Leveraging Artificial Intelligence Is Smart for Explosive Detection
Leveraging Artificial Intelligence Is Smart for Explosive Detection
May 19, 2025
Harnessing the power and possibilities of artificial intelligence (AI) and machine learning (ML) and applying these emerging capabilities to the Department of Homeland Security (DHS) mission has been, and will continue to be, a high priority for the Science and Technology Directorate (S&T). One way S&T is demonstrating this commitment to applying emerging technologies to pressing national threats is by investing in the development of AI/ML technologies. Specifically in this case, the funding is directed at AI/ML that could soon be used to identify dangerous compounds, like those found in explosives and narcotics.
When the DHS Small Business Innovation Research (SBIR) Program released a solicitation back in FY2020, under the topic “Machine Learning Module for Detection Technologies,” the goal was to develop innovative solutions that would ultimately provide DHS operational components with an enhanced ability to identify new threats at aviation checkpoints. In the spring of 2021, following their 6-month Phase I awards to demonstrate concept feasibility, Physical Sciences Inc. (PSI) and Alakai Defense Systems, Inc. (Alakai) were each awarded a $1 million, 24-month SBIR Phase II contract. These awards further lean into the ultimate goal of developing advanced AI/ML-based detection algorithms that can shorten the timeline for deployment of capabilities able to identify threats in the field. The research and development (R&D) being done is important because it addresses a capability gap in the detection of certain types of new threats. S&T believes that AI/ML solutions can help close that gap.
According to Thoi Nguyen, program manager for S&T’s Next Generation Explosives Trace Detection Program, “When the intel, special ops, or law enforcement communities find a new threat, maybe a new explosive compound, the threat is validated and prioritized according to urgency levels. DHS S&T is then tasked to develop an R&D solution to detect and identify the threat. Once the solution is tested, evaluated, and verified that it meets DHS detection requirements, DHS Components go through a lengthy DOTMLPF (Doctrine, Organization, Training, Materiel, Leadership and Education, Personnel and Facilities) process to acquire and deploy the solution. At the end of this process, the chemical ‘signature’ of the threat is uploaded to DHS equipment at airport checkpoints.”
However, adding a new compound to the existing identification library of threat compounds historically has been a slow, meticulous, and labor-intensive process. This can result in a capability gap for updating the database.
The challenge S&T posed with this funding award is to see if an AI/ML solution can significantly expedite the process of updating a detection library, without the intensive human labor.
One of the ways that dangerous compounds are identified at checkpoints is with Raman Spectroscopy. This chemical analytical technique fires a laser at a vaporized and ionized sample that was swabbed from a traveler, or into an object like a closed bottle of liquid. The laser will excite the molecules it encounters in the target, causing them to vibrate. Every type of molecule has its own distinct vibrational frequency. The spectrometer will detect those vibrational frequencies and chart them on a graph. The chemical signature is determined by where specific peaks are found on the graph and the intensity, height, and width of those peaks. Then the system searches the chemical signature library to find a match. If the sample matches an explosive in the database, the alarm is sounded.
So, what’s the problem? “The bottleneck is not in the intel process, the bottleneck is in the R&D process and how to add that new threat intel, the new chemical signature, into the library so we can catch the bad guys,” said Nguyen. “That’s where the AI/ML that our small business partners are developing fits into the equation.”
“We love small businesses because they’re innovative and nimble,” said SBIR Program Director Dusty Lang. “The SBIR program allows us to absorb the risk by funding multiple Phase I proposals to explore feasibility, then move forward to Phase II with the best solutions for DHS needs.”
Traditionally, when a new threat compound is introduced into the library, scientists and contractors are brought in to manually create a new classification or channel for it. At that point, the tedious work to enter all the spectrographic characteristics of the chemical into the library begins. The programing of the chemical traits for the channel must be extremely precise to ensure they get the highest Probability of Detection (PD) and the lowest Probability of False Alarm (PFA) when the library is queried with a sample at a checkpoint.
One of the complicating factors for achieving high PD and low PFA is that the software analyzing the compound must be able to see through the background noise in the sample and identify the compound for what it really is.
“For example, pure TNT from a lab may appear different from TNT in a real-world scenario because there may be additives to the TNT, or there may be other environmental interference. So, even though it might have spectrographic peaks at the right places, they might be somewhat obscured by these other excited molecules and their signatures. If you’re creating a TNT channel, we would have to account for myriad factors. That’s what takes so much time and that’s where accuracy is so important. It has to be calibrated perfectly. What we’re trying to do here with the AI and the ML is that we want to bypass that slow process.”
The first part of that bypass is training the AI to recognize a specific compound. However, the AI can’t teach itself. It still needs to be taught how to do it. The ML-based detection algorithm starts as a blank sheet, and it must be taught which peaks on the graph represent which chemicals. “It’s like teaching a child what sugar tastes like,” said Nguyen. “When you taste this, that is sugar. That’s what we call sweet. And this is sugar with a little bit of lemon. You taste the sour lemon, but it’s still sugar. It’s the same thing with teaching the AI to not get confused by the background noise.”
In Nguyen’s example, the important thing for the child to understand is that the sample is still sugar, and the lemon is just an additive. In the explosive detection world, that lemon might be a fuel added to TNT to make it more powerful. Making sure that the explosives detection algorithm is smart enough to determine that the TNT is mixed with another fuel compound is incredibly important.
That brings us to the second part, which is validation. Once the AI is taught the signature characteristics of the compound, and potential noise distractions have been accounted for, the AI is evaluated for accuracy by running tests designed to trick it. Chemicals are added to the original compound in attempts to shield or mask the spectrographic signature behind other noise.
Nguyen emphasizes the importance of this part, adding that, “We don’t just trust AI completely. We say, ‘trust, but verify,’ to see whether or not the alarm that was just triggered complies with our understanding of how the vibration of the molecules we are testing should present themselves.”
For a limited set of explosives, S&T demonstrated that the AI/ML solution identified explosives with very high PD, yet low PFA—a major success by itself. Even more remarkable is the way that this AI/ML solution has closed the critical time capability gap.
“What traditionally can take as many as one to two years, the AI/ML that our partners developed can now learn, classify, and upload new threats to the library in a matter of days or weeks,” said Nguyen. “That has significant real-world impact. And I want to make sure that we give credit to SBIR, because without their collaboration, funding and support, this project would never have happened.”
SBIR’s Lang added, “These two companies, PSI and Alakai, demonstrate the impact small business can have and why we are always working to strengthen the SBIR reach and support. It is very rewarding to be able to work with program managers like Thoi to facilitate the connections of ideas and needs.”
This round of Phase II funding from the SBIR Program resulted in confirmation that AI/ML has a place in the future of explosive detection. The shortened deployment cycle to chemical libraries in the field, coupled with maintaining the high PD and low PFA, is something that human hands can’t match. That’s the power of trustworthy AI/ML and that’s what S&T is looking to leverage to further secure the nation.
In terms of looking back on the work that has been developed under the program, Nguyen finished up stating, “It was a success beyond our imagination.”
In the future, AI/ML modules will be tested and evaluated at the U.S. Army’s Chemical Biological Center. The goal there will be to determine compatibility between three types of Raman Spectrometers and their interoperative capabilities.
Read the article here: Leveraging Artificial Intelligence Is Smart for Explosive Detection
The Fundamentals of Smart Manufacturing
The Fundamentals of Smart Manufacturing
Manufacturing is changing at a very rapid pace. There is a new type of connected, data-driven, and architecturally open factory emerging in response to these demands, led by the Industrial Internet of Things (IIoT). Along with increased machine automation, other characteristics of this new smart factory include hyper-agility, autonomous production, and data utilization as a tool for business.
Research conducted by Accenture for the World Economic Forum showed that 73% of the C-level executives interviewed were convinced that the IIoT would fundamentally change their industry. But just 20% had a strategy for harnessing it. Companies that want to succeed in the future must master the radical digital transition headed our way by opening themselves to a journey that will change their organization models beyond recognition – the alternative being a catastrophic loss of market share and profitability.
Smart, connected and data-driven
Smart manufacturing is now seen as a natural progression of the “digital convergence” already underway between information technology (IT) and operational technology (OT). There are four essential characteristics that set it apart.
Firstly, the smart factory uses data and IIoT connectivity to easily control all aspects of operations in near real-time, with near full automation across all locations. IoT and digital investment is the foundation for proactive, self-aware factory operations, maintenance and innovation.
Sensor-equipped machines, inter-operable systems and reliable real-time computing are connecting machines across the smart factory. Product, raw materials, equipment, and control systems all have the potential to collect and share data. This data can be analysed in context and in real time to equip workers with actionable information.
Throughout the factory, end-to-end security at both hardware and software levels helps reduce vulnerabilities as more machines are connected. With more than 40 years of experience in both OT and IT solutions, Advantech is uniquely qualified to address the issues of OT-IT convergence which are fundamental to migration from yesterday’s ‘islands of automation’ to tomorrow’s smart factories.
A self-managing “Systems of Systems”
Secondly, the smart factory is based on multiple interconnected systems, each with a high level of flexibility, efficiency, and autonomy. Future factories will eventually become one large system comprising hundreds of smaller systems independently working toward the same goal. From production and maintenance to supply chain and security, each system and subsystem uses AI, machine vision, deep learning, and edge analytics to control everything on the factory floor.
This environment of machine-to-machine communication improves operational efficiencies and reduces unplanned downtimes. Production becomes so responsive to custom requests and material variations that the factory essentially operates at “economies of one” to compete with today’s economies of scale.
Self-monitoring equipment using sensors such as Advantech’s LoRaWAN smart vibration sensor can detect when quality could suffer due to degradation and then schedule its own service. Materials follow the most efficient path, and workloads are consolidated at all architecture layers to provide the flexibility to respond to fast-changing demands. Orchestration of applications and services across hardware enables data aggregation and control to provide new levels of performance.
Human-machine collaboration
A third aspect of the smart factory is its emphasis on machine-to-human collaborations, allowing employees to work more safely and empowering them to make faster, more educated, innovative responses to business needs. As smart factories reduce the number of humans on the floor, workers are helped by collaborative ‘co-bots’ on complex tasks, while repetitive, injurious work is handled by robots.
Workers use augmented reality and data visualisation to overlay information about production, maintenance and product status. A digital culture encourages the use of data for daily work, freeing employees to respond with greater creativity to resolve issues and support business success. A younger workforce is attracted through updated technology, safer work environments, and roles better suited to their generation.
Autonomous and self-adapting
Through autonomy and adaptability, the smart factory enables manufacturers to expand IIoT’s application and value to support changing business strategies.
The factory is becoming smarter and more autonomous over time, using data to optimise resource allocation and transform businesses. As more machines and systems are connected, manufacturing matures into an intelligent factory model in which OT and IT converge and strategically engage in business decisions.
AI and deep learning produce increasingly detailed, accurate and meaningful digital models of equipment and processes, enabling data-driven decision-making; and devices will grow more intelligent over time and respond to events more efficiently. Production controls become self-running, and new business approaches emerge.
Aided by the insights from data, the main manufacturing drivers have expanded from efficiency and product quality to also include production flexibility. Amid this continually evolving environment, the factory systems become increasingly intelligent and autonomous with systems beyond themselves.
Key trends shaping the future of manufacturing
In a smart factory environment, “data collection” refers to the process of gathering information from the manufacturing processes, equipment, and systems involved in production. Data collection in a smart factory is typically facilitated by various sensors, IoT devices, and automation systems that continuously monitor and record relevant information in real-time. This data is then aggregated, processed, and analysed using advanced analytics, machine learning algorithms, and other tools to gain insights, optimize processes, improve efficiency, and make data-driven decisions.
EdgeLink, a versatile IoT gateway software, is engineered to connect with over 200 edge devices and diverse platforms. It adeptly supports multiple protocols, unifying data sources, optimizing data processing, and publishing data to mainstream platforms or other automation systems through cellular, 4G, 5G, Wi-Fi networks, and VPN connections.
“Edge AI” and “Edge Computing” refer to the processing and analysis of data at or near the ‘edge’ of the source of data generation, rather than relying on centralized cloud computing resources. In the context of an intelligent or smart factory, edge computing and AI technologies are employed to perform data processing, analysis, and decision-making tasks directly within the factory environment. They play a crucial role in enabling the responsiveness required in modern manufacturing environments, contributing to the concept of Industry 4.0 and the evolution of smart factories.
MES (Manufacturing Execution System) and ERP (Enterprise Resource Planning) are software systems used to manage manufacturing operations and overall business processes, while OEE (Overall Equipment Effectiveness) is a performance metric used to assess the efficiency of equipment or processes. These concepts are all essential components of modern manufacturing management practices.
In the context of a smart factory, “Analytics AI” and “Machine Learning” (ML) are critical components that enable data-driven decision-making, optimization, and automation. They play crucial roles in leveraging the vast amount of data generated in a smart factory environment to drive insights, improve decision-making, optimize processes, and ultimately enhance productivity and profitability. They enable the transformation of data into actionable intelligence, empowering manufacturers to adapt quickly to changing conditions and stay competitive in today’s fast-paced business environment.
Industrial connectivity refers to the network infrastructure and technologies used to connect various devices, systems, and components within an industrial environment. It enables seamless communication and data exchange between machines and other equipment, facilitating industrial processes. Industrial connectivity is a foundational element of Industry 4.0 and the concept of the IIoT.
Overall, industrial connectivity is essential for enabling automation, data-driven decision-making, and optimization in industrial environments. It forms the foundation for Industry 4.0 initiatives aimed at improving efficiency, productivity, and competitiveness in industrial sectors.
Advantech’s Role in Driving Smart Manufacturing
Software tools play a crucial role in the optimization of smart factories. These tools encompass a wide range of applications and platforms designed to support manufacturing planning, execution, monitoring, analysis, and optimization. Advantech’s WebAccess and other IoT software products provide a complete solution for device and data management to enable edge intelligence.
Advantech has implemented Industry 4.0 in its manufacturing centres, including a ‘situation room’ as the factory’s central brain where data is collected, analysed and visualised for real-time management. The equipment connectivity solution – consisting of an edge data gateway and distributed digital I/O – facilitates machine connections without replacing existing equipment while also collecting data. The industrial computer with Advantech software enables data transfer between production and management systems.
The process visualisation solution enables production monitoring, data integration with MES, and visualisation on the situation room dashboards. This allows production optimization and data-driven decision making. The Advantech WebAccess app gives push notifications of unexpected downtime, allowing immediate action to be taken.
The fundamentals of smart manufacturing aren’t slowing down. When combined together, they usher a new Industrial Internet of Things (IIoT) era where machine automation, hyper-agility, autonomous production, and data utilization deliver transform business processes.
Read the article here: The Fundamentals of Smart Manufacturing
Smart Farming: 6 Emerging Trends for Agritech Startups to Watch
Smart Farming: 6 Emerging Trends for Agritech Startups to Watch
The agriculture sector is changing, and the revolution is as a result of technological development. The concept of smart farming involving the use of advanced technologies such as AI, IoT, and big data analytics is defining the future of agricultural practices and is currently generating a tidal wave of opportunities for agritech startups. These startups are well placed to meet the growing demand for food around the world while at the same time offering efficient, sustainable farming solutions.
Here are six emerging trends and the opportunities that they present for agritech startups:
- Precision Agriculture: Data-Driven Farming for Maximum Efficiency
Precision agriculture is an important pillar of smart farming, which includes the use of data in enhancing farming methods. Precision agriculture helps to make real-time assessments of factors like soil, water, and crop health. The use of satellite imaging, GPS, and sensory technology enables the farmer to apply water, fertilizers, and pesticides appropriately, increasing production and minimizing wastage.
For agritech startups, this presents the opportunity to create systems that collect data and present this to farmers so they can make better decisions. Such sensors, drones, or software solutions can be created by startups, and this real-time data can help farmers to work most efficiently, with the lowest operational expenses and with reduced emission of pollutants to the environment. The possibility of customization of these solutions depends on the type of crops and geographic location, which only adds value to these solutions.
2. AI-Driven Crop Management: Enhancing Productivity and Decision-Making
Artificial intelligence (AI) is rapidly transforming crop management, where it performs tasks which were previously handled manually and also carries out risk assessment. Automated crop management employs big data analytics by relying on machine learning (ML) algorithms to predict climate changes, disease risks, and the optimum time for harvesting. This makes it possible for the farmers to prevent anything that might hinder the growth of the crop and also improve the yield of the crop.
Agritech startups can leverage artificial intelligence to create apps that will assist farmers in managing the growth of crops, pinpointing the onset of diseases, and assisting in informed decision-making. Their products might also ensure optimal use of materials and manpower, reduce costs on labor, and boost the management of the supply chain.
If the startups manage to tackle such essential problems as crop losses and resource wastage, they will have a great potential for enhancing food safety and environmentally friendly technologies.
3. IoT Integration: Connected Devices for Smarter Farms
The arrival of the Internet of Things (IoT) in the agtech space is making everything more connected by using devices such as smart devices, automatic irrigation machines, and drones. These IoT devices help farmers monitor and manage their farms even from far away by providing real-time information about the soil, weather, and the performance of the equipment on the farm.
When it comes to agritech startups, IoT offers themselves a grand avenue to grow solutions that address the multiplicities of the farming value chain. Startups can develop software that optimizes irrigation based on forecasts or work on drones, which will help to control the state of huge fields and identify if crops are stressed.
4. Robotics and Automation: Redefining Labor and Productivity
Robotics and automation convergence in agriculture is mitigating the growing shortage of labor force and increasing efficiency. In terms of reliability, autonomous machines do relieve people of responsibilities in activities such as planting, weeding, and harvesting among other responsibilities.
From an innovation perspective, technologists in startups can identify the low-hanging fruit of using robots in the agricultural industry regarding repetitive duties that require human intervention in undertaking the task.
Automation technology offers scalability, enabling farmers to manage larger areas of land without compromising productivity. With advancements in AI and machine learning, these robots can adapt to different environments and crops, making them a versatile solution for diverse farming needs.
5. Blockchain for Traceability and Transparency
A trend that recent studies have identified is the application of that technology in the food supply chain which primarily guarantees the traceability of products. With the use of blockchain technology, all the information relating to farming, such as what seeds were planted, how the farm was cared for, and how the crops were transported, is stored in a secure, tamper-proof mode. This kind of transparency is not only appealing to the consumer who wishes to trace the sources of the delicacy but also helps the growers in defending their earnings and minimizing swindles.
Such agritech startups can easily leverage blockchain to create ecosystems that will guarantee proper quality assurance at every level of food production to combat and prevent any violations of the quality standards or environmental laws. It will increase the level of transparency, and assist in trusting farmers in terms of their ability to meet consumer needs and be competitive in the market.
6. Sustainability and Climate Resilience: Future-Ready Farming
However, because climate change is an ongoing process that impacts agriculture in a negative way, climate-friendly and sustainable farming practices are required more than ever before. There are increased uses of practices like regenerative agriculture farming and efficient water use, while innovations such as artificial intelligence and the internet of things are making farming more efficient.
The major opportunity that presents itself to agritech startups is to design solutions that ensure farmers make minimal negative impacts on the environment and are sustainable. This ranges from developing tools that help in the conservation of water, reducing the use of chemicals, and generally improving the ground. Climate-smart agriculture is one of the key areas that start-up companies will be able to address in a short time, hence becoming profitable ventures, especially due to the increasing demand for environmentally friendly farming practices.
Seizing the Opportunities: The Role of Agritech Startups
With the advent of artificial intelligence, IoT, robotics, and data analytics in the agricultural industry, there is a conducive environment for agritech startups to thrive. As those startups come up with solutions that address some of these major challenges in farming, such as food production, sustainability, and resources, they will also shape the future of farming while occupying a large niche in the global agritech market.
It will be startups that are innovative, scalable, and sustainable that will drive the change in traditional farming practices. Working hand in hand with farmers, scientists, and governments, agritech startups have the potential to widely implement smart farming tools and establish a better, more productive farming industry.
To summarize the above thoughts, the future of agriculture will belong to people creating better technological solutions for farming and optimizing farming for the benefit of both people and the environment. This is the moment for agritech startups to come up with solutions and capture the market. Agricultural startups have a unique opportunity to look for a solution for current problems and shape the future of agriculture with a global trend for food security and new sustainable agricultural practices.
Read the article here: Smart Farming: 6 Emerging Trends for Agritech Startups to Watch
Smart Cities And E-Health: The Convergence Of Urban Infrastructure And Digital Healthcare
Smart Cities And E-Health: The Convergence Of Urban Infrastructure And Digital Healthcare
In an era where digital transformation is reshaping industries, the convergence of smart cities and e-health is redefining urban living. Smart cities are no longer just about optimizing transportation, utilities and governance. They are evolving into intelligent ecosystems where digital healthcare is becoming a core element of urban planning. As cities grow and technology advances, integrating healthcare into urban infrastructure is reshaping how medical services are delivered, accessed and managed.
The convergence of smart cities and e-health is about reimagining healthcare delivery in a way that is more accessible, efficient and responsive. Cities are moving beyond traditional models of care by leveraging AI, IoT and real-time data analytics to improve patient outcomes and healthcare accessibility.
Specifically, the increasing adoption of agentic AI systems provides sophisticated and real-time monitoring and decision making for enhanced services. Agentic AIs are capable of taking autonomous actions to achieve specific goals or objectives, typically based on predefined rules, learned patterns or programmed behaviors. The shift toward technology-driven healthcare solutions is setting new standards for urban well-being and quality of life.
Smart Cities As Enablers Of Digital Healthcare
The United Nations Department of Economic and Social Affairs estimates that 68% of the global population will live in urban areas by 2050. Many of these cities will be built using smart infrastructure principles that rely on real-time data, AI and IoT-driven systems to improve efficiency and sustainability.
By integrating e-health solutions into urban infrastructure, cities can make healthcare services more connected, data-driven and patient-centric. Digital transformation is ensuring that healthcare is no longer confined to hospitals and clinics.
A study by Fardin Quazi (2024) on “eHealth Services in Comprehensive Smart Environments” highlights the role of urban infrastructure in supporting digital healthcare and the seamless interaction between them. The research emphasizes seamless interactions between smart environments in enhancing patient care and streamlining healthcare operations through advanced digital technologies.
How Smart City Infrastructure Supports E-Health
The global e-health market is currently valued at $274.35 billion and is expected to reach $576.73 billion by 2030. The U.S. remains at the forefront of this growth, driven by the increasing demand for smart, technology-enabled healthcare solutions. Urban infrastructure plays a crucial role in supporting this transformation in several ways.
Real-Time Health Monitoring And IoT Connectivity
In a smart city, healthcare is no longer limited to in-person visits. Wearable devices, home-based health monitoring systems and IoT-powered medical sensors provide real-time data on patients’ vital signs, such as heart rate, oxygen levels and glucose levels. Agentic AI complements this by analyzing the data in real time and triggering actions without waiting for manual input.
This data is transmitted securely to healthcare providers, enabling remote monitoring and timely medical intervention. By integrating real-time health tracking with urban data systems, cities can create more proactive healthcare models that focus on preventive care rather than reactive treatment. This shift reduces hospital overcrowding and enhances medical efficiency.
Emergency Response Optimization
Cities with AI-powered emergency response systems and real-time traffic monitoring are improving the efficiency of medical services. By leveraging real-time GPS data, ambulances can navigate faster routes, bypass congested areas and reduce response times in critical situations.
Additionally, AI-assisted surveillance systems can detect accidents, medical emergencies or sudden health incidents in public spaces, triggering automatic alerts to emergency responders. These innovations are particularly valuable in large urban centers where delays in emergency response can have life-threatening consequences.
The increasing use of agentic AI in smart traffic management is aiding in better monitoring and real-time response. For instance, if a pedestrian meets with an accident or faces a health emergency in a public space, agentic AI surveillance can identify the incident, alert emergency responders and analyze environmental factors such as air quality or crowd density to determine potential causes.
Telemedicine And Virtual Healthcare Services
Telemedicine is becoming a mainstream mode of healthcare delivery rather than an alternative to traditional consultations. With 5G connectivity, cloud-based healthcare platforms and AI-powered diagnostics, patients can now consult doctors remotely without visiting a hospital. This transformation is particularly beneficial for elderly residents, individuals with mobility challenges and underserved communities.
Smart city infrastructure is also facilitating the deployment of virtual health kiosks, allowing residents to access medical consultations and conduct basic health screenings conveniently. Beyond facilitating virtual consultations, agentic AI systems can autonomously schedule follow-ups, analyze symptoms during video calls and recommend diagnostic tests based on patient data.
Recognizing the growing impact of digital healthcare, the U.S. Department of Health and Human Services (HHS), through HRSA, allocated $55 million to 29 health centers to expand access to telehealth, remote patient monitoring and AI-driven health technologies. These investments are reinforcing the role of smart infrastructure in supporting accessible healthcare.
Challenges In Smart Healthcare Integration
Despite its potential, integrating e-health into smart cities presents significant challenges. Data privacy and security remain primary concerns, as healthcare data must be securely transmitted and protected from cyber threats. Additionally, ensuring interoperability between different healthcare platforms, IoT networks and urban systems is an ongoing challenge that requires industry-wide standardization.
Another critical issue is bridging the digital divide. While smart healthcare solutions are advancing, not all urban residents have equal access to digital health services. Investments in affordable digital literacy programs, healthcare technology accessibility and public-private collaborations will be necessary to ensure inclusivity.
Collaboration between healthcare providers, technology developers and policymakers is essential to overcoming challenges in digital healthcare integration.
The Future Of Smart Cities And Healthcare
As cities continue to evolve into data-driven, intelligent environments, healthcare will become an even more central component of urban planning. Future innovations in AI-driven personalized medicine, blockchain-secured health records and 5G-enabled smart hospitals will further revolutionize how cities manage healthcare services.
Public-private partnerships will play a key role in scaling digital healthcare initiatives, bringing together tech companies, government agencies and healthcare providers to create sustainable solutions.
Healthcare is no longer just a standalone service—it is an intrinsic part of modern urban infrastructure. The cities of the future will not only be smarter and more efficient but also healthier and more resilient.
Read the article here: Smart Cities And E-Health: The Convergence Of Urban Infrastructure And Digital Healthcare
Prioritize play to help your city thrive in a post-pandemic world
Prioritize play to help your city thrive in a post-pandemic world
August 14, 2024
We are at a pivotal moment in urban development, facing a housing crisis that affects cities across North America. While addressing the housing shortage is unquestionably critical, we must also remember that cities, especially great cities, are more than shelters.
Cities are the birthplace of inventions, new forms of collaboration and vibrant social interactions. Over the years, much of the social infrastructure that fostered these interactions — such as corner stores, bowling alleys, clubs and bustling main streets — has been stripped away. Therefore, as we work to provide shelter and basic security, we must also rekindle the idea of cities as habitats for the human spirit, laying the foundations for a united, collaborative and flexible society capable of tackling the complex, interconnected issues of our age.
Often, traditional methods of city-building can obscure new opportunities. Perhaps we are now at a point where the erosion of old principles can allow us to leap forward with innovative ideas.
Historically, the relationship between a city and its residents was framed by the Live, Work, Play planning model. This model assumed that these three attributes, in that order, were what people looked for in a potential city. A core pillar of Live was housing and, in North America, home ownership. However, while cities are working diligently to catch up with the housing problem, the underlying causes and the attribute Live are often beyond a city’s control.
Another sign of a weakening city-resident relationship is the post-pandemic shift to flexible work models, especially in the innovation economy. Work is becoming less of a determining factor in where people live. Last year, 35% of workers did some or all of their work at home, according to a U.S. Bureau of Labor Statistics survey, meaning that Work is also an attribute not fully within a city’s control.
These changes suggest that cities are losing relevance in their relationship with residents, potentially leading to an era of mediocre cities. But mediocrity is not sufficient for social, economic and environmental reasons. Cities need to thrive. Anything less will accelerate social isolation and division.
If we move past the old, ineffective priorities, we can see a new opportunity in Play. Traditionally, Play was the afterthought attribute of city building — prioritized last, funded with leftover money and created on land that wasn’t useful for anything else. Given the tenuous state of Live and Work, how a city facilitates social interaction between residents (Play) is now the best way to differentiate its offering and directly improve social and economic prosperity. Moreover, Play is entirely within the control of cities.
Play, as a city attribute, means connecting residents and making them feel they belong. It means celebrating a city’s uniqueness and identity, putting inclusiveness into action, supporting an innovative entrepreneurial ecosystem, fostering trust and compassion, and offering vibrancy that helps a generation often cut out of homeownership feel like fully valued residents of a city.
Practically speaking, Play and the collective joy it creates can help address the housing crisis and other contemporary issues. Joyful cities redefine what it means to live in urban density in a “smaller” home by devoting public space to playful participation. In this model, neighborhoods become the best amenity for a home, and the city becomes everyone’s communal backyard.
Joyful cities are also more competitive in today’s innovation economy, which thrives on ideas, invention and the people who create them. A city’s ability to attract creative talent through vibrant living, collaborative spaces and a lifestyle that blends work and play will determine its economic future.
Despite our efforts to solve the housing problem, cities are unlikely to revert to what they once were. But we can move forward with a Play+Live+Work = Thriving joyful cities prioritization.
It starts with asking, “How do we want our city to play?”
Read the article here: Prioritize play to help your city thrive in a post-pandemic world
What Is a Smart Home?
What Is a Smart Home?
July 12, 2024
The term ‘smart home’ has become an increasingly popular buzzword in the world of home security. Every aspect of our home life seems to become increasingly digitized, with the realm of domotics —a contraction originating from the Latin word ‘domus’, meaning home, and the term ‘robotics’— being front and center throughout this process.
But what does having a smart home even mean and how can homeowners use this technology to increase the peace of mind in their home? Read on to learn more about the nuts and bolts surrounding this increasingly popular term.
What is a smart home?
A smart home is a living space with home automation devices that use an internet connection. Connected devices can communicate with each other and synchronize tasks through a common network. This differs from home automation in general, which can include devices connected through other means such as bluetooth and local networks.
Smart home devices are usually connected through Wi-Fi and are included in the broader term of the Internet of Things (IoT), which includes devices connected through local networks. A smart home can increase the energy efficiency in your home, improve your home security system and make your daily task easier to manage.
History of smart homes
Although we may not think of it this way now, technically, a washing machine is an example of home automation. A task that was once commonly done by hand and took a considerable amount of time and energy was now processed automatically by a machine. In this way, the rise of home appliances in the beginning of the 20th century was the first wave of home automation.
The first main communication protocol for electrical devices, X10, was invented in 1975. The protocol uses power line wiring for signaling and control between appliances and is still widely used today. Modern interest with home automation started in the late 1990s and kept growing as Wi-Fi access and new connecting technologies became more prevalent.
How does smart home technology work?
Smart appliances can synchronize tasks in a specific sequence, known as a routine. These appliances communicate through home automation connectivity standards —technical specifications that ensure devices from different manufacturers can communicate with each other. Some, such as Z-Wave and Zigbee, are available only for specific brands, while the recent advent of Matter as a common standard across companies has gained traction.
Smart home products can also be activated through voice commands, usually with the aid of a voice assistant. The most common of these assistants are Amazon Alexa, Google Assistant and Apple’s Siri. Whether operated through a smartphone or a smart home hub, voice controlled assistants help you control multiple appliances at once and start routines that facilitate your daily life.
Examples of smart home technologies
- Smart lighting (such as smart light bulbs)
- Smart thermostats
- Smart home security appliances (such as security cameras)
- Smart locks
- Smart plugs
- Refrigerators
- Dishwashers
- Smart speakers
- Video doorbells
- Washers and dryers
- Ovens
- Sprinklers
- Motion sensors
- Televisions
- Automated garage door openers
Reasons to invest in a smart home system
A smart home can make your house more energy efficient by automating turning off lights at a certain time or optimizing your energy consumption. It can also improve your home security by integrating your home automation system with security cameras and motion sensor technology. This can then be controlled through a central hub or your smartphone.
However, smart home systems also can expose you to security risks in terms of data privacy —some gadgets connected through the IoT lack reliable encryption. Smart homes also need a consistent and reliable internet connection, which is not available in every part of the US. Ultimately, your home’s particular situation and needs are the factors you should consider to determine if smart home automation is right for you.
Read the article here: What Is a Smart Home?
Rise in AI Adoption Prompts Global Push for Regulation
Rise in AI Adoption Prompts Global Push for Regulation
June 14, 2004
The rapid expansion and deployment of generative artificial intelligence (gen AI) and AI more broadly across organizations worldwide has resulted in a global push for regulation.
In the US, President Joe Biden signed an executive order on AI in October 2023, laying out AI standards that are set to be eventually codified by financial regulators. Over the past five years, 17 US states have enacted 29 bills focused on regulating the design, development and use of AI, according to the Council of State Governments.
In China, President Xi Jinping introduced last year the Global AI Governance Initiative, outlining a comprehensive plan focusing on AI development, safety and governance. Authorities have also issued “interim measures” to regulate the provision of gen AI services, imposing various obligations relating to risk assessment and mitigation, transparency and accountability, as well as user consent and authentication.
Recently, Japanese Prime Minister Fumio Kishida unveiled an international framework for the regulation and use of gen AI called the Hiroshima AI Process Friends Group. The group, which focuses on implementing principles and code of conduct to address gen AI risks, has already gained support from 49 countries and regions, the Associated Press reported on May 03.
Impact of EU’s AI Act on financial services firms
The European Union’s AI Act is perhaps the most impactful and groundbreaking regulation to date. Approved by the EU Parliament in March 2024, the regulatory framework represents the world’s first major law for regulating AI and is set to serve as a model for other jurisdictions.
According to Dataiku, an American AI and machine learning (ML) company, the EU AI Act will have considerable impact on the financial services industry and firms should prepare for compliance now.
Under the AI Act, financial firms will need to categorize AI systems into one of four risk levels and take specific mitigation steps for each category. They will need to explicitly record the “Intended Purpose” of each AI system before they start developing the model. While Dataiku says that there’s some uncertainty about how this will be interpreted and enforced, it notes that this indicates a stricter emphasis on maintaining proper timelines than current regulatory standards.
Additionally, the AI Act introduces “Post Market Monitoring (PMM)” obligations for AI models in production. This means that firms will be required to continually monitor and validate that their models remain in their original risk category and maintain their intended purpose. Otherwise, reclassification will be needed.
Dataiku recommends financial services companies to promptly familiarize themselves with the AI Act’s requirements and assess whether current practices meet these standards. Additionally, documentation should begin at the inception of any new model development, particularly when models are likely to reach production, it says.
Moreover, Dataiku warns that the EU’s proactive stance may encourage other regions to accelerate the development and implementation of AI regulations. By 2026, tech consulting firm Gartner predicts 50% of governments worldwide will enforce use of responsible AI through regulations, policies and the need for data privacy.
A groundbreaking regulatory framework
The EU’s AI Act is the world’s comprehensive regulatory framework specifically targeting AI. The legislation adopts a risk-based approach to products or services that use AI, and impose different levels of requirements depending on the perceived threats the AI applications pose to society.
In particularly, the law prohibits applications of AI that pose an “unacceptable risks” to the fundamental rights and values of the EU. These applications include social scoring systems and biometric categorization systems.
High-risk AI systems, such as remote biometric identification systems, AI used as a safety component in critical infrastructure, and AI used in education, employment and credit scoring, are forced to comply with stringent rules relating to risk management, data governance, documentation, transparency, human oversight, accuracy and cybersecurity, among others.
Gen AI systems are also subject to a set of obligations. In particular, these systems must be developed with advanced safeguards against violating EU laws, and providers must document their use of copyrighted training data and uphold transparency standards.
For foundation models, which include gen AI systems, additional obligations are imposed, such as demonstrating mitigation of potential risks, using unbiased datasets, ensuring performance and safety throughout the model’s lifecycle, minimizing energy and resource usage and providing technical documentation.
The AI Act was finalized and endorsed by all 27 EU member states on February 02, 2024, and by the EU Parliament on March 13, 2024. After final approval by the EU Council on May 21, 2024, the AI Act is now set to be published in the EU’s Official Journal.
Provisions will start taking effect in stages, with countries required to ban prohibited AI systems six months after publication. Rules for general purpose AI systems like chatbots will start applying a year after the law takes effect, and by mid-2026, the complete set of regulations will be in force.
Violations of the AI Act will draw fines of up to EUR 35 million (US$38 million), or 7% of a company’s global revenue.
AI adoption surges
Globally, jurisdictions are racing to regulate AI as adoption of the technology surges. A McKinsey survey found that adoption of AI has reached a remarkable 72% this year, up from 55% in 2023.
Gen AI is the number one type of AI solution adopted by businesses worldwide. A Gartner study conducted in Q4 2023 found that 29% of respondents from organizations in the US, Germany, and the UK are using gen AI, making it the most frequently deployed AI solution.
Read the article here:
https://fintechnews.ch/aifintech/rise-in-ai-adoption-prompts-global-push-for-regulation/71038/
Smart farming with ‘AI at the edge’
admin
Cambridge Consultants has announced to bring artificial intelligence (AI) to the edge of the network, using low-cost, low-power devices to perform complex machine learning tasks
‘AI at the edge’ is set to enable AI to solve many of the real-world challenges, out in the field. The approach is demonstrated by Fafaza, a precision crop spraying technology that performs plant recognition and individual treatment in real time.
Precision agriculture means harnessing technology to optimise production. It relies on precise granular data at the individual plant level, on the scale of large industrial farms, supporting everything from weed identification to crop health and yield estimation. This understanding can inform real-time actions, for example, the application of herbicide to an individual weed. This is the challenge that Fafaza addresses: deploying AI ‘at the edge,’ on the back of a moving tractor and without the need for connectivity.
Fafaza is designed to spot broadleaved weeds amongst the grass and to treat individual target leaves with herbicide. The system identifies, classifies and applies treatment in real time while moving at tractor speed. The Cambridge Consultants team chose this tough ‘green on green’ challenge to demonstrate the potential of state-of-the-art machine vision and AI.
Although AI techniques have been able to achieve plant recognition for a number of years, the challenge has been in moving from powerful specialist platforms with delayed processing of data, to processing and acting in real time: this is ‘AI at the edge’. To be technically practical, a system must be fast enough to distinguish and identify plants using ambient light and to apply treatment while the plant is still in view. To be commercially viable, a system must be rugged and affordable.
Fafaza has been developed to run on off-the-shelf components, including a low-cost camera that can capture images at around 20 frames per second and an AI platform that costs less than US$100. Major processor vendors continue to invest heavily in devices that can run AI inference algorithms, bringing costs down further. These developments are opening up new areas for real-time AI processing in the field, without the need to rely on a communications infrastructure or the cloud.
Read the article here: Smart farming with ‘AI at the edge’
Smile, Your City Is Watching You
admin
Local governments must protect your privacy as they turn to “smart city” technology.
Walking through the streets of New York City, you can feel the thrill of being lost in the crowd. As throngs of people filter past, each going about their days, it seems possible to blend in without being noticed.
But as municipalities and companies pursue the dream of “smart cities,” creating hyper-connected urban spaces designed for efficiency and convenience, this experience is receding farther and farther from reality.
Consider the LinkNYC kiosks installed across New York City — more than 1,700 are already in place, and there are plans for thousands more. These kiosks provide public Wi-Fi, free domestic phone calls and USB charging ports.
Yet the LinkNYC kiosks are not just a useful public service. They are owned and operated by CityBridge (a consortium of companies that includes investment and leadership from Sidewalk Labs — a subsidiary of Alphabet, the parent company of Google) and are outfitted with sensors and cameras that track the movements of everyone in their vicinity. Once you connect, the network will record your location every time you come within 150 feet of a kiosk.
And although CityBridge calls this information “anonymized” because it doesn’t include your name or email address — the system instead records a unique identifier for each device that connects — when millions of these data points are collected and analyzed, such data can be used to track people’s movements and infer intimate details of their lives.
In other words, this free Wi-Fi network is funded the same way as Google itself: using data to sell ads. As Dan Doctoroff, a deputy mayor in the Bloomberg administration and now the founder and C.E.O. of Sidewalk Labs, told a conference in 2016, the company expects to “make a lot of money from this.”
LinkNYC exemplifies the trend in “smart cities” today: the deployment of technologies that expand the collection of personal data by government and corporations. Certainly, this data can be used for beneficial outcomes: reducing traffic, improving infrastructure and saving energy. But the data also includes detailed information about the activities of everyone in the city — data that could be used in numerous detrimental ways.
Whether we recognize it or not, technologies that cities deploy today will play a significant role in defining the social contract of the future. And as it stands, these smart city technologies have become covert tools for increasing surveillance, corporate profits and, at worst, social control. This undemocratic architecture increases government and corporate power over the public.
First, smart city technologies make it easier than ever for local and federal law enforcement to identify and track individuals. The police can create and gain access to widespread surveillance by acquiring their own technology, partnering with companies and requesting access to data and video footage held by companies. In Los Angeles, for example, automatic license plate readers recorded the location of more than 230 million vehicles in 2016 and 2017, information that, through data-sharing agreements, could have found its way into the hands of Immigration and Customs Enforcement. Similarly, the police in suburban Portland, Ore., hoping to aid crime investigations, have used Amazon’s facial-recognition software to identify more than 1,000 people who have appeared in camera footage.
Second, the smart city is a dream come true for companies eager to increase the scale and scope of data they collect about the public. Companies that place cameras and sensors on Wi-Fi kiosks, trash cans and streetlights will gain what had been unattainable insights about the behavior of individuals. And given the vast reach of hard-to-trace data brokers
that gather and share data without the public’s knowledge or consent, one company’s data can easily end up in another’s hands. All of this data can be used to exclude people from credit, jobs, housing and health care in ways that circumvent anti-discrimination laws.
Once these smart city technologies are installed, it will be almost impossible for anyone to avoid being tracked. Sensors will monitor the behavior of anyone with a Bluetooth- or Wi-Fi-connected device. Given the expansive reach of cameras and the growing use of facial-recognition software, it is increasingly impossible to escape surveillance even by abandoning one’s personal digital technology.
This reality suggests that if you want to avoid being tracked in a smart city, you must stay out of that city.
Read the article here: Smile, Your City Is Watching You
5G to play a significant role in entertainment and education
admin
With the deployment of a fifth-generation network in China that comes with high speed and low latency, industrial applications including education and entertainment will discover new market opportunities.
At the Mobile World Congress Shanghai, China Mobile signed a partnership agreement with firms like NetDragon and TAL on smart education applications to be run on 5G networks.
Interactive education will soon become a reality with high-speed and low-latency 5G-enabled data transfer, especially in the developing and rural regions which struggle for quality education. It will make educational resources more accessible and interactive, said Xiong Li, CEO of NetDragon.
Fuzhou-based NetDragon has developed a virtual reality lab for physical and chemical experiments and digital board for schools. With 5G development and cooperation with China Mobile, the new services will be available online and in more schools nationwide.
Kazakhstan’s Ali Almira, founder of AR in Education, is showcasing its AR applications that are used in education, health care and marketing. She attended the MWC Shanghai to look for potential partners to promote AR application that is specifically designed in Chinese for kids and students.
China Mobile’s subsidiary Migu also signed an agreement with Mango Media to establish a joint 5G lab. Besides Mango, it has invited overseas partners like BBC, NBA and Discovery to seek opportunities on HD contents in the 5G era.
Migu broadcasts 350 sports and entertainment events now. It will increase the number of broadcasting programs after the 5G deployment. The company also works with Sichuan’s panda base to produce documentaries and ringtones and music with panda themes.
China Unicom has established a 5G research center with iQiyi on edge computing, super high definition, VR and AR applications. Both sides will try to build a new ecosystem for VR, said iQiyi, China’s leading online video website with 100 million paid users already.
BOE, China’s biggest LCD panel vendor, showcased its latest technologies in Shanghai such as ultra-high-definition screens and foldable display.
4K or 8K high-definition screens will become more popular in China with a jump in contents. 5G, with high-speed data transmission, will help content providers and journalists to obtain and produce more HD contents, including live broadcasting with 5G networks, analysts said.
Consumers want to deal with more contents as the picture, webpage, social network, game and video on smartphones will require bigger screen spaces. It’s a potential opportunity for BOE, which is offering foldable screens with Huawei’s Mate X phones.
Read the article here: 5G to play a significant role in entertainment and education