Forbes LEADERSHIP published Pioneering The Future Of Smart Cities With AI And Generative AI through the foreseeable availability of all AI technology. It would have been a good piece of insight if the level of uncertainty was minimised. Let us see what it’s all about.
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Pioneering The Future Of Smart Cities With AI And Generative AI
AI enables real-time insights by analyzing vast data, transforming smart cities with edge-acquired intelligence.
The future of our world is urban. By 2050, more than two-thirds of the world’s population will reside in cities1. This significant demographic shift presents both a challenge and an opportunity to develop smarter and more efficient community-oriented cities. To address this challenge, stakeholders are increasingly turning to cutting-edge technologies like artificial intelligence (AI) and generative artificial intelligence (GenAI) to revolutionize how cities are designed, built and managed to better serve their ever-increasing populations.
The role of AI and GenAI in shaping smart cities
As intelligent communities with highly connected technology centers become the standard for the future of urban living, data analysis and actionable insights are strategically vital to elevating the standard of living, improving operational efficiency and enhancing city-wide sustainability. At the center of this transformation are AI and GenAI, which are revolutionizing public services, urban mobility, city planning, disaster management and sustainable practices. This transformation leads to a more livable and efficient urban environment that can address citizen needs effectively within the current resource framework, reducing operational costs without replacing human jobs.
These advanced technologies create a cohesive framework of connected systems that not only cater to current needs but are also adaptive to meet future challenges, ensuring a citizen-focused urban future. Continuing to explore and enhance AI and GenAI is vital to establishing smart cities as a global benchmark for urban living and sustainability.
Public services and safety: A new paradigm
AI and GenAI are revolutionizing public services by enabling interactive services, improving multilingual communication for diverse populations and enhancing health services through outbreak awareness and resource allocation. In the public safety sphere, these technologies use predictive analytics for crime prevention and more efficient fire detection and response systems, significantly improving the efficacy and responsiveness of emergency services.
Urban mobility: The veins of the smart city
Traffic congestion plagues cities. In 2022, the average American driver lost 51 hours due to traffic2. Beyond being an ineffective use of time, traffic significantly affects air quality and public health. AI-powered adaptive traffic management can dynamically adjust signal timings based on real-time traffic patterns, incidents and weather conditions, reducing greenhouse gas (GHG) emissions and improving emergency response times. These systems embody the principles of sustainability that are crucial to the smart city vision.
Master planning and infrastructure development
Urban expansion requires infrastructure that keeps up with the pace of change. AI supports this evolution by enabling digital twin modeling and simulation, helping city planners make informed decisions about infrastructure development. This technology optimizes critical infrastructure placement, from fiber networks to public amenities, adapting to meet the community’s immediate and long-term needs.
Sustainable development: A cornerstone of smart cities
Sustainability is a core tenet of the smart city framework, where new technology is integral to managing and reclaiming resources. AI and GenAI enable smarter energy use, improved air and water quality monitoring and more efficient waste management. Cities will also be able to better forecast energy needs, optimize resource allocation and improve their carbon footprint, a crucial endeavor as cities account for 75% of carbon emissions despite only occupying 3% of the earth3.
Smart cities require scalability and efficiency
To effectively navigate the complexities of smart city technologies, an integrated approach is required to scale and maintain efficiencies achieved through innovative developments. Dell NativeEdge securely simplifies the deployment and management of devices at the edge, providing a unified platform that brings together the various strands of smart city operations. This single-pane-of-glass experience is crucial for city administrators who need to efficiently orchestrate a myriad of interconnected technologies. By leveraging NativeEdge, cities can ensure their smart infrastructures are not only more manageable and secure, but also primed for future expansion.
The path forward
AI and GenAI will play a critical role in the evolution and future of smart cities. These innovative technologies are key to transforming urban spaces so they become more than just places to live; they can be designed to improve livability. Integrating AI with urban development sets the stage for smarter, more sustainable cities, improving the services and functional provisions to communities, infrastructures and citizens.
Riyam Chaar wonders if Artificial Intelligence in education is a threat or a blessing, putting forward that artificial intelligence proves to be time-saving, offering uncountable avenues of personalized learning accessible around the clock and much more . . .
The prevalence of any invention is usually confronted with fear. People at first dread it and feel threatened by its existence. Artificial intelligence has become an inevitable part of our daily lives nowadays. Without even acknowledging it, AI tools accompany us all day long, from Google Maps or Waze while driving to Google Translate, ChatGPT, Siri while in the office, and all the other tools that continuously escort us on our phone devices. In brief, the range of available tools that are at our fingertips does not only speed up the tasks that we attempt to achieve but also facilitates our day-to-day ongoings, making them simpler to accomplish.
In the field of education, artificial intelligence, up to this minute, is considered by plenty of educators as a source of danger. Instructors fear that students’ reliance on AI tools will limit their critical thinking, problem-solving skills, and human connection and eventually lead them to become passive learners by copying and pasting information without even comprehending its context. Moreover, they believe that such AI tools bring several issues, such as bias, concerns over privacy, a lack of transparency, and ethical dilemmas. The information displayed in AI tools might not be accurate or credible. Automated translation tools, for instance, might supply the learner with word-to-word translation that is not applicable or contains many mistakes.
On the other hand, artificial intelligence proves to be time-saving, offering uncountable avenues of personalized learning that are accessible around the clock. With one click and in a matter of seconds, the learner can access tons of information that was previously reached by unlimited research and sleepless nights. Moreover, not only does artificial intelligence have the potential to improve educational tasks by delivering tailored learning experiences, but it also provides feedback in a timely manner.
When it comes to the subject of whether or not educators should make use of it, the question that remains at the forefront is whether or not they should make use of it. Should they consider it a blessing or a threat? In a nutshell, artificial intelligence has evolved to the point that it is now an integral part of our everyday lives. It is essential that educators acknowledge this fact and refrain from disregarding the merits of its usage.
However, in order to successfully incorporate artificial intelligence (AI) into educational settings, it is necessary to conduct a thorough analysis of both the benefits and drawbacks associated with AI.
Additionally, appropriate safeguards and ethical standards must be put in place to ensure that artificial intelligence is utilized in a manner that is both responsible and equitable.
Therefore, academics ought to be selective and change whatever needs to be adapted to fit the requirements of the tasks they tend to do. It is necessary for them to handle it with care and acknowledge the limitations it imposes. Considering all of the challenges that these instruments provide, it is imperative that instructors remain and exercise attentiveness while using them.
Riyam Chaar, is the author of “Your Guide to Writing”, a trainer on educational pedagogies, and the academic controller at Global Academy International (Thumama Branch).
AIport, an online community dedicated to covering the latest international ML developments, has crafted the first volume of its Global Generative AI Landscape 2024.
This initial edition examines notable GenAI players worldwide across several key categories. This is the first generative AI landscape analysis to emphasize regional attributes and encompass four times more nations than the average GenAI landscape available to the public.
The research process involved examining all 62 countries invested in the AI market, as featured in the Global AI Index by Tortoise. In-house model developers were identified, filtered by the team of editors and data scientists, and subsequently cross-referenced with current GenAI landscapes from Sequoia Capital, Antler, Base10, and others, before being segmented into ten GenAI categories. As the final step, the data was divided into continental regions: North America, South America, Europe, Asia, Oceania, and Africa.
The first volume of the global GenAI landscape from AIport aims to present a balanced view of international companies, encompassing not only Western firms, but also those from other regions. The landscape offers a comprehensive analysis, detailing which players are developing GenAI solutions, their locations, and the specific nature of their contributions. It contains a total of 128 generative models from 107 companies.
As Avi Chawla, a data scientist and community manager at AIport, put it:
“We noticed that many generative AI landscapes tend to focus either on the Silicon Valley giants or the tech powerhouses of Europe, covering no more than 10 countries on average. While this approach does serve its purpose, it can’t really offer a complete picture. To address this, we decided to dig deeper, and this is what we came up with after weeks of research. We believe Volume 1 of our Global Generative AI Landscape 2024 provides an objectively international outlook. And we’re also planning to delve into other aspects of GenAI more closely in the future.”
The landscape and key highlights
Of the 62 countries listed in the Global AI Index, only 35 develop their GenAI solutions in-house. Roughly 90% of them focus on one model type.
Regional leaders by the number of active GenAI companies are North America – USA; South America – Argentina; Europe – UK and France; Asia – China and Israel; Oceania – Australia and New Zealand; Africa – South Africa.
The average number of GenAI models per company is the highest in North America, being the only region to have at least one model from each of the 10 model categories.
Approximately 10% of all companies covered in the study have implemented multimodality in their GenAI models, with a majority of these developers located in the US. This indicates that while multimodality represents an emerging trend, its adoption outside North America still remains in the nascent stages.
A total of 11 companies worldwide have developed more than one type of GenAI model. Stability AI leads with five distinct GenAI model types (image, video, audio, 3D, and code), followed closely by OpenAI (chatbot, audio, video, and multimodal) and Google (text, image, audio, and multimodal) – both with four model types.
Microsoft, Meta, Tencent, Baidu, and Yandex are among those companies that developed between two to three types of distinct GenAI models.
13 companies have developed multiple models within a single GenAI category. AssemblyAI has two speech-to-text models, MosaicML offers two iterations of its MPT for code generation, while IPOXCap has introduced two chatbots designed for business intelligence applications.
AIport is an online community of AI writers, researchers, and data scientists that aims to provide a transnational perspective on AI. Recognizing that most ML-related publications primarily focus on the “big leagues” in the West, AIport seeks to be more inclusive by widening the angle and broadening the narrative. This approach ensures a more diverse and impartial representation, offering a well-rounded take to the global AI community.
From the traditional cities to the present-day smart cities, urban environments have demonstrated an adaptive nature when faced with evolving challenges and opportunities. So, Revolutionising Urban Landscapes with GenAI is in the air as well as on the ground.
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Revolutionising Urban Landscapes with GenAI
March 03, 2024
The potential of GenAI is vast and has far-reaching implications on our urban spaces. Welcome to the cognitive city that can enhance efficiency, sustainability, and livability.
Evolution of cities
Cities have undergone significant changes in the past few decades, adapting to challenges and advancements in technology in order to become more sustainable and efficient. From the traditional cities to the present-day smart cities, urban environments have demonstrated an adaptive nature when faced with evolving challenges and opportunities. The initial challenges faced by cities often stemmed from rapid industrialisation and urbanisation, leading to overcrowding, housing shortage, inadequate infrastructure, environmental degradation. Efforts to address these challenges focused on expanding and modernising urban infrastructure through smart solutions.
The shift towards smart infrastructure has been driven by advancements in information and communication technologies (ICT), particularly the integration of IoT devices, big data analytics, and digital connectivity into urban management systems. Despite advancements, cities today face new challenges including climate change, resilience to pandemics, social inequality, the need for more participative governance models, cross-integration of solutions and leveraging the full potential of data from across the sectors. The journey towards smarter infrastructure and the integration of advanced technologies has set the stage for the next evolutionary step: the birth of cognitive cities, where AI and machine learning further enhance urban efficiency, sustainability, and livability.
Establishing pathways for cities to leverage GenAI potential
The potential of Generative AI (GenAI) is vast and has far-reaching implications for cities, as demonstrated by the use cases being implemented globally. GenAI can enhance urban services, simulate urban environments, and offer valuable insights for decision-making processes. This fosters the development of more efficient, livable, and sustainable cities. However, the adoption of GenAI in cities is an ongoing innovation process that covers various aspects, including but not limited to:
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Collaborations and partnerships
Facilitation of active collaboration and participation from all city stakeholders while also focusing their efforts on partnering with leading technology providers to unlock the true potential of GenAI accelerating innovation across the city.
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Ethical and responsible usage
Prioritisation of ethical and responsible considerations with a target on actively mitigating biases across GenAI algorithms ensuring inclusivity across seamless interactions and building trust through transparency and enablement of responsible AI.
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Data privacy and security
Establishment of clear data governance framework to manage and safeguard privacy of the abundant user data utilised within the GenAI models and implementation of robust security measures to protect user data against advanced cyber threats.
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Regulatory compliance
Align usage of GenAI capabilities to comply with the various local and international regulatory compliances as applicable to the cities with a constant oversight to ensure all considerations are observed throughout the GenAI journey.
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Read more in our Revolutionising Urban Landscapes with GenAI paper
AI could help us in tackling the climate crisis. But technology is as much a part of the climate problem as a solution. Nevertheless, AI has a large and growing carbon footprint, but there are potential solutions on the horizon
Given the huge problem-solving potential of artificial intelligence (AI), it wouldn’t be far-fetched to think that AI could also help us in tackling the climate crisis. However, when we consider the energy needs of AI models, it becomes clear that the technology is as much a part of the climate problem as a solution.
The emissions come from the infrastructure associated with AI, such as building and running the data centres that handle the large amounts of information required to sustain these systems.
But different technological approaches to how we build AI systems could help reduce its carbon footprint. Two technologies in particular hold promise for doing this: spiking neural networks and lifelong learning.
The lifetime of an AI system can be split into two phases: training and inference. During training, a relevant dataset is used to build and tune – improve – the system. In inference, the trained system generates predictions on previously unseen data.
For example, training an AI that’s to be used in self-driving cars would require a dataset of many different driving scenarios and decisions taken by human drivers.
After the training phase, the AI system will predict effective manoeuvres for a self-driving car. Artificial neural networks (ANN), are an underlying technology used in most current AI systems.
They have many different elements to them, called parameters, whose values are adjusted during the training phase of the AI system. These parameters can run to more than 100 billion in total.
While large numbers of parameters improve the capabilities of ANNs, they also make training and inference resource-intensive processes. To put things in perspective, training GPT-3 (the precursor AI system to the current ChatGPT) generated 502 metric tonnes of carbon, which is equivalent to driving 112 petrol powered cars for a year.
GPT-3 further emits 8.4 tonnes of CO₂ annually due to inference. Since the AI boom started in the early 2010s, the energy requirements of AI systems known as large language models (LLMs) – the type of technology that’s behind ChatGPT – have gone up by a factor of 300,000.
With the increasing ubiquity and complexity of AI models, this trend is going to continue, potentially making AI a significant contributor of CO₂ emissions. In fact, our current estimates could be lower than AI’s actual carbon footprint due to a lack of standard and accurate techniques for measuring AI-related emissions.
The previously mentioned new technologies, spiking neural networks (SNNs) and lifelong learning (L2), have the potential to lower AI’s ever-increasing carbon footprint, with SNNs acting as an energy-efficient alternative to ANNs.
ANNs work by processing and learning patterns from data, enabling them to make predictions. They work with decimal numbers. To make accurate calculations, especially when multiplying numbers with decimal points together, the computer needs to be very precise. It is because of these decimal numbers that ANNs require lots of computing power, memory and time.
This means ANNs become more energy-intensive as the networks get larger and more complex. Both ANNs and SNNs are inspired by the brain, which contains billions of neurons (nerve cells) connected to each other via synapses.
Like the brain, ANNs and SNNs also have components which researchers call neurons, although these are artificial, not biological ones. The key difference between the two types of neural networks is in the way individual neurons transmit information to each other.
Neurons in the human brain communicate with each other by transmitting intermittent electrical signals called spikes. The spikes themselves do not contain information. Instead, the information lies in the timing of these spikes. This binary, all-or-none characteristic of spikes (usually represented as 0 or 1) implies that neurons are active when they spike and inactive otherwise.
Just as Morse code uses specific sequences of dots and dashes to convey messages, SNNs use patterns or timings of spikes to process and transmit information. So, while the artificial neurons in ANNs are always active, SNNs consume energy only when a spike occurs.
Otherwise, they have closer to zero energy requirements. SNNs can be up to 280 times more energy efficient than ANNs.
These properties render SNNs useful for broad range of applications, including space exploration, defence and self-driving cars because of the limited energy sources available in these scenarios.
Lifelong learning
L2 is another strategy for reducing the overall energy requirements of ANNs over the course of their lifetime that we are also working on.
Training ANNs sequentially (where the systems learn from sequences of data) on new problems causes them to forget their previous knowledge while learning new tasks. ANNs require retraining from scratch when their operating environment changes, further increasing AI-related emissions.
L2 is a collection of algorithms that enable AI models to be trained sequentially on multiple tasks with little or no forgetting. L2 enables models to learn throughout their lifetime by building on their existing knowledge without having to retrain them from scratch.
The field of AI is growing fast and other potential advancements are emerging that can mitigate the energy demands of this technology. For instance, building smaller AI models that exhibit the same predictive capabilities as that of a larger model.
Advances in quantum computing – a different approach to building computers that harnesses phenomena from the world of quantum physics – would also enable faster training and inference using ANNs and SNNs. The superior computing capabilities offered by quantum computing could allow us to find energy-efficient solutions for AI at a much larger scale.
The climate change challenge requires that we try to find solutions for rapidly advancing areas such as AI before their carbon footprint becomes too large.
A three-day forum in Morocco emphasized the importance of environmentally friendly, socially resilient, and technologically innovative approaches to urban development. Key discussions included the role of smart cities in achieving sustainable development and the significance of digitalization and AI in transforming societies. Participants highlighted the need for smarter planning strategies and collaboration across African cities.
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