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At opposite ends of Illinois, two leaders in emerging construction technologies are collaborating to demonstrate how AI, ML (machine learning) and CV (computer vision) can validate constructions digital-data mandate: Save time, save money, save lives.

For the fourteenth consecutive year, U.S. construction valuation has grown and is now at $1.98 trillion, noted Mani Golparvar, professor of civil engineering, computer science and technology entrepreneurship at the Grainger College of Engineering/University of Illinois. But, hes reported, 53% of construction projects are behind schedule, 66% are over budget and nearly all carry cost overruns.

Golparvar estimated that $280 billion in potential added value exists through improved coordination. As an industry, weve significantly improved the way we plan projects. But the way we plan the job, execute, monitor our execution and use whatever we monitor to update a planthese four problems all contribute to why projects are behind schedule and over budget. If youre looking to these issues, 80% are preventable: Project team members do not have visibility early enough to be able to come up with a remedy.

To that end, Golparvar is founder and chief strategy officer of Reconstruct, a Visual Command Center that uploads schedules, 2D and 3D models, and reality capture so users can track progress as well as coordination and communications problems. Reconstruct says its benefits include a 30% reduction in time reporting field progress, and 25%-40% in improved schedule management and proactive risk mitigation. Eighty-five companies and several universities now partner with it.

Burcin Kaplanoglu mirrored Golparvars remarks. Speaking from the Oracle Industry Lab he heads just outside Chicago, he noted that U.S. infrastructure is graded as a C- by the American Society of Civil Engineers. At the same time, he said, we [would] need to hire 500,000 people a year, on top of regular hiring, to meet demand.

Consequently, he added, we need to automate processes, gain efficiency and improve safety with technology. Talking to your data is going to have tremendous benefits for the construction engineer. Getting that response really quickly is going to change how we interact with technology.

Golparvar and Kaplanoglu recently joined forces around a Reality Mapping Experiment (see Constructing the Future sidebar, pg. 46) at the Oracle lab to explore what combination of tools, processes and teams can best conduct reality capture and mapping on-site, reducing time and cost and ensuring safety.

Thats of great value to an industry facing an ever-growing tech stack, as Jennifer Suerth, senior vice president of Pepper Construction, which built the lab, said in an Oracle TV video. Autonomy allows us to continue to get the work done but be efficient with the resources we have.

Golparvar and Kaplanoglu offered quick definitions of ML, CV and AI.

Machine learning allows computers to learn from data to improve and streamline processes. Machine learning is continuing to improve the construction industry, Kaplanoglu said. We need to figure out how to optimize and how to forecast better.

Computer vision is a form of AI, Golparvar said. It involves the process of analyzing pictures and videos and generating actionable insights from them, doing image processing and understanding the geometry of the scene. Recognizing objects by automatically analyzing them can track progress, detect and recognize anomalies, and coordinate and optimize operations.

To solve that coordination issue requires everyone to be on the same page in terms of what was there versus what should be there, Golparvar continued. In construction, weve really taken advantage of design information that we call BIM, building information models, 3D representation of design. What if you tie them against the schedule? At any snapshot in time, you can click on any two points, you can measure the length, area and volume. The picture shows you what was promised to be done; the picture in the background shows you what has been achieved on the job. And the delta between the twowhat needs to be coordinated in terms of quality, safety and progresscan be color-coded in red and green: what is on the schedule, what is behind.

Generative AI is the third leg of this digital triad. AI involves machines mimicking human cognitive functions, but theres more to that than large language models. GenAI is going to help us do project management and planning, Kaplanoglu said. AI can inform the entire construction cycle, from estimating preliminary costs, to tracking quality and productivity, to the more tactical tasks of ordering and payment.

Kaplanoglu ventured predictions for AI, ML and CV in construction:

Unified, intelligent clouds will unite software, platforms and infrastructure within end-to-end solutions, easing management and accelerating change.

Oracle is a major player in the world of project management and design, and in 2018 Kaplanoglu co-founded the Oracle Industry Lab to advance the companys innovation in construction and engineering. He explained the rationale. When youre running day-to-day operations, its very hard to stop what youre doing and try these new technologies. So, by creating a testbed where we bring the technology and they bring their problems, we are creating a neutral space to try and learn from it.

To realize this, writing a data strategy around machine learning was followed by building an ecosystem because our products do scheduling, safety, risk management, cost. We had almost 2,000 engagements in the first two years, he said. The pandemic only spurred a concentration on autonomy. All of a sudden, we did remote site monitoring, cameras and drone flights, route inspections. We could work with architects and designers remotely; they didnt have to come to the site. Everything was video.

The original sandbox gave way to a much larger, industry-benchmarking construction space for investigatingrealistic job site situations. It opened in April 2022on time, on budget. With analogs now operating in the United Kingdom and Australia, it has passed 13,000 engagements. I think construction really got used to digital imagery, 3D point clouds, using drones, capturing data, Kaplanoglu said.

Construction has some relatively unique characteristics, from a constantly changing environment to wind and weather exposure. Its like building a prototype each time, Kaplanoglu added. Data can bridge that. For example, Komatsu, the massive construction and mining equipment manufacturer, is using Nvidias Jetson edge AI platform to impart intelligence to trucks, excavators and the like. AI and machine learning are critical to providing real value to the construction space, a Nvidia spokesman toldInside Unmanned Systems.

In terms of project management, Kaplanoglu said, theres tons of opportunities when we want to use machine learning, computer vision and GenAI.

The need to test construction innovation, validation and measurement in real-world situations led to a collaboration between Oracles. Smart Construction Platform ecosystem and founding partner tenant Reconstructs visualization tools. The goal was to create guidelines and tool utilizations best suited for particular use cases, improving documentation of and guidelines for progress, quality and assessment of as-built conditions through a structures lifecycle.

The Report explains costs, time spent on different tools, and what kind of results you should be expecting from each, Golparvar explained about the 2023 document.

Pepper Construction and Clayco offered project expertise on the construction side, with leading drone company Skydio and 3D measurement company FARO Technologies providing technical expertise.

Eight different ways of capturing and mapping data were scanned across resolutions, speed, cost and deliverables. These included manual and autonomous drones, 360 and smartphone cameras, and various stationary and mobile LiDAR techniques. Capture data was processed into Oracles platform and then into Reconstruct, which could process them for a consistent viewing experience. Post-processing, photogrammetry and 4D simulations could be viewed for actionable and exportable results.

Construction is said to be the number one civil market for drones, which can take pictures quickly in hard-to-access areas while their sensors allow for obstacle avoidance. For the experiment, a fully autonomous Skydio drone used its Indoor Capture vision-based autonomous software over several iterations to, as Solutions Engineering Manager Colin Romberger put it on Oracle TV, get total coverageto have the best data to put into photogrammetry software for Reconstruction.

Kaplanoglu discussed gains from using autonomous drones. The time savings dropped significantly, because youre already preprogrammed. Human drone flights also improved with repetition. Theres still some autonomy, like it avoids obstacles and you can do flight plans.

But the biggest drop happens when its fully autonomous.

You always want to know about what youre trying to solve, what kind of tools you have and what kind of resources you can leverage to solve that problem, Golparvar said.

He and Kaplanoglu enumerated how AI and associated technologies can self-direct and break down planning and actions.

Site selection already involves machine learning, Kaplanoglu said, because you have certain parameters to make sure you comply with, like height restrictions, zoning. You can use machine learning to pick the optimal location.

Design can use machine learning to investigate bigger datasets and find helpful patterns. Golparvar: You want to capture the architects intent and transform it into a document that can be used as a base of design. GenAI can provide and reconcile design alternatives, incorporating everything from routing mechanical systems to complying with local codes. The way it works, Kaplanoglu added, you sketch things and then people try to visualize it and build models. Learning from them can offer significant time savings. Im talking about doing this in an hour versus doing it in months.

Construction has its own mini cycle. You define your scope, you hire engineers and architects, and now you need a contractor to build it Kaplanoglu said. You send an RFP for a contractor to build it.

RFPs, however, can be limited and schedulers may cut and paste. Kaplanoglu offered a solution: We can upload an RFP document to Oracle Cloud infrastructure. It reads the document and gives you a summary and then it asks you questions. Its going to show you an early-stage prototype, and it builds a schedule for you. Its going to be a good template for you to build your own schedule.

Now something that would have taken you three days is going to take you maybe two to three hours.

Operations can use computer vision. How many times does someone show up at their location and they dont even know what the specifications are or what theyre supposed to do, Kaplanoglu said. You can take a picture or video and then computer vision can tell you, Its this manufacturer, this model, or vision can process the video or image and say, Oh, theres rust in this corner.

Predictive maintenance can significantly reduce costs and downtime. Safety also can be empowered by AI. Many of the AI tools that we have are completely focusing on offering better awareness for our workers, Golparvar said.

Skydio has participated in the Oracle labs work, and Kaplanoglu offered an example of its drones adding value. We have a great relationship with Skydio. Instead of taking images that humans need to tag, locate and input, that data goes to our product, and then our Vision Services recognizes the rust and registers it to our work order system. But it doesnt just register; it tells you the lat[itude] and long[itude], the location, the severity of the damage, it helps you create a ticket.

Basically, we automate the whole thing.

Imprecise capture, incorrect inputting and lack of updating can undermine results. Still, the industry has significantly improved its use of cloud, Kaplanoglu said. The data is more accessible, you can put more line, compute is easier. There are a lot of benefits to it, and our industry really took that to heart.

Golparvar advocates a step-by-step process. We do a maturity assessment on the readiness of a company for adopting and adapting AI-driven products, and what we can do to make sure that these products fit into the existing workflows. Because people are resistant to change.

We also need to demonstrate the return on investment, at the project level and the individual levelquality, speed, time and money saved. So, the best strategy is to kind of introduce one next step to that project team so we can take them from where they are to that future where many of their steps will be fully automated, and make sure they see how AI is verifiable, to have that element of trust.

Kaplanoglu is bullish in terms of technology adoption. Machine learning, computer vision and GenAI are all going to have a big impact in the next three to five years.

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Inside AI: Talking to the Data - Inside Unmanned Systems

A new plugin from French developer Anemond utilizes machine learning to split sounds up into individual layers that can be remixed and randomized, generating thousands of rhythmic and melodic variations of any sample or loop.

Based on the same machine learning engine as Anemond's Factorsynth, a "fully-fledged sound design studio", Factoid 2 offers a simpler interface, reduced feature set and a lighter CPU load than its bigger and more expensive brother.

Described as a "loop revamper", Factoid's machine learning engine can extract between 2 and 8 layers from any sample. Anemond explains that Factoid isn't a stem separation tool, but instead a unique kind of processor that decomposes, or "factorizes" audio into a set of "temporal and spectral components".

This is "not intended to unmix instruments," Anemond says, "but to discover and extract unpredictable but interesting sound elements with a certain degree of structure", like notes, drum hits, and rhythmic or melodic motifs.

Once these layers are extracted, Factoid can remix and manipulate them in a variety of ways to create new ideas. You're able to randomize sample slices on a quantized grid to create glitchy patterns, transform melodies into textures, and solo, mute and adjust the levels for each factorized layer. After manipulating your sample, you can drag and drop the results from Factoid into your DAW.

Factoid 2 is priced at 29 and is available as a standalone app or VST3/AU plugin for macOS and Windows.

Find out more on Anemond's website.

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Anemond's Factoid 2 is an experimental sampler plugin that uses machine learning to "decompose", remix and ... - MusicRadar

Key Takeaways:

Researchers from Carnegie Mellon University and Los Alamos National Laboratory have used machine learning to create a model that can simulate reactive processes in a diverse set of organic materials and conditions.

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"It's a tool that can be used to investigate more reactions in this field," said Shuhao Zhang, a graduate student in Carnegie Mellon University'sDepartment of Chemistry. "We can offer a full simulation of the reaction mechanisms."

Zhang is the first author on the paper that explains the creation and results of this new machine learning model, "Exploring the Frontiers of Chemistry with a General Reactive Machine Learning Potential," which was published in Nature Chemistry on March 7.

Though researchers have simulated reactions before, previous methods had multiple problems. Reactive force field models are relatively common, but they usually require training for specific reaction types. Traditional models that use quantum mechanics, where chemical reactions are simulated based on underlying physics, can be applied to any materials and molecules, but these models require supercomputers to be used.

This new general machine learning interatomic potential (ANI-1xnr) can perform simulations for arbitrary materials containing the elements carbon, hydrogen, nitrogen, and oxygen and requires significantly less computing power and time than traditional quantum mechanics models. According to Olexandr Isayev, associate professor of chemistry at Carnegie Mellon and head of the lab where the model was developed, this breakthrough is due to developments in machine learning.

"Machine learning is emerging as a powerful approach to construct various forms of transferable atomistic potentials utilizing regression algorithms. The overall goal of this project is to develop a machine learning method capable of predicting reaction energetics and rates for chemical processes with high accuracy, but with a very low computational cost," Isayev said. "We have shown that those machine learning models can be trained at high levels of quantum mechanics theory and can successfully predict energies and forces with quantum mechanics accuracy and an increase in speed of as much as 6-7 orders of magnitude. This is a new paradigm in reactive simulations."

Researchers tested ANI-1xnr on different chemical problems, including comparing biofuel additives and tracking methane combustion. They even recreated the Miller experiment, a famous chemical experiment meant to demonstrate how life originated on Earth. Using this experiment, they found that the ANI-1xnr model produced accurate results in condensed-phase systems.

Zhang said that the model could potentially be used for other areas in chemistry with further training.

"We found out it can be potentially used to simulate biochemical processes like enzymatic reactions," Zhang said. "We didn't design it to be used in such a way, but after modification it may be used for that purpose.

In the future, the team plans to refine ANI-1xnr and allow it to work with more elements and in more chemical areas, and they will try to increase the scale of the reactions it can process. This could allow it to be used in multiple fields where designing new chemical reactions could be relevant, such as drug discovery.

- This press release was originally published on the Carnegie Mellon University website

Link:
Advancing Chemistry with AI: New Model for Simulating Diverse Organic Reactions - Lab Manager Magazine

Generative artificial intelligence (AI) allows anyone to leverage machine learning (ML) capabilities using natural language, and it is extremely intuitive to use. When users are able to search, analyze, and draw conclusions in secondsfrom extensive information that exists across their organization or the internetthey can make more informed decisions at speed. This can help them answer customer queries efficiently, pinpoint significant changes to contracts, and assess risks such as fraud more accurately. Organizations can make more effective use of resources and provide better services by gaining useful insights, such as peak use patterns or the likelihood of good outcomes in different scenarios.

Generative AI models are trained on a large volume of datasets, which gives them the ability to generate answers to a range of questions and summarize findings in a meaningful way for the user. Common use cases in public sector could be determining the best way to reduce Friday afternoon congestion, or how to manage building utilities more efficiently.

To suggest answers, generative AI systems can combine and cross-analyse a diverse range of data in milliseconds to produce a spoken, graphical, or easy-to-understand written summary.

Generative AI models are as reliable as the data theyre trained on and can access. There is a risk of hallucination, which is when the models make something up that may sound plausible and factual but which may not be correct. Anyone who bases decisions and actions on the results of an AI-based query needs to be able to stand by that choice and articulate how it was reached, to avoid unfair targeting or other forms of bias, resource waste, or other questionable decisions.

Any organizations or teams that use generative AI to make decisions or prioritize actions, must build responsible AI systems that are fair, explainable, robust, secure, transparent, and that safeguard privacy. Good governance is fundamental for responsible systems. Its important to be able to justify how these process-support systems arrived at choices.

Organizations need to design and use a proven, well-architected AI framework and operating model to provide for continuous monitoring of the system in use. There has to be full awareness of potential issues and whats needed to mitigate them. Those issues could involve limitations with the data (its quality, level of standardization, currency, and completeness) and any risk of bias, data-protection breaches, or other regulatory or legal infringement.

Systems must be transparent: if someone challenges a decision supported by the AI system, they can track the reasoning behind it. Examples of this could be citing specific sources used in summarisation or tracking the customer data that was used in any ML models.

For a deeper dive, watch our four-part AWS Institute Masterclass series on AI/ML:

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Generative AI: Understand the challenges to realize the opportunities | Amazon Web Services - AWS Blog

Artificial intelligence (AI) is revolutionizing the way we work and live. From helping us solve social challenges and improving business outcomes to boosting our productivity and enhancing our creativity, the possibilities and potential of AI seem endless.

But while recent innovations have brought AI into the spotlight, the artificial intelligence field is not new, says Dr. Suman Kumar, Associate Professor and Computer Science Department Chair at Troy University.

It has been evolving for decades, but now, with memory advances and graphic processing units (GPUs), we can use AI far more effectively as compared to 30 or 40 years ago, Dr. Kumar explains. Now, almost every industry and field uses deep learning. It is one of the most profound shifts. AI now touches almost every aspect of human life.

These advancements have created a heightened demand for computer scientists, machine learning engineers, data scientists, software engineers, research scientists and more. So how can you become part of this promising field? By learning how to specialize in artificial intelligence.

Artificial Intelligence Job Opportunities Abound

AI is in the news today because of advances in large language models like ChatGPT and Google Bard, both powerful AI systems that can be used to produce content, conduct research and more. Stronger machine-learning capabilities have also greatly expanded what we can do with AI, from improving security measures with facial recognition to helping us improve our health with wearable devices that track our heart rates and activity levels.

AI, Dr. Kumar says, is here to stay.

All kinds of organizations, whether its big box retailers or health care, are becoming more and more data-driven. Algorithmic trading in the stock market is also data-driven. AI can help us make sense of that data. Government organizations, such as the IRS, can use AI to help detect fraud. Video game designers use it in game design. Cities can use it to predict traffic patterns. Ive even seen research about its applications in dentistry. Whether someone is using it predictively to get ahead of a problem or generativity to help them create something, AI is applicable to almost any discipline.

That means, now more than ever, professionals who can design, develop, train and improve AI are in high demand. Take computer and information research scientists, for example.

According to the U.S. Bureau of Labor Statistics (BLS), jobs within this field will grow by 23% through 2032, which is much faster than average. The average annual salary is $136,620, the BLS reports.

There are a lot of misconceptions about AI, Dr. Kumar shares. One of those misconceptions is that AI can never fail. AI does fail, and the field needs more humans behind it because it doesnt have human intelligence. We design it to operate like human intelligence, but it will never truly be human.

So, what kinds of other careers in AI are out there?

Many artificial intelligence job opportunities will be in health care, shares Dr. Kumar. Health care increasingly employs artificial intelligence to help understand patient data and prevent disease. Bioinformatics, a field in which you analyze biological data, also holds promise for those interested in AI.

Other artificial intelligence jobs include machine learning specialist, AI researcher, data scientist, video game designer, research engineer, AI systems engineer and AI specialist. But the AI career thats right for you depends on how you want to contribute to the field.

As an AI researcher, you can contribute to the development of new AI systems, says Dr. Kumar. As a machine learning specialist, youll help train those systems. If you enjoy working with data, you can become a data scientist and use data to make predictions or decisions. When you have a background in AI, you have a lot of opportunities.

How To Start an Artificial Intelligence Career

If youre wondering how to start an artificial intelligence career, the first step is to learn AI. You may qualify for some roles with a bachelors degree, AI courses or related career experience. However, according to the BLS, a graduate degree in computer science or a related field is needed for most computer and information research jobs, including artificial intelligence job opportunities.

If youre already working in an entry-level role, a masters degree can help you advance because youll develop the mathematical knowledge, programming skills and research experience you need to excel in AI jobs.

The artificial intelligence field and, more broadly, computer science grew out of mathematics, notes Dr. Kumar. To be successful in AI jobs, you have to be skilled in math. Much of machine learning is math, so the better you are at math, the better youll perform. A masters in computer science program provides you with a strong foundation in math, programming and data science to prepare you for careers in AI.

Working on AI-related projects and taking graduate courses in machine learning and data science can give you an advantage in the job market. Youll want to choose a graduate program led by faculty who are knowledgeable about trends in the field. You may also want to choose an area of specialization, such as bioinformatics, to expand your career opportunities.

How To Specialize in Artificial Intelligence

While there are many paths to an artificial intelligence specialization, earning TROYs masters in computer science is one of the most effective ways to enter the AI field. TROY offers an M.S. in computer science with a specialization in artificial intelligence to help you develop technical knowledge and skills for AI jobs.

Artificial intelligence is a branch of computer science which is interdisciplinary, says Dr. Kumar. An artificial intelligence specialization will give you interdisciplinary study in computer science along with experience in designing and implementing AI systems. These skills are among the most needed in the AI industry.

Choosing an M.S. in computer science with a specialization in artificial intelligence will ensure you have those skills, along with machine learning, programming and data analysis. TROYs masters in computer science program, with a thesis and non-thesis track, will also help you gain research experience and build your portfolio. TROYs program emphasizes project-based learning to give you further advantage.

Our program is very hands-on, shares Dr. Kumar. In our courses, we try to come up with real-life problems for our students to solve. Then, students practice creating solutions to those real-world problems, learning advanced research skills, machine learning and other skills along the way.

For example, students in the artificial intelligence specialization at TROY have worked on using AI to prevent cybersecurity attacks. Theyve also completed projects in transportation and routing. One student even used AI to predict winning baseball teams, which could have applications in TROYs sports management program.

All of our projects are practical and applicable in real life. What the students find in their projects is fascinating, but more importantly, useful, says Dr. Kumar.

Learn More About TROYs Graduate Program Interested in preparing for AI jobs? Learn more about how TROYs M.S. in computer science and artificial intelligence specialization can help you launch or advance your career.

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How To Specialize in Artificial Intelligence - Troy Today - Troy University