#watsonx.ai

[govindhtech]

IBM Watsonx.ai Management Tools Release GenAI Possibility

IBM Watsonx.ai Management Tools Unleash GenAI Potential.

US regulators like the FRB, SEC, and OCC require financial services firms to show that their risk governance structure addresses laws, rules, and regulations (LRRs). This monitoring helps maintain a safe and dependable control environment that meets tighter rules and the organization’s risk tolerance.

However, determining the applicability of banking regulations to particular sections of a legislation can be a difficult and subjective process that calls for expert judgment. Based on the bank’s attributes, such as being a Global Systemically Important Bank (GSIB) or providing certain goods and services, banks frequently depend on outside suppliers to evaluate LRRs and generic controls.

Furthermore, LRRs are always changing, as are other industry frameworks like the Control Objectives for Information and Related Technologies (COBIT), Information Technology Infrastructure Library (ITIL), and National Institute of Standards and Technology (NIST).

This ongoing development necessitates constant work to help guarantee that the organization’s control environment is free of holes. Regretfully, it takes a lot of time and frequently causes delays to manually link LRRs to rules, standards, procedures, risk metrics, and controls. This procedure creates a discrepancy between the organization’s capacity to prove compliance with LRRs and regulatory expectations.

For instance, a bank may have a policy requiring the protection of its clients’ personal information, and the standard may call for the encryption of such information. In that scenario, the control would assist in guaranteeing that personal data is encrypted, and the procedure would specify the steps to encrypt it. However, the bank may not be able to prove compliance with the encryption standard, putting them at risk of noncompliance, if the links between LRRs and controls are not updated promptly.

The watsonx Regulatory Compliance Platform reduces manual effort for control owners, compliance, risk and legal teams

Legal and regulatory requirements can be mapped to a risk governance framework using IBM Watsonx, which also automates the identification of regulatory duties. This solution facilitates the verification of compliance with current responsibilities by examining governance documents and controls and connecting them to relevant LRRs. By using this technology, audit, compliance, risk, legal, IT, and business control owners can construct and maintain LRR libraries with a great deal less human labor.

For instance, Watson Discovery can undertake an effect analysis by actively searching the internet for regulatory revisions for a certain group of LRRs. Watson Assistant can be utilized as an interactive Q&A tool to answer questions from external parties, auditors, and regulators regarding the risk and control environment in a conversational fashion. A risk and compliance program is increasingly using large language model (LLM), which need little to no training.

To apply the banks’ different process, risk, and control taxonomies, LLMs stored in Watsonx augment LRR and governance data. A prompt evaluates an obligation using a programmed approach. For instance, every risk category of the company, including strategic, reputational, wholesale, interest rate, and liquidity risks, would be examined to see what applies. The matching categories to internal controls and other pertinent policy and governance datasets are supported by the improved metadata.

When the content is publicly accessible, whether from third parties or is curated by the organization in an obligation’s library, the procedure is uniform and repeatable across regulations. IT and cybersecurity frameworks like NIST, ITIL, COBIT, Cloud Security Alliance Control Matrix, Federal Financial Institutions Examination Council (FFIEC), and others are included in the mapping and coverage capabilities that are not exclusive to LRRs.

The solution may link the pertinent LRRs to the applicable NIST controls, for example, if a bank wishes to guarantee adherence to the NIST cybersecurity framework. This gives the bank a clear and thorough picture of its cybersecurity posture.

IBM Watsonx.ai

How the watsonx Regulatory Compliance Platform accelerates risk management

The platform’s advanced artificial intelligence (AI) modules, IBM Watsonx.ai, watsonx.gov, and watsonx.data, provide a variety of cutting-edge technical features tailored to the particular requirements of the sector. These components, which may be installed on-premises or in any cloud, are based on IBM’s cutting-edge AI technology.

Users can participate in the whole lifecycle management of generative AI (gen AI) solutions within the IBM Watsonx.ai platform, which includes training, validation, tuning, and deployment processes. Watsonx.ai supports a variety of natural and programming language use cases by facilitating the development of expanded language models through the usage of foundation models from IBM and other sources.

The platform includes the cutting-edge Prompt Lab tool, which was created especially to expedite prompt engineering procedures. By using pre-written sample prompts, customers may confidently start their regulatory and compliance projects quickly and save successful prompts as notebook entries or reusable resources.

Interestingly, the prompt engineering parameters, model references, and prompt wording are all carefully formatted as Python code inside notebooks, enabling smooth programmable interaction. Additionally, IBM Watsonx.ai provides the Tuning Studio function, which enables users to iteratively steer foundation models toward outputs that are more in line with their particular needs.

Watsonx.governance‘s comprehensive suite of tools allows customers to quickly construct responsible, transparent, and explainable AI workflows that are suited to both machine learning and generative AI models. When installed, watsonx.governance combines the features of AI factsheets and Watson OpenScale with the Model Risk Governance features of OpenPages into a single service.

Watsonx.governance also expands its governance features to include generative AI assets. This platform enables users to evaluate machine learning models and foundation model prompts, build AI use cases for the methodical tracking of solutions addressing relevant business concerns, and develop processes while precisely monitoring lifecycle activities.

By supporting data from various sources and removing the requirement for migration or cataloging through open formats, IBM Watsonx.data enables scalable analytics and AI initiatives. This method reduces data duplication and extract, transform, and load (ETL) operations while allowing centralized access and sharing. Data preparation for a variety of applications, including retrieval augmented generation (RAG) and other machine learning and generative AI use cases, is made easier by integrated vectorized embedding capabilities.

Without the need for SQL knowledge, a conversational interface driven by Gen AI makes data discovery, augmentation, and visualization easier. Interoperability is ensured by smooth interface with current data stacks, tools, and databases.

All things considered, using Watsonx for regulatory compliance provides a revolutionary method of transparently and responsibly managing risk and AI projects. Organizations may easily handle the intricacies of regulatory requirements by utilizing its full range of capabilities. This makes it easier to guarantee ethical AI practices throughout the whole lifecycle, from data management to model training. IBM Watsonx.ai enables users to confidently evaluate, track, and improve AI workflows, promoting creativity and confidence in AI-driven solutions while easing regulatory compliance.

Read more on Govindhtech.com

[groovy-computers]

🚀 Elevate your AI prowess with Intel's Gaudi 3 now on IBM Cloud! 🌐 Discover scalability & savings like never before within IBM's ecosystem. IBM Cloud makes the first-of-its-kind move to integrate Intel's Gaudi 3 AI accelerator, offering a cost-effective alternative to NVIDIA's options. Available in Frankfurt & Washington, D.C., with Dallas expansion set for Q2 2025. This launch, unveiled at Intel Vision 2025, promises enterprises a transformative AI experience. Gaudi 3 presents: - Standalone server control within IBM Cloud VPC 🌐 - Red Hat OpenShift AI for containerized environments arriving Q2 2025 🚀 - Seamless integration with watsonx.ai 🤝 Priced to challenge NVIDIA, Gaudi 3 combines power efficiency with secure, scalable AI solutions. Intel's strengthened position delivers innovations for generative AI workloads. Are you ready to transform your AI infrastructure? 🔧 #IntelAI #Gaudi3 #IBMCloud #TechInnovation #AIFuture #NVIDIAalternatives #EnterpriseTechnology #GenerativeAI #ScalableAI #CloudComputing #Watsonx #CostEffectiveness

[rutukadam]

Enterprise AI Market Projected to Reach $171.2 Billion by 2031

 Meticulous Research®—leading global market research company, published a research report titled, ‘Enterprise AI Market by Offering (Solutions, Services), Deployment Mode, Organization Size, Technology (ML, NLP), End-use Industry (IT & Telecom, Healthcare, Retail & E-commerce, Media & Advertisement) and Geography - Global Forecast to 2031.’

According to this latest publication from Meticulous Research®, the global enterprise AI market is projected to reach $ 171.2 billion by 2031, at a CAGR of 32.9% from 2024–2031. The growth of the enterprise AI market is driven by enterprises’ increasing need to enhance customer satisfaction and the growing implementation of enterprise AI solutions in the IT & telecom sectors. However, the high costs of enterprise AI solutions restrain the growth of this market. Furthermore, the increasing need for conversational AI solutions for optimized sales & marketing management and the growing need to automate business processes are expected to generate growth opportunities for the players operating in this market. However, data privacy & security concerns are a major challenge impacting market growth. Additionally, the growing adoption of AI chatbots for customer interaction and the increasing integration of Machine Learning (ML) technology into enterprise AI solutions are prominent trends in this market.

The global enterprise AI market is segmented by offering (solutions and services [professional services and managed services]), deployment mode (cloud-based deployment and on-premise deployment), organization size (large enterprises and small & medium-sized enterprises), technology (machine learning, image processing, natural language processing, and speech recognition), end-use industry (media & advertising, healthcare, retail & e-commerce, BFSI, government, automotive, IT & telecom, and other end-use industries), and geography. The study also evaluates industry competitors and analyses the market at the country and regional levels.

Based on offering, the global enterprise AI market is segmented into solutions and services. In 2024, the solutions segment is expected to account for the larger share of the global enterprise AI market. The segment's large market share is attributed to the growing adoption of enterprise AI solutions to solve specific business challenges or streamline business processes, the rising need to improve productivity and customer experience, and the growing implementation of these solutions to automate tasks, analyze data, and provide insights. As more organizations leverage enterprise AI to drive efficiency and innovation across multiple business functions, several companies are increasingly offering and launching AI solutions tailored to these specific needs. For instance, in February 2024, Wipro Limited (India) partnered with International Business Machines Corporation (U.S.) to launch the Wipro Enterprise Artificial Intelligence (AI)-Ready Platform. This new service will allow clients to create their enterprise-level, fully integrated, and customized AI environments. The Wipro Enterprise AI-Ready Platform leverages IBM's Watsonx AI and data platforms such as watsonx.ai, watsonx.data, and watsonx.governance and AI assistants to deliver clients an interoperable service that helps accelerate AI adoption.

However, the services segment is expected to register a higher CAGR during the forecast period. The growth of this segment is driven by the growing need for AI consulting, data analysis, and enterprise-grade AI solution development, maintenance, and support and the rising adoption of services to automate tasks and help improve business operations efficiently.

Based on deployment mode, the global enterprise AI market is segmented into cloud-based deployment and on-premise deployment. In 2024, the on-premise deployment segment is expected to account for the larger share of the global enterprise AI market. The segment’s large market share is attributed to the increasing on-premise deployment of enterprise AI solutions by large enterprises and the growing demand for service flexibility, enhanced customer experience, and efficiency in managing risks and compliance.

However, the cloud-based deployment segment is expected to register a higher CAGR during the forecast period. The growth of this segment is driven by benefits associated with cloud-based deployment, including easy maintenance of customer data, cost-effectiveness, and scalability, and the increasing demand for enterprise AI solutions that support multi-cloud deployments. Several providers are also developing enterprise AI offerings tailored for private cloud environments and providing end-to-end solutions for multi-cloud deployments. For instance, in March 2023, NVIDIA Corporation (U.S.) launched NVIDIA DGX Cloud. This AI supercomputing service offers enterprises immediate access to infrastructure and software essential for training advanced models, including generative AI and other groundbreaking applications.

Based on organization size, the global enterprise AI market is segmented into large enterprises and small & medium-sized enterprises. In 2024, the large enterprises segment is expected to account for the larger share of the global enterprise AI market. The segment's large market share is attributed to the growing emphasis on developing strategic IT initiatives among large enterprises, the increasing need to manage large volumes of customer-level data, the early adoption of advanced technologies across various sectors such as retail, manufacturing, healthcare, and automotive, and the rising need for deeper insights into customer responses.

However, the small & medium-sized enterprises segment is expected to register a higher CAGR during the forecast period. The growth of this segment is driven by the increasing need for chatbots and digital assistants among small & medium-sized enterprises, the notable benefits offered by chatbots for small & medium-sized enterprises, and the increasing need to improve performance, quality management, and customer satisfaction in call centers.

Based on technology, the global enterprise AI market is segmented into machine learning, image processing, natural language processing, and speech recognition. In 2024, the machine learning segment is expected to account for the largest share of the global enterprise AI market. The segment's large market share is attributed to the growing adoption of enterprise AI solutions with machine learning capabilities to analyze historical data and identify patterns, the rising need for threat detection, anomaly detection, and malware analysis, and the increasing use of these solutions in e-commerce, streaming platforms, and content websites. These systems are extensively utilized in e-commerce, streaming platforms, and content websites. Thus, several companies are partnering to provide enterprise AI solutions with machine learning capabilities. For instance, In May 2023, NVIDIA Corporation (U.S.) collaborated with Microsoft Corporation (U.S.) to accelerate enterprise-ready generative AI. NVIDIA AI Enterprise is integrating with Azure machine learning that provides an end-to-end cloud platform for developers to build, deploy, and manage AI applications for large language models.

However, the natural language processing segment is expected to register the highest CAGR during the forecast period. The growth of this segment is driven by the growing need to understand, interpret, and generate human language data and the rising adoption of NLP to analyze user preferences, behaviors, and interactions to deliver personalized content.

Based on end-use industry, the global enterprise AI market is segmented into media & advertisement, healthcare, retail & E-commerce, BFSI, government, automotive, IT & telecom, and other end-use industries. In 2024, the IT & telecom segment is expected to account for the largest share of the global enterprise AI market. The segment’s large market share is attributed to the increasing demand for personalized customer experiences enabled by AI technologies, the rising adoption of AI for analyzing data from network sensors to optimize operations, and the growing utilization of AI to enhance network performance and deliver customized services. As AI adoption continues to rise in the IT & telecom industry, numerous AI solution providers are forming partnerships and introducing AI solutions. For instance, In February 2024, ServiceNow, Inc. (U.S.) partnered with NVIDIA Corporation (U.S.) to launch telco-specific generative AI solutions to elevate service experiences. The Now Assist solution for Telecommunications Service Management (TSM) is built on the Now Platform and uses NVIDIA AI to boost agent productivity, speed time to resolution, and enhance customer experiences.

Moreover, the IT & telecom segment is expected to register the highest CAGR during the forecast period.

Based on geography, the global enterprise AI market is segmented into North America, Europe, Asia-Pacific, Latin America, and the Middle East & Africa. In 2024, North America is expected to account for the largest share of the global enterprise AI market. North America’s significant market share can be attributed to the growing adoption of enterprise AI in retail, healthcare, and finance sectors, the rising implementation of AI to enhance customer engagement, inventory management, and personalized shopping experience, and the increasing use of chatbots on websites, social media platforms, and messaging apps to respond customer inquiries. Several companies are launching enterprise AI solutions for the healthcare industry in North America. For instance, in November 2021, Pegasystems Inc. (U.S.) collaborated with Google LLC (U.S.) to help improve healthcare experiences with better data insights and personalization. This partnership with Google Cloud will allow Pegasystems’s clients to configure their applications on Google Cloud’s Healthcare Data Engine. It will also enable the implementation of advanced analytics and AI in a secure, compliant, and scalable cloud environment and seamlessly work with Pega’s healthcare solutions.

However, Asia-Pacific is expected to register the highest CAGR during the forecast period. The growth of this regional market is driven by the growing emphasis by companies to launch chatbots and virtual assistants, growing demand for chatbots and voice assistant solutions, and increasing demand for AI-powered customer support services.

Key Players

The key players operating in the enterprise AI market are NVIDIA Corporation (U.S.), Google LLC (A subsidiary of Alphabet Inc.) (U.S.), Amazon Web Services, Inc. (A Subsidiary of Amazon.com, Inc.) (U.S.), International Business Machines Corporation (U.S.), Microsoft Corporation (U.S.), Verint Systems Inc. (U.S.), SAP SE (Germany), Pegasystems Inc. (U.S.), Wipro Limited (India), Intel Corporation (U.S.), Oracle Corporation (U.S.), Hewlett Packard Enterprise (U.S.), MicroStrategy Incorporated (U.S.), Amelia US LLC (U.S.), Sentient.io (Singapore).

Download Sample Report Here @ https://www.meticulousresearch.com/download-sample-report/cp_id=5806

Key Questions Answered in the Report:

·  Which are the high-growth market segments in terms of offering, deployment mode, organization size, technology, and end-use industry?

·  What is the historical market size for the global enterprise AI market?

·  What are the market forecasts and estimates for 2024–2031?

·  What are the major drivers, restraints, opportunities, challenges, and trends in the global enterprise AI market?

·  Who are the major players in the global enterprise AI market, and what are their market shares?

·  What is the competitive landscape like?

·  What are the recent developments in the global enterprise AI market?

·  What are the different strategies adopted by major market players?

·  What are the trends and high-growth countries?

·  Who are the local emerging players in the global enterprise AI market, and how do they compete with other players?

Contact Us: Meticulous Research® Email- sales@meticulousresearch.com Contact Sales- +1-646-781-8004 Connect with us on LinkedIn- https://www.linkedin.com/company/meticulous-research

[dr-iphone]

聯邦銀行導入 IBM watsonx.ai　探索生成式 AI 助攻數位轉型

近年來，台灣金融業積極運用 AI 技術來提升風險管理與精準行銷，但如何進一步利用生成式 AI 加速數位轉型，已成為各大銀行關注的重點。聯邦銀行 近期攜手 IBM watsonx.ai，��過 AI 技術培訓、內部種子團隊建置與設計思維工作坊，積極探索 AI 在金融領域的應用可能性，為未來數位轉型奠定基礎。 Continue reading 聯邦銀行導入 IBM watsonx.ai　探索生成式 AI 助攻數位轉型

[aiforbusinessuk]

AI for Business : Geospatial

The Convergence of Geospatial Data and Artificial Intelligence

In recent years, the intersection of geospatial data and artificial intelligence has opened up new frontiers in data analysis and decision-making across various industries. This convergence is revolutionizing how we understand and interact with our world, from urban planning to environmental conservation.

Understanding Geospatial Data

Geospatial data encompasses information that identifies the geographic location and characteristics of natural or constructed features on Earth. This data comes in various formats, from simple map coordinates to complex satellite imagery, and is collected through methods ranging from aerial flyovers to UAVs and small drones.

The evolution of geospatial data mirrors technological advancement. What began as basic mapping and location services has transformed into intricate layers of information, including real-time traffic data and detailed environmental attributes. Advancements in satellite imagery resolution and the increasing affordability of consumer-grade drones have made high-quality geospatial data more accessible than ever before.

Applications Across Industries

Geospatial data finds applications in numerous fields:

Urban Planning: Designing smarter, more efficient cities

Environmental Monitoring: Tracking climate change and managing natural resources

Transportation: Optimizing routes and managing traffic

Business: Conducting market analysis and identifying prime locations for expansion

The AI Revolution in Geospatial Analysis

Traditionally, analyzing geospatial data was labor-intensive, often relying on manual labeling or specialized software that required extensive expertise. However, the parallel growth of geospatial data availability and AI capabilities has transformed this landscape.

Early AI applications in this field focused on specific tasks. For instance, Microsoft's open-source projects demonstrated AI's potential in automatically identifying damage to buildings in disaster-affected areas and mapping new solar farms using basic deep learning architectures.

Recent advancements have expanded both the scale and scope of AI in geospatial analysis. A prime example is the watsonx.ai geospatial foundation model from IBM and NASA, which leverages 250,000 terabytes of NASA's satellite data, including hyperspectral imagery. This state-of-the-art model can be fine-tuned for various tasks such as land use identification and vegetation type classification.

AI Consulting in Geospatial Applications

AI consulting companies are at the forefront of applying these technologies to real-world challenges. For example:

Processing orthomosaic drone imagery to determine rock particle sizes in quarry blasts, improving blasting practices and reducing CO2 emissions

Developing state-of-the-art AI models for automated labeling of peatlands, significantly reducing the time investment required from human experts in land conservation and restoration projects

AI developers specializing in geospatial applications are continually pushing the boundaries of what's possible, creating custom solutions that transform raw data into actionable insights.

The Future of Geospatial AI

As we move forward, the synergy between geospatial data and AI promises to unlock even more potential. AI consultants are playing a crucial role in this transformation, applying their expertise to convert complex geospatial data into valuable, actionable intelligence across various sectors.

The future of geospatial AI lies in more sophisticated models, integration of diverse data sources, and increasingly automated analysis processes. As these technologies continue to evolve, they will undoubtedly shape how we understand and interact with our world, driving innovation and informed decision-making in countless fields.

[y2fear]

IBM adds Mistral Large language model to watsonx.ai

[govindhtech]

IBM API Connect Presents API Assistant Powered By Watsonx.ai

IBM Watson Assistant API

With IBM API Connect, you can now develop better APIs more quickly with the help of a generative AI API Assistant tool.

Grounded on experimentation, generating artificial intelligence (gen AI) has emerged as a major player in numerous software applications in the last few years, disrupting industries and changing the way we tackle activities that were previously assumed to require human creativity and judgment. Gen AI has welcomed in a new era of efficiency, productivity, and creativity, spanning from digital art and music production to personalized customer care and software code creation.

Growing popularity of AI personal assistants

3 out of 4 top-performing CEOs concur that having the most sophisticated generation AI is necessary to get a competitive edge, according to a recent survey. AI assistants that assist clients and staff are a crucial component of how companies are operationalizing modern AI. Artificial intelligence (AI) assistants can enhance customer experience, IT operations, productivity, and application modernization by streamlining information access and automating tasks across enterprises. A different survey revealed that by 2025, 60% of executives predicted AI helpers would carry out the majority of traditional tasks. It is anticipated by nearly two-thirds (64%) that throughout the same time period, workers will communicate primarily with AI assistants for transactional tasks.

API administration is changing thanks to AI assistance

API management is now essential to guaranteeing that the numerous APIs inside a company’s ecosystem are regularly managed, secured, and governed due to the explosive rise in API usage in recent years. Developers may find it laborious and time-consuming to do manual tasks related to API maintenance, such as composing API documentation. Throughout the API lifespan, developers and users may accomplish API administration activities more quickly and easily by integrating Gen AI capabilities into API management through the use of AI assistants. This covers all aspects, such as developing and overseeing APIs as well as testing, developing, and integrating them.

Introducing the IBM API Connect’s API Assistant

IBM is announcing that IBM API Connect, its industry-leading and multi-award winning API management platform, now has the API Assistant feature globally available. Users of IBM API Connect will be able to construct better APIs more quickly with the aid of API Assistant, powered by Watsonx.ai.

Let’s examine the features that the IBM API Connect API Assistant now offer

Improve governance and increase API usage by updating your API documentation

API documentation requirements are nothing new. It is now imperative to provide comprehensive documentation for APIs, though, as AI and humans alike are consuming them.

For users human or artificial to quickly find, understand, and apply the different APIs available to them within their company, well-documented APIs are a must. To ensure consistency, compliance, and appropriate management of API usage throughout the organization, well-structured documentation is also essential to efficient API governance.

The problem lies in how time-consuming and laborious it is to write excellent API documentation. When it comes to producing comprehensive descriptions and examples for their API specifications, API developers would much rather solve complex coding and integration problems.

At this point, IBM API Connect‘s API Assistant becomes useful. The API Assistant scans the API definition in a matter of seconds, locates any gaps, and recommends context-specific examples and descriptions with a few clicks. By simply reviewing and implementing these suggestions, the developer may increase API discoverability, consistency, and adoption by both humans and other AI while also producing thorough documentation.

Smart error remediation can hasten API development and increase API dependability

All APIs need to be dependable, scalable, and quick to respond. Finding and correcting any mistakes and inconsistencies before deployment is a crucial step in the development process. To find and fix those mistakes, though, requires going through the API definition page by page. When an error occurs in the API definition, the IBM API Connect tool’s API Assistant may quickly identify and recommend ways to fix it.

An instance of a typical validation problem that the API Assistant can detect is when there are duplicate lines of code, missing parameters, or improper data types. Then, in order to swiftly correct the problems and reduce development time while enhancing code quality, you can examine and implement any or all of the recommended remedies.

With IBM API Connect Advanced tier (SaaS), the API Assistant is now accessible. Find out which plan best suits your needs by reading more about API Connect’s available options. To view the API Assistant in action, you may also register for a live IBM API Connect demo.

Read more on govindhtech.com

[omegatrades]

Why IBM is a Strong Buy to Ride the AI Wave

IBM Hit a Five Year High This Past Week

Recent Purchase for a Boost

IBM decided to drop $2.33 billion to buy two parts from German tech company Software. The goal? To make their artificial-intelligence platform better. They grabbed Software's StreamSets and webMethods businesses, known for handling data well.

Why Data Matters

IBM wants to amp up its data game. This move fits into their bigger plan for AI. By getting these business divisions, IBM aims to make their watsonx.ai software more attractive. Why? Because efficiently putting data into AI models is a big deal for clients wanting to make their apps.

Making It Work for Clients

Rob Thomas, IBM's Chief Commercial Officer, says this buy complements IBM's watsonx.ai and data platform. He also mentions it works well with their application modernization, data fabric, and IT automation products. The idea is to help clients make the most out of their apps and data.

Money Matters

IBM is paying for this move with cash it already has. The deal is expected to be done by the second quarter of 2024.

Stock Numbers and Comparison

IBM stocks were at $162.03 in early Monday trading, a tiny 0.1% drop. This year, they've gone up by 15%. Not bad, but when you look at other big tech players like Microsoft (up 55%) and Google's parent, Alphabet (up 50%), IBM seems a bit behind.

Market Vibes and Ownership

Software, the German tech company IBM is buying from, saw a 1.5% rise in local trading in Germany. This shows people there are feeling positive about this business move.

Summary: Why IBM is a Buy

So, is IBM stock a good buy? The recent buy and IBM's push for better data capabilities seem to be key. With the world going AI-crazy, this move fits right in. IBM might not have matched up to some other tech giants in stock performance, but these recent moves show they're trying to stay ahead. If you're looking to ride the "AI Wave", this may be a hot stock right now. IBM's focus on data and these strategic moves might make it worth a look at.

Want More? Get AI Stock Reports.

[bankedtrackrollerderby]

Cloudera DataFlow Real-Time Ingegration of IBM WatsonX.AI Granite 13B - ...

[jcmarchi]

Wipro and IBM collaborate to propel enterprise AI

New Post has been published on https://thedigitalinsider.com/wipro-and-ibm-collaborate-to-propel-enterprise-ai/

Wipro and IBM collaborate to propel enterprise AI

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In a bid to accelerate the adoption of AI in the enterprise sector, Wipro has unveiled its latest offering that leverages the capabilities of IBM’s watsonx AI and data platform.

The extended partnership between Wipro and IBM combines the former’s extensive industry expertise with IBM’s leading AI innovations. The collaboration seeks to develop joint solutions that facilitate the implementation of robust, reliable, and enterprise-ready AI solutions.

The Wipro Enterprise AI-Ready Platform harnesses various components of the IBM watsonx suite, including watsonx.ai, watsonx.data, and watsonx.governance, alongside AI assistants. It offers clients a comprehensive suite of tools, large language models (LLMs), streamlined processes, and robust governance mechanisms, laying a solid foundation for the development of future industry-specific analytic solutions.

Jo Debecker, Managing Partner & Global Head of Wipro FullStride Cloud, said: “This expanded partnership with IBM combines our deep contextual cloud, AI, and industry expertise with IBM’s leading AI innovation capabilities.”

A key aspect of this collaboration is the establishment of the IBM TechHub@Wipro, a centralised tech hub aimed at supporting joint client pursuits. This initiative will bring together subject matter experts, engineers, assets, and processes to drive and support AI initiatives.

Kate Woolley, General Manager of IBM Ecosystem, commented: “We’re pleased to reach this new milestone in our 20-year partnership to support clients through the combination of Wipro’s and IBM’s joint expertise and technology, including watsonx.”

The Wipro Enterprise AI-Ready Platform offers infrastructure and core software for AI and generative AI workloads, enhancing automation, dynamic resource management, and operational efficiency in the enterprise. Moreover, it caters to specialised industry use cases, such as banking, retail, health, energy, and manufacturing, offering tailored solutions for customer support, marketing, feedback analysis, and more.

Nagendra Bandaru, Managing Partner and President of Wipro Enterprise Futuring, highlighted the flexibility of the platform, stating: “Wipro’s Enterprise AI-Ready Platform will allow clients to easily integrate and standardise multiple data sources augmenting AI- and GenAI-enabled transformation across business functions.”

In addition to facilitating AI governance through the AI lifecycle, the platform prioritises responsible AI practices, ensuring transparency, data protection, and compliance with relevant laws and regulations.

As part of this collaboration, Wipro associates will undergo training in IBM hybrid cloud, AI, and data analytics technologies, further enhancing their capabilities in developing joint solutions.

(Photo by Carson Masterson on Unsplash)

See also: Reddit is reportedly selling data for AI training

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

Tags: ai, artificial intelligence, enterprise, ibm, ibm watson, platform, watsonx, wipro

[crypto-chronicles]

IBM Launches "Granite": AI Foundation Models for Business

IBM has unveiled its latest advancement in the realm of artificial intelligence (AI) for business: a series of foundation models named “Granite.” These models, designed for the watsonx.ai platform, aim to harness the power of generative AI for both linguistic and coding applications. Granite Models: A Deep Dive Developed by IBM Research, the Granite models, specifically Granite.13b.instruct and…