Ways in which Machine Learning and AI can improve your ERP

Artificial Intelligence and machine learning have the ability to restructure the world. These two areas have witnessed exponential advancement due to which companies are able to benefit the most. Improvements to the ERP software has also been made with the help of artificial intelligence and machine learning. Furthermore, machine learning is beneficial because it quickly evaluates a problem which thus results in taking necessary measures on time. With such technological advancement, it is easier for companies to prevent shutdowns or waste their resources. Let’s dig in further and know how these two technologies can help in improving your existing ERP.

Spotlight

Dispersive Technologies, Inc

Dispersive provides programmable networking for mission-critical solutions. Our radically different, 100% software approach to networking delivers new levels of security, reliability, and performance and provides a foundation for innovation and transformation across industry verticals. Inspired by battlefield-proven wireless radio techniques, the Dispersive™ Virtualized Network dynamically splits session-level IP traffic at the edge device into smaller, independent and individually encrypted packet streams.

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Software, Low-Code App Development

Empowering Industry 4.0 with Artificial Intelligence

Article | June 7, 2024

The next step in industrial technology is about robotics, computers and equipment becoming connected to the Internet of Things (IoT) and enhanced by machine learning algorithms. Industry 4.0 has the potential to be a powerful driver of economic growth, predicted to add between $500 billion- $1.5 trillion in value to the global economy between 2018 and 2022, according to a report by Capgemini.

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DevOps

How Artificial Intelligence Is Transforming Businesses

Article | April 18, 2024

Whilst there are many people that associate AI with sci-fi novels and films, its reputation as an antagonist to fictional dystopic worlds is now becoming a thing of the past, as the technology becomes more and more integrated into our everyday lives. AI technologies have become increasingly more present in our daily lives, not just with Alexa’s in the home, but also throughout businesses everywhere, disrupting a variety of different industries with often tremendous results. The technology has helped to streamline even the most mundane of tasks whilst having a breath-taking impact on a company’s efficiency and productivity

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Application Development Platform

The advances of AI in healthcare

Article | March 14, 2024

With the Government investing £250 million into the project, the Lab will consider how to use AI for the benefit of patients – whether this be the deployment of existing AI methods, the development of new technologies or the testing of their safety. Amongst other things, the initiative will aim to deliver earlier diagnoses of cancer. It is estimated that in excess of 50,000 extra patients could see their cancer being detected at an early stage, thus boosting survival rates. More specifically, a study has shown that AI is quicker in identifying brain tumour tissue than a pathologist.This would have a positive knock-on effect in other areas, such as enabling money to be saved (that otherwise would have been spent on further treatment) and reducing the workload of staff (at a time when there is a crisis in NHS workforce numbers).

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Three Keys to Successful AI Adoption

Article | February 10, 2020

Over the past several years, we have begun to see the emergence of artificial intelligence (AI) in businesses. According to a study for the AI Index 2019 Annual Report, more than half of respondents report their companies are using AI in at least one function or business unit. Thirty percent report they have AI embedded across multiple areas of their business. As businesses continue to develop their understanding of what is possible with AI, we can expect to see a continued increase in AI adoption.

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Spotlight

Dispersive Technologies, Inc

Dispersive provides programmable networking for mission-critical solutions. Our radically different, 100% software approach to networking delivers new levels of security, reliability, and performance and provides a foundation for innovation and transformation across industry verticals. Inspired by battlefield-proven wireless radio techniques, the Dispersive™ Virtualized Network dynamically splits session-level IP traffic at the edge device into smaller, independent and individually encrypted packet streams.

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Software

BMC Software Announces AIOps in BMC Helix for AI-Optimized IT Ops

BMC Software | November 06, 2023

BMC Software's new AIOps capabilities in BMC Helix Operations Management use AI for quick IT issue resolution. The solution boosts IT operations in hybrid, multi-cloud environments, enhancing visibility and service performance. New features like service blueprints, causal AI-powered explainability, and AIOps situation fingerprinting expedite incident resolution and risk recovery. BMC Software, a global leader in IT solutions, announces new AIOps capabilities for its BMC Helix Operations Management solution using the BMC HelixGPT capability. The solution uses advanced AI to find problems' root causes more quickly. It changes the way IT works by adding dynamic service modeling, situation explainability, and deep container auto-detection to better understand containerized environments. As businesses grapple with complex hybrid, multi-cloud environments and increasing data volume and complexity, the need for advanced AI and machine learning to drive visibility, observability, and optimum business service performance is paramount. Nancy Gohring, research director for IDC's Enterprise System Management, Observability and AIOps program, emphasized the importance of modernizing IT operations in line with the adoption of hybrid and cloud-native technologies. AIOps capabilities that leverage AI to pinpoint problem causes, guide users to the correct response, and predict potential future issues are key to ensuring service delivery aligns with business outcomes. The BMC Helix Operations Management solution combines advanced causal AI to identify issue root causes, predictive AI for proactive problem identification and resolution, and generative AI for automating event summaries and best action recommendations for complex problems. These innovations enable IT operations to deliver higher service availability and resilience to businesses, driving efficient operational performance with greater tool silo visibility and superior AI-driven insights for significantly improved problem identification and repair times. The new BMC Helix Operations Management innovations include out-of-the-box service blueprints, situation explainability powered by causal AI, and AIOps situation fingerprinting powered by AI, GPT, and NLP. These features ensure accurate service models in ever-changing IT environments, swift incident resolution, and faster recovery from service outages and other potential risks. While the new AIOps capabilities in BMC's Helix Operations Management solution offer a host of benefits, they also present potential challenges. The complexity of AI systems can lead to difficulties in understanding and controlling their operations, which could pose challenges in troubleshooting and rectifying issues. Additionally, the heavy reliance on AI might reduce the level of human oversight in IT operations, which could be risky in certain scenarios. The effectiveness of the solution is also heavily dependent on the quality and quantity of data it receives, which might not always be optimal in real-world scenarios. On the brighter side, the benefits of this solution are substantial. The use of advanced AI capabilities allows for swift identification and resolution of IT issues, greatly improving operational efficiency. The solution's ability to enhance IT operations in complex hybrid, multi-cloud environments is a significant advantage, as it provides much-needed visibility and service performance. The new features, including out-of-the-box service blueprints, causal AI-powered explainability, and AIOps situation fingerprinting, ensure swift incident resolution and faster recovery from potential risks. These innovations lead to higher service availability and resilience, which are crucial for businesses in today's digital age. Overall, despite some potential challenges, the BMC Helix Operations Management solution's new AIOps capabilities present a promising advancement in the field of IT operations management.

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Software

Unveiling Java 21: Oracle’s Quantum Leap in AI-Driven Development

Oracle | September 25, 2023

Oracle releases the new Java 21 to boost productivity, performance, and AI/ML support. Its features include string templates, record patterns, virtual threads, and more. Java 21 introduces the Vector API and aims to be the go-to for ML libraries. Oracle unveiled Java 21, marking the second update to the programming language in 2023, following Java 20's release in March. The latest iteration of Java brings a slew of enhancements designed to boost developer productivity and application performance and cater to artificial intelligence (AI) and machine learning (ML) development needs. Notably, Oracle's commitment to long-term support for Java 21, spanning at least eight years, aims to provide organizations with flexibility in migrating their applications. Additionally, support for Java 11 has been extended until January 2032. Key updates in Java 21 include: JEP 430: String Templates (Preview): Simplifying Java program development by enabling the incorporation of real-time-calculated values into strings JEP 440: Record Patterns (Third Preview): Empowering developers to expand pattern matching for complex data queries, enhancing productivity JEP 441: Pattern Matching for Switch: Enhancing the efficiency and reliability of projects by enriching the semantic nature of Java JEP 443: Unnamed Patterns and Variables (Preview): Improving code readability and maintainability by enhancing record patterns JEP 439: Generational Z Garbage Collector (ZGC): Boosting developer productivity by reducing heap memory overhead and garbage collection CPU requirements JEP 444: Virtual Threads: Optimizing the creation, maintenance, and monitoring of high-throughput, concurrent applications with lightweight virtual threads JEP 446: Scoped Values (Preview): Enabling sharing of immutable data within and across threads JEP 448: Vector API (Sixth Incubator): Introducing an API for reliable runtime compilation of vector computations on supported CPU architectures JEP 453: Structured Concurrency (Preview): Streamlining error handling, cancellation, improving reliability, and enhancing observability A standout feature in Java 21 is the finalization of virtual threads (JEP 444) within Project Loom, according to Georges Saab, Oracle's senior vice president of Oracle Java Platform and chair of the OpenJDK governing board. Virtual threads simplify the development of scalable and responsive applications, especially for those not well-versed in low-level threading APIs. This feature allows libraries and frameworks to build highly concurrent applications without requiring direct thread management, facilitating the creation of scalable programs that harness all available CPUs. Georges Saab, Oracle's senior vice president of Oracle Java Platform and chair of the OpenJDK governing board, reportedly remarked, Our answer is, we don't want to add a machine learning library, we want to make Java the platform to run all machine learning libraries on. [Source – IT Pro Today] Java 21 is geared toward AI, with JEP 448's introduction of a vector API as a prominent AI-focused feature. This API aids runtime execution, an essential component in modern AI applications utilizing vector embeddings in databases. Furthermore, projects like Valhalla, Panama, and improvements to the garbage collector aim to support AI and ML workloads by optimizing Java for processing large, intricate datasets. The overarching goal is to make Java the platform of choice for running all machine learning libraries, rather than adding a standalone machine learning library to the language.

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AI Tech, General AI, Software

DataRobot Announces New Generative AI Offering

Businesswire | August 11, 2023

DataRobot, the leader in Value-Driven AI, today announced a new generative AI offering, including platform capabilities and applied AI services, to accelerate the path from concept to value with generative AI. This offering uniquely brings both generative and predictive AI capabilities together in the DataRobot AI Platform, delivering an open and end-to-end solution for you to experiment, build, deploy, monitor and moderate enterprise-grade AI applications and assistants, and drive impact for your business. “We’ve talked to hundreds of customers looking to adopt generative AI who have concerns about existing tools on the market, including security and reputational risks, vendor lock-in and mounting technical debt from piecemeal solutions,” said Jay Schuren, Chief Customer Officer, DataRobot. “With over a decade at the forefront of AI innovation, we understand what it takes to deliver AI successfully and safely. Our new offering gives your teams everything they need to experiment quickly, deploy in production, monitor to ensure quality and ultimately get value from your generative AI projects.” The new offering builds on the DataRobot AI Platform to accelerate your generative AI initiatives by unifying best-in-class components and providing critical capabilities in an open and multi-cloud environment, including: - Generative AI Models Extended for the Enterprise: Seamlessly integrate large language models (LLMs), vector databases and prompting strategies with your enterprise data directly within DataRobot hosted notebooks. With a code-first experience and pre-built assistant recipes, you can rapidly develop customized solutions that meet your unique needs. - Enterprise-Grade Generative AI Observability: Gain confidence operating all of your generative and predictive AI assets with advanced monitoring, management and governance. Measure what matters, from operational and data drift metrics to generative AI-specific metrics like toxicity and truthfulness, and ensure applications stay “on-topic” using use case-specific guardrails. - Easy-to-Build Generative AI Applications: Quickly prototype, build and deploy end-to-end applications and assistants to deliver a complete generative AI powered experience to business stakeholders and end users with just a few lines of code, using a DataRobot-hosted Streamlit application sandbox. DataRobot is also introducing new generative AI services focused on end-to-end implementation of custom use cases as well as dedicated programming to upskill your workforce, designed and delivered by our applied AI experts: - Generative AI Training & Enablement for executives and practitioners, enabling leaders to quickly establish the level of generative AI proficiency that is necessary to remain competitive in today’s market. - Generative AI Ideation & Roadmapping Workshops for teams to go from use case ideation to implementation by systematically identifying and prioritizing high-value opportunities, and aligning leaders, data teams and stakeholders. - Generative AI Trust & Compliance Framework to support responsible generative AI governance processes and better prepare your business to meet existing guidelines and anticipate pending regulations. DataRobot supports customers from all industries to solve real-world business problems with generative and predictive AI. "The generative AI space is changing quickly, and the flexibility, safety and security of DataRobot helps us stay on the cutting edge with a HIPAA-compliant environment we trust to uphold critical health data protection standards," said Rosalia Tungaraza, AVP, Artificial Intelligence, Baptist Health South Florida. “We’re harnessing innovation for real-world applications, giving us the ability to transform patient care and improve operations and efficiency with confidence.” Connecting Ford Motor Company with over 3,800 Ford and Lincoln dealerships across the U.S. and Canada, FordDirect leverages the DataRobot AI Platform to better engage and anticipate customer needs. “DataRobot is an indispensable partner helping us maintain our reputation both internally and externally by deploying, monitoring, and governing generative AI responsibly and effectively,” said Tom Thomas, Vice President of Data Strategy, Analytics & Business Intelligence, FordDirect. “We are on the cusp of a major transition. Global organizations are excited about the possibilities to transform their businesses with generative AI while at the same time faced with risks ranging from hallucinations and toxicity, to governance and bias,” said Ritu Jyoti, Group Vice President, Worldwide Artificial Intelligence and Automation Research Practice Global AI Research Lead at IDC. “That’s why AI platforms like DataRobot are critical in unlocking business value with generative AI and predictive AI alongside robust monitoring, governance, and a broad ecosystem. They create a competitive edge for enterprises.” About DataRobot DataRobot is the leader in Value-Driven AI, a unique and collaborative approach to generative and predictive AI that combines an open platform, deep expertise and broad use-case experience to improve how organizations run, grow and optimize their business. The DataRobot AI Platform is the only complete AI lifecycle platform that interoperates with an organization’s existing investments in data, applications and business processes, and can be deployed on any cloud environment. Global organizations, including 40% of the Fortune 50, rely on DataRobot to drive greater impact and value from AI. Learn more at datarobot.com and follow us on LinkedIn and X (@DataRobot).

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Software

BMC Software Announces AIOps in BMC Helix for AI-Optimized IT Ops

BMC Software | November 06, 2023

BMC Software's new AIOps capabilities in BMC Helix Operations Management use AI for quick IT issue resolution. The solution boosts IT operations in hybrid, multi-cloud environments, enhancing visibility and service performance. New features like service blueprints, causal AI-powered explainability, and AIOps situation fingerprinting expedite incident resolution and risk recovery. BMC Software, a global leader in IT solutions, announces new AIOps capabilities for its BMC Helix Operations Management solution using the BMC HelixGPT capability. The solution uses advanced AI to find problems' root causes more quickly. It changes the way IT works by adding dynamic service modeling, situation explainability, and deep container auto-detection to better understand containerized environments. As businesses grapple with complex hybrid, multi-cloud environments and increasing data volume and complexity, the need for advanced AI and machine learning to drive visibility, observability, and optimum business service performance is paramount. Nancy Gohring, research director for IDC's Enterprise System Management, Observability and AIOps program, emphasized the importance of modernizing IT operations in line with the adoption of hybrid and cloud-native technologies. AIOps capabilities that leverage AI to pinpoint problem causes, guide users to the correct response, and predict potential future issues are key to ensuring service delivery aligns with business outcomes. The BMC Helix Operations Management solution combines advanced causal AI to identify issue root causes, predictive AI for proactive problem identification and resolution, and generative AI for automating event summaries and best action recommendations for complex problems. These innovations enable IT operations to deliver higher service availability and resilience to businesses, driving efficient operational performance with greater tool silo visibility and superior AI-driven insights for significantly improved problem identification and repair times. The new BMC Helix Operations Management innovations include out-of-the-box service blueprints, situation explainability powered by causal AI, and AIOps situation fingerprinting powered by AI, GPT, and NLP. These features ensure accurate service models in ever-changing IT environments, swift incident resolution, and faster recovery from service outages and other potential risks. While the new AIOps capabilities in BMC's Helix Operations Management solution offer a host of benefits, they also present potential challenges. The complexity of AI systems can lead to difficulties in understanding and controlling their operations, which could pose challenges in troubleshooting and rectifying issues. Additionally, the heavy reliance on AI might reduce the level of human oversight in IT operations, which could be risky in certain scenarios. The effectiveness of the solution is also heavily dependent on the quality and quantity of data it receives, which might not always be optimal in real-world scenarios. On the brighter side, the benefits of this solution are substantial. The use of advanced AI capabilities allows for swift identification and resolution of IT issues, greatly improving operational efficiency. The solution's ability to enhance IT operations in complex hybrid, multi-cloud environments is a significant advantage, as it provides much-needed visibility and service performance. The new features, including out-of-the-box service blueprints, causal AI-powered explainability, and AIOps situation fingerprinting, ensure swift incident resolution and faster recovery from potential risks. These innovations lead to higher service availability and resilience, which are crucial for businesses in today's digital age. Overall, despite some potential challenges, the BMC Helix Operations Management solution's new AIOps capabilities present a promising advancement in the field of IT operations management.

Read More

Software

Unveiling Java 21: Oracle’s Quantum Leap in AI-Driven Development

Oracle | September 25, 2023

Oracle releases the new Java 21 to boost productivity, performance, and AI/ML support. Its features include string templates, record patterns, virtual threads, and more. Java 21 introduces the Vector API and aims to be the go-to for ML libraries. Oracle unveiled Java 21, marking the second update to the programming language in 2023, following Java 20's release in March. The latest iteration of Java brings a slew of enhancements designed to boost developer productivity and application performance and cater to artificial intelligence (AI) and machine learning (ML) development needs. Notably, Oracle's commitment to long-term support for Java 21, spanning at least eight years, aims to provide organizations with flexibility in migrating their applications. Additionally, support for Java 11 has been extended until January 2032. Key updates in Java 21 include: JEP 430: String Templates (Preview): Simplifying Java program development by enabling the incorporation of real-time-calculated values into strings JEP 440: Record Patterns (Third Preview): Empowering developers to expand pattern matching for complex data queries, enhancing productivity JEP 441: Pattern Matching for Switch: Enhancing the efficiency and reliability of projects by enriching the semantic nature of Java JEP 443: Unnamed Patterns and Variables (Preview): Improving code readability and maintainability by enhancing record patterns JEP 439: Generational Z Garbage Collector (ZGC): Boosting developer productivity by reducing heap memory overhead and garbage collection CPU requirements JEP 444: Virtual Threads: Optimizing the creation, maintenance, and monitoring of high-throughput, concurrent applications with lightweight virtual threads JEP 446: Scoped Values (Preview): Enabling sharing of immutable data within and across threads JEP 448: Vector API (Sixth Incubator): Introducing an API for reliable runtime compilation of vector computations on supported CPU architectures JEP 453: Structured Concurrency (Preview): Streamlining error handling, cancellation, improving reliability, and enhancing observability A standout feature in Java 21 is the finalization of virtual threads (JEP 444) within Project Loom, according to Georges Saab, Oracle's senior vice president of Oracle Java Platform and chair of the OpenJDK governing board. Virtual threads simplify the development of scalable and responsive applications, especially for those not well-versed in low-level threading APIs. This feature allows libraries and frameworks to build highly concurrent applications without requiring direct thread management, facilitating the creation of scalable programs that harness all available CPUs. Georges Saab, Oracle's senior vice president of Oracle Java Platform and chair of the OpenJDK governing board, reportedly remarked, Our answer is, we don't want to add a machine learning library, we want to make Java the platform to run all machine learning libraries on. [Source – IT Pro Today] Java 21 is geared toward AI, with JEP 448's introduction of a vector API as a prominent AI-focused feature. This API aids runtime execution, an essential component in modern AI applications utilizing vector embeddings in databases. Furthermore, projects like Valhalla, Panama, and improvements to the garbage collector aim to support AI and ML workloads by optimizing Java for processing large, intricate datasets. The overarching goal is to make Java the platform of choice for running all machine learning libraries, rather than adding a standalone machine learning library to the language.

Read More

AI Tech, General AI, Software

DataRobot Announces New Generative AI Offering

Businesswire | August 11, 2023

DataRobot, the leader in Value-Driven AI, today announced a new generative AI offering, including platform capabilities and applied AI services, to accelerate the path from concept to value with generative AI. This offering uniquely brings both generative and predictive AI capabilities together in the DataRobot AI Platform, delivering an open and end-to-end solution for you to experiment, build, deploy, monitor and moderate enterprise-grade AI applications and assistants, and drive impact for your business. “We’ve talked to hundreds of customers looking to adopt generative AI who have concerns about existing tools on the market, including security and reputational risks, vendor lock-in and mounting technical debt from piecemeal solutions,” said Jay Schuren, Chief Customer Officer, DataRobot. “With over a decade at the forefront of AI innovation, we understand what it takes to deliver AI successfully and safely. Our new offering gives your teams everything they need to experiment quickly, deploy in production, monitor to ensure quality and ultimately get value from your generative AI projects.” The new offering builds on the DataRobot AI Platform to accelerate your generative AI initiatives by unifying best-in-class components and providing critical capabilities in an open and multi-cloud environment, including: - Generative AI Models Extended for the Enterprise: Seamlessly integrate large language models (LLMs), vector databases and prompting strategies with your enterprise data directly within DataRobot hosted notebooks. With a code-first experience and pre-built assistant recipes, you can rapidly develop customized solutions that meet your unique needs. - Enterprise-Grade Generative AI Observability: Gain confidence operating all of your generative and predictive AI assets with advanced monitoring, management and governance. Measure what matters, from operational and data drift metrics to generative AI-specific metrics like toxicity and truthfulness, and ensure applications stay “on-topic” using use case-specific guardrails. - Easy-to-Build Generative AI Applications: Quickly prototype, build and deploy end-to-end applications and assistants to deliver a complete generative AI powered experience to business stakeholders and end users with just a few lines of code, using a DataRobot-hosted Streamlit application sandbox. DataRobot is also introducing new generative AI services focused on end-to-end implementation of custom use cases as well as dedicated programming to upskill your workforce, designed and delivered by our applied AI experts: - Generative AI Training & Enablement for executives and practitioners, enabling leaders to quickly establish the level of generative AI proficiency that is necessary to remain competitive in today’s market. - Generative AI Ideation & Roadmapping Workshops for teams to go from use case ideation to implementation by systematically identifying and prioritizing high-value opportunities, and aligning leaders, data teams and stakeholders. - Generative AI Trust & Compliance Framework to support responsible generative AI governance processes and better prepare your business to meet existing guidelines and anticipate pending regulations. DataRobot supports customers from all industries to solve real-world business problems with generative and predictive AI. "The generative AI space is changing quickly, and the flexibility, safety and security of DataRobot helps us stay on the cutting edge with a HIPAA-compliant environment we trust to uphold critical health data protection standards," said Rosalia Tungaraza, AVP, Artificial Intelligence, Baptist Health South Florida. “We’re harnessing innovation for real-world applications, giving us the ability to transform patient care and improve operations and efficiency with confidence.” Connecting Ford Motor Company with over 3,800 Ford and Lincoln dealerships across the U.S. and Canada, FordDirect leverages the DataRobot AI Platform to better engage and anticipate customer needs. “DataRobot is an indispensable partner helping us maintain our reputation both internally and externally by deploying, monitoring, and governing generative AI responsibly and effectively,” said Tom Thomas, Vice President of Data Strategy, Analytics & Business Intelligence, FordDirect. “We are on the cusp of a major transition. Global organizations are excited about the possibilities to transform their businesses with generative AI while at the same time faced with risks ranging from hallucinations and toxicity, to governance and bias,” said Ritu Jyoti, Group Vice President, Worldwide Artificial Intelligence and Automation Research Practice Global AI Research Lead at IDC. “That’s why AI platforms like DataRobot are critical in unlocking business value with generative AI and predictive AI alongside robust monitoring, governance, and a broad ecosystem. They create a competitive edge for enterprises.” About DataRobot DataRobot is the leader in Value-Driven AI, a unique and collaborative approach to generative and predictive AI that combines an open platform, deep expertise and broad use-case experience to improve how organizations run, grow and optimize their business. The DataRobot AI Platform is the only complete AI lifecycle platform that interoperates with an organization’s existing investments in data, applications and business processes, and can be deployed on any cloud environment. Global organizations, including 40% of the Fortune 50, rely on DataRobot to drive greater impact and value from AI. Learn more at datarobot.com and follow us on LinkedIn and X (@DataRobot).

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