Predicting Earthquakes

Researchers from Stanford's School of Earth, Energy & Environmental Sciences have developed a deep-learning model to detect seismic activity. The algorithms can identify the start of two different types of seismic waves and detect even weak earthquakes that current methods often overlook.

Scientists applied the model to five weeks of continuous earthquake data and located 200% more earthquakes than traditional technology found. If this type of AI software catches on, it could help people evacuate their homes before an earthquake occurs. It could also prevent people from returning home too early and encountering aftershocks.

Forecasting Floods

Climate change has caused a dramatic increase in flooding. In July 2022, the U.S. experienced two one-in-1,000-year rainfall events within two days of each other, leading to devastating floods that engulfed homes and claimed several lives. Although floods cause billions of dollars in damage annually and affect hundreds of millions of people, current forecasting technology often fails to help people evacuate in time.

Now, some researchers hope AI can help predict heavy rainfall. Google's AI-based Flood Hub software is available in 80 countries, warning people of floods up to a week in advance. Users can look at the world map to see rainfall and river level predictions for each region, with a red icon indicating the highest risk. Google is working on making the technology available in Search and Maps.

Detecting Wildfires

By the time firefighters extinguished the 2018 Camp Fire, it had claimed the lives of 85 people and burned for two weeks, making it the deadliest wildfire in California's history. Could AI have predicted the disaster and saved the towns of Paradise and Concow?

The California Department of Forestry and Fire Protection has started using high-tech cameras and AI to detect smoke and fire. A network of cameras mounted on platforms scan the horizon for wildfires, and researchers are training the software on what is and is not a fire.

One benefit of using cameras is that they can be where people cannot, such as in remote wilderness locations. Hopefully, this new technology will learn to alert firefighters when it detects a blaze and help prevent future disasters.

Predicting Hurricanes

NASA's IMPACT team recently partnered with tech company Development Seed to track Hurricane Harvey. Using machine learning and satellite imagery, the Deep Learning-based Hurricane Intensity Estimator estimates a hurricane's wind speed as soon as satellite data reaches Earth.

The software's neural networks essentially automate the Dvorak technique that matches satellite imagery to known patterns. By analyzing hurricane data in almost real time, meteorologists may be able to warn the public of impending hurricanes before disaster strikes.

Issuing Smarter Alerts

In addition to predicting disasters, AI could help by sending out timely alerts to save money, keep people informed and aid in the evacuation process.

For example, U.S. Coast Guard Command Centers have people listening for radio distress calls for 12 hours a day, which entails listening almost entirely to hoax calls or static. AI could relieve employees of this tedious duty by analyzing radio traffic to detect distress signals. This technology could help issue faster alerts to activate Coast Guard rescue missions.

Another potential use for AI would be to analyze CCTV footage in real time inside buildings, sounding an alarm if it detected smoke or earthquake-related tremors. A rapid response time would allow people to evacuate quickly.

Harnessing the Power of AI

Artificial intelligence is revolutionizing disaster forecasting. Meteorologists have already used it to evacuate people who would otherwise be in the direct path of oncoming storms, such as during India's Cyclone Phailin in 2013.

The technology will likely save countless lives as it becomes even more refined. Someday, instead of looking at the skies, we may only have to look at a screen to know when to board up the windows.

The post Why We Need AI to Keep People Safe from Natural Disasters appeared first on Datafloq.

What it demonstrates can guide governments of all levels toward the most productive use of time and resources when shifting toward an EV-based and sustainable future for mobility.

How Did AI Perform a Study About EVs and the Grid?

Utilidata partnered with UMTRI to install smart grid chips in local EV chargers to get more data about how it impacts the grid. The never-before-seen study incorporated artificial intelligence to determine voltage and current patterns as people use it regularly. It could determine everything from how drive-time impacts charging to brand-specific trends.

They needed to incorporate AI in this groundbreaking study to have two million EVs on Michigan roads by 2030 to follow their environmental objectives.

Almost every state and nation in the world has these metrics to meet to collaborate in reducing the adverse effects of climate change, which is why studies like these that accumulate easy-to-parse data that are easily shareable and accessible are paramount in progress.

What Is the Impact of AI-Based Studies Like These?

There is an immediate need for researchers to use AI in projects because it can expedite stalling sectors. AI mediation eliminates the time between investments and government intervention when it can provide real-time data. The data reveals where budgets can allocate funds and where to install EV chargers for the most value. However, the grid has to keep up, and that's the most complicated factor.

First, it will perpetuate the value of AI in data-driven studies. They are a priceless supplement to manual data collection, especially in already-smart technology where machinery integrates smoothly. The return on investment is incomparable, as companies make the upfront investment for the technology, and they save countless down the line in wasted hours of humans poring over data that human error is more likely to taint. With AI, human oversight can verify the validity, getting the best of both strengths.

Additionally, it will inspire everyone worldwide to see AI as a resource in accelerating climate-friendly advocacy and research and development previously seen as too expensive, complex or inaccessible.

For example, lithium-ion batteries are costly to the environment and for manufacturers' pockets – how can these develop or shift to combine with other renewable technologies to make them more sustainable? They have a 10-to-15-year lifespan, but what if cars or chargers combined with solar power or additional renewable energy?

For sectors like the grid, which require a near-complete overhaul to meet projected EV demand, it helps everyone from engineers to city planners to electricians collaborate with clear-cut data on the next steps.

What Will Happen Because of the Study?

What has the data revealed to researchers, and how will they apply these findings? The results will not become public until late 2023. Still, they continue their hard work by collaborating with the U-M Electric Vehicle Center for more research – $130 million funded by the state. They will announce a roadmap soon. In the meantime, leadership in the project claims the extension of the original study will elaborate on how the findings will influence consumer behavior and policy.

The funds will also spread education about the sector for more skilled workers and focus on honing in on battery engineering and manufacturing to make the process more streamlined and efficient. It's particularly relevant as EV batteries haven't garnered the cleanest reputation for their lack of recycling infrastructure and environmental abuse from raw material extraction.

Related studies are happening simultaneously that validate and expand the potential of what AI has expounded. A recent MIT study – that didn't employ AI – postulates that the foundation for EV innovation is the strategic placement of charging stations. EV stations could go anywhere there's room, but that isn't how humanity should install them. Home charging provides more opportunities than it seems, giving policymakers ideas for government-funded incentives for contributing to EV charger and grid development.

AI Will Drive the Future of EVs and the Grid

Michgian's AI-driven study concerning EVs and the grid will change renewable mobility infrastructure worldwide. It will normalize the usage of AI in industry-shifting research and development while catalyzing necessary pushes toward productive eco-friendly progress. Setbacks in EV development, from supply chain disruptions to inadequate recycling, have misconstrued the sector's potential to eliminate the transportation sector's greenhouse gas emissions.

Studies like these will be the starting point for efficient and sustainable grid analysis based on empirical data from regular EV users.

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A comprehensive deliverable is a tough ask when it needs to demonstrate its value in such a short period. Still, UI design principles and additional tips can craft an app worthy of five-star reviews and years-long use.

1. UI Determines UX

UX is how users feel when using an app, and interactivity, ease-of-access and aesthetics often contribute to those feelings more than designers realize. Design principles guide users in the right direction when it comes to responding emotionally and mentally to the app.

For example, the UI principle of Structure prioritizes app architecture that appeals to how the brain categorizes like and unlike things. The Feedback principle allows user participation in the design by recording their reactions and opinions to change the app's state or presentation to adjust their UX.

2. Never Stop Testing

A 2017 survey uncovered that 88% of app users would stop using an app if they encountered too many bugs. Users may not immediately uninstall it – though this is possible – but they would at least close out and consider deletion later.

Frequent testing will prevent daily bugs from creeping into an application. Additionally, transparent patch notes demonstrate attentiveness to detail and responsiveness to feedback for improved UX and UI.

3. Make It Accessible

Not every app user has the same capabilities, so it's critical to consider UI elements that appeal to as wide an audience as possible. With testing in mind, app makers should consider finding diverse individuals to provide feedback. It could reveal the answers to questions like:

• Does the app have colorblind settings for color changes?
• Is the text adjustable in size?
• Are there audio or visual aids for people with visual or audio concerns?
• Does the app use gender-neutral language?
• Are there diverse representations of bodies, if applicable?

4. Keep It Simple

The Simplicity and Visibility principles of UI explain why a more approachable, less flashy design is to the advantage of app designers. Users want to be able to find all the information and settings they are curious about without seeking an FAQ or customer service representative.

The app text should be free of jargon – unless the target audience demands otherwise – so navigation is straightforward. App users could also leverage images, diagrams and other visual aids instead of walls of text. The fewer requests app makers get from users, the more they know their design prioritizes ease of use.

5. Make It Compatible

Platform availability is crucial – nobody wants to discover a valuable app and find it's unavailable for their phone model. App wars like the battle between Google and Apple Maps show these contentions firsthand, despite the apps offering nearly similar features. Ideally, app makers will make their app brand agnostic while including functionality and integrations for other devices like wearable tech or seamless car connectivity.

6. Leverage White Space for Readability

App designers are creatives at heart, so it might be an exercise in self-restraint to pull back on elaborate design elements for the sake of simplicity. Negative space between graphics and paragraphs helps scannability. Put them under and beside images and columns to discourage long text blocks or oversized visual elements.

7. Use Breadcrumb Navigation

Breadcrumb navigation uses logical menu mapping that appeals to the audience. Whether it's attribute- or history-based navigation or anything in between, users shouldn't have to search for long to find the landing page they need. The key to smart breadcrumbs is to have the right amount – there is no need to be hyperspecific. Determine what users want to find and make links accordingly.

8. Spell Out Data Transparency and Protection

Apps collect data – users may not love it, but they're willing to agree to terms and conditions that are clear and concise while providing them the convenience they desire from the app. The app should have a dedicated area to explain why and how it collects data and uses it per federal compliance and regulations.

9. Increase Load Speeds

Everyone's internet speeds and data accessibility is different. Still, app designers have a lot of influence on UX if they can decrease file sizes, simplify source codes and update software to load as quickly as possible without depending on those external influences. UX improves if users don't experience lag or distrust servers.

10. Hone Brand Consistency

Brand awareness and tone should resonate with the app's users and the company's story. Every subsequent style update should only be degrees from the original concept – not a massive divergence. Otherwise, it could confuse users. Are they in a different app? Did someone buy it out? Spend time in the early development and design stages to ensure a solid brand foundation for the app's longevity.

Ensure Content UX With Quality UI

Following design principles to forge a simple, practical and easy-to-boot application will make users happy. App design is rarely a stagnant process – it's a series of updates to improve quality, appearance and experience. It's a rare opportunity to hone a product long after its release, so app makers should want to make it perfect.

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Whether a personalized recommendation popping up on a user's screen, a helpful chatbot or a seamless mobile app experience, these technologies can turn a one-time buyer into a lifelong patron. The transformative power of technology offers a path to impress and engage people like never before. Here is how technology can improve customer experiences exponentially.

The Role of Personalization in Customer Experience

Personalization is the practice of tailoring a service or a product to accommodate specific needs. Consider Netflix recommending a film based on your recent viewings or Amazon suggesting products inspired by your search history.

This is personalization in action and is crucial in elevating customer service. A survey revealed that 74% of consumers said feeling valued and understood was the primary factor contributing to brand loyalty.

Leveraging technology like artificial intelligence (AI) and data analytics lets businesses predict customer preferences and offer uniquely tailored experiences, resulting in increased satisfaction.

10 Ways Technology Improves the Customer Experience

These concepts will help you understand how companies use technology to improve the customer experience.

1. AI and Machine Learning for Predictive Personalization

AI and machine learning are changing the face of predictive personalization. These technologies analyze past interactions and customer behavior to anticipate future needs, recommend products and services, and create highly customized experiences.

Spotify uses AI and machine learning to analyze users' listening habits, genres and artists of choice. It uses this data to create personalized playlists like “Discover Weekly” to offer songs that listeners might enjoy. This predictive personalization keeps users engaged and enhances their experience, encouraging them to spend more time on the platform.

2. Flexible Delivery and Fulfillment Options for Meeting Needs

Flexible delivery and fulfillment options are essential in meeting diverse customer needs, and technology is significant in enabling this. Advanced logistics and delivery tracking software let customers order products online and provide real-time updates about their delivery status. This adds transparency to the process and allows shoppers to adjust delivery times.

3. Chatbots for 24/7 Customer Service

Chatbots are revolutionizing customer service by offering 24/7 assistance. These virtual assistants can handle several queries promptly, reducing wait times. For example, banking websites often use chatbots to guide customers through common issues, like resetting passwords or checking account balances, offering immediate solutions and greatly improving customer satisfaction.

4. Augmented Reality for Product Visualization

Augmented reality (AR) brings an innovative approach to product visualization, enabling customers to experience products before purchasing. A notable example is IKEA's AR app, IKEA Place, which allows users to virtually place furniture in their homes. This realistic visualization facilitates confident purchase decisions.

5. Internet of Things for Smart Devices

The Internet of Things (IoT) is pivotal in creating smart devices that uplift customer experiences. For example, products like Google Nest or Amazon Echo leverage IoT to provide personalized experiences, such as adjusting the room temperature to the user's preferences or playing their favorite music.

6. Mobile Technology for On-the-go Access

Mobile technology empowers customers by providing on-the-go access to services and products. The rise of user-friendly apps has revolutionized the global food delivery market, which has grown over 200% since 2017, catapulting its worth to exceed $150 billion.

This market expanded twofold in the United States during the COVID-19 pandemic. This highlights mobile technology's crucial role in offering customers convenience and flexibility.

7. Social Media Platforms for Customer Engagement

Social media platforms offer businesses a powerful avenue for engaging with customers. Companies can communicate directly with shoppers, address their concerns, share updates and collect feedback. Additionally, livestreams or stories can be useful for product reveals or behind-the-scenes glimpses.

8. Blockchain for Enhanced Security and Trust

Security is paramount in building customer trust. Blockchain technology is a potent tool ensuring secure and transparent transactions. The decentralized and tamper-resistant system eliminates vulnerabilities associated with traditional purchases. This reassures customers about their information's safety and fosters trust.

9. Data Analytics for Customer Insights

Data analytics plays a key role in understanding customer behavior and preferences. Companies can analyze interactions and gain valuable insights into what people value most. This can inform strategies to improve product offerings, ultimately leading to more satisfying customer experiences.

10. Cloud-based Solutions for Seamless Service

Cloud-based solutions offer seamless service that significantly enhances customer experiences. Businesses that store their data in the cloud can ensure uninterrupted service, quick response times and real-time updates. For instance, Netflix uses cloud technology to provide users instant access to movies and shows.

The Future of Customer Experience

Technology continues to play a large role in shaping customer experiences. These advancements hold immense potential for businesses to create more personalized, secure and seamless interactions. Companies should embrace these innovations to build a sustainable customer-centric future.

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Modern Observation Equipment

Most disasters are weather related, which means predicting them well in advance is often possible. Currently, more than 40% of Americans live in counties that were affected by climate disasters in 2021. The first step in preventing future climate-related deaths is by detecting disasters early.

Modern disaster detection equipment may be active or passive and includes Doppler radar, automated surface observing systems, geostationary and polar-orbiting satellites and wind-profiling systems. This equipment sends data to local weather stations and large, central supercomputing facilities for interpretation.

The ability to remotely sense physical and environmental changes without installing permanent instruments is one of the greatest advances in modern technology. Tools such as lidar, GPS and satellite imagery work together to inform scientists about upcoming weather events. They also allow meteorologists to gather data in dangerous conditions with less personal risk.

Predictive AI Software

After compiling data, the next step is to use it to make predictions. Artificial intelligence shines when it comes to analyzing vast stores of information, and it can track metrics like weather conditions and intensity over time. This quality makes it an excellent tool for predicting natural disasters.

Researchers are working to develop better predictive AI software. With the help of flood simulations and rainfall records, artificial intelligence software may be able to forecast upcoming floods. AI can already use geological and seismic data to predict volcanic eruptions.

AI software also uses seismic data to look at the magnitude and locations of past earthquakes, speculating where the next tremors and aftershocks will hit. Additionally, AI uses satellite imagery to predict the path and intensity of damaging weather events like hurricanes and tornadoes.

Artificial intelligence is still in its infancy. However, the machine learning field has seen such rapid growth in recent years that AI could emerge as a leading technology for weather prediction.

Improved Warning Systems

The last component of disaster mitigation is to use predictions to warn people, informing them how dangerous conditions could be and whether they should evacuate. Early warning systems are one of the most effective types of technology for preventing disaster-related injuries and death.

Location-based text messaging systems are one way governments prepare people for impending disasters. Text messages can convey the predicted time of the disaster, who should evacuate, which roads are open and other crucial information. Since its 2012 debut, public officials have used the Wireless Emergency Alert system 78,000 times to issue alerts on everything from severe weather to missing children.

In the absence of cell service – due to downed cell towers or power lines – officials often use LoRa communication technology. Although the name stands for “long range,” this form of radio communication works best for sending messages to recipients no more than 10 miles away.

IoT-connected warning systems are another new type of disaster prediction technology. For example, a network of electronic alarms in South Africa surveys for wildfires, providing real-time monitoring and text alerts to nearby residents. An expanded network of internet-connected cameras and sensors in cities, rural regions and even uninhabited locations could give valuable insights into upcoming weather conditions.

Using Tech for Good

Disaster prediction technology has saved untold numbers of lives. However, it still has room for improvement. Technological advancements in the form of predictive AI, improved observation equipment and better warning systems will likely save millions of people from the effects of natural disasters. And as the climate rapidly changes, that technology cannot come soon enough.

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The Building Blocks of Self-Driving Cars

Self-driving cars fall into two broad categories of hardware and software components that work in tandem to enable autonomous operation. The hardware mainly consists of the sensor, actuators and onboard computers. Cameras, LiDAR, radar and ultrasonic sensors collect vital data and information about the vehicle's surroundings, while actuators control acceleration, braking and steering in response to onboard computers' decisions.

The software components are responsible for processing the data gathered in the hardware and making appropriate driving decisions. These include perception, localization, planning and control. Perception focuses on detecting and identifying objects in the environment, while localization determines the vehicle's position relative to a map. Planning generates a safe and efficient path to the destination, and control executes the route by sending commands to the actuators.

However, safety concerns still lie ahead for self-driving cars, as the technology must consistently demonstrate its ability to navigate complex traffic situations and avoid accidents. Addressing these issues requires continuous improvements in autonomous systems' reliability and performance, along with rigorous testing to validate their safety under diverse conditions.

Advanced Battery Technologies and Their Role in Autonomous Electric Vehicles

Advanced battery technologies also play a critical role in developing autonomous electric vehicles (EVs) by providing efficient energy storage solutions to meet the increased power demands of self-driving systems. Lighter, higher-capacity batteries enable longer ranges and faster charging times, leading to enhanced performance and convenience for EV users.

One innovative solution is lithium batteries that are up to 80% lighter than lead-acid versions. This weight reduction provides considerable benefits for EVs, including improved energy efficiency and vehicle handling. This exemplifies the potential impact of advanced battery technologies on autonomous EVs.

Overall, cutting-edge battery solutions are essential for successfully integrating autonomous driving capabilities into EVs – paving the way for a cleaner, smarter transportation future.

ML and the IOT's Impact on Autonomous Driving

Machine learning (ML) and the Internet of Things (IoT) are crucial in advancing autonomous driving by enabling vehicles to learn from data and communicate with their surroundings. ML, particularly deep learning, allows self-driving cars to detect and classify objects in their environment. On the other hand, reinforcement learning helps vehicles make real-time decisions and plan optimal routes based on data gathered from various sensors.

IoT contributes significantly to autonomous driving by enabling vehicle-to-everything (V2X) communication. This includes vehicle-to-vehicle (V2V) communication, which allows cars to share information and coordinate actions, improving traffic flow and safety. Vehicle-to-infrastructure (V2I) communication facilitates the interaction between cars and traffic management systems, resulting in more efficient road usage.

Moreover, IoT-enabled smart cities can enhance autonomous driving by offering connected infrastructure and real-time traffic data. However, data privacy and security are still concerns as more devices connect to transportation networks. Regulatory and legal hurdles pose challenges in creating a comprehensive framework governing self-driving cars, including insurance, liability and traffic regulations.

ML and IoT technologies improve decision-making capabilities and facilitate a more intelligent transportation ecosystem. Addressing challenges and technological advancements will be crucial for the widespread adoption of self-driving cars.

The Current State and Future of Self-Driving Cars

Self-driving cars include various automation levels – from driver assistance features to fully autonomous vehicles. Major players in the automotive and tech industries continue to develop and test self-driving technologies, with some real-world deployments already underway.

Significant advancements in autonomous driving technology will address current limitations and pave the way for widespread adoption. As regulations evolve to accommodate self-driving cars and the technology matures, drivers will likely witness greater integration of autonomous vehicles into transportation systems. The future of self-driving cars promises increased safety, reduced traffic congestion and improved accessibility – transforming people's relationships with personal transportation.

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The Factors Influencing AI Reliability in Contingency Planning

Consider the following factors that influence AI's reliability in contingency planning.

Data Quality

Data quality largely influences the reliability of AI in contingency planning, as the success of its algorithms depends largely on accurate, consistent and complete data sets. If the data is high-quality, this provides a solid foundation for AI models to analyze patterns, detect trends and generate reliable predictions that inform contingency planning.

However, poor data quality leads to misleading results, undermining the effectiveness of AI-based planning processes. To ensure high data quality, you must address data representation, consistency and completeness – alongside potential biases that may skew AI insights.

Algorithms and Models

Algorithms and models are fundamental components that determine AI's reliability. That's why suitable models based on specific problems and available data are crucial for accurate predictions and recommendations. Moreover, regularly updating and refining these models ensures the AI system remains adaptive and effective.

Recognizing that no single algorithm or model is universally applicable is important. Hence, a thoughtful approach to selecting, combining and fine-tuning algorithms can significantly enhance the usefulness of AI-driven contingency plans.

Human-AI Collaboration

Human and AI collaboration is crucial for ensuring AI's reliability in contingency planning. When organizations effectively combine human expertise and AI capabilities, they benefit from both strengths while mitigating individual weaknesses.

Effective communication is essential for interpreting and contextualizing AI-generated insights – and human oversight can prevent errors or biases from affecting the planning process. Additionally, a collaborative environment encourages continuous learning and improvement for companies to enhance the overall effectiveness of AI-driven contingency plans.

The Advantages and Disadvantages of AI in Contingency Planning

To maximize AI in contingency planning, organizations should explore the pros and cons and leverage this information for balanced planning processes.

Advantages

AI offers several advantages in contingency planning. One of the most significant is improved efficiency, as AI can analyze data and generate insights faster than human experts.

Enhanced predictive capabilities are another key benefit, with AI models capable of detecting patterns that may be challenging for humans to discern. As a result, this allows for more proactive and informed decisions when creating plans that respond to emergencies.

Additionally, AI can reduce human errors by automating repetitive tasks and providing consistent data-driven recommendations. Automation leads to more robust and reliable contingency plans.

Disadvantages

Despite its advantages, AI presents some challenges in contingency planning. Data limitations, such as privacy concerns and potential biases, can hinder the plan's effectiveness. The output's quality is only as good as the data fed into the system.

The black-box nature of AI can also be problematic. Complex algorithms may be difficult to understand and explain, raising ethical concerns and potentially limiting trust in AI-generated insights.

Furthermore, overreliance on AI may lead to a loss of human expertise and complacency in decision-making. Organizations may be tempted to rely solely on AI for recommendations without considering the nuances and complexities of the real world.

How to Adopt a Balanced Approach to AI in Contingency Planning

Consider the following strategies to ensure your reliability on AI is up to the standards for effective contingency planning:

• Continuous improvement of AI models: Regularly update and refine models to accommodate new data and changing circumstances, ensuring optimal performance.
• Collaboration between AI and human experts: Foster effective communication and teamwork between AI systems and humans to leverage their combined strengths.
• Adopt a hybrid approach to decision-making: Combine AI-generated insights with human expertise and judgment to make well-rounded decisions in contingency planning.
• Ensuring data quality and addressing biases: Prioritize data accuracy, consistency and completeness while proactively addressing potential biases to maintain reliability.

Leverage AI Responsibly for Effective Contingency Planning

AI has the potential to revolutionize contingency planning, offering improved efficiency while reducing human errors. However, organizations must address AI's challenges and limitations to ensure effective contingency planning. It's appropriate to adopt a balanced approach so organizations can leverage AI responsibly to create a robust contingency plan.

The post How Reliable Is AI for Contingency Planning? appeared first on Datafloq.

Why Do EVs Need AI and ML to Be Successful?

AI needs machine learning to assess information for practical applications. EV drivers often overlook their functionality, misconstruing them as built-in. Every hookup and data point creates a more competent industry, making the vehicle more aware of personal driving and charging patterns. ML guides numerous behaviors of EVs without drivers realizing by:

• Optimizing charging based on user driving patterns.
• Adjusting pricing based on electricity demand and grid use.
• Informing installers where to implement more infrastructure.
• Educating engineers on battery optimization and development.

Autonomous vehicles also need AI and ML to stay relevant. Though sensors provide visual awareness to cars, allowing them to keep between road lines and stop at lights, they cannot predict pedestrian behavior or how other drivers react in unpredictable traffic. For self-driving cars to become reliable, ML must educate AI about driving patterns down to geographic specificity for citizens to remain safe.

How Does the Grid Play a Role?

The sustainability narrative relies on technologies to be self-sustaining. While technology could have two separate sectors for energy generators and users, it makes more sense to set a precedent that every environmentally friendly technology operates as both. With the help of AI and ML, EVs can redistribute extra energy stores back to the grid to help during peak times, also known as vehicle-to-grid (V2G) services.

The climate-neutral revolution must advocate for eliminating sole ownership of energy resources. Informing EV drivers how AI and ML increase energy accessibility and ownership is crucial for setting realistic expectations for how practical EV operations will function. Single entities cannot hoard energy, and democratized power-sharing will become the norm.

EV infrastructure has to support its longevity to continue this synergetic relationship. Charging stations can't actively destroy batteries. Therefore, AI is vital in developing smart charging. Drivers going long distances must trust they'll arrive in places that won't harm their vehicle and perpetuate range anxiety. Every hookup matters because it tells manufacturers how to stay relevant to the environment and their customers.

How Will AI and ML Improve EVs?

EVs couldn't get better with data. Manufacturers wouldn't know the average commuter distance in New England if it weren't for driving data informing future battery designs. AI couldn't determine and draw up digital twins about future EV performance without predictive analysis that only ML can generate from years of data mining. The projected 50% emissions reduction from battery electric vehicles (BEVs) could increase exponentially with a bit of help from ML decision-making.

How fast will the batteries deplete before customers need a fresh one? What's the end-of-life cycle for these products? These technologies raise endless new questions, such as whether these maintenance behaviors will impact purchasing decisions and if the hardware is resilient against cyberthreats.

The only technologies capable of answering these questions with as little human interference as possible for EV makers are AI and ML. Trials for technological and chemical adjustments to EVs have never been more valuable, because the data informs blueprints logically. The decisions manufacturers make will have so much evidence backing them that customers won't be able to find faults with these products in the future.

Why EVs Need AI

Nothing is more valuable for tech companies than information. For an industry as necessary as it is politically charged, makers must have intimate consumer awareness to maintain buy-in. Not only will these technologies keep EVs successful in the long term, but they will keep vehicles advancing at a pace that could exceed humanity's expectations.

A fully-functioning self-driving car could be on doorsteps soon – but only because AI and ML invested in collecting the information manufacturer needed to make productive resource allocations.

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What Is Predictive Maintenance?

Predictive maintenance uses Internet of Things (IoT) sensors to monitor equipment health in real-time. When vibrations, temperatures or other maintenance-related factors fall outside of normal parameters, these sensors alert relevant employees that the machine needs inspection. Manufacturers can then schedule timely maintenance stops according to each machine's specific needs.

As IoT technology improves and becomes more accessible, these data-driven repair strategies are quickly gaining popularity. As of 2021, 31% of manufacturing companies were actively using PdM, with another 48% planning on implementing it in the near future. If adoption continues to follow this trend, predictive maintenance will become the norm in manufacturing before long.

Impact of Predictive Maintenance

This rapid growth stems from several tangible business benefits. As manufacturers face increasing pressure to optimize operations, predictive maintenance's advantages become all the more valuable.

Improved Uptime

One of the most impactful benefits of predictive maintenance is how it extends equipment uptime. Since PdM uses on-machine sensors, it can catch issues early. Consequently, manufacturers can prevent breakdowns more effectively and spend less time repairing equipment.

Traditional preventive maintenance can prevent malfunctions, but since it doesn't account for real-time needs, it may also introduce unnecessary repair stops. PdM's need-based repair schedules reduce downtime from both breakdowns and unneeded maintenance. These savings can add to a 10%-20% increase in machine uptime and availability.

Reduced Costs

Predictive maintenance will also make manufacturing operations more cost-efficient. Even a conventional preventive strategy can reduce repair costs by 25%, and PdM takes those savings further by eliminating superfluous maintenance-related downtime.

Repairs cost less when workers can catch issues earlier, as they can fix the problem before it damages more components within a system. The increased uptime PdM provides over preventive programs leads to further cost reductions by letting manufacturers run machines longer. As a result, PdM enables savings of up to 40% over reactive maintenance and 12% over standard preventive approaches.

Higher Efficiency

As manufacturers employ predictive maintenance across more machines, this equipment will perform better. Data-driven insights from the IoT sensors in these systems let facilities ensure their machinery runs as accurately and efficiently as possible. Those improvements, in turn, will translate into fewer production errors and longer operating cycles.

This optimization reduces material waste by preventing errors and enables higher output. In addition to enabling more cost-effective operations, these improvements help manufacturers become more sustainable. Consumers are more likely to purchase from sustainable brands, which can boost companies' revenue.

Improved Safety

Rising PdM adoption will also help make manufacturing a less hazardous industry. Incidents involving contact with equipment accounted for more than 39,000 manufacturing injuries in 2020 alone. While some of those cases may arise from operator error, machine malfunctions play a significant role in these hazards.

Predictive maintenance can reduce these injuries by ensuring all equipment works as it should. Eliminating malfunctions allows manufacturers to prevent incidents in which the inability to control a machine or an equipment error endangers operators or other nearby employees. These safety improvements will protect workers' health, reduce related costs and could improve workplace morale.

PdM Will Change the Manufacturing Sector

Since manufacturing relies so heavily on equipment, optimizing machinery through predictive maintenance has far-reaching effects. As more organizations learn of these potential advantages, PdM adoption will continue to surge.

This growth will substantially alter the manufacturing industry. If enough key players in the sector embrace this technology, manufacturing will become safer, more cost-effective and more efficient. Those that capitalize on this movement early could reap significant benefits, while those that don't may soon fall behind the competition.

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Problems With Air Quality Sensors

Governments often install permanent air quality sensors throughout urban areas. However, these monitors can be few and far between, making it difficult to understand local pollution levels. Additionally, many sensors only provide real-time air quality reports, which doesn't help with making plans based on predictions.

Satellites play a key role in gathering global air quality readings but don't take frequent measurements. This leads them to miss localized events – such as heavy traffic or new construction projects – that temporarily worsen air quality. Their precision is also limited near the planet's surface where people breathe.

Furthermore, they don't take readings inside people's homes. Indoor volatile organic compound (VOC) levels can be two to five times higher than outdoors. People also smoke, cook with propane and use kerosene lamps in their homes, which all cause significant air pollution.

How AI Can Help

Crowdsourcing projects are underway to use mobile air quality meters to gather more data. Citizen scientists are placing these mobile sensors in public places such as community centers, schools and buses to understand air pollution levels better. Some projects employ AI to decide where to take measurements – it helps reduce the number needed by identifying the most critical areas to examine.

The large volume of information gathered via these projects allows researchers to train machine learning algorithms. AI software looks for patterns in air quality measurements, current weather conditions, traffic and the built environment to predict upcoming pollution levels. People can then use apps, like IQAir, to check the forecast in various locations.

In Delhi, India, students created an app called Air Cognizer that estimates air pollution based on photos. They developed a data set of almost 5,000 images and trained the AI to recognize the color and transparency of the sky. Now, app users can simply snap a picture to get an idea of current air quality.

Air quality data helps schools, governments and individuals alter their behavior based on current and forecasted conditions. For example, employers might allow people to work from home on highly polluted days when taking public transit could pose a health risk. Hospitals can make staffing decisions based on upcoming pollution levels. Cities can warn people not to drive on days with severe traffic emissions.

Air quality data is also important for enacting regulatory changes. Policymakers who see how severe the pollution problem is in their cities may be more likely to set guidelines to limit emissions and encourage green energy initiatives. The Climate and Clean Air Coalition estimates that reducing methane emissions by 40%, hydrofluorocarbon by 100% and black carbon by 70% could save 2.4 million lives annually.

Although air quality predictions aren't perfect, apps and citywide alerts can include an uncertainty analysis to give people an idea of the prediction's accuracy, just like a weather forecast. This data would integrate well with smart cities using IoT to gather information and provide updates.

The Role of AI in Air Quality

Artificial intelligence can significantly affect how people measure, analyze and forecast air pollution levels. Individuals can use this data to make more informed decisions regarding staffing and when to go outside. It also helps with route planning, issuing citywide alerts and changing emissions policies. AI will play a key role in air quality management going forward.

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