Evolving from a popular word in the tech domain, artificial intelligence (AI) has been lapped up by governments and public administrations in recent years. More specifically, embedded AI is often in focus, but some major challenges are connected to these systems' deployment and implementation.
Embedded AI refers to integrating AI technologies into existing systems or applications, such as government services, to improve functionality and efficiency. Governments and their public administrations worldwide are exploring the possibilities of deploying embedded AI in various applications, including transportation, healthcare, education, and public safety.
There are benefits of embedded AI in many different areas for any institution, such as:
* Improved decision-making (embedded AI can help make better decisions by providing insights from large datasets and automating complex processes)
* Increased efficiency (by automating routine tasks and providing real-time data analysis, embedded AI can help improve the speed and accuracy of government services),
* Better citizen engagement (embedded AI can help provide better services to citizens by enabling personalized and timely communication and support)
* Cost savings (automating tasks and improving efficiency can help save money and reduce operational costs)
* Enhanced security (embedded AI can help detect and prevent security threats by providing real-time monitoring and analysis of data)
* Improved public safety (monitoring and responding to emergencies in real-time can make communities safer and more secure).
It seems pretty clear that embedded AI can help governments provide better, more efficient public administration services to their citizens while reducing costs and improving safety and security. However, it's essential to ensure that embedded AI gets implemented ethically and transparently to maintain public trust and confidence in government and public administration services because deploying embedded AI also comes with a spectrum of challenges.
One main challenge is ensuring that the technology is transparent and accountable. Embedded AI often relies on complex algorithms that can be difficult to understand, even for the developers who create them. This makes it hard for government officials and the public to understand how the AI system makes decisions.
Governments may need to develop new standards and regulations to ensure that embedded AI is transparent and accountable. This could include requirements for companies and organizations to disclose how their AI systems work, how they make decisions, and how they handle their data.
Secondly, ensuring that the technology is ethical and fair is necessary. AI systems are only as unbiased as the data they are trained on. If the data used to train an embedded AI system is biased or discriminatory, the system will also be biased and discriminatory.
To ensure that embedded AI is ethical and fair, governments need to establish guidelines for using AI in different sectors, as well as guidelines for collecting and using data. Governments and public administrations may also need to work with organizations and companies to ensure that their AI systems are not discriminatory and do not perpetuate biases.
A third challenge, especially when deploying embedded AI, is ensuring that the technology is secure and protected from cyber threats. Embedded AI systems can be vulnerable to cyber attacks, and if compromised, they can pose a significant risk to both public safety and national security.
Currently, governments and public administrations need to invest in cybersecurity measures to protect embedded AI systems. This includes developing new security standards and protocols and establishing regulatory frameworks to ensure that companies and organizations are taking appropriate steps to protect their AI systems.
A last challenge that has to be addressed and lies on the government level is the reskilling and training of workers who embedded AI systems may displace. As AI technology advances, many jobs will likely be automated. Workers in specific sectors may need to acquire new skills to remain employable.
Governments may need to invest in programs to reskill and upskill workers in sectors likely to be impacted by embedded AI. One way forward might be to provide education and training programs and financial assistance to help workers transition to new jobs.
IoE Corp has developed a solution that helps solve the third and most crucial of these four challenges, the security issue. The underlying security risk, which enables most kinds of cyber terrorism, is that embedded AI systems are almost always centralized and depend on cloud storage.
Distributed Denial of Service (DDoS) attacks, targeted ransomware, common malware, DNS hijacking, and other tools these criminals and terrorists use all exploit centralized infrastructure solutions, be it at the index, data, service, or user level. And these attacks are not run by some lone hacker in a basement but by sophisticated and well-funded organizations with highly educated staff.
Cyber security in its current form of attacker/defender will never be foolproof. When takeouts of data flow or service are directly and immensely disruptive to our economy, security, and lives, we must take these risks seriously. Moving data to a cloud server center and then moving it back to where it's used is, in most cases, not a viable solution. Data must be informed as generated; there should be no intermediate steps.
In conclusion, deployments using current infrastructure solutions, such as cloud technology, are catastrophes waiting to happen. In this sense, a fully decentralized infrastructure and service platform are the keys to success. The service infrastructure enabling the scalability of embedded AI needs to be decentralized, which is why IoE Corp has built the Eden System.
Eden is a decentralized, autonomous, portable, secure, virtual infrastructure for managing clustered workloads over depos (decentralized pods) and services facilitating declarative configuration and automation. The system is based on scalable device clustering, making adding new devices as nodes easy. This makes it possible for any device to contribute computing resources over an intelligent mesh network so that computing can happen where needed and close to where it will be used.
IoE Corp, in addition, has developed quantum-safe tunnels using polymorphic encryption keys and uses a blockchain with consensus to verify the data moved between the nodes over the tunnels, thus creating trusted data walled gardens. The orchestration of computing and storage is done via service manifests that describe service rules, policies, and logic. An autonomous knowledge-based AI manages the underlying orchestration mechanics using network consensus over the blockchain as a deciding mechanism.
Deploying the Eden System architecture inside public administrations presents many benefits for all citizens and institutions to obtain a high level of trust. Beyond the security value, Eden is transparent and accountable. Using Eden services provides institutions (administrations) and end-users (citizens) with:
* Defense against denial of service attacks (Eden being fully decentralized mitigates DDoS attacks because there are no centralized points to takeout).
* Detection of Malware trying to replicate itself to other nodes (Eden verifies data traffic between nodes over a blockchain; malware can be detected and the infected node identified).
* Bad data and bad player detection (A decentralized network like Eden uses verification and sanity checks on data entering and exiting the network).
* Service discovery and dynamic load balancing (On a real-time basis, the network can expose anyone using the Service name from the service manifest or using their Eden service over their IP address. A tracking system alerts when traffic services are high, resulting in the orchestrator load balancing and distributing the network traffic to stabilize the service).
* Storage flexibility (Eden provides access to decentralized storage, be it data-lakes, temporary storage for AI crunches, or persistent storage, and allows you to mount storage systems of your choice, such as local storage or public cloud providers).
* Automated rollouts and rollbacks (Eden permits you to describe the desired state for your deployed services using Service Manifests, changing the actual state to the desired state at a controlled rate. For example, you can automate Eden System to create new services for your deployment, remove existing depos and adopt all their resources to the new service).
* Automatic scaling (You provide Eden with the size of the starting cluster of nodes that it can or should use to run service tasks. Then Eden will optimize how much CPU and RAM each task needs. It can also be installed onto your nodes to use your resources best).
* Self-healing (The Eden System Orchestrator automatically restarts on depos that fail, replaces depos, and kills depos that don't respond to the service manifest-defined health check. These won't be advertised to users until they are ready to serve).
* Secret and configuration management (Eden lets you securely store and manage sensitive information, such as passwords, OAuth tokens, and encryption keys. Possible because you can deploy and update secrets and application configuration without rebuilding your depos and exposing secrets in your service).
Read more: https://ioecorp.com/
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