1 December 2023 12:14 am

AI Edge Computing and its Impact on Urban Infrastructure

Aug 3, 2023

Advertisements
The above featured-image is for illustration and is credit to aaeon
.
The Future of Smart Cities: AI Edge Computing and its Impact on Urban Infrastructure

Fagen Wasanni Technologies published this article on AI Edge Computing in urban development that’s worth reading.

Exploring the Future of Smart Cities: The Role and Impact of AI Edge Computing on Urban Infrastructure

As we stand on the precipice of a new era in urban development, the future of smart cities is being shaped by the rapid advancements in technology. One of the most transformative technologies is Artificial Intelligence (AI) Edge Computing, which is poised to have a profound impact on urban infrastructure.

AI Edge Computing is a paradigm that brings computation and data storage closer to the location where it’s needed, to improve response times and save bandwidth. This technology is a game-changer for smart cities, as it allows for real-time data processing, enabling cities to become more efficient, sustainable, and livable.

The integration of AI Edge Computing into urban infrastructure is already underway, with cities around the world beginning to harness its potential. For instance, in the realm of traffic management, AI Edge Computing can analyze data from traffic cameras in real-time to optimize traffic light sequences, reducing congestion and improving road safety. This technology can also predict traffic patterns, allowing city planners to make informed decisions about infrastructure development.

In the context of public safety, AI Edge Computing can be used to enhance surveillance systems. By processing data on the edge, these systems can identify potential threats or criminal activity in real-time, enabling quicker response times from law enforcement agencies. Moreover, AI algorithms can learn and adapt over time, improving their accuracy and effectiveness.

The impact of AI Edge Computing extends to environmental sustainability as well. Smart sensors placed throughout a city can monitor air quality, noise levels, and waste management in real-time. This data can then be processed on the edge, providing city officials with actionable insights to address environmental issues promptly and efficiently.

Furthermore, AI Edge Computing can revolutionize the way cities manage their energy consumption. Smart grids powered by this technology can monitor and analyze energy usage in real-time, optimizing the distribution of energy and reducing waste. This not only leads to significant cost savings but also contributes to a city’s sustainability goals.

However, the implementation of AI Edge Computing in urban infrastructure is not without its challenges. Data privacy and security are major concerns, as the technology involves the collection and processing of vast amounts of data. Cities must ensure robust data protection measures are in place to safeguard citizens’ privacy. Additionally, the deployment of AI Edge Computing requires substantial investment in infrastructure and skills training, which may be a hurdle for cities with limited resources.

Despite these challenges, the potential benefits of AI Edge Computing for smart cities are immense. As cities continue to grow and evolve, this technology will play a pivotal role in shaping urban infrastructure, making cities smarter, safer, and more sustainable.

In conclusion, the future of smart cities is intrinsically linked with the advancement of AI Edge Computing. This technology is set to redefine urban infrastructure, transforming the way cities function and improving the quality of life for their residents. As we move forward into this exciting new era of urban development, the possibilities for what our cities could become are truly limitless.

.

.

Read the Fagen Wasanni Technologies

How AI physics has the potential to revolutionise product design

Jun 27, 2023

Advertisements

How AI physics has the potential to revolutionise product design

This article was published by the World Economic Forum on 26 June 2023.
It is part of the Annual Meeting of the New Champions.
Joris Poort, Founder and Chief Executive Officer, Rescale

  • Artificial Intelligence (AI) tools like ChatGPT have become mainstream, but the use of AI in the product design process is less widely known.
  • Engineering and scientific computing are harnessing AI to define a new era of innovation across industries.
  • Computer simulations are increasingly using AI to understand how different product designs will perform.

 

The impact of Artificial Intelligence (AI) on digital services is quickly becoming apparent. Tools like ChatGPT, Bard, and GitHub Copilot are transforming how we work and live.

What is less known is the potential for AI to revolutionise the basic nature of research, engineering, and physical product design.

Engineering and scientific computing is now the foundation of innovation. These methods often require massive computing power from supercomputing clusters (also known as high-performance computing or HPC) to run detailed simulation models that replicate the real world.

Across industries, research and development (R&D) teams use digital simulations to explore the physical world effectively. Use cases vary from inventing life-saving medicine, improving aircraft design and pioneering sustainable energy to creating self-driving vehicles, and refining manufacturing processes, among many other possibilities.

Now, AI offers the potential to supercharge engineering and scientific computing and transform how organizations innovate.

Making R&D more efficient

The computer simulations used today for engineering and scientific computing are increasingly benefiting with additional assistance from AI (and in some cases replaced by AI), dramatically lowering costs and helping engineers find the best answers faster.

Running simulations can be expensive, often requiring supercomputers to crunch massive data sets and execute highly complex calculations. But if you can build a machine learning (ML) model on how the physics works, you don’t need to run simulations every time since your ML inference model can extrapolate the answer from the data.

This means you get the answers on how a particular design will perform faster and cheaper. Over the long term, machine learning, physics-informed neural networks, and other AI-based tools will become standard tools for all engineers and scientists to maximize productivity within R&D development.

The era of AI-assisted engineering

Organizations that become adept at creating well-crafted AI-physics models will gain a solid competitive advantage. Such capabilities will help them establish a fundamental physics understanding of how different product designs will perform in real life. This has huge implications for how organizations retain knowledge in their research and product development.

AI-physics models can capture best practices knowledge about how physical objects behave – information that traditionally has been in the head of an expert, such as a scientist, engineer, or designer.

An aircraft engineer, for example, has accumulated knowledge about the best design approaches for the shape of wings, which then informs their choices regarding the types of design options they explore with digital simulations.

But that kind of information can now be captured with AI, which can then come up with a shortlist of design suggestions that engineers can then further explore with digital simulations.

Critically, suppose a company can create an ML model of best practices for wing designs (by training the AI tool on its body of knowledge about airplane wings). In that case, it can retain that expertise, regardless of if an engineer leaves the company.

This also brings far greater agility to an organization. If a company wants to build a new kind of plane that is more stable in high winds, an ML application can quickly generate the best options for the shape of the wings, helping the organization rapidly spin up new prototypes to enter new markets.

AI will physically shape our world

Given AI’s ability to help us understand the physical world, we are likely to see new shapes in all types of products, from buildings and aircraft to furniture and automobiles. Such innovations are being driven by another variant of AI: generative design.

Generative design works much like generative AI for writing text. By providing some basic guidance (prompts) about what you are trying to design, generative design tools will output many possible options, some of which you would not have thought up on your own.

By letting the software decide the design based on your performance objectives, some fascinating possibilities can result. Generative design, for example, is creating prototypes with a very biological look.

Generative AI for product design evolution and optimization example of a load-bearing bracket. Source: Rescale.

Navigating the AI transition for science and engineering

Organizations that embrace AI will accelerate engineering and scientific discovery while developing innovative new solutions that would be computationally prohibitive using traditional approaches.

Despite the promise of AI, organizations across industries will need to establish engineering and research best practices to help ensure they navigate this transition safely to maximize the benefits to society without needless risk.

Most importantly, AI is only as good as the information it trains on. Organizations will still need to do much work to provide the essential information to make the AI tool smart in the right ways.

Also, legal issues for AI are still very much undefined. Organizations must carefully review the outputs of AI to ensure accuracy, as well as watching for any ethical red flags.

Security is also another important consideration to make sure AI practices don’t accidentally expose intellectual property or proprietary information.

Certainly, guardrails for how organizations use AI are essential as we work through the early days of this new technology. But with some thoughtful measures in place, AI can safely open up all new possibilities for research and development, helping organizations move faster, become more agile, and discover better ways to invent the future.

Supporting the adoption of AI physics will help us make better products faster, accelerate the R&D innovation process, and explore the boundaries of knowledge to develop new engineering breakthroughs and scientific discoveries.

Don’t miss any update on AMNC 2023

.

Have you read?

.

 

Exploring the Intersection of AI and Sustainable Architecture

Jun 19, 2023

Advertisements

The image above is for illustration and credit to GettyImages.

AI and the Built Environment: The Next Generation of Design Solutions

Exploring the Intersection of AI and Sustainable Architecture

Artificial intelligence (AI) has been making waves in various industries, and it’s no surprise that it’s now finding its way into the world of architecture and design. As we strive to create more sustainable and efficient buildings, AI has the potential to revolutionize the way we approach the built environment. By exploring the intersection of AI and sustainable architecture, we can unlock the next generation of design solutions that will help shape the future of our cities and communities.

One of the most significant ways AI can contribute to sustainable architecture is through the optimization of building design. Traditionally, architects and engineers have relied on their experience and intuition to create energy-efficient buildings. However, AI algorithms can analyze vast amounts of data and consider numerous design variables to identify the most sustainable and cost-effective solutions. This data-driven approach can lead to more innovative designs that minimize energy consumption, reduce waste, and lower the overall environmental impact of buildings.

For example, AI can be used to optimize the orientation, shape, and size of a building to maximize natural light and minimize heat gain. This can result in a more comfortable indoor environment while reducing the need for artificial lighting and air conditioning. Similarly, AI can help architects select the most appropriate materials and construction techniques to improve a building’s thermal performance and reduce its carbon footprint.

Another area where AI can make a significant impact is in the design of urban environments. As cities continue to grow and urban populations increase, there is a pressing need to create more sustainable and livable urban spaces. AI can be used to analyze complex urban systems and identify the most effective strategies for improving air quality, reducing traffic congestion, and promoting walkability and public transportation. By using AI to inform urban planning decisions, we can create more sustainable and resilient cities that are better equipped to face the challenges of the future.

In addition to optimizing design, AI can also play a crucial role in the ongoing management and maintenance of buildings. By integrating AI with building management systems, it’s possible to monitor and analyze the performance of a building in real-time. This can help identify inefficiencies and potential issues before they become significant problems, allowing for more proactive maintenance and reducing the overall environmental impact of a building throughout its lifecycle.

Furthermore, AI can be used to create more responsive and adaptive buildings that can adjust to changing conditions and occupant needs. For instance, AI-powered systems can learn from occupants’ behavior and preferences to optimize lighting, heating, and cooling, resulting in a more comfortable and energy-efficient environment. This level of personalization can not only improve the overall user experience but also contribute to greater sustainability by reducing energy waste.

As we continue to explore the intersection of AI and sustainable architecture, it’s essential to consider the ethical implications of these emerging technologies. While AI has the potential to revolutionize the built environment, it’s crucial to ensure that these advancements are used responsibly and equitably. This includes addressing issues related to data privacy, algorithmic bias, and the potential displacement of human workers in the design and construction process.

In conclusion, AI offers a wealth of opportunities for creating more sustainable and efficient buildings and urban environments. By harnessing the power of AI, architects, engineers, and urban planners can develop innovative design solutions that minimize environmental impact, improve building performance, and enhance the overall quality of life for occupants. As we continue to explore the intersection of AI and sustainable architecture, we can look forward to a future where our built environment is smarter, more resilient, and more sustainable than ever before.

 

.

.

 

AI and Smart Cities–Improving Urban Life

May 25, 2023

Advertisements
AI and Smart Cities are they meant for Improving Urban Life?  Let us see what AI could bring to Smart Cities.
The image above is credit to World Economic Forum.
.

AI and Smart Cities–Improving Urban Life

By Ale Oluwatobi Emmanuel

The world as we have it today is not static. At the snap of a finger, there’s a new innovation in town that everyone makes a fuss about. Over the years and through generations, we’ve witnessed a series of disruptions in various sectors that have impacted our lives and activities.

You’d want to see what the first generation of computers in the 19th century looked like when they were invented. Take your time. They took up the size of an entire room.

Here is the question–who would have thought the same large computers could be compressed into smaller sizes? Today, with a size of 0.3 millimeters, the Michigan micro mote boasts of being the most miniature computer, and guess what? That’s a size smaller than a grain of rice.

What’s more? As humans, there is an exciting future ahead, and we’d have it with artificial intelligence at our beck and call. Recently, you’ve noticed how AI is disrupting virtually all sectors worldwide. Talk of banking, transportation, health, military, and even sports.

As we see with other sectors, our city centers are included in these disruptions, especially now that urban areas are getting more crowded and complex. It’s time to make our cities smart with AI.

What are smart cities, and how do we make our cities smart with this unique technology? There is no better time to have the discussion. Let’s dive in.

Smart Cities: What exactly are they?

If you’ve ever wondered–everyone is talking about smart cities, what’s the fuss about them? A city is smart if it incorporates technology and other digital solutions for its processes.

A smart city would utilize information and communication technologies to improve the quality of life of the citizens and the way the government serves the people.

It utilizes innovative technologies for a more interactive and responsive city administration, improved water supply, innovative urban transport networks, waste management, and many more.

A city is termed smart not by the number of smart technologies it’s got but by how it has effectively used these technologies to positively impact its citizens and drive economic growth.

Here is the catch– Artificial intelligence has a huge potential to access the activities of urban dwellers to bring about urban planning and management.

Talk of handling data from different sources to gain insights for effective municipal operations. Guess what? It also reduces associated expenses. Let’s assess some more use cases of AI in Smart cities.

Artificial Intelligence and Smart City Infrastructures

According to research conducted by the World Bank, 56% of the world’s population, which is about 4.4 billion people, live in cities. By 2050, this figure is expected to have doubled its current size.

At that point, 7 out of 10 people you meet would live in the city. Hence, there is a need to leverage artificial intelligence to enhance infrastructure and create more sustainable and livable urban environments.

For example, in public transit, cities with vast transit infrastructure have much to gain regarding making their processes seamless.

With the power of AI, commuters using major routes can offer real-time information through hands-on devices to communicate the situation of things on the road. This can enable other commuters to decide the ways they’d be taking faster.

As a case study, Dubai initiated a smart city project to monitor bus drivers’ condition, contributing to a 65% reduction in accidents caused by fatigue and stress.

In the same vein, AI can enhance the safety of power grids to improve performance management. Smart grids, such as generation plants, can be created backed by computer technology.

Moreso, prediction models can be set up on these grids to make smart meter readings of large quantities of data. They can also forecast the demand and price at given moments.

Artificial Intelligence and Smart City Services

Today, there is a need for cities around the world to provide improved delivery and quality of services through continuous monitoring of residents. For example, an AI-driven system in Los Angeles monitors air quality in real-time.

This system helps the city reduce air pollution and improve public health. It uses data from air quality sensors to prompt city officials about air pollution hotspots. It helps guide citizens to safe travel places.

Below are some other service sectors experiencing the disruptions of artificial intelligence.

  • Customer Service:

AI is disrupting customer service. Natural language processing (NLP) algorithms in chatbots are now available. The chatbots let customer support executives work effectively by getting information about customers’ issues.

So, it means if you own a business that relies majorly on customer service, you can hire an AI developer that can build chatbots to meet your specific business needs. Due to the accuracy of chatbots, there are speculations that they’d take over customer service roles, but only time will tell.

  • Health care:

In the healthcare service sector, introducing AI can bring about predictive healthcare. By leveraging predictive analytics, AI can help doctors make accurate decisions about the health of their patients. Asides from this, AI can also help streamline the analysis of scan results via image recognition. Doctors diagnose symptoms more accurately and effectively. With the rise of IoT-enabled embedded devices, they can remotely monitor their patient’s health conditions.

  • Banking:

AI is a valuable tool in a field such as the financial sector, which is prone to fraudulent activities. Artificial intelligence helps banks automate processes that are typically carried out by humans, reducing the time and effort it takes if done manually. Interestingly, AI can also help track customers’ credit history. AI’s predictive technology shows the likelihood of an individual not paying a loan back based on the information it analyzes.

That way, financial institutions and other loan services can streamline the process of getting new customers likely to repay their loans.

  • Transportation

Autonomous vehicles are here to stay, and they’re powered by AI. Who would have thought there’d be a time when cars could navigate their ways without human control? Well, it’s happening now. Kudos to Tesla and other big technology companies. Autonomous vehicles can also be used for deliveries and for transporting goods. Self-driving trucks can deliver packages more efficiently. We already see Tesla’s AI-powered Semi automobile do well in this regard.

Artificial Intelligence–The Tool for a Smarter World

No doubt, it is a visible phenomenon that the world of technology and innovation constantly changes. It’s exciting to let you know that we’re still at the early stage of the deployment of AI. Although we’ve seen its applications in diverse sectors, its long-term benefits will start unfolding. If you’re reading this now, you’re lucky. You must begin to adapt and position well for a new world driven by artificial intelligence.

Moving forward, a lot of changes will happen. From lifestyle changes to improvements in societal processes and operations. Welcome to the world of AI.

.

Read original TechDay article.

Is generative AI bad for the environment?

May 24, 2023

Advertisements

Is generative AI bad for the environment? A computer scientist explains the carbon footprint of ChatGPT and its cousins

By Kate Saenko, Boston University

The image above is on The Generative AI Race Has a Dirty Secret, credit to WIRED

AI chatbots and image generators run on thousands of computers housed in data centers like this Google facility in Oregon.
Tony Webster/Wikimedia, CC BY-SA

Generative AI is the hot new technology behind chatbots and image generators. But how hot is it making the planet?

As an AI researcher, I often worry about the energy costs of building artificial intelligence models. The more powerful the AI, the more energy it takes. What does the emergence of increasingly more powerful generative AI models mean for society’s future carbon footprint?

“Generative” refers to the ability of an AI algorithm to produce complex data. The alternative is “discriminative” AI, which chooses between a fixed number of options and produces just a single number. An example of a discriminative output is choosing whether to approve a loan application.

Generative AI can create much more complex outputs, such as a sentence, a paragraph, an image or even a short video. It has long been used in applications like smart speakers to generate audio responses, or in autocomplete to suggest a search query. However, it only recently gained the ability to generate humanlike language and realistic photos.

Using more power than ever

The exact energy cost of a single AI model is difficult to estimate, and includes the energy used to manufacture the computing equipment, create the model and use the model in production. In 2019, researchers found that creating a generative AI model called BERT with 110 million parameters consumed the energy of a round-trip transcontinental flight for one person. The number of parameters refers to the size of the model, with larger models generally being more skilled. Researchers estimated that creating the much larger GPT-3, which has 175 billion parameters, consumed 1,287 megawatt hours of electricity and generated 552 tons of carbon dioxide equivalent, the equivalent of 123 gasoline-powered passenger vehicles driven for one year. And that’s just for getting the model ready to launch, before any consumers start using it.

Size is not the only predictor of carbon emissions. The open-access BLOOM model, developed by the BigScience project in France, is similar in size to GPT-3 but has a much lower carbon footprint, consuming 433 MWh of electricity in generating 30 tons of CO2eq. A study by Google found that for the same size, using a more efficient model architecture and processor and a greener data center can reduce the carbon footprint by 100 to 1,000 times.

Larger models do use more energy during their deployment. There is limited data on the carbon footprint of a single generative AI query, but some industry figures estimate it to be four to five times higher than that of a search engine query. As chatbots and image generators become more popular, and as Google and Microsoft incorporate AI language models into their search engines, the number of queries they receive each day could grow exponentially.

AI chatbots, search engines and image generators are rapidly going mainstream, adding to AI’s carbon footprint.
AP Photo/Steve Helber

AI bots for search

A few years ago, not many people outside of research labs were using models like BERT or GPT. That changed on Nov. 30, 2022, when OpenAI released ChatGPT. According to the latest available data, ChatGPT had over 1.5 billion visits in March 2023. Microsoft incorporated ChatGPT into its search engine, Bing, and made it available to everyone on May 4, 2023. If chatbots become as popular as search engines, the energy costs of deploying the AIs could really add up. But AI assistants have many more uses than just search, such as writing documents, solving math problems and creating marketing campaigns.

Another problem is that AI models need to be continually updated. For example, ChatGPT was only trained on data from up to 2021, so it does not know about anything that happened since then. The carbon footprint of creating ChatGPT isn’t public information, but it is likely much higher than that of GPT-3. If it had to be recreated on a regular basis to update its knowledge, the energy costs would grow even larger.

One upside is that asking a chatbot can be a more direct way to get information than using a search engine. Instead of getting a page full of links, you get a direct answer as you would from a human, assuming issues of accuracy are mitigated. Getting to the information quicker could potentially offset the increased energy use compared to a search engine.

Ways forward

The future is hard to predict, but large generative AI models are here to stay, and people will probably increasingly turn to them for information. For example, if a student needs help solving a math problem now, they ask a tutor or a friend, or consult a textbook. In the future, they will probably ask a chatbot. The same goes for other expert knowledge such as legal advice or medical expertise.

While a single large AI model is not going to ruin the environment, if a thousand companies develop slightly different AI bots for different purposes, each used by millions of customers, the energy use could become an issue. More research is needed to make generative AI more efficient. The good news is that AI can run on renewable energy. By bringing the computation to where green energy is more abundant, or scheduling computation for times of day when renewable energy is more available, emissions can be reduced by a factor of 30 to 40, compared to using a grid dominated by fossil fuels.

Finally, societal pressure may be helpful to encourage companies and research labs to publish the carbon footprints of their AI models, as some already do. In the future, perhaps consumers could even use this information to choose a “greener” chatbot.

Kate Saenko, Associate Professor of Computer Science, Boston University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

 

This site uses functional cookies and external scripts to improve your experience.