Bitdeer AI, part of Bitdeer Technologies Group, a leading AI Cloud service provider, has announced the launch of its advanced AI Training Platform, designed to provide fast and scalable AI/ML inference with serverless GPU infrastructure. With the newest AI Training Platform, Bitdeer AI becomes one of the first NVIDIA Cloud Service Providers (CSP) in Asia to offer both cloud service and an AI training platform.

The Bitdeer AI Training Platform empowers everyone to build, train, and fine-tune AI models at scale through notebooks and organized resources on a project basis. Based on the pre-configured guides and customizable parameters, the innovative platform simplifies the process of developing and refining AI models, making them accessible to a wider audience. It further allows different teams within the same organization to collaboratively build and develop AI models without the need to manage their own servers, setting a new benchmark in efficiency and performance.

Also Read: AiThority Interview with Kunal Purohit, President – Next Gen Services, Tech Mahindra

High-Performance AI Infrastructure

The newly announced platform offers seamless access to high-performance AI infrastructure and resources of NVIDIA DGX SuperPOD with H100 GPUs, DDN Storage, and InfiniBand Networks. It also improves the efficiency and scalability of AI/ML training processes by utilizing multi-GPUs across various servers simultaneously. By distributing the workload across several GPUs, Bitdeer AI’s services can handle extensive and sophisticated training tasks, making it the optimal choice for organizations aiming to accelerate their AI initiatives.

Addressing Key Business Challenges

• Optimizing Development Costs: With Bitdeer AI, businesses can optimize costs through a pay-as-you-go model, only being charged when notebooks are in service mode. This approach ensures that organizations only pay for the resources they use, making AI development more cost-effective.
• Simplifying Complex GPU Infrastructure Setups: The serverless infrastructure provides a comprehensive integrated development environment for ML, including pre-built algorithms and support for popular frameworks like TensorFlow and PyTorch. This significantly reduces the complexity and time required to develop and train ML models, streamlining the AI development process.
• Ensuring Reproducibility and Environment Consistency: Bitdeer AI ensures consistency and reproducibility in the build environment, crucial for managing ML model deployment. This consistency prevents unexpected errors when restarting CI/CD jobs or migrating from one platform to another, avoiding costly build errors in long-running ML jobs.

Bitdeer AI collaborated with a software engineering team from the SMU School of Computing and Information Systems to test, verify, and fine-tune the platform, ensuring its robustness and effectiveness. Looking ahead, Bitdeer AI plans to collaborate with NVIDIA to enhance the AI Training Platform by integrating with the NVIDIA AI Enterprise (NVAIE) cloud services such as NIM. This collaboration will enable businesses to customize, test, and scale AI agents efficiently, further solidifying Bitdeer AI’s commitment to providing top-tier AI solutions.

Also Read: AiThority Interview with Yair Amsterdam, CEO of Verbit 

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AMD announced the completion of its acquisition of Silo AI, the largest private AI lab in Europe. The all-cash transaction valued at approximately $665 million furthers the company’s commitment to deliver end-to-end AI solutions based on open standards and in strong partnership with the global AI ecosystem.

Also Read: AiThority Interview with Kunal Purohit, President – Next Gen Services, Tech Mahindra

Silo AI brings a team of world-class AI scientists and engineers to AMD experienced in developing cutting-edge AI models, platforms and solutions for large enterprise customers including Allianz, Philips, Rolls-Royce and Unilever. Their expertise spans diverse markets and they have created state-of-the-art open source multilingual Large Language Models (LLMs) including Poro and Viking on AMD platforms. The Silo AI team will join the AMD Artificial Intelligence Group (AIG), led by AMD Senior Vice President Vamsi Boppana.

“AI is our number one strategic priority, and we continue to invest in both the talent and software capabilities to support our growing customer deployments and roadmaps,” said Vamsi Boppana, AMD senior vice president, AIG. “The Silo AI team has developed state-of-the-art language models that have been trained at scale on AMD Instinct accelerators and they have broad experience developing and integrating AI models to solve critical problems for end customers. We expect their expertise and software capabilities will directly improve the experience for customers in delivering the best performing AI solutions on AMD platforms.”

Also Read: AiThority Interview with Seema Verma, EVP and GM, Oracle Health and Life Sciences

For more than 50 years AMD has driven innovation in high-performance computing, graphics and visualization technologies. Billions of people, leading Fortune 500 businesses and cutting-edge scientific research institutions around the world rely on AMD technology daily to improve how they live, work and play. AMD employees are focused on building leadership high-performance and adaptive products that push the boundaries of what is possible.

Also Read: AiThority Interview with Yair Amsterdam, CEO of Verbit 

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The only way to keep businesses safe is to be aware of generative AI’s threats and cultivate a fraud-prevention ecosystem.

Veriff, a preferred global identity verification provider, released a report, Deepfakes Deep Dive, that examines the rise of deepfake technology, its implications for the digital economy, and strategies for mitigating associated risks. As AI-generated deepfakes become more sophisticated, the report provides insights for businesses and individuals on safeguarding against fraud.

Also Read: AiThority Interview with Kunal Purohit, President – Next Gen Services, Tech Mahindra

“Deepfakes aren’t necessarily a new phenomenon. We’ve dealt with the threat for years. However, the GenAI-driven ability to impersonate a user’s likeness creates serious security problems for online businesses,” says Rishi Chauhan, Director of Product for Identity and Fraud at Veriff.

In the report, Veriff has identified four common techniques adopted by fraudsters as they deploy AI and deep learning in their activities: Face Swaps, Lip Sync, Puppets, and GANs & Autoencoders. Chauhan names Face Swapping, in particular, as an area that is improving constantly. “It doesn’t leap forward every year, but every few months. Videos are also getting better—today, there are many ways that we can see whether it’s a deepfake video, but at some point, it will be difficult for humans to see these details,” he adds. In 2023 alone, approximately 500,000 voice and video deepfakes were circulated globally.

The danger of deepfakes is that they can be used to attack existing accounts and fraudulently open new ones. Enterprises with disjointed and inconsistent identity management processes and poor cybersecurity are especially vulnerable.

Also Read: AiThority Interview with Seema Verma, EVP and GM, Oracle Health and Life Sciences

“To stay ahead of fraudsters, companies need a constantly evolving, multi-layered approach that combines a range of threat mitigation tools. Unfortunately, no single solution exists,” comments Rishi Chauhan. He suggests that there are some essential elements that a coordinated strategy should include:

• Comprehensive checks on identity documents
• Examination of key device attributes
• Treating data absence as a risk factor
• Counter-AI to identify manipulated images
• Pattern detection across sessions and customers
• Biometric analysis of photographic and video images

“Whether you use IDV, document verification, or other tools, the more controls and data points you have, the harder it is for the fraudsters,” adds Rishi Chauhan.

Also Read: AiThority Interview with Yair Amsterdam, CEO of Verbit 

[To share your insights with us as part of editorial or sponsored content, please write to psen@itechseries.com]

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Apiiro shifts risk detection left before code is written, re-inventing Secure-by-Design for today’s agile software environments

Apiiro, the leading application security posture management (ASPM) platform, today introduced Risk Detection at Design Phase, a new, AI-driven capability that automatically analyzes feature requests to identify risks and proactively initiate security reviews or threat models at the earliest stage of the application development lifecycle. With this new, first-of-its-kind capability, application security (AppSec) practitioners can now scale their secure software development lifecycle (SSDLC) processes by mitigating security and compliance concerns before a single line of code is written.

Also Listen: AI Inspired Series by AiThority.com: Featuring Bradley Jenkins, Intel’s EMEA lead for AI PC & ISV strategies

Security products on the market today detect risks only after the development process has begun. This results in wasted time for developers due to manual risk assessment questionnaires, which impact release velocity and business value. With the detection of risks at the design phase, Apiiro customers can proactively address security, data privacy, infrastructure, compliance, and other risks at the onset of development, saving significant time and costs while minimizing rework and accelerating secure software delivery.

Apiiro’s detection of risky feature requests is built on cutting-edge AI technology, including Apiiro’s native private LLM. This model, not accessible by ChatGPT or any other public LLM services, ensures customer privacy and compliance by automatically analyzing feature requests and proactively identifying potential risks associated with:

• Generative AI technology: adding or changing generative AI tools, frameworks, technologies, and the data that is exposed to them.
• Sensitive data handling: storing and/or processing sensitive information like PII, PHI payment data fields as part of the application data flow, changing encryption mechanisms, data migrations, writing sensitive data to logs, and using sensitive data as an API return type.
• User permissions and access management: user authentication and authorization, login or registration processes, and changing user permissions.
• Third-party integrations, and open source dependencies: changing or adding open source dependencies and integrations with third-party services.
• Architecture design and security controls: requests for changes in APIs, network, databases, web servers, web clients, logging, serialization and other component configurations, architecture designs, and deployment of new or changed components.

For each risky feature request, enriched by the code architecture generated by its Deep Code Analysis (DCA) technology, Apiiro’s native private LLM model automatically generates contextual questions for a security review and produces threat stories using the STRIDE threat model. This automation eliminates the need for manual security processes, accelerating development velocity and deployment of secure code to the cloud, ultimately driving business growth. In addition, Apiiro enhances design risk context by automatically mapping to specific code commits, repositories, and pull requests, providing deeper insight into how potential risks may manifest in the actual codebase.

Also Read: Humanoid Robots And Their Potential Impact On the Future of Work

“Detecting potential risk at the design phase gives us the opportunity to remediate risks before they exist, and in the most efficient way for our developers. However, it’s challenging to do this at scale and to ensure full coverage of features our development team are building. Apiiro’s design phase risk detection engine is a unique capability in the ASPM space. It allows us to modernize our approach to Secure-by-Design, scale and strengthen our security engagement, and provide some automation to our threat modeling and security requirements processes.” -Head of Security Engineering at Fortune 100 retail company.

“Amidst the ever-changing complexity of modern software development processes and application architectures, Apiiro is committed to delivering complete risk-based visibility and protection from design to runtime,” said Moti Gindi, chief product officer at Apiiro. “Building secure software starts with secure design, and the new AI-Driven Risk Detection at Design Phase from Apiiro takes the ‘shift left’ approach a step further, addressing risks even before a single line of code is written. This first-of-its-kind functionality leverages the power of AI to ensure customers have the context required to facilitate efficient security reviews and evolve from a reactive to a proactive approach to application security.”

Don’t miss this out: More than 500 AI Models Run Optimized on Intel Core Ultra Processors

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Aurascape AI, a pioneering “born in the AI age” cybersecurity company, today announced the successful closure of its oversubscribed $12.8 million seed funding round, led by Mayfield Fund. Other investors and shareholders included Celesta Capital, StepStone Group and AISpace, as well as several industry leaders including Mark McLaughlin, former Chairman and CEO of Palo Alto Networks.

Don’t miss this out: More than 500 AI Models Run Optimized on Intel Core Ultra Processors

Aurascape is poised to transform cybersecurity from the ground up, leveraging patent-pending AI innovations to address the challenges exacerbated by the widespread enterprise adoption of AI. The company’s founding team is comprised of seasoned entrepreneurs and technologists from leading cybersecurity and AI companies such as Palo Alto Networks, Google, and Amazon. Collectively, the team has a stellar track record of creating market-leading products that have generated over $10 billion in revenue for multi-billion-dollar cybersecurity companies and unicorns. Aurascape AI draws inspiration from the emergence of Checkpoint during the internet’s infancy, Palo Alto Networks during the application explosion, and leading cybersecurity companies born in the cloud era.

“With the rise of AI, cybersecurity threats to enterprises have never been greater. Traditional solutions with AI add-ons and veneers are ineffective against rapidly expanding attack surfaces,” said Rajiv Khemani, Co-founder and CEO of Aurascape AI. “At Aurascape, we are building a ground-up, cloud-native cybersecurity solution with capabilities far beyond current architectures, to deliver unparalleled protection for enterprises.”

Also Listen: AI Inspired Series by AiThority.com: Featuring Bradley Jenkins, Intel’s EMEA lead for AI PC & ISV strategies

“We are thrilled to invest in Aurascape AI as it harnesses the disruptive potential of AI to reimagine cybersecurity,” said Navin Chaddha, Managing Partner at Mayfield. “At Mayfield, we have a people-first approach to investing, and we’re excited to back this world-class team with deep, proven expertise in AI and cybersecurity.”

“Aurascape AI boasts a team of industry veterans with extensive expertise in cybersecurity and AI,” added Mark McLaughlin, former Chairman and CEO of Palo Alto Networks. “I am excited by their pioneering work and potential to deliver transformative cybersecurity solutions for the AI era.”

“There is a critical need for AI-native security tools purpose-built to secure new Gen AI applications and increasing multimodal content,” said Sriram Viswanathan, Founding Managing Partner at Celesta. “Celesta Capital is excited to support Aurascape AI, its world-class team, and its leading-edge approach to cybersecurity.”

Aurascape AI is currently engaged in proof-of-concepts (POCs) with select customers and plans to unveil its first commercial product later in 2024. This funding will bolster Aurascape AI’s product development efforts and support its go-to-market plans, driving innovation at the intersection of cybersecurity and artificial intelligence.

[To share your insights with us as part of editorial or sponsored content, please write to psen@itechseries.com]

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Secrets are most commonly found in GitHub, with nearly 350 secrets leaked per year and eight passwords leaked per week per 100 employees

Nightfall AI, the leading enterprise data leak prevention (DLP) platform for SaaS, generative AI (GenAI), email and endpoints, today published findings from its annual State of Secrets Report. This research revealed that secrets like passwords and API keys were most often found in GitHub, with nearly 350 total secrets exposed per 100 employees every year. What’s more concerning is that 35% of all API keys discovered were still active — posing a major risk for privilege escalation attacks, data leaks, data breaches and more. Many of the secrets discovered had already been exposed for several months.

Also Listen: AI Inspired Series by AiThority.com: Featuring Bradley Jenkins, Intel’s EMEA lead for AI PC & ISV strategies

Companies who have embraced modern cloud, SaaS and GenAI environments have only just begun to uncover the hidden risks of secret sprawl, which occurs when sensitive information like API keys or passwords are spread to apps, files and messages where they don’t belong. From within apps like Slack, GitHub, Jira and Google Drive, threat actors can easily find and leverage company secrets to compromise organizations to a devastating degree, as we’ve seen in numerous high-profile incidents at major brands, such as The New York Times and Sisense. Nightfall’s research aimed to bring this challenge to light and help companies understand where their secrets are sprawled—as well as how they can clean up their tech stack.

In its research, Nightfall scanned hundreds of terabytes of data looking for sensitive secrets — passwords, API keys, database connection strings and cryptographic keys — shared across cloud systems and applications over the past year, and found more than 171,000 secrets exposed across SaaS apps, GenAI tools, email and endpoints. While GitHub had the highest volume of secret sprawl, 54% of exposed secrets were found in other developer and productivity apps, including Confluence (134 per 100 employees), Zendesk (110), Slack (64) and Google Drive (34). This is notable because gaining visibility into sensitive data across a multitude of different SaaS platforms is a significant challenge for companies.

Also Read: Humanoid Robots And Their Potential Impact On the Future of Work

In its research summary, Nightfall breaks its findings down with a focus on passwords and API keys. Here are a few of the findings:

Passwords were the most commonly exposed secrets.

• 59% of the secrets discovered were passwords
• 8 passwords were discovered per 100 employees per week
• Passwords were most commonly found in GitHub (54%), Confluence (23%), Zendesk (15%) and Slack (8%)

API Keys were found across many popular SaaS and development platforms.

• 39% of the secrets discovered were API keys
• API keys were most commonly found in GitHub (71%), Slack (6.6%), Google Drive (6.6%) and Jira (6.6%)
• 7 API keys were discovered per 100 employees per week
• The most risky types of API keys commonly discovered were JSON web tokens, and API keys for Slack, AWS, GitHub, Gitlab, Google Cloud and Azure

“Secret sprawl is a pervasive and ever-present problem that companies must address now,” said Rohan Sathe, co-founder and CTO, Nightfall. “Fortunately, it is easily preventable. It’s important for security teams to know what secrets are being shared and where they’re being shared in order to t********** and minimize secret exposure.”

Combatting Secret Sprawl

Continuous monitoring and automated remediation can dramatically reduce the time it takes to identify and mitigate risk associated with secret sprawl. Nightfall also recommends that companies implement end-to-end encryption, use password managers and rotate API keys regularly to stave off data leaks and breaches. Nightfall also highlights the importance of educating employees about the safest ways to share secrets, and enforcing those teachings throughout the year as opposed to with annual security training alone.

Don’t miss this out: More than 500 AI Models Run Optimized on Intel Core Ultra Processors

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Lasso Security, a Generative AI security company, announces a new approach to access control for GenAI users. Context-Based-Access Control (CBAC) from Lasso Security allows enterprise security professionals to address access control and data leak prevention in GenAI solutions with a few simple prompts.

Retrieval-Augmented Generation (RAG) is a technique that enhances the capabilities of LLMs by integrating them with external data sources. This approach retrieves relevant documents to provide additional context, significantly improving the accuracy and relevance of LLM outputs without the need for retraining. However, RAG’s lack of native access control poses a significant security risk to enterprises, as it could allow unauthorized users to access sensitive information.

Also Read: Extreme Networks and Intel Join Forces to Drive AI-Centric Product Innovation

Current access control methods like Role-Based Access Control (RBAC) and Attribute-Based Access Control enhance security by restricting access based on roles, permissions or attributes. However, in the world of RAG, these methods often lead to increased system complexity, data duplication and slower query performance. They also require meticulous implementation and ongoing maintenance, making them challenging to scale and manage effectively.

To address this issue, Lasso Security developed and introduced CBAC, which focuses on the context of both requests and responses. CBAC ensures that only authorized users access specific information, preventing unauthorized exposure and handling of documents with mixed relevant and out-of-scope information. This approach offers organizations a higher level of security and control, managing data access in a context-aware and efficient manner.

With CBAC, users can:

• Precisely Manage Access: Ensure that only authorized users can access specific pieces of information based on the context of their request.
• Prevent Unauthorized Information Exposure: Block sensitive information from being retrieved and displayed to users who shouldn’t see it, even if they have broader permissions.
• Handle Nuanced Data: Manage documents that contain both relevant and out-of-scope information by evaluating the context of each request.

This new access control and data leak prevention feature is integrated into Lasso Security’s GenAI security suite, which offers protection of employees’ use of GenAI based Chatbots, Applications, Agents, Code Assistants and integrated Models into production environments. Regardless of how you deploy LLMs, Lasso Security monitors every interaction involving data transfer to or from the LLM. It also swiftly identifies any anomalies or violations of organizational policies, ensuring a secure and compliant environment at all times.

Also Read: AI Inspired Series by AiThority.com: Featuring Bradley Jenkins, Intel’s EMEA lead for AI PC & ISV strategies

In the attached images, Lasso Security demonstrates how CBAC is implemented within a company using free-form text to enforce access control and security policies. As seen, a member of a finance team interacting with a GenAI tool is blocked from accessing information not related to their department. In contrast, an R&D person asking the same questions to the same GenAI tool is allowed access to the information.

“CBAC is a game-changer in the world of context-aware data security. By focusing on the knowledge level and not patterns or attributes, CBAC ensures that only the right information reaches the right users, providing a level of precision and security that traditional methods can’t match,” noted Ophir Dror, CPO & Co-Founder of Lasso Security. “This innovative approach allows organizations to harness the full power of RAG while maintaining stringent access controls, truly revolutionizing how we manage and protect data.”

Also Read: More than 500 AI Models Run Optimized on Intel Core Ultra Processors

[To share your insights with us as part of editorial or sponsored content, please write to psen@itechseries.com]

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BIGG Digital Assets a leading innovator in the digital assets space and owner of Netcoins, Blockchain Intelligence Group, and TerraZero is pleased to announce TerraZero’s collaboration with artificial intelligence company Qeleo (Qeleo.com); Predictive Technologies d/b/a Qeleo, to accelerate the scalability of TerraZero’s core product, Intraverse, and other operational initiatives.

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• TerraZero and Qeleo have collaborated and will continue to collaborate on artificial intelligence tools specific to TerraZero’s pipeline of development.
• These AI tools, by design, foster the creation of assets to be used within virtual experiences deployed by TerraZero, and utilized across TerraZero’s Intraverse ecosystem.
• TerraZero and Qeleo plan to develop additional tools that further the scaling of 3D models and full 3D environments following creative input from staff and contractor artists and developers.
• TerraZero aims to use these AI tools to scale thousands of virtual environments, as well as build new revenue streams for the company.
• As TerraZero continues to scale the development of Intraverse PRO following activations with Napster and others, these AI tools will allow artists, creators and brands to generate virtual environments and utilize the Intraverse’s ecommerce, data and omnichannel features.

In working with Qeleo, TerraZero has three primary goals. First, develop unique AI tools that were up to par with their high quality standards when it came to compelling virtual experiences. Second, TerraZero is pursuing an engine they can use to create environments faster without losing their signature sense of design and innovative feature set. Third, the company wants to save their artists’ time and energy–so their efforts can go towards custom asset creation and development for larger, bespoke experiences for TerraZero’s ongoing portfolio of partners and clients.

Dan Reitzik, founder of TerraZero and Interim CEO of BIGG Digital Assets writes, “We are on the precipice of customizable, consumable content tailored to the individual. If you look at the market and where technology is headed, it feels only right that TerraZero takes this leap with Qeleo and continues to be an innovative player in the space. We have been impressed by Qeleo’s ability and confidence as an enterprise AI specialist. Smart AI needs to be driven by smart people. I have full faith in what Qeleo is doing with TerraZero.”

Also Read: Extreme Networks and Intel Join Forces to Drive AI-Centric Product Innovation

Tarka L’Herpiniere, CTO of Qeleo, adds, “Working with TerraZero to build their custom AI solution was a truly rewarding experience. Their vision for the metaverse, combined with their commitment to innovation, made this project particularly interesting. We were able to combine multiple Qeleo AI models to deliver a solution that automates the creation of 3D metaverse environments, including architectural and asset generation. The result is an extremely powerful tool that has enabled TerraZero to scale their operations and create more immersive experiences – faster.”

TerraZero will seek more opportunities to better deploy operational capital on revenue generating business, and support the Intraverse and its core products and features to serve larger onboarding efforts to grow a more robust user base.

The goal of both parties is to build an engine that can not only serve TerraZero’s development needs, but, in the future, users who come onto the platform and want to see their own design tastes reflected in the Intraverse.

lso Read: More than 500 AI Models Run Optimized on Intel Core Ultra Processors

[To share your insights with us as part of editorial or sponsored content, please write to psen@itechseries.com]

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Anyscale, the company behind Ray, the open source framework for scalable AI, named industry veteran Keerti Melkote as chief executive officer following a year of 4x revenue growth and explosive open source adoption.

Melkote brings extensive experience in leading and scaling global technology organizations. After founding Aruba Networks in his garage in 2001, Melkote led the company through nearly two decades of growth, including its IPO in 2007, multi-billion dollar acquisition by HPE in 2015. Melkote continued to lead Aruba at HPE and also led HPE’s Intelligent Edge business helping transform it into a high-growth multi-billion dollar business.

Also Read: Extreme Networks and Intel Join Forces to Drive AI-Centric Product Innovation

“AI represents a generational opportunity for enterprises to create value and differentiate themselves. However, AI workloads are very compute intensive and Anyscale’s founders created Ray as the software layer to easily scale AI workloads. Thanks to their vision, Ray now powers AI workloads such as training, inference, and data processing at companies ranging from Uber to Canva to Coinbase. I am excited to join Robert, Philipp, and Ion along with the incredibly talented team at Anyscale to capitalize on the head start Ray has in this emerging category of AI infrastructure.” – Keerti Melkote

“Anyscale has already assembled one of the premier teams in the critically important and rapidly growing AI Infrastructure market. By adding Keerti, the brilliant and successful former founder and CEO of Aruba Networks, Anyscale has the opportunity to become a truly special company.” – Ben Horowitz, Anyscale board member, cofounder of Andreessen Horowitz

“We believe Anyscale is one of a few companies positioned to win in this rapidly evolving space. Very few entrepreneurs have founded a company and scaled it through every stage the way Keerti has. Keerti is the ideal leader to take Anyscale through its current and future stages of growth and ascension.” – Pete Sonsini, Anyscale board member, NEA

The amount of compute power needed to run AI workloads like model training, data processing, and model inference is growing astronomically. However, there are many hard software engineering challenges that need to be solved when running AI workloads in the cloud or across large GPU clusters, challenges spanning scalability, reliability, performance, and cost efficiency. Today, the leading AI companies like Google and Meta employ massive AI infrastructure teams to solve these challenges in-house. These challenges need to be solved for the industry as a whole, and that’s what Anyscale is solving.

Ray, the open source framework for scalable AI, was created by Ion Stoica, prior CEO Robert Nishihara, and Philipp Moritz out of UC Berkeley’s RISELab to solve the hard scaling challenges around AI. Today, Ray is growing 6x year over year, is used by over 50,000 developers, and is emerging as the open source standard for scaling AI. The trio founded Anyscale to commercialize Ray and to enable every business to move quickly to bring AI to production.

Also Read: AI Inspired Series by AiThority.com: Featuring Bradley Jenkins, Intel’s EMEA lead for AI PC & ISV strategies

Examples of Ray successes include:

• Pinterest runs 5000 training jobs and 300 batch inference jobs every month on Ray.
• Runway used Ray to help build Gen-3 Alpha.
• OpenAI used Ray to train GPT-4.
• Uber standardized model training on Ray.
• Amazon processes exabytes of data with Ray.
• Ant Group serves over one million requests per second with Ray.
• Similar stories abound at Canva, ByteDance, Instacart, Spotify, Niantic, Samsara, Netflix, Airbnb, Shopify, Zoox, Snorkel, LinkedIn, Handshake, and many many more.

“As AI workloads make their way from exploration to production, the need for scale and the associated challenges are exploding. Keerti is a powerhouse and is the right leader to bring Anyscale into every enterprise and business that hopes to deploy AI. I’m overjoyed to be working with him and look forward to partnering in the years to come.” – Robert Nishihara. Nishihara will spearhead customer interactions and help shape product roadmap and strategy in his new role.

“The adoption of AI over the next few years will be unlike any technology shift we’ve seen so far. We are thrilled to have Keerti lead Anyscale. His deep background in technology and enterprise software are perfectly suited for navigating the unprecedented pace of innovation and change that we are experiencing.” – Ion Stoica, cofounder and executive chairman of Anyscale. In addition to cofounding Anyscale, Stoica is a UC Berkeley professor and was a co-founder and the first CEO at Databricks.

Anyscale will host its third annual in-person Ray Summit in San Francisco on October 1-2. Ray Summit is the industry conference on scalable AI. Speakers include:

• Mira Murati – CTO, OpenAI
• Anastasis Germanidis – Co-founder & CTO, Runway
• Marc Andreessen – Co-founder & General Partner, Andreessen Horowitz
• Joe Spisak – Head of Generative AI Open Source, Meta
• Harrison Chase – Co-founder & CEO, LangChain
• Jerry Liu – Co-founder & CEO, LlamaIndex
• John Bicket – Co-founder & CTO, Samsara
• Stephen MacKinnon – VP Applied Machine Learning, Recursion

Also Read: More than 500 AI Models Run Optimized on Intel Core Ultra Processors

[To share your insights with us as part of editorial or sponsored content, please write to psen@itechseries.com]

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Strategic partnership utilizes Hybrid Intelligence and AI to streamline data management to deliver optimal asset performance for industry.

Radix, a global technology solutions company, announced a strategic partnership with Cognite, the globally recognized authority in Data and AI for industry. This partnership aims to accelerate the use of Artificial Intelligence (AI) to streamline and contextualize data management and asset performance across oil and gas, energy, petrochemicals, and manufacturing industries.

The partnership pairs Cognite’s leading Industrial DataOps platform, Cognite Data Fusion, with Radix’s unrivaled expertise in engineering, data and software technology, and operations to allow customers to intelligently analyze and extract critical information from their data, driving improvements in operational efficiency, uptime optimization, and cost reductions.

Also Read: AI Inspired Series by AiThority.com: Featuring Bradley Jenkins, Intel’s EMEA lead for AI PC & ISV strategies

Transforming operations using Hybrid Intelligence with AI

Radix and Cognite have collaborated for the past several years, drawing on each other’s strengths to deliver innovative solutions for one of our most strategic customers. The combination of Cognite Data Fusion’s innovative technology and Radix’s engineering intelligence will help tackle the industry’s long-standing problem of extracting key information from large data pools in non-integrated systems. By utilizing Hybrid Intelligence with AI to intelligently sort through data in a more refined manner, companies will quickly isolate problem areas and work on solutions with more accurate and effective timelines. This will be a tremendous advantage across business applications, such as energy optimization, mass balance for production accounting, and inventory management for critical materials. Hybrid Intelligence also helps to accelerate access to data across various independent systems, making it easier to coordinate for quicker analysis and troubleshooting across industrial operations.

“Our partnership with Cognite has shown that we can bring our unique expertise together to empower companies with the hybrid intelligent tools they need to get to the data that becomes valuable and actionable information,” said Flavio Guimarães, Global Head of Alliances & Practices at Radix. “With Cognite Data Fusion, we help businesses streamline their data, thus helping to boost decision-making with real-time insights and drive cost reductions across the organization.”

An essential example of the transformative power of Cognite Data Fusion is the work done with one of the largest chemical manufacturing companies. It highlights how Cognite Data Fusion^®, partnering with Radix Hybrid Intelligence using AI can leverage information from legacy data stacks and combine them into an integrated manufacturing portal that presents the information in a much more accessible manner.

Delivering an Industrial Applications Library for industry

Radix is thrilled to unveil a rich set of industrial applications built on top of Cognite Data Fusion. With deep domain expertise and engineering intelligence, these selected industrial application solutions enhance scalability, usability, and overall value for users and businesses. Some solutions will be showcased at Cognite Impact 2024 in Houston, Texas, including an operational view on actionable insights, OEE monitoring and control, Preventative Insights for Monitoring, and Improvement Workflows for field process improvements and operational efficiency.

Also Read: Extreme Networks and Intel Join Forces to Drive AI-Centric Product Innovation

“Cognite was founded to solve the industrial data problem and deliver simple access to complex industrial data,” said Laxmi Akkaraju, Chief Customer Officer at Cognite. “Cognite Data Fusion liberates siloed data and enables reliable responses from Generative AI. By layering Radix’s expertise on top of our platform, we can quickly identify the root causes of operational problems and empower our customers to solve some of their most complex business problems.”

“The Industrial Applications Library creates added value to the digital transformation journey helping companies to achieve optimal operational excellence and significant cost savings for our customers,” added Trudi Hable, Head of Strategic Alliances, North America at Radix. “Radix’s expertise and intelligence will ensure that real-time information is being relayed to Cognite Data Fusion in an efficient manner, allowing for the right data to be brought to the right people.”

The strong partnership is poised to drive innovation and sustainability across industry. Cognite and Radix partner together to deliver best-in-class technology solutions that utilize AI and genuine intelligence for Cognite Data Fusion to optimize operational performance. The collaboration helps promote energy efficiency and substantially improve previously unaccounted utility costs. Combining hybrid intelligence in asset performance with deep industrial intelligence accelerates a sustainable future.

Also Read: More than 500 AI Models Run Optimized on Intel Core Ultra Processors

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