#anomaly detection

[fridge-reviews]

The Exit 8 - Blast Review

Developer: Kotake Create Steam Deck Compatibility?: Playable Rrp: £3.39

Now this is a genre I’ve not come across before, anomaly hunting. It feels like it’s an offshoot from walking simulators since strictly speaking the only thing you do is walk. The thing is, that’s a bit of an unfair statement. You see, in this game genre, you may interact with the game world by walking around but the game itself requires you to be observant and detail oriented.

In The Exit 8 you are stuck in an endlessly repeating passageway for what seems like a train station. If you see an anomaly you’re supposed to walk back the way you came, otherwise continue on. Each time you do this you’ll pass a large yellow exit sign, if you were right about there being an anomaly or not the number on that sign goes up, but if you’re wrong it reduces down to zero. The aim of the game is to get that sign to eight and finally exit the passageway.

The anomalies vary wildly, sometimes they’re very obvious but often they’re some kind of hidden detail such as a missing doorknob or a strange stain on the ceiling.

I absolutely loved this game, I’ve always enjoyed playing ‘spot the difference’ as a kid and this game gives the same sense of satisfaction that those did. It’s just in this case there’s also a sense of liminal horror as well, but don’t worry there aren’t any jumpscares.

---- If you’d like to support me I have a Ko-fi, the reviews will continue to be posted donation or not.

[megayogiposts]

Corruption in UP Forest Department Tenders

Mahesh Pratap Singh <yogimpsingh@gmail.com> Corruption in Procurement Process of UP Forest Department1 message Abhinav Sharma <sharmaabhinav6766@gmail.com>28 January 2025 at 15:23To: Mahesh Pratap Singh <yogimpsingh@gmail.com>Three Divisions of Uttar Pradesh Forest Department namely Hardoi, Firozabad & Amethi have invited Tenders from the Participants for the Supply of Agriculture Grade Gypsum…

[oksoimstilllongforusernames]

To this day I still wonder how The Knight is in my photo album but like, from way back in 2014 (NEARLY 10 YEARS AGO)

[2sillylittleguys]

I hate when this shit happens :/

[ontonix]

Ontonix Develops Risk Stratification Tool for Multimorbidity AF Patients

In the framework of the European Horizon Project AFFIRMO, grant 899871, Ontonix has developed a Risk Stratification tool which provides a probability score of patient hospitalization within a 1-year period. The specific aim of the AFFIRMO project is to implement and test the effectiveness of an integrated patient-centered holistic care pathway for the management of older patients with AF and…

[firsteigen-databuck]

What is Anomaly Detection? Anomaly detection – also known as outlier analysis – is an approach to data quality control that identifies those data points that lie outside the norms for that dataset. 

[usaii]

A Comprehensive Introduction to Anomaly Detection in Machine Learning | USAII®

An in-depth score on Anomaly detection techniques and more awaits you. Explore the most comprehensive take on anomaly detection and become an ML engineering asset.

Read more: https://shorturl.at/LHKo5a, Anomaly detection techniques, Anomaly detection algorithms, machine learning engineers, Clustering, machine learning techniques, anomaly detection systems, Machine Learning Certifications, Machine Learning Certification programs, machine learning models

[margaret-mead]

Optimize Network Performance Through Anomaly Detection

Companies should have professional tools that can help to detect minor changes while transferring the information. It can access the resources online that use anomaly detection to know about abnormal activities within the enterprise. Selecting a professional network traffic monitoring solution will help in identifying major threats and maintaining cybersecurity. Also, the IT administrator in a company can easily get notifications of outside threats with them.

[ordazzle01]

https://ordazzle.com/staying-ahead-in-e-commerce-with-ai-driven-anomaly-detection-techniques/

Embark on a profound exploration into the revolutionary realm of e-commerce, where the transformative power of AI-driven anomaly detection takes center stage. This enlightening journey invites you to delve into the profound significance, multifaceted features, and the unparalleled competitive edge that AI-driven anomaly detection bestows upon online businesses. Uncover the intricate layers of this cutting-edge technology as it reshapes the landscape of e-commerce security and operational efficiency. From its nuanced role in identifying irregularities to its capacity for predictive insights, this comprehensive exploration unveils the transformative potential that AI-driven anomaly detection holds for businesses navigating the ever-evolving e-commerce terrain. Join us in unraveling the complexities and unlocking the strategic advantages that this technological innovation brings to the forefront of e-commerce excellence.

[meteoroby]

Eccezionali ondate di calore fuori stagione ma anche siccità, incendi e alluvioni lampo si celano dietro a questo inarrestabile trend climatico del nuovo millennio. Intanto, quasi con certezza, il 2023 si avvia a essere l'anno più caldo mai registrato

[cimcondigital]

Transforming Predictive Maintenance with CIMCON Digital’s IoT Edge Platform: Unlocking Proactive Asset Management

Introduction

In today’s fast-paced and technologically advanced world, the need for efficient and proactive asset management is paramount for businesses to stay competitive. CIMCON Digital’s IoT Edge Platform emerges as a game-changer in the realm of Predictive Maintenance, empowering organizations to detect anomalies in advance using ML algorithms. This capability not only enables customers to plan schedules well in advance and avoid costly downtime but also provides real-time visibility into the remaining useful life of assets. In this article, we delve into how CIMCON Digital’s IoT Edge Platform revolutionizes Predictive Maintenance with practical examples of proactive asset management.

1. The Challenge of Reactive Maintenance

Traditionally, companies have been plagued by reactive maintenance practices, where assets are repaired or replaced only after failures occur. This reactive approach leads to unexpected downtime, reduced productivity, and increased maintenance costs. Predicting asset failures and planning maintenance schedules in advance is critical to ensure smooth operations, optimize resource allocation, and minimize overall downtime.

2. Empowering Proactive Maintenance with ML Algorithms

CIMCON Digital’s IoT Edge Platform is equipped with advanced Machine Learning algorithms that analyze real-time data from connected assets and machines. By continuously monitoring sensor data and historical performance trends, the platform can accurately detect anomalies and deviations from normal operating patterns. This proactive approach allows businesses to predict potential asset failures well in advance, providing ample time to schedule maintenance activities before any critical failures occur.

3. Planning Ahead to Avoid Downtime

Imagine a scenario in a manufacturing facility where a critical piece of equipment experiences an unexpected failure. The consequences could be disastrous, leading to costly downtime and missed production targets. With CIMCON Digital’s IoT Edge Platform in place, the same equipment would be continuously monitored in real-time. As soon as the platform detects any unusual behavior or signs of potential failure, it triggers an alert to the maintenance team.

Armed with this early warning, the maintenance team can plan the necessary repairs or replacements well in advance, avoiding unplanned downtime and minimizing disruption to production schedules. This capability not only ensures smooth operations but also optimizes maintenance resources and lowers the overall maintenance costs.

4. Real-Time Visibility into Asset Health

The IoT Edge Platform goes beyond detecting anomalies; it also provides real-time insights into the remaining useful life of assets. By analyzing historical performance data and asset health indicators, the platform estimates the remaining operational life of an asset with high accuracy.

Consider a scenario in a utility company managing a fleet of aging turbines. The maintenance team needs to know the remaining useful life of each turbine to plan proactive maintenance and avoid sudden breakdowns. With CIMCON Digital’s IoT Edge Platform, the team can access real-time information on the health of each turbine, enabling them to make data-driven decisions about maintenance schedules, parts replacement, and resource allocation.

5. Benefits of CIMCON Digital's IoT Edge Platform

CIMCON Digital’s IoT Edge Platform offers a host of benefits to businesses seeking to enhance their Predictive Maintenance capabilities:

a) Proactive Decision-making: By detecting anomalies in advance, the platform enables proactive decision-making, reducing reactive responses and enhancing overall operational efficiency.

b) Minimized Downtime: With the ability to schedule maintenance activities in advance, businesses can avoid costly downtime, leading to increased productivity and higher customer satisfaction.

c) Optimal Resource Allocation: The platform’s real-time visibility into asset health allows for better resource allocation, ensuring that maintenance efforts are targeted where they are most needed.

d) Cost Savings: By avoiding unexpected failures and optimizing maintenance schedules, businesses can significantly reduce maintenance costs and improve their bottom line.

Conclusion:

CIMCON Digital’s IoT Edge Platform empowers businesses to transcend traditional reactive maintenance practices and embrace a proactive approach to asset management. With the platform’s advanced ML algorithms, businesses can detect anomalies in advance, plan maintenance schedules proactively, and gain real-time visibility into asset health. This transformative capability results in minimized downtime, optimized resource allocation, and substantial cost savings. As CIMCON Digital’s IoT Edge Platform continues to revolutionize Predictive Maintenance, businesses can embark on a journey towards greater efficiency, productivity, and long-term sustainability.

[ontonix]

Next-gen Monitoring and Protection of Complex, Critical Systems

A New Paradigm in Early Anomaly Detection. Artificial Intuition identifies problems in complex, critical systems and helps solve them. Before they materialise. In real time. Highly complex systems are exposed to a new form of risk: complexity-induced risk. MORE What Does Artificial Intuition Do? Artificial Intuition analyses data from large, complex systems, infrastuctures or processes,…

[jcmarchi]

The modern ROI imperative: AI deployment, security and governance

New Post has been published on https://thedigitalinsider.com/the-modern-roi-imperative-ai-deployment-security-and-governance/

The modern ROI imperative: AI deployment, security and governance

Ahead of the TechEx North America event on June 4-5, we’ve been lucky enough to speak to Kieran Norton, Deloitte’s US Cyber AI & Automation leader, who will be one of the speakers at the conference on June 4th. Kieran’s 25+ years in the sector mean that as well as speaking authoritatively on all matters cybersecurity, his most recent roles include advising Deloitte clients on many issues around cybersecurity when using AI in business applications.

The majority of organisations have in place at least the bare minimum of cybersecurity, and thankfully, in most cases, operate a decently comprehensive raft of cybersecurity measures that cover off communications, data storage, and perimeter defences.

However, in the last couple of years, AI has changed the picture, both in terms of how companies can leverage the technology internally, and in how AI is used in cybersecurity – in advanced detection, and in the new ways the tech is used by bad actors.

As a cybersecurity tool, AI can be used in network anomaly detection and the smart spotting of phishing messages, among other uses. As a business enabler, AI means that the enterprise has to be proactive to ensure AI is used responsibly, balancing the innovation AI offers with privacy, data sovereignty, and risk.

Considered a relatively new area, AI, smart automation, data governance and security all inhabit a niche at present. But given the growing presence of AI in the enterprise, those niches are set to become mainstream issues: problems, solutions, and advice that will need to be observed in every organisation, sooner rather than later.

Governance and risk

Integrating AI into business processes isn’t solely about the technology and methods for its deployment. Internal processes will need to change to make best use of AI, and to better protect the business that’s using AI daily. Kieran draws a parallel to earlier changes made necessary by new technologies: “I would correlate [AI] with cloud adoption where it was a fairly significant shift. People understood the advantages of it and were moving in that direction, although sometimes it took them more time than others to get there.”

Those changes mean casting the net wide, to encompass the update of governance frameworks, establishing secure architectures, even leveraging a new generation of specialists to ensure AI and the data associated with it are used safely and responsibly. Companies actively using AI have to detect and correct bias, test for hallucinations, impose guardrails, manage where, and by whom AI is used, and more. As Kieran puts it: “You probably weren’t doing a lot of testing for hallucination, bias, toxicity, data poisoning, model vulnerabilities, etc. That now has to be part of your process.”

These are big subjects, and for the fuller picture, we advocate that readers attend the two talks at TechEx North America that Kieran’s to give. He’ll be exploring both sides of the AI coin – issues around AI deployment for the business, and the methods that companies can implement to deter and detect the new breed of AI-powered malware and attack vectors.

The right use-cases

Kieran advocates that companies start with smaller, lower-risk AI implementations. While some of the first sightings of AI ‘in the wild’ have been chatbots, he was quick to differentiate between a chatbot that can intelligently answer questions from customers, and agents, which can take action by means of triggering interactions with the apps and services the business operates. “So there’s a delineation […] chatbots have been one of the primary starting places […] As we get into agents and agentic, that changes the picture. It also changes the complexity and risk profile.”

Customer-facing agentic AI instances are indubitably higher risk, as a misstep can have  significant effects on a brand. “That’s a higher risk scenario. Particularly if the agent is executing financial transactions or making determinations based on healthcare coverage […] that’s not the first use case you want to try.”

“If you plug 5, 6, 10, 50, a hundred agents together, you’re getting into a network of agency […] the interactions become quite complex and present different issues,” he said.

In some ways, the issues around automation and system-to-system interfaces have been around for close on a decade. Data silos and RPA (robotic process automation) challenges are the hurdles enterprises have been trying to jump for several years. “You still have to know where your data is, know what data you have, have access to it […] The fundamentals are still true.”

In the AI era, fundamental questions about infrastructure, data visibility, security, and sovereignty are arguably more relevant. Any discussions about AI tend to circle around the same issues, which throws into relief Kieran’s statements that a conversation about AI in the enterprise has to be wide-reaching and concern many of the operational and infrastructural underpinnings of the enterprise.

Kieran therefore emphasises the importance of practicality, and a grounded assessment of need and ability as needing careful examination before AI can gain a foothold. “If you understand the use case […] you should have a pretty good idea of the ROI […] and therefore whether or not it’s worth the pain and suffering to go through building it.”

At Deloitte, AI is being put to use where there is a clear use case with a measurable return: in the initial triage-ing of SOC tickets. Here the AI acts as a Level I incident analysis engine. “We know how many tickets get generated a day […] if we can take 60 to 80% of the time out of the triage process, then that has a significant impact.” Given the technology’s nascence, demarcating a specific area of operations where AI can be used acts as both prototype and proof of effectiveness. The AI is not customer-facing, and there are highly-qualified experts in their fields who can check and oversee the AI’s deliberations.

Conclusion

Kieran’s message for business professionals investigating AI uses for their organisations was not to build an AI risk assessment and management programme from scratch. Instead, companies should evolve existing systems, have a clear understanding of each use-case, and avoid the trap of building for theoretical value.

“You shouldn’t create another programme just for AI security on top of what you’re already doing […] you should be modernising your programme to address the nuances associated with AI workloads.” Success in AI starts with clear, realistic goals built on solid foundations.

You can read more about TechEx North America here and sign up to attend. Visit the Deloitte team at booth #153 and drop in on its sessions on June 4: ‘Securing the AI Stack’ on the AI & Big Data stage from 9:20am-9:50am, and ‘Leveraging AI in Cybersecurity for business transformation’ on the Cybersecurity stage, 10:20am – 10:50am.

Learn more about Deloitte’s solutions and service offerings for AI in business and cybersecurity or email the team at uscyberai@deloitte.com.

(Image source: “Symposium Cisco Ecole Polytechnique 9-10 April 2018 Artificial Intelligence & Cybersecurity” by Ecole polytechnique / Paris / France is licensed under CC BY-SA 2.0.)

[kbvresearch]

Anomaly Detection: Uncovering Hidden Insights in Your Data

In today’s data-driven world, the ability to detect anomalies is crucial for businesses and organizations across various industries. Anomaly detection, also known as outlier detection, is a technique used to identify patterns or data points that deviate significantly from the norm. In this blog, we will delve into the fascinating world of anomaly detection, exploring its importance, methods, and…