaled their plans to adopt one of the top-five cloud databases and platforms (Amazon Redshift, Amazon Athena, Google BigQuery, Databricks, and Snowflake) in the months ahead.

While 64 percent of those polled come from organizations already collecting and storing sensitive data, the vast majority (88 percent) indicated that their firms are subject to one or more data use rules or regulations like GDPR, HIPAA, CCPA, and SOC 2. Additionally, 30 percent of respondents reported a need to comply with internal, company-specific rules around data. Somewhat concerning is that more than a quarter of all those polled were unsure of what (if any) data quality solution their organization is currently using.

The data engineering landscape is changing and maturing. Whereas years ago there were few, if any, tools to solve data challenges, a plethora of technologies both commercial and open-source are now available. These technologies are helping organizations leverage their sensitive data for real-time access and analytics, all while protecting it in accordance with a growing body of regulatory requirements. There are also an entirely new crop of data engineering training courses and online certification options available (tto enable technical and non-technical users alike to develop on-the-fly skills.

Tim is Solutions Review's Editorial Director and leads coverage on big data, business intelligence, and data analytics. A 2017 and 2018 Most Influential Business Journalist and 2021 "Who's Who" in data management and data integration, Tim is a recognized influencer and thought leader in enterprise business software. Reach him via tking at solutionsreview dot com.

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Engineering and IT jobseekers connect with employers at career fair – Chicago Daily Herald

After going virtual a year ago, the 6th annual Engineering and Information Technology Internship & Career Fair returns to an in-person setting. The event, hosted by the College of Lake County's (CLC) Career and Job Placement Center (CJPC), will run from 1-3 p.m. on Friday, Jan. 7 in the A-Wing of the Grayslake Campus.

"We are very excited to have the fair return to an in-person event," Workforce Development Manager Gina Smith said. "Historically, this is our largest recruiting event of the year, attracting students from engineering and IT programs across the country."

The career fair allows students and other jobseekers to meet with an expected 20 employers this year, who are eager to bring on top talent. Students from top engineering schools such as University of Illinois, Purdue, Michigan State, Ohio State and others, along with professional-level job seekers will be in attendance.

"The IT focus is a new addition in the past two years," Smith said. "Local employers expressed a desire to target this highly skilled group of students, new graduates and the wealth of experienced talent in Lake County."

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Engineering and IT jobseekers connect with employers at career fair - Chicago Daily Herald

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PhD Studentship, Aeronautical and Astronautical Engineering job with UNIVERSITY OF SOUTHAMPTON | 275929 – Times Higher Education (THE)

Aeronautical and Astronautical Engineering

Location: Highfield CampusClosing Date: Monday 28 February 2022Reference: 1648321DA

Predicting unknown flow physics by integrating experimental measurements into low-fidelity simulations

Supervisory Team: Sean Symon and Bharath Ganapathisubramani

Project description

Numerical simulations of fluid flows play an important role in aerodynamic design since experimental measurements are typically limited and difficult to measure in all regions of the flow. Simulations can provide significantly more information than experiments, but modelling assumptions are necessary since it is not computationally tractable to simulate realistic flow conditions. This project investigates a more active role for experimental data by using it as an input to simulations.

Experimental measurements, which are incomplete and uncertain, are fed into a low-fidelity simulation to produce a hybrid flow field that mimics large-scale features in the experiment. The objective is to assimilate experimental measurements of three-dimensional velocity fields around a finite-aspect ratio wing. The experimental data will be time-resolved and fed into a low-fidelity CFD solver. Once this has been achieved, the framework will consider external disturbances, such as gusts, which lead to transient flow behaviour. The assimilated flow fields will uncover additional flow physics and be used to construct reduced-order models.

Entry Requirements

A very good undergraduate degree (at least a UK 2:1 honours degree, or its international equivalent).

Closing date:applications should be received no later than 28 February 2022 for standard admissions, but later applications may be considered depending on the funds remaining in place.

Funding: For UK students, Tuition Fees and a stipend of 15,609 tax-free per annum for up to 3.5 years.

How To Apply

Applications should be made online. Select programme type (Research), 2022/23, Faculty of Physical Sciences and Engineering, next page select PhD Engineering & Environment (Full time). In Section 2 of the application form you should insert the name of the supervisor Sean Symon

Applications should include:

Apply online: https://www.southampton.ac.uk/courses/how-to-apply/postgraduate-applications.page

For further information please contact: feps-pgr-apply@soton.ac.uk

The School of Engineering is committed to promoting equality, diversity inclusivity as demonstrated by our Athena SWAN award. We welcome all applicants regardless of their gender, ethnicity, disability, sexual orientation or age, and will give full consideration to applicants seeking flexible working patterns and those who have taken a career break. The University has a generous maternity policy, onsite childcare facilities, and offers a range of benefits to help ensure employees well-being and work-life balance. The University of Southampton is committed to sustainability and has been awarded the Platinum EcoAward.

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PhD Studentship, Aeronautical and Astronautical Engineering job with UNIVERSITY OF SOUTHAMPTON | 275929 - Times Higher Education (THE)

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Tax issue and pandemic delay liquidation process of UB Engineering – Deccan Herald

Four years after a court ordered the winding up of Vijay Mallyas UB Engineering, its creditors have received about Rs 50 crore so far from the liquidation proceedings, according to sources close to the resolution process professional.

That is less than half of the companys liquidation value of Rs 106 crore. UB Engineering, which counted Axis bank, YES bank, Corporation Bank, IDBI Bank and Laxmi Vilas Bank as some of its lenders, owed them over Rs 450 crore. The company, which was first sent to the insolvency court for resolution, was then forced to liquidate after it failed to find a buyer under the Insolvency and Bankruptcy Code.

Its liquidation proceedings have taken longer than usual due to a tax issue and the pandemic-induced lockdowns, the sources said.

I think in infrastructure cases, especially in dormant companies, liquidation should not take more than one years time, New Delhi-based insolvency professional Nilesh Sharma said, adding the liquidator could consider selling the companys assets through auctions to hasten the process.

IBCs last newsletter for the July-September quarter showed 39% of the liquidation cases have been going on for over two years and 29% have been going for 1-2 years.

There were impediments like service tax liens not being lifted from property. Now that has been cleared. That is helping us finish the liquidation, a source close to the liquidation proceedings said.

The source added that only the secured creditors of UB engineering would get their dues.

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Machine Learning Democratized: Of The People, For The People, By The Machine – Forbes

Supporters raise signs as Democratic presidential hopeful Bernie Sanders campaign rally in downtown ... [+] Grand Rapids, Michigan, on March 8, 2020. - Democratic presidential hopefuls Joe Biden and Bernie Sanders secured crucial endorsements Sunday from prominent black supporters just days ahead of the first round of voting to pit them in a head-to-head contest. (Photo by JEFF KOWALSKY / AFP) (Photo by JEFF KOWALSKY/AFP via Getty Images)

Technology is a democratic right. Thats not a legal statement, a core truism or even any kind of de facto public awareness proclamation. Its just something that we all tend to agree upon. The birth of cloud computing and the rise of open source have fuelled this line of thought i.e. cloud puts access and power in anyones hands and open source champions meritocracy over hierarchy, an action which in itself insists upon access, opportunity and engagement.

Key among the sectors of the IT landscape now being driven towards a more democratic level of access are Artificial Intelligence (AI) and the Machine Learning (ML) methods that go towards building the smartness inside AI models and their algorithmic strength.

Amazon Web Services (AWS) is clearly a major player in cloud and therefore has the breadth to bring its datacenters ML muscle forwards in different ways, in different formats and at different levels of complexity, abstraction and usability.

While some IT democratization focuses on putting complex developer and data science tools in the hands of laypeople, other democratization drives to put ML tools in the hands of developers not all of whom will be natural ML specialists and AI engineers in the first instance.

The recently announced SageMaker Studio Lab is a free service for software application developers to learn machine learning methods. It teaches them core techniques and offers them the chance to perform hands-on experimentation with an Integrated Development Environment (in this case, a JupyterLab IDE) to start creating model training functions that will work on real world processors (both CPU chips and higher end Graphic Processing Units, or GPUs) as well as the gigabytes of storage these processes also require.

AWS has twinned its product development with the creation of its own AWS AI & ML Scholarship Program. This is a US$10 million investment per year learning and mentorship initiative created in collaboration with Intel and Udacity.

Machine Learning will be one of the most transformational technologies of this generation. If we are going to unlock the full potential of this technology to tackle some of the worlds most challenging problems, we need the best minds entering the field from all backgrounds and walks of life. We want to inspire and excite a diverse future workforce through this new scholarship program and break down the cost barriers that prevent many from getting started, said Swami Sivasubramanian, VP of Amazon Machine Learning at AWS.

Founder and CEO of Girls in Tech Adriana Gascoigne agrees with Sivasubramanians diversity message wholeheartedly. Her organization is a global nonprofit dedicated to eliminating the gender gap in tech and she welcomes what she calls intentional programs like these that are designed to break down barriers.

Progress in bringing more women and underrepresented communities into the field of Machine Learning will only be achieved if everyone works together to close the diversity gap. Girls in Tech is glad to see multi-faceted programs like the AWS AI & ML Scholarship to help close the gap in Machine Learning education and open career potential among these groups, said Gascoigne.

The program uses AWS DeepRacer (an integrated learning system for users of all levels to learn and explore reinforcement learning and to experiment and build autonomous driving applications) and the new AWS DeepRacer Student League to teach students foundational machine learning concepts by giving them hands-on experience training machine learning models for autonomous race cars, while providing educational content centered on machine learning fundamentals.

The World Economic Forum estimates that technological advances and automation will create 97 million new technology jobs by 2025, including in the field of AI & ML. While the job opportunities in technology are growing, diversity is lagging behind in science and technology careers.

The University of Pennsylvania Engineering is regarded by many in technology as the birthplace of the modern computer. This honor and epithet is due to the fact that ENIAC, the worlds first electronic, large-scale, general-purpose digital computer, was developed there in 1946. Professor of Computer and Information Science (CIS) at the university Dan Roth is enthusiastic on the subject of AI & ML democratization.

One of the hardest parts about programming with Machine Learning is configuring the environment to build. Students usually have to choose the compute instances, security polices and provide a credit card, said Roth. My students needed Amazon SageMaker Studio Lab to abstract away all of the complexity of setup and provide a free powerful sandbox to experiment. This lets them write code immediately without needing to spend time configuring the ML environment.

In terms of how these systems and initiatives actually work, Amazon SageMaker Studio Lab offers a free version of Amazon SageMaker, which is used by researchers and data scientists worldwide to build, train, and deploy machine learning models quickly.

Amazon SageMaker Studio Lab removes the need to have an AWS account or provide billing details to get up and running with machine learning on AWS. Users simply sign up with an email address through a web browser and Amazon SageMaker Studio Lab provides access to a machine learning development environment.

This thread of industry effort must also logically embrace the use of Low-Code/No-Code (LC/NC) technologies. AWS has built this element into its platform with what it calls Amazon SageMaker Canvas. This is a No-Code service intended to expands access to Machine Learning to business analysts (a term that AWS uses to broadly define line-of-business employees supporting finance, marketing, operations and human resources teams) with a visual interface that allows them to create accurate Machine Learning predictions on their own, without having to write a single line of code.

Amazon SageMaker Canvas provides a visual, point-and-click user interface for users to generate predictions. Customers point Amazon SageMaker Canvas to their data stores (e.g. Amazon Redshift, Amazon S3, Snowflake, on-premises data stores, local files, etc.) and the Amazon SageMaker Canvas provides visual tools to help users intuitively prepare and analyze data.

Amazon SageMaker Canvas uses automated Machine Learning to build and train machine learning models without any coding. Businesspeople can review and evaluate models in the Amazon SageMaker Canvas console for accuracy and efficacy for their use case. Amazon SageMaker Canvas also lets users export their models to Amazon SageMaker Studio, so they can share them with data scientists to validate and further refine their models.

According to Marc Neumann, product owner, AI Platform at The BMW Group, the use of AI as a key technology is an integral element in the process of digital transformation at the BMW Group. The company already employs AI throughout its value chain, but has been working to expand upon its use.

We believe Amazon SageMaker Canvas can add a boost to our AI/ML scaling across the BMW Group. With SageMaker Canvas, our business users can easily explore and build ML models to make accurate predictions without writing any code. SageMaker also allows our central data science team to collaborate and evaluate the models created by business users before publishing them to production, said Neumann.

As we know, with all great power comes great responsibility and nowhere is this more true than in the realm of AI & ML with all the machine brain power we are about to wield upon our lives.

Enterprises can of course corral, contain and control how much ML any individual, team or department has access to - and which internal and external systems it can then further connect with and impact - via policy controls and role-based access systems that make sure data sources are not manipulated and then subsequently distributed in ways that could ultimately prove harmful to the business, or indeed to people.

There is no denying the general weight of effort being applied here as AI intelligence and ML cognizance is being democratized for a greater transept of society and after all who wouldnt vote for that?

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Machine Learning Democratized: Of The People, For The People, By The Machine - Forbes

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Top Python Machine Learning Libraries to Explore in 2022 – Analytics Insight

These 10 python machine learning libraries are the best

Python is the most popular programming language for data science projects. And on the other side, machine learning is a trending topic that is across the globe these days. Python machine learning libraries have become the language for implementing machine learning algorithms. To grasp data science and machine learning, you need to learn Python. Here are the top Python machine learning libraries to explore in 2022.

TensorFlow is an open-source numerical computing library for machine learning based on neural networks. It was created by the Google Brain research team in 2015 to use internally in Google products. Later, it started to gain a lot of popularity among many companies and start-ups such as Airbnb, PayPal, Airbus, Twitter, and VSCO using it on their technology stacks. It is one of the top Python machine learning libraries to explore.

PyTorch is one of the largest machine learning libraries that was designed and developed by Facebooks AI research group. It is used for natural language processing, computer vision, and other similar kinds of tasks. It is one of the top python machine learning libraries to explore. It is used by companies such as Microsoft, Facebook, Walmart, Uber, and others.

Keras is a fast experimentation platform with deep neural networks but it has soon gained a standalone Python ML library. It has a comprehensive ML toolset that aids companies such as Square, Yelp, Uber, and others to handle text and image data effectively. It has a user-friendly interface and has multi-backend support. It has a modular and extensible architecture. It is one of the top Python machine learning libraries to explore.

Orage3 is a software package that includes tools for machine learning, data mining, and data visualization. It was developed in 1996, the scientists at the University of Ljubljana created it with C++. It is one of the top Python machine learning libraries to explore. The features that make Orange3 qualify for this top list are powerful prediction modeling and algorithm testing, widget-based structure, and ease of learning.

Python wasnt initially developed as a tool for numerical computing. The advent of NumPy was the key to expanding Pythons abilities as mathematical functions, based on which machine learning solutions would be built. Using this library is beneficial because of robust computing capabilities, the large programming community, and high performance. It is one of the top Python machine learning libraries to explore.

Along with NumPy, this library is a core tool for accomplishing mathematical, engineering computations, and scientific. The main reasons why Python specialists appreciate SciPy are its easy-to-use library, fast computational power, and improved computations. SciPy is built on top of NumPy and can operate on its arrays, ensuring higher quality and faster execution of computing operations. It is one of the top python machine learning libraries to explore.

Scikit-learn was firstly made as a third-party extension to the SciPy library. It is one of the top libraries on GitHub. The library is an indispensable part of the technology stacks of Booking.com, Spotify, OkCupid, and others. It is one of the top python machine learning libraries to explore. Scikit-learn also found a place on our list because it is great at classical machine learning algorithms, easily interoperable with other SciPy stack tools.

Pandas is a low-level Python library built upon NumPy. Everything started with the AQR financial company that needed help with quantitative analysis of its financial data. Wes McKinney is a developer at the company who started the creation of Pandas. Pandas have powerful data frames and flexible data handling. It is one of the top Python machine learning libraries to explore.

A unity of NumPy, Matplotlib, and SciPy is supposed to replace the need to use the proprietary MATLAB statistical language. Python packages are also available for free and more flexibly which can make a choice of many data scientists. It is one of the top Python machine learning libraries to explore. The reason to include Matplotlib is because of its comprehensive set of plotting tools.

In 2007, the Montreal Institute of Learning Algorithms was created by Theano for evaluating and manipulating various mathematical expressions. Based on these expressions, the Python machine learning library allows building optimized deep learning neural networks. It has a stable simultaneous computing, fast execution speed, and optimized stability. It is one of the top python machine learning libraries to explore.

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