Wednesday 16 October 2024
How well do you know your hypervisor and firmware?
Friday 11 October 2024
How a solid generative AI strategy can improve telecom network operations
Generative AI (gen AI) has transformed industries with applications such as document-based Q&A with reasoning, customer service chatbots and summarization tasks. These use cases have demonstrated the impressive capabilities of large language models (LLMs) in understanding and generating human-like responses, particularly in fields requiring nuanced language understanding and inferencing.
However, in the realm of telecom network operations, the data is different. The observability data comes from proprietary sources and encompasses a wide variety of formats, including alarms, performance metrics, probes and ticketing systems capturing incidents, defects and changes. This data, whether structured or unstructured, is deeply embedded in a domain-specific language. This includes terms and concepts from technologies like 5G, IP-MPLS and other network protocols.
A notable challenge arises from the fact that standard foundational LLMs are not typically trained on this highly specialized and technical data. This needs a careful strategy for integrating gen AI into the telecom operations domain, where operational efficiencies and accuracy are paramount.
Successfully using gen AI for network operations requires tailoring the models to this niche context while addressing unique challenges around data specificity and system integration.
How generative AI addresses network operations challenges
The complexity and diversity of network data, along with rapidly changing technologies, presents several challenges for network operations. Gen AI offers efficient solutions where traditional methods are costly or impractical.
- Time-consuming processes: Switching between multiple systems (such as alarms, performance or traces) delays problem resolution. Generative AI centralizes data into one interface providing natural language experience, speeding up issue resolution by reducing system toggling.
- Data fragmentation: Scattered data across platforms prevents a cohesive view of issues. Generative AI consolidates data from various sources based on the training. It can correlate and present data in a unified view, enhancing issue comprehension.
- Complex interfaces: Engineers spend extra time adapting to various system interfaces (such as UIs, scripts and reports). Generative AI provides a natural language interface, simplifying navigation across complex systems.
- Human error: Manual data consolidation leads to misdiagnoses due to data fragmentation challenges. AI-driven data analysis reduces errors, helping ensure accurate diagnosis and resolution.
- Inconsistent data formats: Varying data formats make analysis difficult. Gen AI model training can provide standardized data output, improving correlation and troubleshooting.
Challenges in applying generative AI in network operations
While gen AI offers transformative potential in network operations, several challenges must be addressed to help ensure effective implementation:
- Relevance and contextual precision: General-purpose language models perform well in nontechnical contexts, but in network-specific use cases, models need to be fine-tuned with domain-specific terminology to deliver relevant and precise results.
- AI guardrails and hallucinations: In network operations, outputs must be grounded in technical accuracy, not just linguistic sense. Strong AI guardrails are essential to prevent incorrect or misleading results.
- Chain-of-thought (CoT) loops: Network use cases often involve multistep reasoning across multiple data sources. Without proper control, AI agents can enter endless loops, leading to inefficiencies due to incomplete or misunderstood data.
- Explainability and transparency: In critical network operations, engineers must understand how AI-derived decisions are made. AI systems must provide clear and transparent reasoning to build trust and help ensure effective troubleshooting, avoiding “black box” situations.
- Continuous model enhancements: Constant feedback from technical experts is crucial for model improvement. This feedback loop should be integrated into model training to keep pace with the evolving network environment.
Implementing a workable strategy to maximize business benefits
Key design principles can help ensure the successful implementation of gen AI in network operations. These include:
- Multilayer agent architecture: A supervisor/worker model offers modularity, making it easier to integrate legacy network interfaces while supporting scalability.
- Intelligent data retrieval: Using Reflective Retrieval-Augmented Generation (RAG) with hallucination safeguards helps ensure reliable, relevant data processing.
- Directed chain of thought: This pattern helps guide AI reasoning to deliver predictable outcomes and avoid deadlocks in decision-making.
- Transactional-level traceability: Every AI decision should be auditable, ensuring accountability and transparency at a granular level.
- Standardized tooling: Seamless integration with various enterprise data sources is crucial for broad network compatibility.
- Exit prompt tuning: Continuous model improvement is enabled through prompt tuning, ensuring that it adapts and evolves based on operational feedback.
- Faster mean time to repair (MTTR): Achieve a 30-40% reduction in MTTR, resulting in enhanced network uptime.
- Reduced average handle time (AHT): Decrease the time network operations center (NOC) technicians expenditure addressing field technician queries by 30-40%.
- Lower escalation rates: Reduce the percentage of tickets escalated to L3/L4 by 20-30%.
Tuesday 8 October 2024
New IBM study: How business leaders can harness the power of gen AI to drive sustainable IT transformation
As organizations strive to balance productivity, innovation and environmental responsibility, the need for sustainable IT practices is even more pressing. A new global study from the IBM Institute for Business Value reveals that emerging technologies, particularly generative AI, can play a pivotal role in advancing sustainable IT initiatives. However, successful transformation of IT systems demands a strategic and enterprise-wide approach to sustainability.
The power of generative AI in sustainable IT
Generative AI is creating new opportunities to transform IT operations and make them more sustainable. Teams can use this technology to quickly translate code into more energy-efficient languages, develop more sustainable algorithms and software and analyze code performance to optimize energy consumption. 27% of organizations surveyed are already applying generative AI in their sustainable IT initiatives, and 63% of respondents plan to follow suit by the end of 2024. By 2027, 89% are expecting to be using generative AI in their efforts to reduce the environmental impact of IT.
Despite the growing interest in using generative AI for sustainability initiatives, leaders must first consider its broader implications, particularly energy consumption.
64% say they are using generative AI and large language models, yet only one-third of those report having made significant progress in addressing its environmental impact. To bridge this gap, executives must take a thoughtful and intentional approach to generative AI, asking questions like, “What do we need to achieve?” and “What is the smallest model that we can use to get there?”
A holistic approach to sustainability
To have a lasting impact, sustainability must be woven into the very fabric of an organization, breaking free from traditional silos and incorporating it into every aspect of operations. Leading organizations are already embracing this approach, integrating sustainable practices across their entire operations, from data centers to supply chains, to networks and products. This enables operational efficiency by optimizing resource allocation and utilization, maximizing output and minimizing waste.
The results are telling: 98% of surveyed organizations that take a holistic, enterprise-wide approach to sustainable IT report seeing benefits in operational efficiency—compared to 50% that do not. The leading organizations also attribute greater reductions in energy usage and costs to their efforts. Moreover, they report impressive environmental benefits, with two times greater reduction in their IT carbon footprint.
Hybrid cloud and automation: key enablers of sustainable IT
Many organizations are turning to hybrid cloud and automation technologies to help reduce their environmental footprint and improve business performance. By providing visibility into data, workloads and applications across multiple clouds and systems, a hybrid cloud platform enables leaders to make data-driven decisions. This allows them to determine where to run their workloads, thereby reducing energy consumption and minimizing their environmental impact.
In fact, one quarter (25%) of surveyed organizations are already using hybrid cloud solutions to boost their sustainability and energy efficiency. Nearly half (46%) of those report a substantial positive impact on their overall IT sustainability. Automation is also playing a key role in this shift. With 83% of leading organizations harnessing its power to dynamically adjust IT environments based on demand.
Sustainable IT strategies for a better tomorrow
The future of innovation is inextricably linked to a deep commitment to sustainability. As business leaders harness the power of technology to drive impact, responsible decision-making is crucial, particularly in the face of emerging technologies such as generative AI. To better navigate this intersection of IT and sustainability, here are a few actions to consider:
1. Actively manage the energy consumption associated with AI: Optimize the value of generative AI while minimizing its environmental footprint by actively managing energy consumption from development to deployment. For example, choose AI models that are designed for speed and energy efficiency to process information more effectively while reducing the computational power required.
2. Identify your environmental impact drivers: Understand how different elements of your IT estate influence environmental impacts and how this can change as you scale new IT efforts.
3. Embrace sustainable-by-design principles: Embed sustainability assessments into the design and planning stages of every IT project, by using a hybrid cloud platform to centralize control and gain better visibility across your entire IT estate.
Source: ibm.com
Saturday 5 October 2024
Using AI to conserve the endangered African forest elephant
In the Congo Basin, the second-largest rainforest in the world, the African forest elephant population has been in drastic decline for decades. This decline is the result of habitat loss caused by deforestation and climate change, along with rampant poaching.
We can observe the beneficial environmental effects of these species starting to disappear. As a keystone species in the habitat, the dwindling presence of the elephants has major implications you might not imagine. African forest elephants have been shown to increase carbon storage in their habitats. They are “ecosystem engineers” according to the World Wide Fund for Nature, clearing out lesser vegetation and making room for stronger, more resilient, flora to thrive.
While we know these changes will occur as the elephant population shrinks, actually seeing it happen presents challenges. The World Wide Fund for Nature-Germany aims to track and identify individual elephants to count them. With help from IBM, the WWF will be able to use a system of camera traps connected to software that enables automatic tracking as opposed to manual tracking.
Augmenting our vision with tech
That is where computer vision can serve as a fresh set of eyes. IBM announced earlier this year that it would team with WWF to pair camera traps with IBM Maximo® Visual Inspection (MVI) to help monitor and track individual elephants as they pass by the camera traps.
“MVI’s AI-powered visual inspection and modeling capabilities allow for head- and tusk-related image recognition of individual elephants similar to the way we identify humans via fingerprints,” explained Kendra DeKeyrel, Vice President ESG and Asset Management Product Leader at IBM.
These capabilities allow for not only counting or spotting the individual elephants, but also tracking some of their behaviors to better understand their movement patterns and impact in the ecosystem. MVI particularly offers help in automating the process of identifying these elephants instead of having staff manually look at the images. Additionally, the AI’s advanced visual recognition capabilities can pull the identity of an elephant from an image that is blurry or incomplete.
“Counting African forest elephants is both difficult and costly,” Dr. Thomas Breuer, WWF’s African Forest Elephant Coordinator, said. “The logistics are complex and the resulting population numbers are not precise. Being able to identify individual elephants from camera trap images with the help of AI has the potential to be a game-changer.”
Strengthening our connection to the natural world
As more about the movement and migration of the African forest elephant is gleaned, more additional information can be pulled from our increased understanding of how the species is behaving and interacting with its environment. “IBM is exploring how to leverage IBM Environmental Intelligence above ground biomass estimates to better predict elephants’ future locations and migration patterns, as well as their impact on a specific forest,” DeKeyrel said.
That includes determining how much the African forest elephants can help with mitigating climate change. It’s understood that the presence of elephants helps to increase the carbon storage capacity of the forest. “African forest elephants play a crucial role in influencing the shape of the forest structure, including helping increase the diversity, density, and abundance of plant and tree species,” Oday Abbosh, IBM Global Sustainability Services Leader, explained. It’s estimated that one forest elephant can increase the net carbon capture capacity of the forest by almost 250 acres, the equivalent of removing a full year’s worth of emissions from 2,047 cars from the atmosphere.
Having a more accurate image of the elephant population allows for performance-based conservation payments, such as wildlife credits. In the future, this could help enable organizations to better assess the financial value of nature’s contributions to people (NCP) provided by African forest elephants, such as carbon sequestration services.
We know the animal kingdom is constantly shaping the planet, and being affected by our own activity even when we can’t see it. Due to continuing breakthroughs in technology, we’re increasingly getting a clearer picture of the world of wildlife that was previously difficult to capture. When we can see it, we can react to it, helping to protect species that need help and strengthening our connection to the natural world.
“Our collaboration with WWF marks a significant step forward in this effort,” Abbosh said, “By combining our expertise in technology and sustainability with WWF’s conservation expertise, we aim to leverage the power of technology to create a more sustainable future.”
Source: ibm.com
Tuesday 24 September 2024
Internet of Animals: A look at the new tech getting animals online
All living things on Earth are connected, in that we all affect one another, directly and indirectly. But more often than not, we don’t see or know what is happening in the lives of animals. Deep in the jungles and forests, far off in the deserts and prairies, many species of animals are seeing their behavior change as the planet warms in ways we can’t see.
Thanks to technological achievements in recent years, we are starting to have a clearer look into these environments that have previously been obscured from our view. Modern breakthroughs have made tracking tools less invasive, easier to manage, and have created the conditions for better seeing and understanding of wildlife, including how they move and behave.
A team of researchers has tapped these innovations to create a global network of animals, tracking the movement of thousands of creatures in a way that reveals never-before-seen activity. Through this data, we’re gaining a new understanding of animal migration, what is causing it, and how different species are adapting to climate change and rapid changes to their ecosystems.
Getting animals online
In 2001—before the Internet of Things was much more than a sci-fi-like fantasy, before even half of the United States was regularly online—professor of ecology and evolutionary biology Martin Wikelski had an idea for a global network of sensors that could provide never-before accessible insight into the activities of animals who live well outside of the human-dominated parts of the planet.
The “Internet of Animals” known as ICARUS (International Cooperation for Animal Research Using Space) went from idea to reality in 2018 when, after nearly two decades of laying groundwork, a receiver was launched to the International Space Station and embedded on the Russian portion of the orbiting science laboratory, where it functioned as a central satellite-style receiver, collecting data from more than 3,500 animals that had been tagged with tiny trackers.
According to Uschi Müller, ICARUS Project Coordinator and member of the Department of Migration Team at the Max Planck Institute of Animal Behavior in Germany, the ICARUS receiver collected the data from the trackers and sent it to a ground station, where the information was then uploaded to Movebank, an open source database that hosts animal sensor data for researchers and wildlife managers to freely access.
The original version of ICARUS was groundbreaking but limited. “The ISS only covers an area up to 55 degrees North and 55 degrees South within its flight path,” explained Müller. Mechanical issues on ISS knocked the network offline in 2020, and Russia’s invasion of Ukraine in 2022 brought the tracking activity to a grinding halt.
Expanding the vision
“The dependence on a single ICARUS payload…demonstrated the vulnerability of the former infrastructure,” Müller said. Animals continued to carry the trackers, a burden that was no longer producing benefits for potentially understanding and protecting them. And the sudden absence in the database that counts on regular updates carried the potential for harmful consequences to scientific research.
While it’s hard to say getting plunged back into darkness is ever a benefit to those who value data and information, the event was illuminating on its own. It sent the ICARUS team back to the drawing board, which also allowed them to build a system that wouldn’t just get them back online but would offer fail-safes that could mitigate risks of future outages.
“What was initially a shock for all the scientists involved very quickly turned into a euphoric ‘Plan B’ and the development of a new, much more powerful and much cheaper CubeSat system, flanked by a terrestrial observation system,” Müller said.
The space segment of the new system will include multiple payloads, the first of which will be launched in 2025 in partnership with the University of the Federal Armed Forces in Munich. It will be the first five planned launches, which will send CubeSat satellites, nanosatellites that will hang in polar orbit and provide coverage across the entire planet rather than a limited range.
They will work in collaboration with a terrestrial “Internet of Things” style network that will be able to generate real-time data from the ground. The result, according to Müller, will be “tagged animals can be observed much more frequently, more reliably and in every part of the world.”
These receivers will be picking up data from upgraded tags, which the ICARUS team has been working tirelessly to shrink down to a size that minimizes invasiveness for the animal. The tags that will be used for the latest version of the ICARUS system will weigh just 0.95 grams, but according to Müller, their transmitters have gotten incredibly small in recent years.
“Thanks to the continuous technical development of animal transmitters, which now weigh just as little as 0.08 grams and are extremely powerful, even insects such as butterflies and bees as well as the smallest bats can be tagged for the first time,” she said.
Once the new ICARUS system is online, Müller and the team expect to see the clouded vision of the animal kingdom continue to clear up. “The migration routes and the behavior and interactions of animals about which almost nothing is known to date can be researched,” she said of the project. “We continue to expect great interest in the scientific world to use this system and to continuously develop and optimize it.”
Source: ibm.com
Saturday 21 September 2024
IBM Planning Analytics: The scalable solution for enterprise growth
Platform architecture and scalability
IBM Planning Analytics Architecture
Scalability without limits
Performance that keeps pace with your business
Performance benchmarks
- Solar Coca-Cola: Simulates the impact of stock keeping unit (SKU) price changes on margins and profits in real time, eliminating the need for manual spreadsheets.
- Mawgif: Manages and analyzes data in real time, optimizing revenue and efficiency.
- Novolex: Reduced its 6-week forecasting process by 83%, bringing it down to less than a week.
Data handling and performance
IBM Planning Analytics Data Handling
Performance benchmarks
Modeling flexibility and customization
IBM Planning Analytics Modeling
Customization capabilities
Integration and data connectivity
IBM Planning Analytics Integrations
- ODBC connection using TM1 Turbo Integrator: This powerful utility enables users to automate data import, manage metadata and perform administrative tasks.
- Push-pull using flat files: Turbo Integrator supports reading and writing flat files, which is useful for pushing data from TM1 to a relational database.
- Using the REST API: This increasingly popular option opens up possibilities for a single tool to manage data push-pull operations.
- Microsoft Office 365 integration: Seamless integration fosters effortless collaboration.
- ERP system connectivity: Our solution connects with major enterprise resource planning (ERP) systems such as SAP, Oracle and Microsoft Dynamics, helping to ensure smooth financial and operational data flow.
- Customer relationship management (CRM) integration: Integrations with systems such as Salesforce provide access to crucial sales and customer data.
- Data warehouses and business intelligence (BI) tools: Our solution interfaces with data warehouses and BI tools, enabling advanced analytics and comprehensive reporting.
Connectivity options
Experience IBM Planning Analytics
Monday 16 September 2024
Data observability: The missing piece in your data integration puzzle
Hidden dangers in your data pipeline
- High incidence of incorrect, inconsistent or missing data can be attributed to data quality issues. Even if you can spot the issue, it becomes a challenge to identify the origin of the data quality problem. Often, data teams must follow a manual process to help ensure data accuracy.
- Recurring breakdowns in data processing workflows with long downtime might be another signal. This points to data pipeline reliability issues when the data is unavailable for extended periods, resulting in a lack of confidence among stakeholders and downstream users.
- Data teams face challenges in understanding data relationships and dependencies.
- Heavy reliance on manual checks and alerts, along with the inability to address issues before they impact downstream systems, can signal that you need to consider observability tools.
- Difficulty managing intricate data processing workflows with multiple stages and diverse data sources can complicate the whole data integration process.
- Difficulty managing the data lifecycle according to compliance standards and adhering to data privacy and security regulations can be another signal.
Prioritize observability so bad data doesn’t derail your projects
Saturday 14 September 2024
How Data Cloud and Einstein 1 unlock AI-driven results
Data Cloud as the foundation for data unification
Einstein 1 Studio provides enhanced AI tools
Salesforce AI capabilities without Data Cloud
CRM + AI + Data + Trust
Friday 13 September 2024
How digital solutions increase efficiency in warehouse management
In the evolving landscape of modern business, the significance of robust maintenance, repair and operations (MRO) systems cannot be overstated. Efficient warehouse management helps businesses to operate seamlessly, ensure precision and drive productivity to new heights. In our increasingly digital world, bar coding stands out as a cornerstone technology, revolutionizing warehouses by enabling meticulous data tracking and streamlined workflows.
With this knowledge, A3J Group is focused on using IBM Maximo Application Suite and the Red Hat® Marketplace to help bring inventory solutions to a wider audience. This collaboration brings significant advancements to warehouse management, setting a new standard for efficiency and innovation.
To achieve the maintenance goals of the modern MRO program, these inventory management and tracking solutions address critical facets of inventory management by way of bar code technology.
Bar coding technology in warehouse management
Bar coding plays a critical role in modern warehouse operations.Bar coding technology provides an efficient way to track inventory, manage assets and streamline workflows, while providing resiliency and adaptability. Bar coding provides essential enhancements inkey areas such as:
Accuracy of data: Accurate data is the backbone of effective warehouse management. With barcoding, every item can be tracked meticulously, reducing errors and improving inventory management. This precision is crucial for maintaining stock levels, fulfilling orders and minimizing discrepancies.
Efficiency of data and workers: Barcoding enhances data accuracy and boosts worker efficiency. By automating data capture, workers can process items faster and more accurately. This efficiency translates to quicker turnaround times and higher productivity, ultimately improving the bottom line.
Visibility into who, where, and when of the assets: Visibility is key in warehouse management. Knowing the who, where and when of assets helps ensure accountability and control. Enhanced visibility allows managers to track the movement of items, monitor workflows and optimize resource allocation, leading to better decision-making and operational efficiency.
Auditing and compliance: Traditional systems often lack robust auditing capabilities. Modern solutions provide comprehensive auditing features that enhance control and accountability. With these capabilities, every transaction can be recorded, making it easier to identify issues, conduct audits and maintain compliance.
Implementing digital solutions to minimize disruption
Implementing advanced warehouse management solutions can significantly ease operations during stressful times, such as equipment outages or unexpected order surges. When systems are down or demand spikes, having a robust management system in place helps leaders continue operations with minimal disruption.
During equipment outages, quick decision-making and efficient processes are critical. Advanced solutions help leaders manage these scenarios by providing accurate data, efficient workflows and visibility into inventory levels, which enables swift and informed decisions.
Implementing software accelerators to address warehouse needs
Current trends in warehouse management focus on automation, real-time data tracking and enhanced visibility. By adopting these trends, warehouses can remain competitive, efficient and capable of meeting increasing demands.
IBM and A3J Group offer integrated solutions that address the unique challenges of warehouse management. Available on IBM Red Hat Marketplace, these solutions provide comprehensive features to enhance efficiency, accuracy and visibility.
IBM Maximo Application Suite
IBM® Maximo® Manage offers robust functionality for managing assets, work orders and inventory. Its integration with A3J Group’s solutions enhances its capabilities, providing a comprehensive toolkit for warehouse management.
A3J Group accelerators
A3J Group offers several accelerators that integrate seamlessly with IBM Maximo, providing enhanced functionality tailored to warehouse management needs.
MxPickup
MxPickup is a material pickup solution designed for the busy warehouse manager or employee. It is ideal for projects, special orders and nonstocked items. MxPickup enhances the Maximo receiving process with superior tracking and issuing controls, making it easier to receive large quantities of items and materials.
Unlike traditional systems that force materials to be stored in specific locations, MxPickup allows flexibility in placing and tracking materials anywhere, including warehouse locations, bins, any Maximo location, and freeform staging and delivery locations. Warehouse experts can choose to place or issue a portion or all of the received items, with a complete history of who placed the material and when.
MxPickup also enables mass issue of items, allowing warehouse experts to select records from the application list screen and issue materials directly, streamlining the process and saving valuable time.
A3J Automated Label Printing
The Automated Label Printing solution is designed to notify warehouse personnel proactively when items or materials are received through a printed label report. This report includes information about the received items with bar coded fields for easier scanning. Labels can be automatically fixed to received parts or materials, containing all the necessary information for warehouse operations staff to fulfill requests. The bar codes facilitate quick inventory transactions by using mobile applications, enhancing efficiency and accuracy.
Bringing innovative solutions to warehouse management
The collaboration between IBM and A3J Group on Red Hat Marketplace brings innovative solutions to warehouse management. By using advanced bar coding, data accuracy, efficiency and visibility, warehouses can achieve superior operational performance. Implementing these solutions addresses current challenges and prepares warehouses for future demands, supporting long-term success and competitiveness in the market.
Source: ibm.com