HellasQCI and Nokia lead way to the future of Quantum-Safe Networks

• Trial showcases hybrid approach with both classic and quantum physics key generation to help HellasQCI reach its goal of Quantum-Safe Networks for critical infrastructure security across Greece

• Results demonstrate critical interoperability in the quantum key distribution (QKD) architecture and how Nokia’s portfolio is equipped to help secure the network against quantum attacks at the lowest latency and lowest cost per bit

• HellasQCI project consortium is led by GRNET – National Infrastructures for Research and Technology, and operates under the auspices of the Greek Ministry of Digital Governance. GRNET provides networking, cloud computing and high-performance computing connecting more than 150 organizations across 50 cities and serves hundreds of thousands of users

Nokia announced it has successfully completed a proof of concept (PoC) of Quantum-Safe Networks (QSN) in a complex optical network ring topology across three locations in Greece with HellasQCI, the Greek National Quantum Communication Infrastructure Consortium. Nokia successfully demonstrated hybrid key generation using both classic and quantum physics to generate and distribute Quantum-Safe keys for encrypted optical services. The goal of the PoC was to run use cases with the HellasQCI consortium members from government, research and education, defense, law enforcement and private sector critical infrastructure owners to ensure Quantum-Safe connectivity infrastructure.

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The PoC was performed on a ring connecting three nodes in GRNET’s Data Center, the National Kapodistrian University of Athens (NKUA) Network Operations Center (NOC), and the NKUA Department of Informatics and Telecommunications (DIT), Optics Communications and Photonic Technology Laboratory (OPTCOMMS-PTL).

The positive potential of quantum computing is enormous. Today, governments and research organizations are investing in the technology to address issues such as sustainability, defense, and other societal challenges. Enterprises are using it across industries for applications like advanced financial computations, shaping the future of electric vehicles, or to better understand the impact of climate change on the power grid. However, the development of quantum computing also introduces an ability for this technology to easily penetrate systems previously regarded as secure, for example, by breaking existing encryption. This threat could easily damage life sustaining critical infrastructure. Hence, there is an urgent need to ensure networks are ready for quantum attacks through consortiums like HellasQCI who conduct rigorous tests and trials on their systems before bad actors can attempt to leverage quantum computing, overwriting current encryption technologies.

The HellasQCI consortium consists of 14 public organizations and private corporations, and is led by GRNET as project coordinator and NKUA as its technical coordinator. GRNET operates under the auspices of the Greek Ministry of Digital Governance, providing networking, cloud computing, high-performance computing, and e-Infrastructures. It connects more than 150 organizations across 50 cities and serves hundreds of thousands of users daily in public administration, education, research, health and culture.

The Nokia QSN solution showcased the value added by the Nokia Security Management Server (SMS) to orchestrate Quantum-Safe keys and provide continuous monitoring and management of quantum secured connectivity. This particular SMS is the Quantum-Safe Key orchestrator, managing key requests and assuring Quantum-Safe services for the given crypto period. Upon continuous monitoring of Quantum-Safe connectivity service, the system’s SMS can increase secured connectivity resiliency automatically by reverting to classic physics-based keys in case of issues on the QKD layer.

Dr. Ognjen Prnjat, Director for European Infrastructures and Projects Directorate at GRNET, said: “We are very pleased with the successful completion of the PoC with Nokia, which is one of the key milestones for the HellasQCI project that establishes the National Quantum Communication Infrastructure in Greece. GRNET plays a crucial role in various European infrastructures for research and education, including EOSC, EUDAT, EGI, GÉANT, PRACE, and EuroHPC, and we are honored to have been given the responsibility by the Ministry of Digital Governance to lead the implementation of this innovative quantum network infrastructure as part of the pan-European Quantum Communication Infrastructure (EuroQCI) initiative. The PoC demonstrated the feasibility and the benefits of Quantum-Safe Networks for securing critical communications in Greece and in Europe and we look forward to continuing our collaboration with Nokia and other partners to develop and deploy an operational and secure EuroQCI.”

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James Watt, Head of the Optical Networks Division at Nokia, said: “We are proud to partner with HellasQCI and its consortium members to demonstrate our QSN solution in this challenging and innovative PoC. Test environments like this are crucial to ensure networks are ready for quantum-level cybersecurity attacks, which are inevitable as quantum computing becomes more accessible around the world. Nokia brings a structured framework and a collaborative partner-based approach combined with our world-leading networking expertise with extensive experience managing large-scale and complex deployments with multi-connectivity domain and multi QKD vendor technologies.”

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 WiMi Hologram Cloud a leading global Hologram Augmented Reality (“AR”) Technology provider announced that it developed a deep reinforcement learning-based task scheduling algorithm in cloud computing to improve the performance and resource utilization of cloud computing systems. Deep reinforcement learning can solve complex decision-making problems by learning and optimizing strategies. By using deep reinforcement learning, the task scheduling problem can be transformed into a reinforcement learning problem by training a deep neural network to learn the optimal strategy for task scheduling. The advantage of reinforcement learning is that it can automatically adjust the policy according to the changes in the environment and can be adapted to complex task scheduling scenarios. Deep reinforcement learning has the advantages of adaptivity, nonlinear modeling, end-to-end learning, and generalization ability in task scheduling, and it can comprehensively consider factors such as the execution time of the task, the resource demand, the load situation of the virtual machine, and the network latency, so as to carry out the task scheduling more accurately, and to improve the performance of the system and the utilization rate of resources.

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WiMi’s deep reinforcement learning-based task scheduling algorithm in cloud computing includes state representation, action selection, reward function and training and optimization of the algorithm. State representation is an important link. By transforming various information in the cloud computing environment into a form that can be processed by the machine learning model, it can help the model to better understand the current task scheduling situation, so as to make more reasonable and accurate task scheduling decisions. Action selection is also a key step, where at each time step, the agent needs to select an action to execute to decide the task scheduling strategy at the current moment. Such an algorithm can select an optimal action based on the current system state to achieve efficient cloud computing task scheduling. The reward function, on the other hand, is used to evaluate the reward value obtained by the agent after executing an action, which in turn guides the decision-making process of the agent. The reward function can enable the agent to learn and optimize better during the task scheduling process.

In addition, the training and optimization of the deep reinforcement learning-based task scheduling algorithm in cloud computing are also very critical. First, a reinforcement learning environment applicable to the task scheduling problem needs to be constructed, including the definition of states, actions and reward functions. The state can include information such as the current system load situation, attributes and priority of the task; the action can choose to assign the task to a certain virtual machine or decide whether to delay the processing of the task; and the reward function can be defined based on the completion time of the task, resource utilization and other metrics. The algorithm is then trained using a deep reinforcement learning algorithm such as Deep Q-Network (DQN), a neural network-based reinforcement learning algorithm that can make decisions by learning a value function. During the training process, by interacting with the environment, the algorithm continuously updates the parameters of the neural network to optimize the decision-making strategy for task scheduling. In addition, some optimization techniques, such as experience playback and objective networks, can be used to further improve the performance and stability of the algorithm. Through continuous training and optimization, the algorithm will gradually learn the optimal strategy for task scheduling, thus improving the performance and efficiency of the system.

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The deep reinforcement learning-based task scheduling algorithm in cloud computing has achieved significant improvements in both task scheduling effectiveness and system performance. There are still some research directions that can be further explored in this technology area. In the future, WiMi will improve the performance and adaptability of the deep reinforcement learning-based task scheduling algorithm in cloud computing through multi-objective optimization, adaptation in dynamic environments, model uncertainty handling, real-time decision making, and improving the algorithm to provide better support for practical applications.

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AI platform Chai announced an agreement by specialised cloud computing provider CoreWeave to make an investment in Chai. CoreWeave invested in Chai in the form of GPU compute credits, placing Chai at a $450 million valuation. This deal followed shortly after Chai’s initial fundraising, bringing the total capital invested in Chai to over $10 million.

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CoreWeave’s strategic investment in Chai will go towards powering one of the first large-scale AI language model competitions, allowing competitors to submit language models and compete for user engagement in Chai’s popular entertainment app. Winners in the competition are rewarded with cash prizes and the prestige of the Chai Prize title.

Launched earlier this year, the Chai Developer Platform has already resulted in the inception of several natural language modelling techniques, with others being deployed perhaps for the very first time to such a wide audience. With thousands of LLMs created and submitted to the Chai platform, they were the first to deploy techniques such as Direct Preference Optimization (DPO), an advancement on the previous state of the art technique, Reinforcement Learning From Human Feedback (RLHF), which OpenAI used to create ChatGPT.

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Every message sent on the social AI platform is powered by CoreWeave’s cloud. “As the world transitions to an AI economy, everyone will socialize with AI.” said Chai CEO William Beauchamp, “Maybe your next best friend will live in the cloud. We’re glad to be partnering with CoreWeave on our mission.” With Nvidia releasing thousands of H100s in Q1 ’24 we are excited to see the transition accelerate.

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fifty-five, a global martech consultancy that empowers brands to unite and fully optimize siloed data across all channels to drive business results, announced that it has joined the Amazon Web Services (AWS) Partner Network (APN). The APN is a global community of AWS Partners that leverage programs, expertise, and resources to build, market, and sell customer offerings.

A network to accelerate business transformation

Leveraging its three-fold expertise – martech data consulting, data engineering and data science – fifty-five supports its clients in their transformation journey by helping them make the most of their data to develop innovative marketing strategies for a more effective customer experience.

As part of this partnership, fifty-five will be able to deploy trusted machine learning and AI solutions to improve its clients’ performance and competitiveness. The agreement also promotes the development and scaling of generative AI applications within enterprises.

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“AWS provides the wide range of cloud services our customers need to put data and innovation at the heart of their projects. We are very proud to have won an award at the recent Gen AI Partners Demo Showdown (“Account Manager/Sales AWS”). It was an opportunity to demonstrate the relevance of Amazon Bedrock applied to Data Marketing Analytics. This award reinforced our desire to deepen our collaboration with the AWS teams to bring to life business-related, high-performance use cases,” commented Jean-François Wassong, Partner and CTO of fifty-five.

AWS solutions at the service of fifty-five customers

fifty-five has already managed several projects for its customers with the help of AWS. In one instance, a global consumer goods group called on fifty-five to deploy a “datamesh,” an architecture enabling the systematic use of a technical platform and a base of ready-to-use data. On this assignment, fifty-five tapped into the large scope of capabilities from its teams, combined with the power of AWS services and integrated tools such as Snowflake, SageMaker, ECS and EC2, in their day-to-day work.

These data processing tools were replicated and scaled for the CPG company’s full portfolio of brands. By building a continuously updated marketing database and leveraging AWS’ pre-configured processes and tools, fifty-five helped its customer unlock greater end-to-end marketing potential, ranging from data collection to transformation, activation, and measurement.

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AWS offers a wide range of cloud services, from storage, computing and database infrastructure to advanced technologies such as analytics, artificial intelligence (especially generative AI), machine learning and the Internet of Things (IoT). By choosing AWS, businesses benefit from the agility, flexibility and security of cloud computing. These services enable them to deploy applications, store data and access IT resources on demand, without having to invest in a costly physical infrastructure.

As an APN member, fifty-five joins a global network of 100,000 partners from more than 150 countries working with AWS to provide innovative solutions, solve technical challenges, win deals, and deliver mutual value to customers.

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Authenticate.com (“Authenticate”), a leading provider of identity verification and fraud prevention solutions, announced that it has filed a patent for EthicAi, a groundbreaking AI aimed at identifying and reducing bias in policing. The company has assembled a work group of renowned academics, scientists, engineers, and researchers to consult and contribute to the development of this innovative solution. Indeed, there are project participants whose skepticism and doubts are essential to the design and development phase of this initiative.

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The patent filing represents a significant milestone in Authenticate’s effort to create a more fair and equitable policing system. By harnessing the power of artificial intelligence, the company aims to address the long-standing issue of bias in law enforcement, with the goal of ultimately leading to improved decision-making and increased transparency.

“We are excited to announce the filing of our patent for an AI that will revolutionize policing by reducing bias and promoting fairness,” said Steve Ward, CEO of Authenticate. “Bias in policing has long been a concern, and we believe that our innovative solution will play a crucial role in creating a more just society.”

The AI being developed by Authenticate will utilize advanced algorithms and machine learning techniques to analyze data and identify patterns of bias in policing practices. By gathering empirical data from various sources, including social science research, police data, and input from the public, theoretically, the AI should be able to make highly accurate predictions about human behavior, while ensuring fairness and transparency.

To kickstart this groundbreaking effort, the work group Authenticate has assembled consists of renowned experts from diverse fields who have been asked to consult and contribute at will. The team includes psychologists with authority in this specific domain, such as Kevin Jay Jablonski, the once Chief Police Psychologist for the Los Angeles Police Department for twenty-five years, and Jack Glaser, a Professor at the Goldman School of Public Policy at UC Berkeley and social psychologist who has brought his 30 years of research experience on stereotyping, prejudice, and discrimination to bear on the subject of racial disparities in policing, collaborating in multiple efforts to compile and analyze large policing datasets. Sociologists with subject matter expertise on algorithmic injustice. Medical doctors in trauma centers and emergency rooms, neuroscientists, mathematical logicians, physicists, data scientists and system architects are also actively involved and participating in the project.

There is a first principle of equity and inclusion at the forefront of this endeavor, which is why community involvement and participation from NGOs will be crucial to ensuring the research, studies, and more are conducted properly and fairly. The team is actively recruiting lived-experience experts and advocates who have had direct or indirect contact with the justice system. Through the use of high-speed messaging, distributed systems, cloud computing and edge computing, Authenticate theorizes that it can observe and report bias in real time.

“We believe that leveraging edge computing to reduce latency is the key to success for the EthicAi use case,” said System Architect Colin Sullivan, of Luxant Solutions. “Coordinating complex workloads between edge and cloud will provide time-sensitive mission critical insights for law enforcement officers in the field.”

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Authenticate’s AI for bias reduction in policing has the potential to transform the way law enforcement agencies operate. By providing additional perspectives, alternatives, and consequences, the system will assist human decision-making, by attempting to surface unconscious biases, and enhance critical thinking.

The company remains committed to developing the technology ethically and responsibly. Ethical principles, including fairness, equity, accountability, and transparency, will be the guiding light in the ongoing development and use of the AI system. Authenticate recognizes the challenges involved in implementing this technology and is dedicated to building trust with the community by being transparent about how the AI system works, and ensuring its benefits are aligned with community interests. Authenticate is confident that its innovative AI will contribute to a more just and equitable society. The company is dedicated to this newfound mission to reduce bias in policing, improve decision-making, and increase transparency and accountability, aligning itself with the global call for reform in law enforcement practices.

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Partnership seeks to meet the growing demand and upskilling requirements for millions of cloud computing professionals worldwide

Udemy, a leading online skills marketplace and learning platform, announced a new partnership with Google Cloud, joining their new Google Cloud Endorsed Content Program. Through this program, Google Cloud will endorse high-quality Udemy content that current and aspiring cloud professionals can trust to learn more about Google Cloud emerging technologies such as generative AI and prepare for future certification exams. This new Google Cloud Endorsed Content adds to a growing portfolio of Google Cloud training available on Udemy. Additionally, select Udemy instructors will be invited to preview new products and features coming to Google Cloud, allowing them to provide learners with fresh, relevant content the moment a new capability becomes available. This supports Udemy’s commitment to ensuring its content is keeping pace with the rate of technology changes in the marketplace. These new courses will be easily identifiable via Google Cloud Endorsed badges and will be featured on Udemy’s dedicated, Google Cloud Endorsement page. 

“Today, we’re at the forefront of the next digital revolution, and as a result, there’s a critical need for skilled professionals who are well-versed in tools like Google Cloud to help their organizations modernize and take full advantage of all that AI-powered cloud computing platforms have to offer,” said Greg Brown, President and CEO at Udemy. “We’re thrilled to be partnering with an iconic technology leader that’s been at the cutting edge of innovation for more than two decades, so that together, we can help train the next generation of cloud computing professionals.” 

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With demand for cloud computing skills on the rise, Udemy currently offers more than 430 dedicated Google Cloud courses with nearly 500,000 enrollments over the past 12 months. Additionally, as it is becoming an increasing priority for organizations and professionals to understand generative AI, Udemy has seen increased interest in the 124 Google Bard courses available on its platform with more than 73,000 course enrollments since its launch in March 2023. Further, in Udemy’s recently published 2024 Global Learning & Skills Trends Report, Google Cloud’s Professional Cloud DevOps Engineer certification was noted as one of the most in-demand technical skills for 2024 with a 1,454% growth in course consumption over the past year alone. 

As companies increasingly prioritize adoption of skills-based practices, it is critical that they have access to the freshest content available to support certification preparation so professionals can not only acquire new skills, but also validate them and demonstrate mastery to their employers. Udemy’s learning platform does just that and also recently introduced an Integrated Skills Framework earlier this year which provides organizations with a roadmap to help them understand and more easily navigate the fundamental components of a skills-based approach to learning. This approach, coupled with endorsement by Google Cloud, provides learners and organizations with confidence that they are accessing the most current, high quality Google Cloud certification preparation available. 

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In the initial phase of the pilot program, Google Cloud will be focused on endorsing generative AI courses––beginning with courses from world-renowned Udemy instructors Jose Portilla and Ranga Karanam––with plans to review and endorse additional content related to Google Cloud certification preparation, data analytics, developer content and infrastructure and networking capabilities in the coming months. 

“We are thrilled to announce Google Cloud Learning Services’ collaboration with Udemy to bring learners cutting-edge cloud computing and generative AI courses from industry experts like Jose Portilla and Ranga Karanam,” said Natalie Van Kleef, Head of the Current & Future Technologists organization in Google Cloud Learning Services. “This exciting partnership will empower developers to gain the skills and knowledge they need to become confident in these new technologies as generative AI continues to shape the future of work.” 

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Adds capability to CompAnalyst Total Compensation Management Platform

Salary.com, the market leader in total compensation management software and data solutions, announces the acquisition of HCR Software, maker of CompXL’s enterprise incentive compensation planning software. CompXL supports the management of merit raises, bonus, commissions, long term incentive awards, equity, and employee total rewards statements across an entire organization. With this acquisition, Salary.com builds on its core foundation of highly accurate and comprehensive data and software, completing the evolution of CompAnalyst® into the only Total Compensation Management Platform (“TCM”) that pairs Salary.com’s proprietary back-end technologies with a familiar spreadsheet format to make a flexible, scalable, enterprise-ready solution built for the cloud.

Kent Plunkett, CEO and co-founder of Salary.com says, “Most companies struggle to manage decision-making and communications around people’s pay and incentives. Our CompAnalyst Total Compensation Management Platform now offers enterprises everything they need to benchmark, manage, and communicate employee total rewards. Getting pay right is at the heart of the deal between employers and their people. Building trust and engagement are the new imperative for HR, compensation, recruiting and sales leadership teams. With the addition of CompXL’s compensation planning software, and the expertise of their team, Salary.com is creating a way for employers to consistently earn that trust.”

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Yong Zhang, President and co-founder of Salary.com says, “We are profoundly committed to the practice of Total Compensation Management. Accuracy, fairness, and communication are at the core of how you get pay right. The addition of CompXL rounds out our award-winning Suite’s capabilities to offer the most complete and integrated compensation capabilities across the entire enterprise.”

The Benefits of CompAnalyst’s TCM Platform

CompAnalyst’s TCM platform now merges spreadsheet familiarity and flexibility with cloud computing‘s automation, workflow, scalability, and security capabilities. With the ability to create and manage incentive programs that reward and attract the best talent, customers can reduce the administrative burdens often associated with planning and managing compensation.

Other benefits include:

• Ability to create new compensation strategies
• Cycle planning time reduced by up to 75%
• Flexibility to support any incentive plan, including complex incentives like commissions
• Ability to work with both simple and complex plans
• Scalability and support of global plans and multiple currencies across thousands of employees
• Integrations with any HR system (ADP, Ceridian, Oracle, SAP, UKG, Workday, etc.)
• Familiar spreadsheet format offers minimal learning curve

Jamie Davis, CEO of HCR Software says, “I am excited about our future as a part of Salary.com. CompXL has spent the last two decades building the industry’s most configurable enterprise compensation planning application. It is a natural evolution to become part of Salary.com’s CompAnalyst platform. Our people are very excited to join the Salary.com family and continue to care for our hundreds of valued customers.” GlobalLogic, Lantheus, and Natixis Investment Managers are among the many organizations leveraging CompXL for incentive compensation planning.

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AI and Cloud computing pairing is a massive advantage for businesses in the GPT era. While the Cloud computing software industry is expected to grow 2x in the next 5 years, AI computing will grow 5X during the same period. AI’s deep integration with applications in cloud computing technology is associated with revenue-generation opportunities. Tech-driven organizations use AI to scale their revenues in addition to fast-tracking their immediate strategic goals. According to Deloitte, AI not only enables the “mass personalization” of products and services but also intelligently automates a large number of repetitive tasks to free workers who can pursue creative goals.

In this article, we have described the top AI use cases in cloud computing. AI pioneers such as Microsoft, Google Cloud, AWS, IBM, SAP, and Salesforce are constantly developing new-age AI tools and applications that accelerate Cloud computing expertise across numerous fronts. Healthcare, manufacturing, customer service, education, banking and finance, and media intelligence are among the top industries benefitting from the unification of AI and cloud computing.

With the help of artificial intelligence, computers can process vast volumes of information and apply their acquired knowledge to make excellent judgments and discoveries far more quickly than people can.

What Is AI in Computing?

To do jobs normally performed by people, which need human intellect and discernment, a computer or a robot controlled by a computer must have artificial intelligence (AI).

Machine learning techniques require a great deal of mathematical computation, which is why AI cloud computing often makes use of accelerated hardware and software. It can acquire new abilities as it goes, allowing it to extract novel insights from large datasets.

AI computing is the greatest game-changing innovation of our time since we now reside in a data-centric era, and it can discover patterns that no human could. For example, American Express employs AI computing to identify fraud in billions of yearly credit card transactions. Cancer specialists rely on it to sift through reams of medical photos for signs of the disease.

Read the Latest blog from us: AI And Cloud- The Perfect Match

The Unification of AI and Cloud Computing

Automation of tasks including data analysis, data management, security, and decision-making are at the intersection of AI and cloud computing. These efficiencies and potential cost savings may be attributed in large part to AI’s capacity to apply machine learning and extract objective interpretations of data-driven insights.

Artificial intelligence (AI) software built on machine learning algorithms deployed in cloud settings provides users with personalized and contextualized information. This combination, of which Alexa and Siri are only two examples, paves the way for a wide range of actions, such as searching, listening to music, and making purchases.

Mass amounts of data are often utilized to train an ML model’s algorithm. This data might be organized, unstructured, or raw and needs strong CPUs and GPUs to handle. Such massive quantities of processing power can only be provided by the right mix of public, private, or hybrid cloud systems (depending on security and compliance needs). Serverless computing, batch processing, and container orchestration are just some of the ML services made possible by AI cloud computing.

Read: AI and Machine Learning Are Changing Business Forever

Top 20 Uses of Artificial Intelligence In Cloud Computing

1. Cost Optimization: AI can help optimize cloud spending by analyzing usage patterns and suggesting cost-effective configurations, instance types, and scaling strategies. This can lead to significant cost savings for organizations.
2. Resource Scaling: AI can automate the process of scaling cloud resources up or down based on real-time demand. This ensures that applications have the necessary resources available to maintain performance while minimizing idle resource costs.
3. Predictive Maintenance: In cloud infrastructure, predictive maintenance uses AI to monitor the health of cloud resources and predict when hardware components are likely to fail. This can help prevent service interruptions and reduce downtime.
4. Security and Threat Detection: AI can enhance cloud security by analyzing network traffic patterns and identifying potential security threats in real-time. It can detect anomalies, such as unauthorized access or unusual data patterns, and trigger alerts or automatic responses.
5. Natural Language Processing (NLP): Cloud-based NLP services powered by AI can be used to extract insights from unstructured text data, improve customer support, and automate content moderation in cloud-hosted applications.
6. Data Analytics: AI-powered cloud services can perform advanced data analytics, including data mining, predictive analytics, and machine learning, to extract valuable insights from large datasets hosted in the cloud.
7. Image and Video Analysis: Cloud-based AI can process and analyze images and videos stored in cloud storage, enabling applications like facial recognition, object detection, and content tagging.
8. Recommendation Systems: AI algorithms can be deployed in the cloud to build recommendation engines, offering personalized content recommendations to users in various applications, such as e-commerce, streaming platforms, and news websites.
9. Content Generation: AI can be used to generate content, such as text, images, or even music, which can be hosted in the cloud and served to users in real-time. This is particularly useful in chatbots, virtual assistants, and content-creation tools.
10. Optimizing Workflows: AI can help automate and optimize various cloud-based workflows, such as DevOps processes, data pipelines, and data migration tasks.
11. Auto-Scaling Containers: AI-driven container orchestration systems in the cloud can automatically scale containerized applications based on traffic and resource usage, improving efficiency and resource allocation.
12. Performance Optimization: AI algorithms can continuously monitor the performance of cloud applications and suggest optimizations, such as code improvements, database indexing, or caching strategies.
13. Personalization: Cloud-based AI can provide personalized user experiences in applications by analyzing user behavior and preferences, and delivering tailored content or recommendations.
14. Language Translation: AI-driven language translation services can be hosted in the cloud, enabling real-time language translation in various applications, including communication and content localization.
15. Virtual Assistants: Cloud-hosted AI virtual assistants, like chatbots, can provide customer support, answer queries, and perform tasks on behalf of users, improving user engagement and satisfaction.
16. Distributed Computing: AI can optimize the distribution of computing tasks in the cloud, ensuring that workloads are efficiently allocated across a distributed infrastructure.
17. Data Backup and Recovery: AI can improve data backup and recovery processes by identifying critical data, ensuring redundancy, and optimizing data restoration.
18. Resource Provisioning: AI-driven cloud management platforms can predict resource needs and proactively provision resources to meet demand, ensuring optimal application performance.
19. IoT and Edge Computing: AI in the cloud can analyze data from IoT devices and edge nodes, providing centralized processing, analytics, and insights for distributed IoT deployments.
20. Business Intelligence and Reporting: Cloud-based AI can generate advanced reports and visualizations, turning data into actionable insights for organizations.

To What Extent Does Artificial Intelligence Help Cloud Computing?

Artificial intelligence (AI) on the cloud may help businesses in several different ways. The following are some advantages for companies:

• Save Money
  Initially, the price of ML-based models was too exorbitant for most small and medium-sized organizations. Furthermore, the models were executed across numerous GPUs in production data centers. Public and private cloud virtualization advancements have greatly reduced the cost to design, test, and deploy models, allowing more small and medium-sized organizations to benefit from AI and cloud computing.
• Productivity
  The IT department is freed from mundane, repetitive chores when a hybrid or public cloud is used for data storage and processing. Previously, administrators spent a lot more time and energy tending to models that relied on AI-based algorithms.
• Automation
  The incorporation of AI into the cloud’s underlying infrastructure facilitates the automation and simplification of routine processes.
• Management of Information
  Artificial intelligence (AI) enhances data management, and when coupled with cloud computing, it increases data security. Because of this, it is feasible to automatically and effectively manage massive amounts of data. Artificial intelligence is also useful for migrating data from local systems to the cloud.

Read the Latest blog from us: Risks Of IT Integration

Is There a Downside to Using AI on the Cloud?

The use of artificial intelligence in the cloud raises several ethical questions. These worries are addressed in this section.
Security of Stored/Shared Data A data privacy policy needs to be developed when employing AI in cloud computing. Information about customers and suppliers that can be associated with a real person is much more useful than the same information in an anonymous form. It is crucial to have data protection and compliance procedures in place, especially when dealing with personal information.

IT departments require access to the internet to upload data to the cloud. Poor internet connectivity can cause issues and is a drawback of cloud-based machine learning algorithms. Although data processing in the cloud is faster than traditional computing, users of cloud services still need to be concerned about the security of their data in the event of a breakdown in data transmission to the cloud.

The Future of AI Cloud Computing

As cloud computing becomes standard practice across the entire IT sector, the overall industry will experience a slowdown in revenue growth. Consequently, investors anticipate that the AI boom will resuscitate cloud computing as large technology businesses increasingly attempt to exploit AI on the cloud.

Among the many interesting projects involving generative AI on the cloud is Amazon’s new Bedrock service. This service would allow programmers to include AI-generated text into their programs quickly and easily.

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• Combination expected to deliver a more than five-fold expansion in Lumentum’s cloud intra-data center served opportunity

• Served opportunity expected to grow at over 30% CAGR through 2028, driven by investments to support the rapid proliferation of Artificial Intelligence and Machine Learning (AI/ML) applications

• Acquisition expected to be immediately accretive to non-GAAP earnings per share and to more than double Lumentum’s cloud intra-data center infrastructure revenue in the 12-month period following the transaction close

Lumentum Holdings (“Lumentum”) and Cloud Light Technology Limited (“Cloud Light”)announced that they have entered into a definitive agreement under which Lumentum will acquire Cloud Light with a transaction value of approximately $750 million, subject to certain adjustments. At the time of closing, transaction consideration will be paid in cash and the assumption and substitution of outstanding unvested Cloud Light options. The transaction has been unanimously approved by the Boards of Directors of both companies and by Cloud Light’s shareholders.

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“With Cloud Light, we are making a strategic investment to significantly expand our opportunities in the cloud data center and networking infrastructure space”

The acquisition of Cloud Light is expected to accelerate Lumentum’s push into the fastest growing segments of the multibillion-dollar opportunity for optical modules used in cloud computing data center infrastructure. Cloud Light has a demonstrated track record of developing and manufacturing the highest-speed connectivity solutions at the leading edge of new and rapidly growing technology transitions. Nearly all of Cloud Light’s more than $200M revenue in the last 12 months was derived from 400G or higher speed transceiver sales. In the most recent quarter, over half of Cloud Light’s optical transceiver revenue was derived from 800G modules.

With this acquisition, Lumentum will be well-positioned to serve the growing needs of cloud and networking customers, particularly those focused on optimizing their data center infrastructure for the demands of AI/ML. Lumentum will be able to deliver immediate customer value with a more comprehensive product and technology portfolio, enabling customers to more effectively manage the escalating compute and interconnect requirements of AI workloads. The combination also brings best-in-class design and assembly, test, and packaging capabilities together with Lumentum’s global scale and customer reach.

“With Cloud Light, we are making a strategic investment to significantly expand our opportunities in the cloud data center and networking infrastructure space,” said Alan Lowe, Lumentum president and CEO. “Cloud Light provides us with the highest speed transceiver solutions at scale and complements our advanced component capabilities. This results in a broad product and technology portfolio that addresses a wide range of cloud operator needs.”

“We are confident that this transaction will deliver substantial, long-term value to our stockholders, with immediate earnings accretion and accelerated revenue growth. We look forward to welcoming Cloud Light’s very talented team to Lumentum,” concluded Mr. Lowe.

“Today’s announcement is a pivotal milestone in the history of Cloud Light, and a testament to the hard work and dedication of our employees,” said Dr. Dennis Tong, Cloud Light Founder and CEO. “We founded the company with a vision that our deep expertise in high-volume precision manufacturing would result in a superior value proposition for cloud data center customers. Having worked closely with the technology teams within leading cloud operators, we believe we can build upon our success to date and further accelerate cloud data center growth by combining Lumentum’s advanced photonic integration and transmission technologies with our highly automated packaging and manufacturing processes. We look forward to joining the Lumentum team and beginning an exciting new chapter.”

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Compelling Strategic and Financial Benefits

• Captures AI inflection with expanded intra-data center opportunity: The highly complementary combination squarely positions Lumentum as a leader in providing photonics to cloud operators, enabling more than a five-fold expansion in the company’s served opportunity inside of data centers. With the advent of generative AI, cloud network needs for 400G and higher speed optical transceivers have accelerated rapidly, with the opportunity for these intra-data center products anticipated to grow at a 30% CAGR and exceed $10B by 2028. Cloud Light provides the highest speed optical transceiver products to leading hyperscale cloud customers, with even higher-speed solutions well along in their development, complementing Lumentum’s existing portfolio of laser transmitters and other integrated components for data center transceivers.

• Better equipped to address future customer roadmaps: AI models are driving an exponential increase in compute requirements, where performance is now doubling every 3 to 4 months, compared to the historical doubling every two years according to Moore’s Law. Scaling workloads even higher is limited by existing network and interconnect bottlenecks. With the addition of Cloud Light, Lumentum better addresses these challenges – both now and into the future – by combining advanced optical interconnect technologies based on chip-scale photonic integration, together with highly automated packaging and manufacturing technologies.

• More strategic partner to cloud and AI infrastructure customers: With Cloud Light, Lumentum can more effectively address the growing and diverse needs of cloud operator and AI infrastructure providers. This includes providing next-generation optical connectivity and optical switching within data centers, as well as coherent pluggable modules and optical line system components for data center interconnect applications. With Lumentum’s vertical integration capabilities in components and diversity in its global manufacturing footprint, customers can benefit from improved security of supply and superior technology and cost roadmaps.

• Amplifies edge and metro networking opportunities: Cloud Light’s proven capabilities in low-cost product development and high-volume manufacturing are relevant not only for cloud data center and data center interconnect solutions, but also for network edge applications, including those served by direct detect and coherent tunable DWDM transceivers. A broader set of networking customers will benefit from the added advanced packaging technologies and manufacturing capabilities offered by this combination.

• Immediately accretive to Lumentum’s earnings and accelerates Lumentum growth: The transaction is expected to be immediately accretive to Lumentum’s non-GAAP earnings per share and is expected to more than double Lumentum’s cloud data center infrastructure revenue in the 12-month period following the transaction close. In the last twelve months, over 90 percent of Cloud Light’s revenue was derived from 400G and higher speed products, and in their most recent quarter, over half of Cloud Light’s optical transceiver revenue was derived from 800G transceiver products.

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[To share your insights with us, please write to sghosh@martechseries.com]

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FEDML, a rapidly growing startup in artificial intelligence (AI), officially announced the release of FEDML Nexus AI, offering the next generation of cloud services and platform for generative AI.

As large language models (LLMs) and other generative AI applications gain prominence and global GPU demand intensifies, a wave of new GPU providers and resellers has emerged. “Developers need a way to quickly and easily find and provision the best GPU resources across multiple providers, minimize costs, and launch their AI jobs without worrying about tedious environment setup and management for complex generative AI workloads. They may even want to develop privately on their own infrastructure or in a hybrid manner. No such technology exists,” said Salman Avestimehr, the founding CEO of FEDML and the Dean’s Professor and Director of the USC-Amazon Center on Trustworthy AI at USC. “FEDML Nexus AI bridges this gap in the market and provides such capabilities to developers and enterprises.”

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The multi-faceted capabilities of FEDML Nexus AI are as follows:

• GPU Marketplace for AI Development: Addressing the current dearth of compute nodes/GPUs arising due to the skyrocketing demand for AI models in enterprise applications, FEDML Nexus AI offers a massive GPU marketplace with over 18,000 compute nodes. Beyond partnering with prominent data centers and GPU providers, the FEDML GPU marketplace also welcomes individuals to join effortlessly via our “Share and Earn” interface.
• Unified ML Job Scheduler and GPU Manager: With a simple fedml launch your_job.yaml command, developers can instantly launch AI jobs (training, deployment, federated learning) on the most cost-effective GPU resources, without the need for tedious resource provisioning, environment setup and management. FEDML Launch supports any computing-intensive job for LLMs and generative AI, including large-scale distributed training, serverless/dedicated deployment endpoints, and large-scale similarity search in vector DB. It also enables cluster management and deployment of ML jobs on-premises, private, and hybrid clouds.
• Zero-code LLM Studio: As enterprises increasingly seek to create private, bespoke, and vertically tailored LLMs, FEDML Nexus AI Studio empowers any developer to train, fine-tune, and deploy generative AI models code-free. This Studio allows companies to seamlessly create specialized LLMs with their proprietary data in a secure and cost-effective manner.
• Optimized MLOps and Compute Libraries for Diverse AI Jobs: Catering to advanced ML developers, FEDML Nexus AI provides powerful MLOps platforms for distributed model training, scalable model serving, and edge-based federated learning. FEDML Train offers robust distributed model training with advanced resource optimization and observability. FEDML Deploy provides MLOps for swift, auto-scaled model serving, with endpoints on decentralized cloud or on-premises. FEDML Federate extends model training and serving to edge servers and smartphones, enhancing privacy compliance and optimizing costs. For developers looking for quick solutions, FEDML Nexus AI’s Job Store houses pre-packaged compute libraries for diverse AI jobs, from training to serving to federated training.

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“The AI community has made significant progress in developing new paradigms and innovative models for generative AI. However, challenges in cloud computing still hinder the productionization of these new AI models and research frameworks for native AI apps. These challenges include low availability of GPUs, high cloud costs, fragmented software stack for AI, and inefficiency in development and operations. Our latest Nexus AI is a significant step forward in addressing these challenges. It utilizes FEDML Launch, an incredibly versatile cross-cloud scheduler, to coordinate various computing frameworks for training and deployment in a unified and user-friendly manner.

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This not only saves developers and enterprises the time and effort of finding GPU resources and dealing with fragmented multi-stage machine learning pipelines, but also simplifies complex multi-step workflows in cutting-edge directions such as LLM-based AI agents,” said Aiden Chaoyang He, co-founder and CTO of FEDML, and an Internet veteran who is former director and principal engineer in many big techs. “Our unwavering vision is to provide superior AI infrastructure for the rapidly growing AI community. We are fortunate to have climbed this mountain starting with federated learning, where we have innovated the most complex building blocks, such as schedulers, orchestrators, and distributed systems for ML. These innovations have been further leveraged to meet the general demands of model training and deployment.”

[To share your insights with us, please write to sghosh@martechseries.com]

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