What is Artificial Intelligence and Why It Matters in 2024?
Missing Link: AI And Rescue Workers – How Artificial Intelligence Helps Helpers
The warnings about the potential flood disaster in the Ahr valley in the summer of 2021 and the subsequent crisis management are considered suboptimal , which cost the then Rhineland-Palatinate Interior Minister Roger Lewentz (SPD) his job. For example, videos from a police helicopter squadron from the evening of July 14, 2021, were considered lost meanwhile, whereupon it became apparent early on that entire houses, caravans and people had been washed away by the masses of water. Emergency services are therefore now increasingly relying on systems with artificial intelligence (AI) to detect dangerous situations with weather events such as heavy rain and flooding or fires at an earlier stage, inspect them in near real time as they occur and respond appropriately.
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For authorities and organizations with security tasks (BOS), i.E. The emergency services with blue lights, everyday life is often hectic. Maintaining an overview can quickly become a challenge. More and more data and technical aids such as computer programs and, finally, AI applications are available to help rescuers with their tasks. Some solutions have already been launched and many are still in the pipeline. However, as sensitive personal information is often used as a basis, there are also potential pitfalls, for example due to the General Data Protection Regulation (GDPR). If risks are associated with the use of the technology, the new AI Regulation must also be considered.
At a recent online conference on "AI helps helpers" organized by Behörden-Spiegel, experts agreed that the greatest potential of artificial intelligence in the BOS sector lies in situational awareness. The technology is predestined to "get an overview of the scene as quickly as possible", explained Sirko Straube, Deputy Head of the Robotics Innovation Center at the German Research Center for Artificial Intelligence (DFKI). This involves, for example, evaluating images and data from drone cameras or body sensors (wearables) together in the control center and then drawing the right conclusions and networking with other emergency services.
Early warning systems with AIKatharina Weitz, Project Manager at the Fraunhofer Heinrich Hertz Institute (HHI) in Berlin, cites the EU-funded Tema (Trusted Extremely Precise Mapping and Prediction for Emergency Management) project, which aims to improve crisis management in the event of natural disasters, as an example of early warning systems with AI. The HHI is primarily responsible for human-interpretable explanations, AI-generated predictions and recommendations. As part of Tema, heterogeneous data sources such as drones, sensors and satellites are to be evaluated, including topographical information. In addition, there is AI-based object recognition, which should be able to differentiate between pedestrians, bicycles or motorized vehicles and identify phenomena such as mass panics, sources of fire and fires.
Weitz explains that users need to know why the model has classified objects in one way or another. The team relies on Layer-wise Relevance Propagation (LRP) here. This involves interpreting individual predictions by assigning values that quantify the significance of the individual input features for the prediction. Using the implemented neural network, "we send the image backwards again to generate a visual explanation", the computer scientist explains. As a result, individual relevant pixels for the assessment are highlighted in color. These are then signs of smoke and fire, for example. The AI also provides "learned concepts with examples". This is ideal for early warning systems.
There are already models for emergency services "that are based on explainable AI", says Simon Franke, team leader for research projects at the Information and Technology Center (ITC) of the Rhineland-Palatinate branch of the German Red Cross (DRK). The center uses such a system for emergency call support. "It can be rule-based," says the practitioner. For example, the signs mentioned by the caller that could indicate a heart attack can be brought together.
Technology already plays a major role for emergency services - but the use of artificial intelligence is also becoming increasingly important
(Image: Krempl)
"Everyone is a bit scared of the AI Act"In general, Franke also "dreams of" using AI to obtain a uniform picture of the situation at an early stage. The ITC is initially concentrating on "integrated control centers" to achieve data exchange, including with partners in industry such as BASF. Due to the large number of systems in use, the basis for using AI is often still lacking. The order of the day is therefore to break up isolated solutions with standardized tenders and create interfaces. The Federal Ministry for Economic Affairs and Climate Protection (BMWK) is now demanding a uniform data standard for funding - in line with this.
However, Franke does not believe that there is a need for specific AI products for disaster control. Existing technologies already help with live translation, image recognition and the evaluation of traffic cameras, for example regarding the transportation of hazardous goods. There are also already AI-supported solutions to reduce peak deployment times in the event of major emergencies. However, there are often legal concerns before a deployment, as "many things are complicated by data protection". "Everyone is a bit scared of the AI Act," reports the platform manager of the Spell project for innovative technology in rescue control centers. The new regulation is currently creating "a lot of uncertainty".
Franke also points to technological hurdles when using AI: "We can't just attach ourselves to any cloud provider." It is necessary to operate infrastructures for the computer clouds yourself. This leads to "entirely different hardware requirements" than for private users. It is also necessary to convince employees, authorities and ministries. Many employees ask themselves: "Will I be abolished or controlled by AI?" The advantages of the technology should therefore be clear. It must also be easy to use and must not disrupt the workflow of dispatchers in particular. On the other hand, the networker is not particularly worried about mobile network failures such as those in the Ahr valley - radio relay and satellite communication are available.
AI projects for control centers sprout from the groundThe AI in Rescue Chains (Aircis) project, which is funded by the German Federal Ministry for Digital and Transport, has also set itself the task of making the work of rescue services easier with the help of AI. The starting point here is that there are currently no tools to simulate or plan the rescue chain under the influence of extreme weather events. The aim is therefore to use AI to forecast the volume of operations based on real data from the Cottbus control center and to develop a simulation to map the entire rescue chain using a digital twin. In the event of heavy rainfall or during periods of heat, for example, it would then be easier to calculate different routes.
In addition to Tema, the HHI alone is involved in another EU funding initiative, MedEWsa (pronounced Medusa), which aims to use AI to facilitate the prevention of natural hazards ranging from fires and heatwaves to volcanic eruptions. A German variant of this is the Daki (AI for Disaster Early Warning Systems) project, which runs until the end of 2024 and, according to Weitz, will create a dashboard and interfaces to inform industry, the public and politicians in good time. Daki is part of the BMWK's AI innovation competition and is being subsidized with around 12 million euros. One aspect of such initiatives is to use satellite information from the European Space Agency (ESA) to predict the risk of forest fires and flooding.
AI's Emerging Privacy Threats: A Strategic Guide For Business Leaders
Jeremy Bradley-Silverio Donato, COO at Zama.
gettyThe advent of artificial intelligence (AI) has revolutionized various facets of business operations, from enhancing efficiency to unlocking new opportunities for innovation.
However, as highlighted by recent research commissioned by my company, Zama, this also brings with it a new set of challenges, particularly concerning data privacy. The study, which surveyed over 1,000 developers across the U.K. And U.S., underscores AI's burgeoning threat to privacy, a concern that now rivals traditional cybercrime.
For business leaders, this presents a dual challenge: harnessing AI's potential while safeguarding sensitive data.
Proactive Measures For Business LeadersTo effectively address AI's privacy implications, business leaders must first grasp the scope of the threat. Our research indicates that 98% of developers believe regulatory measures are urgently needed to tackle future privacy issues posed by AI. This sentiment is not unfounded, given Statista's projection that cybercrime costs could soar to $13.82 trillion by 2028. With AI enhancing cybercriminals' capabilities, the risks to data privacy could escalate significantly.
Here are some steps businesses can take:
1. Strengthening Regulatory Knowledge And CompliancePrioritize understanding and adhering to emerging regulatory frameworks that address AI and data privacy. Given that 72% of developers in our study feel current regulations are inadequate and 56% perceive them as potential threats, there is a clear call for dynamic and robust regulatory strategies. Leaders must stay informed about legislative developments and advocate for regulations that balance innovation with privacy protection.
2. Investing In Privacy Enhancing Technologies (PETs)A significant portion of developers advocate for the integration of PETs, particularly fully homomorphic encryption (FHE). (Disclosure: My company specializes in FHE solutions.) FHE allows data to be processed without being decrypted, thereby maintaining privacy and security.
For businesses starting with FHE, several key considerations and challenges need addressing:
First, clearly define the use case and understand the value proposition of FHE, or indeed other PETs, within the business context. Determine scenarios where the benefits of introducing this technology, such as enabling privacy-preserving analytics or meeting stringent regulatory requirements, justify its complexity.
Next, assess technical readiness. Evaluate your business's infrastructure and start with a pilot project to understand performance impacts.
While FHE provides robust security, it can lead to latency and performance issues. Exploring hybrid encryption methods and optimizing algorithms can mitigate these concerns. A solid data management strategy is vital, incorporating data governance and compliance with regulation. Implementing data classification and regularly updating compliance strategies can streamline processes and ensure regulatory alignment.
While this may sound daunting, investing in privacy enhancing technologies ensures that sensitive information remains protected even as businesses utilize AI to drive growth and efficiency.
3. Developing A Privacy-First CultureBuilding a culture that prioritizes data privacy involves training people on data protection. Encouraging a privacy-first mindset can mitigate risks and build trust among customers and partners.
At my company, we prioritize data classification and encryption by categorizing data based on sensitivity levels—public, internal, confidential and restricted. Automated tagging and context-aware encryption ensure that sensitive data receives appropriate protection.
Additionally, secure data access policies are vital; we implement role-based access control, multifactor authentication and detailed logging to monitor access and usage patterns. We conduct regular training sessions focused on best practices, integrating privacy into our development processes. This approach, known as "privacy by design," ensures privacy considerations are embedded in every stage of product development, and it's something we strongly encourage our clients and partners to consider as well.
Transparent communication and accountability further reinforce a commitment to privacy, with regular updates and feedback channels. These strategies can collectively enhance a business's ability to manage sensitive information securely and responsibly.
4. Collaborating With Experts And Industry PeersEngaging with cybersecurity experts can provide valuable insights into emerging threats and best practices for mitigation. And collaborative efforts can lead to the development of more effective strategies for managing AI's privacy risks. I suggest business leaders participate in forums, workshops and industry groups dedicated to cybersecurity and AI ethics.
5. Implementing Robust Data Governance FrameworksBusiness leaders should establish comprehensive frameworks that define how data is collected, stored, processed and shared. This includes setting clear policies for data access and usage, regularly auditing data practices and ensuring compliance with relevant regulations.
6. Adopting A Risk Management ApproachTreat AI-related privacy threats as a critical component of the overall risk management strategy. This involves conducting regular risk assessments to identify potential vulnerabilities and implementing measures to mitigate them. Leaders should also develop contingency plans to respond swiftly to data breaches or other privacy incidents.
7. Fostering Innovation With Ethical AI PracticesWhile addressing privacy concerns, I recommend business leaders also promote the ethical use of AI. This can include ensuring transparency in AI decision-making processes, avoiding biases in AI models and prioritizing the ethical implications of AI applications.
Some simple tips can help you get started: Implement explainable AI (XAI) techniques that make AI decisions understandable, allowing stakeholders to see how conclusions are reached. Conduct regular audits and bias assessments to identify and mitigate biases in data and outcomes. Use diverse and representative datasets to train AI systems to reduce inherent biases. If you outsource your AI capabilities, ask your service provider(s) for these kinds of guarantees.
Finally, remember to continually work on fostering a culture of accountability by setting up governance structures that involve cross-functional teams, including ethicists, to review and guide your AI practices. Regularly engaging with stakeholders for feedback further enhances trust and fairness in AI decision-making, just as it does with the implementation of PETs.
Looking Ahead: Balancing Innovation And PrivacyAs AI continues to evolve, its potential to both drive innovation and pose privacy risks will grow. To navigate this complex landscape, I think leaders must strike a balance between harnessing AI's capabilities and protecting data.
Proactive measures, such as investing in advanced encryption technologies, fostering a privacy-first culture and staying abreast of regulations, are essential. By taking these steps, business leaders can safeguard their organizations against AI-related privacy threats and position themselves as responsible innovators.
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Virtualitics' Maintenance Decision Intelligence AI Application Transforms Enterprise Asset Management
New AI-Powered Application Enhances Efficiency of the Scheduling Process by More Than 30%
PASADENA, Calif., June 18, 2024 /PRNewswire/ -- Virtualitics, a leader in AI decision intelligence, today introduced its latest AI-powered application, Maintenance Decision Intelligence (MDI). Designed to transform maintenance operations for asset-intensive industries, MDI drives significant improvements in efficiency, reliability, and decision-making.
New AI-Powered Application Enhances Maintenance Scheduling Efficiency by More Than 30%
Virtualitics AI-driven Decision Intelligence applications are built with Explainable AI (XAI) at their core to ensure that answers provided by the platform can be trusted and empower users to get even more competitive advantage out of their data. MDI delivers AI-powered intelligence and recommendations to guide enterprise maintenance organizations to proactively respond to anticipated failures, improve asset availability, and enhance overall operational efficiency. MDI enables organizations to identify failure points and determine the best corrective actions, significantly reducing unplanned downtime and giving teams unparalleled efficiency and insight.
Developed for the US Department of Defense, the application is a proven solution ready to deliver impact in commercial enterprises. Virtualitics' technology includes multiple U.S. Patents, and the MDI application has already been field-proven capable of analyzing over a decade of historical data and millions of records.
"Our MDI application revolutionizes asset and supply chain management by providing proactive maintenance operations recommendations," said Michael Amori, CEO and co-founder of Virtualitics. "We are setting a new standard for making AI both powerful and explainable, so organizations can ensure asset uptime and confidently accelerate impactful decisions."
MDI integrates asset management and resource planning data sources to inform users when planned or open maintenance jobs cannot be scheduled or executed due to missing resources. This AI-driven approach enables organizations to optimize supply chain and maintenance demands while meeting evolving customer needs.
MDI also benefits from Virtualitics' latest platform feature, Pathfinder, an innovative AI-powered tool designed to simplify data analysis for new or less experienced users. Pathfinder democratizes advanced analytics for maintenance operations use cases by reducing the amount of input required and presenting actionable insights that empower users to confidently make maintenance decisions. By integrating advanced AI routines and Explainable AI capabilities, Pathfinder guides users through key analysis paths—Segmentation, Key Drivers, and Outliers—enabling them to unlock the full potential of their data with minimal effort.
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These easy-to-use tools empower maintenance teams to predict asset failure and optimize resource allocation, which can improve the efficiency of scheduling processes by more than 30% while ensuring maximum asset uptime and operational success. Virtualitics is committed to enhancing the usability and impact of its AI solutions. The MDI application and Pathfinder functionality exemplify this commitment by making advanced analytics accessible to all users, regardless of their technical expertise.
About Virtualitics Virtualitics, a leader in AI decision intelligence, transforms enterprise and government decision-making. Our AI-powered platform applications, built on a decade of Caltech research, enhance data analysis with interactive, intuitive, and visually engaging AI tools. We transform data into impact with AI-powered intelligence, delivering the insights that help everyone get to impact faster. Trusted by governments and enterprises, Virtualitics makes AI accessible, actionable, and transparent for analysts, data scientists, and leaders alike, driving significant business results. For more information, visit virtualitics.Com.
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