Artificial Intelligence, or AI, is easily one of the biggest technology developments in the past few decades. Both exciting and limitless in its possibilities, it is opening the doors to all kinds of creative applications. What started out as a high-tech and seemingly unapproachable technology has now become mainstream, with people using it in one way or another often without even realizing it.

One area that AI has managed to have a real impact on is design. In fact, design has been completely shaken up thanks to this technology and the doors it has opened. But how exactly is AI being used in the design? Let’s peel back the layers and look a little closer.

Use AI for the Monotonous Jobs

There are plenty of jobs that designers have to do that feel monotonous, time-consuming, and frankly not all that creative. This is where AI could really step in and fill a void, freeing up the designers’ time and energy so they can direct it elsewhere.

Some examples of these types of tasks include color correction on various photos, cropping photos, and even resizing images. A fair amount of time can be spent on these types of simple tasks, and AI has the ability to learn what to do and do it for designers.

It Allows for Custom Designs

Then there is the fact that AI can aid in custom designs. So many businesses, clients, and individuals prefer that custom touch to their particular design, as it helps to add a unique feature and makes the finished design more functional.

A really intriguing example is custom stage design, meant for professional stage productions like concerts, plays, and so forth. Because these shows have such specific needs, a custom design from companies like Staging Concepts makes sense, and AI along with other technology makes the design concept that much easier to put together and showcase to the client.

he Opportunity to Co-Create Designs

Here’s a really innovative concept but imagine being able to co-create with AI and/or robot technology? That is quickly becoming the reality, as AI isn’t intended to take the place of a designer in a company rather it is meant to help them.

It can spark creativity, help make the job go smoother and faster, and offer solutions that the designer may not have thought of. Again, the AI is meant to enhance the process not take it over.

Use Voice Commands for Your Designs

Now imagine being able to put together designs via voice commands. It sounds incredibly high-tech, but again it is the reality of where things are today. If you can use voice commands to create designs, then you can expect a much faster design process overall. Think of it AI as your digital assistant.

AI is Transforming the Industry

Sure, it’s a bold statement to say that AI is transforming the design industry, but it’s not an understatement. Artificial technology and innovative technology, in general, are truly shaping the design industry adding more functionality and promise than ever before.

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A long-held goal by chemists across many industries, including energy, pharmaceuticals, energetics, food additives and organic semiconductors, is to imagine the chemical structure of a new molecule and be able to predict how it will function for a desired application. In practice, this vision is difficult, often requiring extensive laboratory work to synthesize, isolate, purify and characterize newly designed molecules to obtain the desired information.

Recently, a team of Lawrence Livermore National Laboratory (LLNL) materials and computer scientists have brought this vision to fruition for energetic molecules by creating machine learning (ML) models that can predict molecules’ crystalline properties from their chemical structures alone, such as molecular density. Predicting crystal structure descriptors (rather than the entire crystal structure) offers an efficient method to infer a material’s properties, thus expediting materials design and discovery. The research appears in the Journal of Chemical Information and Modeling.

“One of the team’s most prominent ML models is capable of predicting the crystalline density of energetic and energetic-like molecules with a high degree of accuracy compared to previous ML-based methods,” said Phan Nguyen, LLNL applied mathematician and co-first author of the paper.

“Even when compared to density-functional theory (DFT), a computationally expensive and physics-informed method for crystal structure and crystalline property prediction, the ML model boasts competitive accuracy while requiring a fraction of the computation time,” said Donald Loveland, LLNL computer scientist and co-first author.

Members of LLNL’s High Explosive Application Facility (HEAF) already have begun taking advantage of the model’s web interface, with a goal to discover new insensitive energetic materials. By simply inputting molecules’ 2D chemical structure, HEAF chemists have been able to quickly determine the predicted crystalline density of those molecules, which is closely correlated with potential energetics’ performance metrics.

“We are excited to see the results of our work be applied to important missions of the Lab. This work will certainly aid in accelerating discovery and optimization of new materials moving forward,” said Yong Han, LLNL materials scientist and principal investigator of the project.

Follow-up efforts within the Materials Science Division have used the ML model in conjunction with a generative model to search large chemical spaces quickly and efficiently for high density candidates.

“Both efforts push the boundaries of materials discovery and are facilitated through the new paradigm of merging materials science and machine learning,” said Anna Hiszpanski, LLNL material scientist and co-corresponding author of the paper.

The team continues to search for new properties of interest to the Lab with the vision of providing a suite of predictive models for materials scientists to use in their research.

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Researchers at Osaka University are using ML to design and simulate molecules for organic solar cells

With the implementation of Machine Learning, technology is increasingly evolving and revolutionizing the environment. Machine learning (ML) and Artificial Intelligence (AI) may tend to be synonymous, but ML is an AI application that allows a program to understand automatically from data input. In a variety of industries, ML’s functional capabilities accelerate operating performance and power automation. ML is progressing at a breakneck pace, fueled primarily by new technological innovations.

Machine Learning in the Solar Energy Industry

Solar energy is a significant renewable energy source, and its demand has been growing rapidly in recent years. Last year, the global solar energy market was worth $52.5 billion, and by 2026, it is expected to be worth $223.3 billion. The possibilities that machine learning approaches are unveiling in the industry are part of the reason for this exponential development. Machine learning technology utilizes complex algorithms to assist in the analysis of the future, enabling businesses to develop more successful strategies. Solar energy companies can significantly boost their profit margins by shifting their market strategy from a conventional approach to modern data-driven competencies.

Osaka University Experiments on Virtually Unlimited Solar Cell

Machine learning is being used by researchers at Osaka University to design and simulate molecules for organic solar cells, which could lead to more efficient usable materials for renewable energy applications.

As per report of EurekAlert, Osaka University researchers employed machine learning to design new polymers for use in photovoltaic devices. After virtually screening over 200,000 candidate materials, they synthesized one of the most promising and found its properties were consistent with their predictions. This work may lead to a revolution in the way functional materials are discovered.

Machine learning is a powerful tool that allows computers to make predictions about even complex situations, as long as the algorithms are supplied with sufficient example data. This is especially useful for complicated problems in material science, such as designing molecules for organic solar cells, which can depend on a vast array of factors and unknown molecular structures. It would take humans years to sift through the data to find the underlying patterns–and even longer to test all of the possible candidate combinations of donor polymers and acceptor molecules that make up an organic solar cell. Thus, progress in improving the efficiency of solar cells to be competitive in the renewable energy space has been slow.

EurekAlert also added that, “This project may contribute not only to the development of highly efficient organic solar cells, but also can be adapted to material informatics of other functional materials,” senior author Akinori Saeki says.

We may see this type of machine learning, in which an algorithm can rapidly screen thousands or perhaps even millions of candidate molecules based on machine learning predictions, applied to other areas, such as catalysts and functional polymers.

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Researchers at Osaka University are using ML to design and simulate molecules for organic solar cells

With the implementation of Machine Learning, technology is increasingly evolving and revolutionizing the environment. Machine learning (ML) and Artificial Intelligence (AI) may tend to be synonymous, but ML is an AI application that allows a program to understand automatically from data input. In a variety of industries, ML’s functional capabilities accelerate operating performance and power automation. ML is progressing at a breakneck pace, fueled primarily by new technological innovations.

Machine Learning in the Solar Energy Industry

Solar energy is a significant renewable energy source, and its demand has been growing rapidly in recent years. Last year, the global solar energy market was worth $52.5 billion, and by 2026, it is expected to be worth $223.3 billion. The possibilities that machine learning approaches are unveiling in the industry are part of the reason for this exponential development. Machine learning technology utilizes complex algorithms to assist in the analysis of the future, enabling businesses to develop more successful strategies. Solar energy companies can significantly boost their profit margins by shifting their market strategy from a conventional approach to modern data-driven competencies.

Osaka University Experiments on Virtually Unlimited Solar Cell

Machine learning is being used by researchers at Osaka University to design and simulate molecules for organic solar cells, which could lead to more efficient usable materials for renewable energy applications.

As per report of EurekAlert, Osaka University researchers employed machine learning to design new polymers for use in photovoltaic devices. After virtually screening over 200,000 candidate materials, they synthesized one of the most promising and found its properties were consistent with their predictions. This work may lead to a revolution in the way functional materials are discovered.

Machine learning is a powerful tool that allows computers to make predictions about even complex situations, as long as the algorithms are supplied with sufficient example data. This is especially useful for complicated problems in material science, such as designing molecules for organic solar cells, which can depend on a vast array of factors and unknown molecular structures. It would take humans years to sift through the data to find the underlying patterns–and even longer to test all of the possible candidate combinations of donor polymers and acceptor molecules that make up an organic solar cell. Thus, progress in improving the efficiency of solar cells to be competitive in the renewable energy space has been slow.

EurekAlert also added that, “This project may contribute not only to the development of highly efficient organic solar cells, but also can be adapted to material informatics of other functional materials,” senior author Akinori Saeki says.

We may see this type of machine learning, in which an algorithm can rapidly screen thousands or perhaps even millions of candidate molecules based on machine learning predictions, applied to other areas, such as catalysts and functional polymers.

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The coleader of computational design at SmithGroup explains how machine learning tools can refine data into information, helping designers work smarter.

Technology is changing the world as we know—and design—it. But have architects and designers unlocked the full potential of cutting-edge digital tools? In this series of comments, practitioners with a visionary approach examine some of the most influential and disruptive tech today—like blockchain technology, VR/AR/MR, spatial computing, machine learning, and cloud computing—and envisage their impact on the practice of architecture and interior design tomorrow. The changes they describe, while forecasts, will likely come to fruition, driving the way we plan, work, and create. Consider this a glimpse of the not-so-distant future.

Machine learning will enable a more integrated, informed design process by disrupting how and when architects engage with data. If we begin by viewing machine learning as a collection of algorithmic tools that refine data into information—just as a saw helps to shape wood into furniture—the opportunities these tools present become more focused. I imagine machine learning tools will help design professionals understand the impact of decisions as they are made—not days or weeks later.

Methods such as surrogate modeling, which uses regressor algorithms to replace slow calculation engines with an instantaneous predictive “surrogate,” will support real-time, data-rich design interfaces that allow teams to react at the speed of a designer’s curiosity. I expect future engineers will operate like data analysts. They will spend most of their time modeling, analyzing, and explaining data rather than manually operating analysis software. For example, once a design challenge is parametrically modeled and translated into structured data—an approach we call design space exploration—simple algorithms like multiple linear regression can measure which parameters have the greatest impact on performance.

Clustering algorithms, classifiers, and dimensionality reduction techniques can then be used to tease out obscure relationships that can provide actionable direction to teams. These algorithms represent a fraction of the machine learning tools that design professionals can and should learn to use. But machine learning algorithms are not magic. They are tools of the information age that we can leverage to better inform the design process moving forward.

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Building upon years of momentum, architects are well in the swing of transitioning from the monolith to microservices. Here are three of the top trends they’ll face in 2021.

Throughout 2020, application architects sought new design methods and tools to help ease the transition from the monolith to a distributed architecture design — and they’re not done yet. Specifically, they are looking to solve issues surrounding consistency in distributed service management, incremental migration to microservices and service granularity.

Let’s take a closer look at some of these distributed architecture trends and examine how these shifts will affect the development landscape in 2021, including the microservices design patterns and tools that are top-of-mind for architects.

The modular monolith reigns

Despite the flexibility and scaling benefits associated with microservices, this type of architecture imposes significant hurdles upfront — specifically regarding observability, inter-service communication and complex deployment cycles. As such, architects must find a way to pursue microservices design patterns while still maintaining the dependability of their existing monolith. In 2021, architects will continue to pursue the modular monolith and establish bounded context by segmenting code, limiting dependencies and isolating data stores. This allows them to introduce the desired cohesion and loose coupling found in microservices, but avoid the headaches of managing multiple runtimes and asynchronous communication.

Loose coupling becomes essential

While a microservices design patterns demand a granular separation of services and responsibilities, development teams continue to apply traditional MVC patterns that demand tight component coupling. To successfully introduce distributed architecture design, more architects should make an effort to implement hexagonal architecture patterns in 2021. This pattern allows developers to create coarse-grained services that can gradually parse individual functions into individual services, but uses proxy components to spawn these services without affecting the underlying process logic.

Distributed services, centralized management

Teams that manage distributed systems must spend considerable time repetitively performing the same management tasks for hundreds of services, such as deployments, monitoring, logging and versioning. In an effort to eliminate waste, it’s likely that teams will increasingly adopt service templates and scaffolding tools in 2021. The hope is that these tools will promote coherence across collections of microservices by providing preconfigured service designs and enforcing standardized management practices.

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Considering that thousands of components must be packed onto a tiny fingernail-sized chip, this can be difficult. The trouble is that it can take several years to design a chip, and the world of machine learning and artificial intelligence (AI) moves much faster than this.

In an ideal world, you want a chip that is designed quickly enough to be optimized for today’s AI challenges, not the AI challenges of several years ago. 

Now, Alphabet’s Google has proposed an AI solution that could advance the internal development of its own chips. The solution? To train AI chips to design themselves. 

Shortening the AI Chip Design Cycle

In a research paper posted to Arxiv on March 23, it is described how the researchers “believe that it is AI itself that will provide the means to shorten the chip design cycle, creating a symbiotic relationship between hardware and AI, with each fuelling advances in the other,”

The research describes how a machine learning program can be used to make decisions about how to plan and layout a chip’s circuitry, with the final design being just good as or better than manmade ones.  

According to Jeff Dean, Google’s head of AI research, this program is currently being used internally for exploratory chip design projects. The company is already known for developing a family of AI hardware over the years, including its Tensor Processing Unit (TPU) for processing AI in its servers. 

The Chip Design Challenge

Planning a chip’s circuitry, often referred to as “placement” or “floor planning”, is very time-consuming. And as chips continually improve, final designs very quickly become outdated and despite being designed to last two-to-five years, there is constant pressure and demand on engineers to reduce the time between upgrades. 

Floorplanning involves placing logic and memory blocks, or clusters of, in a way that maximizes power and performance while concurrently minimizing footprint. This is already challenging enough, however, the process is made all the more challenging by the fact that this must all take place while rules about the density of interconnects are followed at the same time. 

Even with today’s advanced tools and processes, human engineers require weeks of time and multiple iterations to produce an acceptable design for an AI chip.

Using AI for Chip Floor Planning

However, Google’s research is said to have made major improvements to this process. In the Arxiv paper, research engineers Anna Goldie and Azalia Mirhoseini claim to have designed an algorithm that learns how to achieve optimum placement of chip circuitry. It does this by studying existing chip designs in order to produce its own. 

According to Goldie and Mirhoseini, it is able to do this in a fraction of the time currently required by human designers and is capable of analyzing millions of design possibilities as opposed to thousands. This enables it to spit out chip designs that not only utilize the latest developments but are cheaper and smaller, too.

Repeated Tasks Result in Higher Performance

During their research, the duo modeled chip placement as a reinforcement learning problem. These systems, unlike conventional deep learning ones, learn by doing rather than training on a large dataset. They adjust the parameters in their networks according to a “reward signal” that is sent when they succeed in a task.

In the case of chip design, the reward signal is a combined measure of power reduction, area reduction, and performance improvement. As a result, the program becomes better at its task the more times it does it. 

A Solution to Moore’s Law 

If this research is as promising as Google’s researchers would have us believe, it could represent a solution to Moore’s Law—the assertion that the number of transistors on a chip doubles every one-to-two years—by ensuring the continuation of it. In the 1970s, chips generally had a few thousand transistors. Today, some host billions of them.

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So, you want to scale your computing muscle to train bigger deep learning models. Can your data center handle it?

According to Nvidia, which sells more of the specialized chips used in machine learning than any other company, it most likely cannot. These systems often consume so much power, a conventional data center doesn’t have the capacity to remove the amount of heat they generate.

It’s easy to see how customers withoutan infrastructure that can support a piece of Nvidia hardware is a business problem for Nvidia. To widen this bottleneck for at least one of its product lines, the company now has a list of pre-approved colocation providers it will send you to if you need a place that will keep your supercomputers cool and happy.

As more companies’ machine learning initiatives graduate from initial experimentation phases – during which their data scientists may have found cloud GPUs rented from the likes of Google or Microsoft sufficient – they start thinking about larger-scale models and investing in their own hardware their teams can share to train those models.

Among the go-to hardware choices for these purposes have been Nvidia’s DGX-1 and DGX-2 supercomputers, which the company designed specifically with machine learning in mind. When a customer considers buying several of these systems for their data scientists, they often find that their facilities cannot support that level of power density and look to outsource the facilities part.

“This program takes that challenge off their plate,” Tony Paikeday, who’s in charge of marketing for the DGX line at Nvidia, told Data Center Knowledge in an interview about the chipmaker’s new colocation referral program. “There’s definitely a lot of organizations that are starting to think about shared infrastructure” for machine learning. Deploying and managing this infrastructure falls to their IT leadership, he explained, and many of the IT leaders “are trying to proactively get ahead of their companies’ AI agendas.”

Cool Homes for Hot AI Hardware

DGX isn’t the only system companies use to train deep learning models. There are numerous choices out there, including servers by all the major hardware vendors, powered by Nvidia’s or AMD’s GPUs. But because they all pack lots of GPUs in a single box – an HPE Apollo server has eight GPUs, for example, as does DGX-1, while DGX-2 has 16 GPUs – high power density is a constant across this category of hardware. This means that along with the rise of machine learning comes growing demand for high-density data centers.

The trend benefits specialist colocation providers like Colovore, Core Scientific, and ScaleMatrix, who designed their facilities for high density from the get-go. But other, more generalist data center providers are also capable of building areas within their facilities that can handle high density. Colovore, Core Scientific, and ScaleMatrix are on the list of colocation partners Nvidia will refer DGX customers to, but so are Aligned Energy, CyrusOne, Digital Realty Trust, EdgeConneX, Flexential, and Switch.

Partially owned by Digital Realty, Colovore built its facility in Santa Clara in 2014 specifically to take care of Silicon Valley’s high-density data center needs. Today, it supports close to 1,000 DGX-1 and DGX-2 systems, Ben Coughlin, the company’s CFO and co-founder, told us. He wouldn’t say who owned the hardware, saying only that it belonged to fewer than 10 customers who were “mostly tech” companies. (Considering that the facility is only a five-minute drive from Nvidia headquarters, it’s likely that the chipmaker itself is responsible for a big portion of that DGX footprint, but we haven’t been able to confirm this.)

Colovore has already added one new customer because of Nvidia’s referral program. A Bay Area healthcare startup using artificial intelligence is “deploying a number of DGX-1 systems to get up and running,” Coughlin said.

A single DGX-1 draws 3kW in the space of three rack units, while a DGX-2 needs 10kW and takes up 10 rack units – that’s 1kW per rack unit regardless of the model. Customers usually put between nine and 11 DGX-1s in a single rack, or up to three DGX-2s, Coughlin said. Pumping chilled water to the rear-door heat exchangers mounted on the cabinets, Colovore’s passive cooling system (no fans on the doors) can cool up to 40kW, according to him.

In a “steady state,” many of the cabinets draw 12kW to 15kW, “but when they go into some sort of workload state, when they’re doing some processing, they’ll spike 25 to 30 kilowatts,” he said. “You can see swings on our UPSs of 400 to 500 kilowatts at that time across our infrastructure. It’s pretty wild.”

Echoing Nvidia’s Paikeday, Chris Orlando, CEO and co-founder of ScaleMatrix, said typical customers that turn to his company’s high-density colocation services in San Diego and Houston are well into their machine learning programs and looking at expanding and scaling the infrastructure that supports those programs.

A high-density specialist, ScaleMatrix’s proprietary cooling design also brings chilled water directly to the IT cabinets. The company has “more than a handful of customers that have DGX boxes colocated today,” Orlando told us.

High Density Air-Cooled

Flexential, which is part of Nvidia’s referral program but doesn’t have high-density colocation as its sole focus, uses traditional raised-floor air cooling for high density, adding doors at the ends of the cold aisles to isolate them from the rest of the building and “create a bathtub of cold air for the server intakes,” Jason Carolan, the company’s chief innovation officer, explained in an email.

According to him, this approach works fine for a 35kW rack of DGX systems. “We have next-generation cooling technologies that will take us beyond air, but to date, we haven’t had a sizeable enough customer application that has required … it on a large scale,” he said. Five of Flexential’s 41 data centers can cool high-density cabinets today.

As more and more companies use machine learning, it is becoming an important workload for data center providers to be able to support. Adoption of these computing techniques is only in its early phases, and they are likely to become an important growth driver for colocation companies going forward. Not many enterprises are set up to host supercomputers on-premises, and few are going to spend the money to build this infrastructure, so turning to colocation facilities that are already designed to efficiently cool tens of kilowatts per rack is their logical next step.

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