AiThority’s Weekly AI Buzz for 14th -18th October: Major Insights from AI Giants To Read About
Stay in the loop with AiThority’s weekly insights into the ever-evolving world of AI! We dive into the deployment of AI infrastructure and the exciting innovations in purpose-built Artificial Intelligence. Discover how Machine Learning is integrating with cloud computing and the significant impact of Natural Language Processing on product development. Let’s explore these AI advancements together!
Discover the Latest Advances in AI Technology
1.Lenovo Introduces ThinkSmart Core Gen 2 to Future-Proof AI-Optimized Meeting Spaces
Lenovo Group launched the ThinkSmart Core Gen 2, an AI-optimized device for video conferencing. Powered by the Intel Core Ultra processor and an integrated NPU, it delivers high performance for AI-heavy tasks, enhancing collaboration and productivity. It consumes 40% less power and offers smart features like voice detection, meeting summaries, and real-time assistance. The device is secure, easy to install, and works with Microsoft Teams and Zoom. It also includes built-in management tools for IT teams and additional Lenovo security features for seamless operation and support.
2.OutSystems Unveils Mentor, a First-of-Its-Kind AI-Powered Application Generation Experience
OutSystems, unveiled **Mentor**, the first AI-powered digital worker for the full software development lifecycle (SDLC) at the 2024 ONE Conference. Mentor uses low-code and GenAI to enable rapid app generation, delivery, and monitoring, automating tasks like app prototyping, quality checks, and AI-powered code reviews. Developers can create fully functional apps in minutes, boosting productivity while maintaining quality and security. Mentor combines AI innovation with the OutSystems Developer Cloud, transforming traditional development by reducing technical debt and enabling continuous improvement. It marks a significant leap in AI-driven software development, offering a more efficient and secure process.
3. Satori Joins Microsoft Fabric for a Unified, Secure, and Responsible AI Platform
Satori has announced its integration with Microsoft Fabric, allowing users quick and secure access to data without burdening data teams. Microsoft Fabric unifies data from multiple sources like S3 and Azure into OneLake, but managing permissions and security across various tools is complex. Satori simplifies this by providing a platform for creating and enforcing security policies across Fabric, ensuring data privacy and compliance. This integration speeds up data access for consumers using tools like Power BI and Synapse Data Analytics, helping them leverage AI across their projects securely and efficiently.
4. Databricks Strengthens Partnership with AWS to Deliver Advanced Generative AI Capabilities
Databricks announced a strategic collaboration with AWS to accelerate custom model development using Mosaic AI on AWS. Leveraging AWS Trainium AI chips, the partnership enables customers to efficiently pretrain, fine-tune, and serve large language models (LLMs) on private data. This collaboration enhances cost-effective AI-powered applications while ensuring data control and security. The partnership will also expand joint offerings, including model optimization, migration support, and tailored solutions for industries like media and finance. Both companies aim to empower businesses to harness generative AI for innovation across various sectors, supported by AWS’s infrastructure and Databricks’ AI expertise.
5. KPMG and ContractPodAI Announce Strategic Alliance to Transform Managed Legal Services with AI
ContractPodAi has partnered with KPMG to power its managed legal services with Leah, ContractPodAi’s AI platform for legal tasks. This collaboration aims to optimize legal operations, speed up contract management, and improve data accuracy for KPMG’s clients in key global markets like the US, UK, and Germany. Leah’s technology will streamline high-volume legal processes while preserving human oversight. KPMG leaders Andy Giverin and Jeff Catanzaro highlighted the alliance’s goal of reducing operational costs and boosting efficiency, helping clients make smarter, faster decisions in a competitive legal environment.
6. DataStax Announces DataStax AI Platform, Built with NVIDIA AI
DataStax has launched the DataStax AI Platform, built with NVIDIA AI, which reduces AI development time by 60% and speeds up AI workloads 19x. This platform integrates enterprise data and AI tools to help businesses create AI applications that improve in accuracy as customers engage. It supports the entire AI lifecycle, from data ingestion to deployment. Features include the Langflow platform, NVIDIA NeMo tools for custom model training, and multimodal data extraction. The platform is available for both cloud and self-managed environments, offering flexibility for regulated industries like finance and healthcare.
7. Cognizant Announces Multi-Agent Orchestration for its Neuro AI Platform
Cognizant has enhanced its Neuro AI platform to help businesses quickly develop AI use cases that improve decision-making and generate revenue. The platform now includes multi-agent AI orchestration, allowing enterprises to easily identify and pilot AI solutions across various industries. Key features include the Opportunity Finder, which helps businesses discover AI use cases, and Model Orchestrator, a tool for applying machine learning models to data. These updates empower companies to scale AI beyond prediction-based outcomes, with successful applications already seen in sectors like healthcare, agriculture, and finance.
8. Red Hat Accelerates Generative AI Innovation with Red Hat Enterprise Linux AI on Lenovo ThinkSystem Servers
Red Hat, has partnered with Lenovo to integrate Red Hat Enterprise Linux AI (RHEL AI) into Lenovo ThinkSystem SR675 V3 servers. This collaboration provides an AI-optimized platform that enables businesses to develop, test, and deploy AI and generative AI models more efficiently. Pre-loaded with RHEL AI, the servers offer enhanced GPU processing to support demanding AI workloads, accelerating model training and deployment. RHEL AI includes IBM Research’s Granite LLMs and InstructLab tools for streamlined model development. Lenovo Consulting Services will support the deployment, ensuring businesses optimize AI workflows and achieve faster results.
Weekly Roundup: Expert Views on AI Trends
AiThority Interview with Robert Figiel, VP of Centric Market Intelligence R&D at Centric Software
Expert Voices in AI
Robert Figiel, VP of Centric Market Intelligence R&D at Centric Software, discusses using AI to enhance pricing and inventory strategies. Centric Market Intelligence (CMI) helps fashion and beauty brands gain real-time insights to speed up market responses. AI analyzes data to optimize pricing and inventory management, ensuring products align with demand. Future developments in Product Lifecycle Management (PLM) will automate tasks, improving workflows. Key challenges in AI adoption include ensuring data quality, integrating with existing systems, and overcoming resistance to change. Businesses must prioritize interoperability and address data privacy concerns to maximize AI’s benefits.
AiThority Interview with James Alger, Co-founder and COO of Qloo
James Alger, Co-founder and COO of Qloo, shares insights on AI’s role in customer engagement and personalization. With 20 years in advertising, James co-founded Qloo to help brands understand consumer tastes using technology, rather than outdated methods. AI drives Qloo’s Taste AI, enabling businesses to personalize customer experiences based on broad preferences while ensuring data privacy by avoiding personally identifiable information (PII). Key challenges include limited data sources for personalization, but Qloo addresses this with a comprehensive approach. Emerging technologies like large language models (LLMs) and Apple Intelligence will further shape personalization, enhancing user experiences in the future.
Unmissable Articles of the Week on AI
Three Things Retailers Must Understand About AI Adoption
Overview of the Article
AI can transform retail by improving supply chains, store operations, and marketing strategies. Retailers are investing heavily in AI to provide personalized offers and meet consumer demands efficiently. A study forecasts that AI adoption in retail will exceed 80% in three years. However, traditional and digital marketing methods remain intertwined, slowing down AI integration.Customers expect seamless omnichannel experiences, blending online and in-store shopping. Retailers must ensure data quality and quantity for effective AI outputs, balance automation with manual review of AI predictions, and embrace a continuous improvement cycle to gain a competitive edge.
The Essential Automation Toolkit – Tips on How to Succeed
A unified toolkit acts like a digital “Swiss Army Knife,” streamlining operations through intelligent task automation. Companies increasingly adopt platforms that combine Generative AI (Gen AI) and machine learning (ML) for better decision-making and efficient data management. Disparate systems can create service gaps, hindering collaboration. A centralized automation platform optimizes resource use and data collection. For instance, Allianz Group saved 10,000 hours monthly through automation, while Zurich Insurance Group enhanced customer experience. By consolidating various technologies, a unified intelligent automation platform improves control, reduces errors, and ensures consistent customer experiences, accelerating growth and redefining operational excellence.
Revolutionizing Generative AI with Retrieval Augmented Generation
Generative AI is rapidly changing industries, but its swift adoption has outpaced security measures, with 80% of data professionals noting new data security challenges. Retrieval Augmented Generation (RAG) offers a solution by integrating retrieval mechanisms, enhancing the accuracy and relevance of AI outputs. RAG Fusion combines text-generation models with advanced retrieval systems, improving response accuracy. Benefits of RAG include better accuracy, efficiency, flexibility, bias management, and reduced errors. Organizations can leverage RAG for cost-effective, scalable GenAI deployments. Best practices for securing RAG applications include dynamic access controls, automated risk assessments, centralized monitoring, and employee training to safeguard sensitive information.
AI Quote of the Week
“For AI-powered enterprise software it’s important to review IT Security certifications of vendors and understand the vendors’ measures to avoid leakage of sensitive data.”–Robert Figiel, VP of Centric Market Intelligence R&D at Centric Software
[To share your insights with us as part of editorial or sponsored content, please write to psen@itechseries.com]
This Week’s Must-Listen AI Podcast – AI Inspired Series
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A chat with Bradley Jenkins, Intel’s EMEA lead for AI PC & ISV strategies
Bradley Jenkins about the key benefits of Intel’s Core Ultra processor range and how modern enterprises can benefit from systems powered by it.
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