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Exam Code: C1000-083 Practice exam 2023 by Killexams.com team C1000-083 Foundations of IBM Cloud V2 Test Detail:
The IBM C1000-083 exam, Foundations of IBM Cloud V2, is designed to assess the knowledge and skills of individuals in foundational concepts related to IBM Cloud. The exam tests the candidate's understanding of cloud computing principles, IBM Cloud offerings, and the ability to apply IBM Cloud solutions to real-world scenarios.
Course Outline:
The Foundations of IBM Cloud V2 course provides comprehensive training on the fundamental concepts of cloud computing and IBM Cloud solutions. The course covers various topics, including cloud architecture, cloud deployment models, cloud services, and the use of IBM Cloud services for building and deploying applications. The following is a general outline of the key syllabus covered:
1. Cloud Computing Concepts:
- Introduction to cloud computing and its benefits.
- Cloud deployment models: public, private, hybrid, and multi-cloud.
- Cloud service models: IaaS, PaaS, and SaaS.
- Cloud security and compliance considerations.
2. Introduction to IBM Cloud:
- Overview of IBM Cloud offerings and services.
- IBM Cloud architecture and infrastructure components.
- IBM Cloud deployment models and options.
- IBM Cloud pricing and billing models.
3. IBM Cloud Services:
- IBM Cloud compute services: virtual servers, containers, and serverless computing.
- IBM Cloud storage services: object storage, block storage, and file storage.
- IBM Cloud networking services: virtual private networks, load balancers, and DNS.
- IBM Cloud database services: SQL and NoSQL databases.
4. Application Development on IBM Cloud:
- IBM Cloud development tools and environments.
- Building and deploying applications on IBM Cloud.
- Integration with IBM Watson services for AI and machine learning.
Exam Objectives:
The C1000-083 exam assesses the candidate's knowledge and skills in the following areas:
1. Cloud Computing Concepts:
- Understanding of cloud computing principles, deployment models, and service models.
- Knowledge of cloud security and compliance considerations.
2. IBM Cloud Fundamentals:
- Familiarity with IBM Cloud offerings, architecture, and infrastructure components.
- Understanding of IBM Cloud deployment models and pricing models.
3. IBM Cloud Services:
- Knowledge of IBM Cloud compute, storage, networking, and database services.
- Ability to differentiate between different IBM Cloud service offerings.
4. Application Development on IBM Cloud:
- Understanding of IBM Cloud development tools and environments.
- Knowledge of building and deploying applications on IBM Cloud.
- Awareness of integration possibilities with IBM Watson services.
Syllabus:
The C1000-083 course syllabus provides a detailed breakdown of the syllabus covered in the training program. It includes specific learning objectives, hands-on exercises, and case studies to enhance the candidate's understanding and application of IBM Cloud concepts. The syllabus may cover the following areas:
- Cloud Computing Concepts and Models
- Introduction to IBM Cloud
- IBM Cloud Compute Services
- IBM Cloud Storage Services
- IBM Cloud Networking Services
- IBM Cloud Database Services
- Application Development on IBM Cloud Foundations of IBM Cloud V2 IBM Foundations resources Killexams : IBM Foundations resources - BingNews
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https://killexams.com/exam_list/IBMKillexams : Best Free AI Training Courses for 2023
With businesses finding new, inventive ways to use AI almost every day, it's no surprise that AI training courses are becoming increasingly sought after.
Workers in all sorts of industries are looking to upskill themselves in line with the rapid technological changes occurring. Luckily, companies like Microsoft and Google offer free AI training courses, as do some higher education institutions.
In this guide, we cover the best AI training courses currently available, as well as the benefits of learning about AI in the current job market. We've largely focused on free courses that offer immediate, foundational learning opportunities that you can start applying to your job role or career straight away, rather than paid degree courses that cost hundreds or thousands of dollars.
1. Google’s Generative AI Learning Path (10 Courses)
One of the more generous courses available in terms of real hours of learning, Google’s Generative AI Learning path has 10 courses on it. All courses take one day to complete.
Seven of the courses are classified as introductory, including “Introduction to Generative AI”, “Introduction to Large Language Models” and “Generative AI Fundamentals”. There are three courses within the learning path described as intermediate, including “Encoder-Decoder Architecture” and “Attention Mechanism: Overview”.
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The first two introductory courses cover a lot of immediately applicable content, such as how to use prompt tuning to get the best out of large language models. There's also a course on the responsible usage of AI.
Although there's no official qualification, you will be awarded a completion badge that you can attach to your digital resume.
2. Microsoft's “Transform Your Business With AI” course
This Microsoft learning path is designed, as the tech giant says, to help businesspeople acquire “the knowledge and resources to adopt AI in their organizations”, and explores “planning, strategizing, and scaling AI projects in a responsible way.”
Microsoft says the objectives for this course are to become familiar with existing AI tools, understand basic AI terminology and practices, and use prebuilt AI to build intelligent applications.
To enroll in this course, which is 2 hours and 40 minutes long, Microsoft says you’ll need a “basic understanding” of IT and business concepts. Modules included in the pathway are:
Leverage AI tools and resources for your business (55 mins)
Create business value from AI (21 min)
Embrace responsible AI principles and practices (48 mins)
Scale AI in your organization (36 mins)
3. Linkedin’s “Career Essentials In Generative AI” training course
Linkedin’s AI Career Essentials Course is made up of five different videos, with a total run time of around four hours. Each video is hosted by a different AI expert, covering a range of core concepts and ethical considerations relating to AI models.
One of the videos provides a detailed explanation of how to streamline your work with Microsoft Bing Chat, while another discusses the key differences between search engines and reasoning engines. Although there’s no accreditation or certification, completing this course will earn you a badge of completion from LinkedIn, which can be displayed on your profile.
The fifth video in the series, entitled “an introduction to artificial intelligence” is an hour and a half long and provides a simplified overview of the best AI tools for businesses, which is handy for those who haven’t taken the plunge yet and implemented a tool in work.
4. IBM’s “AI Foundations for Everyone” training course
IBM offers a course entitled “AI Foundations for Everyone” through Coursera, of which over 19,000 people have already enrolled in. You can audit the course for free, which will give you access to all of the materials and some of the assignments, but you won't be graded or get a certificate at the end.
It’s geared toward beginners and you don’t need prior experience to enroll, and the schedule is flexible so you can learn at your own pace. Along with AI fundamentals, the course will also ensure you’re familiar with IBM’s own AI services, which help businesses integrate artificial intelligence into their existing infrastructure.
IBM says that, by the end of the course, participants will have had “hands-on interactions with several AI environments and applications”.
The course has three modules: “Introduction to Artificial Intelligence”, “Getting Started with AI using IBM Watson”, and “Building AI-Powered Chatbots Without Programming”. Each module takes between nine and eleven hours to complete.
5. Digital Partner's “The Fundamentals of ChatGPT” training course
Digital Partner’s course entitled “The fundamentals of ChatGPT” is a great option for anyone who wants to take a free, accredited course that covers the basics of Generative AI.
During the course, you’ll spend time learning about OpenAI’s role in global AI development, and be able to learn about how ChatGPT works, its advantages and limitations. There’s also a variety of examples included within the course that will show you how to leverage ChatGPT for different tasks, and you'll learn more about the difference between ChatGPT and ChatGPT Plus.
Modules include “Working With ChatGPT”, “ChatGPT and Its Shortcomings” and “Training a GPT Model”.
This free course is available on Alison.com, and is published by a digital marketing firm called Digital Partner. The course is CPD accredited and a certificate will be awarded upon completion of a small assessment at the end of the 1.5-3 hour program.
6. Phil Ebner’s ChatGPT, Midjourney, Firefly, Bard, DALL-E” AI crash course
While there are some good courses on Udemy that will guide you through the ins and outs of MidJourney and other AI generation tools, this instructor covers the most ground, and almost 12,000 students have already enrolled in the course, which has a 4.6/5 rating on Udemy.
The course is almost two hours long and also includes content that will help you better use tools like ChatGPT to generate text responses as well as images.
The “AI for Visual Creativity” section, however, will show you how to use both MidJourney and Dall-E to create “photorealistic images, illustrations, and digital art in a variety of styles.”
On Udemy, you don’t receive certificates of completion for free courses, but if you're just looking to upskill yourself free of charge, this course is definitely worth a look.
Best Free AI Training courses for Programmers, Developers & Tech Experts
Up next, we have more advanced courses geared towards programming and development.
1. Harvard University’s “Introduction to Artificial Intelligence with Python”
Harvard University offers a self-paced, 7-week course on the “concepts and algorithms at the foundation of modern artificial intelligence”.
The time commitment of between 10-30 hours a week – but it’s completely free to enroll and you’ll be supported as you complete projects and attend lectures. However, you need to have taken Harvard’s “Introduction to Computer Science” course first to enroll.
2. DeepLearning.AI’s “ChatGPT Prompt Engineering For Developers” (Coursera)
This course will help you utilize OpenAI’s API to write more effective prompts, learn how large language models can be used to carry out tasks like text transformation and summarizing, and teach you how to program and build a custom AI chatbot.
The course is run by AI expert and DeelLearning.AI co-founder Andrew Ng and OpenAI’s Isa Fulford, and it’s only an hour long. DeepLearning.AI says the course is “free for a limited time”. A basic understanding of Python is needed, but aside from that, it’s beginner friendly.
3. Intro to TensorFlow for Machine Learning (Udacity)
This course teaches participants how to build deep-learning applications with TensorFlow, one of the most popular open-source Python software libraries.
The estimated completion time for the course is approximately two months, and you should have some experience with Python syntax, including variables, functions, and classes, as well as a grasp of basic algebra.
If you take the course, providers Udacity say, you’ll get “hands-on experience building your own state-of-the-art image classifiers” as well as other types of deep learning models.
This Georgia Tech course is free on Udacity and focuses on exploring “automated decision-making from a computer-science perspective”.
At the end of the course – which takes approximately four months to complete, but is also described as self-paced – participants will recreate a result from a published paper on reinforcement learning.
However, it is recommended you have a graduate-level machine-learning qualification and some prior experience with reinforcement learning from previous studies. Experience with Java is also required.
Although there’s no official certificate awarded for completing the course, you can earn a nano degree program certificate by completing Udacity's 4-month long “Deep Reinforcement Learning”, although this costs $1116.
5. Become an AI-Powered Engineer: ChatGPT, Github Copilot (Udemy)
In this course, students will learn how to create high-quality pieces of code using ChatGPT and integrate it with other text editors. It also covers how to use GitHub Copilot.
This might be a free tutorial, but the course has much better reviews than some of the other AI courses available on Udemy, with 56% of watchers who left a review giving the course five stars, and a further 24% giving it four stars at the time of writing.
The course will be best suited to developers who want to leverage AI tools for coding responsibilities in general, and also, to become more efficient in their coding practices.
6. GreatLearning’s “ChatGPT for Beginners” training course
This is a completely free, two-hour long beginners-focused ChatGPT course. It’s one of the only beginner's courses on the internet that includes a section on coding prompts, although it also covers quite a bit of other ground, including email prompting.
There are no prerequisites needed for this course, and it has an average rating of 4.61/5, with 75% of reviewers giving the course 5 stars.
Free College AI Courses and Training
A number of universities and colleges offer AI-focused courses.
1. Stanford University’s “Introduction to Artificial Intelligence” course (Udacity)
This foundational online program, which takes around 10 months to complete at a rate of 10 hours a week, focuses on fundamental AI concepts and practical machine learning skills but is classified as an intermediate course.
The course, which is split into two umbrella sections (“Fundamentals of AI” and “Applications of AI) is completely free if you sign up for Udacity (which also doesn't cost anything). It consists of 22 different lessons and a string of interactive quizzes.
2. Vanderbilt University’s “Prompt Engineering for ChatGPT”
Jules White, Vanderbilt University’s associate dean for strategic learning programs and associate professor of computer science, has launched a free online course available through Coursera focusing on prompt engineering.
It goes through the most effective approaches for prompt engineering, covering summarization, simulation, programming, and other useful ways you can harness the power of ChatGPT with your inputs.
The course takes around 18 hours to complete and is made up of an introduction to prompts and three separate sessions on prompt patterns, as well as a 2-hour module on examples.
In collaboration with Georgia Tech, Udacity has made an intermediate machine-learning course available for free, which takes around 4 months to complete, although the course listing says you can do it at your own pace.
The course is offered as part of an online master’s degree at Georiga Tech, but taking this course won’t earn you credit toward this degree.
The course covers Supervised and Unsupervised Learning, which are two different types of machine learning, and covers how they're used in AI systems.
However, having a “strong familiarity with Probability Theory, Linear Algebra, and Statistics” and prior experience with statistics is helpful. Students should also have some experience with programming.
4. The Open University’s “AI Matters” course (OpenLearn)
The Open University is a UK-based institution that offers a free course through its learning portal OpenLearn entitled “AI Matters”.
In the course, you'll learn about the “historical, social, political and economic issues in AI”, explore the benefits and limitations of the technology, and discuss ethical risks relating to AI.
The course is six hours long, and you'll be eligible for a certificate of participation.
5. University of Pennsylvania’s “AI For Business” (Coursera)
The University of Pennsylvania's “AI for Business” specialization is made up of four different, free courses:
AI Applications in People Management
AI Fundamentals for Non-Data Scientists
AI Applications in Marketing and Finance
AI Strategy and Finance
According to the University of Pennsylvania's website, although the course itself costs $39 to complete, you can enroll in the four individual modules that take up the course for free. Each module will take up around two hours a week to complete.
6. University of Helsinki's “Elements of AI” and “Ethics of AI”
The University of Helsinki has two, free online courses available. The course entitled “Ethics of AI” is geared towards “anyone who is interested in the ethical aspects of AI”, the university says.
The course will familiarize you with common questions that arise in AI ethics and the various ways to approach them.
“Elements of AI” is a broader course with 6 chapters, focusing on syllabus such as “neural networks”, “machine learning” and “AI problem solving”. All you need to do to access the course materials is sign up.
The Benefits of Learning About AI
Of course, completing an AI training course can have a number of benefits. From a personal learning perspective, it's one of the best ways you can spend your time – AI is here to stay, and getting a better grasp of how it works might just help you out in the near future.
Plus, the things you learn about AI will be applicable to a wide variety of job roles in almost every sector of the economy, so it's arguably a safer bet than completing a course on a niche or industry-specific topic.
What's more, right now, businesses are looking for people who understand how generative AI tools like ChatGPT work, and how to leverage them effectively. Employees that are conscious of the limitations of AI tools and able to generate useful responses using prompts are going to become more sought after than employees without these skills.
Completing an AI training course is going to look good on your CV, which will help if you're applying for a new job. Evidence that you've taken the initiative to explore an emerging technology is definitely something an employer will find desirable.
Of course, if you don't have much of a budget – or you're not entirely sure what AI training course would be the best use of your time – then trying out some free options is a great place to start.
Yes – Google has a free AI learning path that has ten courses you can complete for free. Each course takes around one day to complete. The modules provide an introduction to generative AI, and although there's no official qualification, you will get a completion badge which you can add to your resume.
Yes – you can learn how to harness the powers of AI on your own, through online guides, tutorials, and courses. There are quite a lot of resources out there now that will show you how to get the most out of generative AI tools like ChatGPT. However, if you want to learn how more complex skills, such as programming a large language model, you may need to seek out a paid course
Yes – Microsoft has a free training path available that consists of four short modules, which cover how to leverage AI effectively in business settings and how to scale AI projects in a responsible, impactful way.
Wed, 16 Aug 2023 20:53:00 -0500en-UStext/htmlhttps://tech.co/news/best-free-ai-training-coursesKillexams : IBM Unveils watsonx Generative AI Capabilities to Accelerate Mainframe Application Modernization
ARMONK, N.Y., Aug. 22, 2023 — IBM today announced watsonx Code Assistant for Z, a new generative AI-assisted product that will help enable faster translation of COBOL to Java on IBM Z and enhances developer productivity on the platform. This product will be generally available in Q4 2023, and is being designed to help accelerate COBOL application modernization. Watsonx Code Assistant for Z will preview during TechXchange, IBM’s premier technical learning event in Las Vegas, Sept 11-13.
Watsonx Code Assistant for Z is a new addition to the watsonx Code Assistant product family, along with IBM watsonx Code Assistant for Red Hat Ansible Lightspeed, scheduled for release later this year. These solutions will be powered by IBM’s watsonx.ai code model, which will have knowledge of 115 coding languages having learned from 1.5 trillion tokens. At 20 billion parameters, it is on target to become one of the largest generative AI foundation models for code automation. The watsonx Code Assistant product portfolio will extend over time to address other programming languages, to Strengthen time to value for modernization and address growing skills challenges for developers.
Watsonx Code Assistant for Z is being designed to assist businesses in leveraging generative AI and automated tooling to accelerate their mainframe application modernization – all with the goal of preserving the performance, security and resiliency capabilities of IBM Z.
The COBOL data processing language supports many vital business and operational processes at organizations globally. At scale, using watsonx Code Assistant for Z in comparison to other approaches could make it easier for developers to selectively and incrementally transform COBOL business services into well architected high-quality Java code – with estimated billions of lines of COBOL code as potential candidates for targeted modernization over time. Generative AI can help developers to more quickly assess, update, validate and test the right code, allowing them to more efficiently modernize large applications and focus on higher impact tasks.
IBM is designing these capabilities to provide tooling for each step of the modernization journey. The solution is expected to include IBM’s Application Discovery and Delivery Intelligence (ADDI) inventory and analysis tool. Following ADDI, key steps on the journey include refactoring business services in COBOL, transforming COBOL code to Java code with an optimized design, and validating the resulting outcome, including using automated testing capabilities. Potential benefits for clients include:
Accelerating code development and increasing developer productivity throughout the application modernization lifecycle.
Managing total cost, complexity, and risk of application modernization initiatives, including translation and optimization of code in-place on IBM Z.
Expanding access to a broader pool of IT skills and accelerating developer onboarding.
Achieving high quality, easy to maintain code through model customization and the application of best practices.
“Our collaboration with IBM is an important element in our drive to leverage generative AI interfaces to challenge legacy approaches with material productivity gains, and reinvent our Capital Markets solutions,” said Roger Burkhardt, CTO, Capital Markets and AI, Broadridge Financial. “We have had excellent client response to our generative AI investments and we are intrigued by the opportunity to further our efforts by leveraging IBM watsonx Code Assistant for Z to address a broader range of platforms.”
AI-assisted Mainframe Application Modernization Is an Imperative
According to new research from the IBM Institute for Business Value, organizations are 12x more likely to leverage existing mainframe assets rather than rebuild their application estates from scratch in the next two years. At the same time, however, the study shows that the number one challenge for those same organizations is a lack of resources and skills.
“By bringing generative AI capabilities through watsonx to new use cases, we plan to drive real progress for our clients,” said Kareem Yusuf, PhD, Senior Vice President, Product Management and Growth, IBM Software. “IBM is engineering watsonx Code Assistant for Z to take a targeted and optimized approach. It’s built to rapidly and accurately convert code optimized for IBM Z, accelerate time to market and broaden the skills pool. This can help enhance applications and add new capabilities while preserving the performance, resiliency, and security inherent in IBM Z.”
There are many application modernization approaches available today. Some options include rewriting all application code in Java, or migrating everything to public cloud, which may sacrifice capabilities that are core to the IBM Z value proposition while failing to deliver on expected cost reduction. Tools that convert COBOL applications to Java syntax can produce code that is hard to maintain and can be unrecognizable to a Java developer. Generative AI is promising, but current AI-assisted partial re-write technology lacks COBOL support and doesn’t optimize the resulting Java code for the given task.
The resulting Java code from watsonx Code Assistant for Z will be object-oriented. IBM is designing this solution to be optimized to interoperate with the rest of the COBOL application, with CICS, IMS, DB2, and other z/OS runtimes. Java on Z is designed to be performance-optimized versus a compared x86 platform.
Building on a Foundation of Governance and Innovation
According to a 2023 Gartner report (for Gartner Subscribers only), “by 2028, the combination of humans and AI assistants working in tandem could reduce the time to complete coding tasks by 30%.” The report further states that “the use of AI code generation tools is not replacing the quality assurance (QA) processes and security controls that are needed by developers for robust and secure product development, as well as for mitigation of inherited risks from using generative methods for code.”
Protecting sensitive data and customer intellectual property are critical when it comes to implementing generative AI. IBM for decades has followed core principles, grounded in commitments to Trust and Transparency. With this principle-based approach, the watsonx platform aims to enable enterprises to leverage their own trusted data and IP to build tailored AI solutions that are scalable across operations.
Additionally, IBM Consulting brings deep domain expertise in IBM Z application modernization with a focus on guiding clients that leverage the platform across key industries such as banking, insurance, healthcare and government. These dedicated consultants can help clients identify the right application areas to modernize in order to optimize the potential benefits of watsonx Code Assistant for Z.
For more information about AI-assisted mainframe application modernization, and to get started with IBM’s optimized, targeted approach, please visit our website here and join us at TechXchange. Register today for our watsonx Code Assistant for Z webinar on Sept. 21 at 11 am ET here and learn how IBM is bringing Gen AI to mainframe application modernization. You can also schedule a live demo with IBM’s team here.
About IBM
IBM is a leading provider of global hybrid cloud and AI, and consulting expertise. We help clients in more than 175 countries capitalize on insights from their data, streamline business processes, reduce costs and gain the competitive edge in their industries. More than 4,000 government and corporate entities in critical infrastructure areas such as financial services, telecommunications and healthcare rely on IBM’s hybrid cloud platform and Red Hat OpenShift to affect their digital transformations quickly, efficiently and securely. IBM’s breakthrough innovations in AI, quantum computing, industry-specific cloud solutions and consulting deliver open and flexible options to our clients. All of this is backed by IBM’s legendary commitment to trust, transparency, responsibility, inclusivity and service.
Source: IBM
Tue, 22 Aug 2023 05:11:00 -0500text/htmlhttps://www.datanami.com/this-just-in/ibm-unveils-watsonx-generative-ai-capabilities-to-accelerate-mainframe-application-modernization/Killexams : IBM and NASA open-source foundation AI model for analyzing satellite data
IBM Corp. and NASA todayreleased an advanced artificial intelligence model designed to help researchers analyze satellite data faster.
The model is available on Hugging Face, a popular GitHub-like platform for sharing open-source neural networks. The next phase of IBM’s collaboration with NASA will focus on extending their AI to additional use cases. They will partner with Worcester, Massachusetts-based Clark University on the initiative.
“The essential role of open-source technologies to accelerate critical areas of discovery such as climate change has never been clearer,” said Sriram Raghavan, vice president of IBM Research AI.
IBM says the new model is designed to help researchers identify areas in the continental U.S. that may be at risk of flooding and wildfire. According to the company, the model can analyze geospatial data up to four times faster than state-of-the-art neural networks. It also takes less data to train.
IBM describes the AI as a foundation model, or a model that can perform a wide range of advanced computing tasks. It’s based on the Transformer architecture, a popular approach to designing neural networks. Transformer models can take a large amount of contextual information into account when reasoning about a piece of data, which allows them to make more accurate decisions than other AI systems.
The technology underpins many of the most advanced AI systems on the market. That includes GPT-4, OpenAI LP’s latest large language model.
IBM and NASA jointly trained their model on a geospatial dataset called Harmonized Landsat Sentinel-2. The dataset includes images of the Earth’s surface that were taken by NASA’s Landsat-8 satellite. It also contains measurements from Sentinel-2, a satellite constellation operated by the European Space Agency.
IBM trained the AI model using its internally-developed Vela supercomputer. The system, which the companyrevealed earlier this year, is powered by chips from Nvidia Corp.’s A100 series of data center graphics cards. Vela uses a high-end version of the A100 with a particularly large pool of onboard memory for storing AI models.
Alongside Nvidia silicon, the supercomputer includes IBM-developed virtualization software. Virtualization makes certain AI development tasks easier, but that simplicity comes at the cost of reduced processing power. IBM says that it lowered the performance impact to less than 5%, which its researchers describe as “lowest overhead in the industry that we’re aware of.”
Though IBM and NASA optimized their model to detect areas at risk of flooding and wildfires, they estimate it can be adapted to other use cases as well. Tracking deforestation is one task that the model could speed up. IBM says that it can also be used to help researchers monitor carbon emissions and forecast crop yields.
Down the road, the company plans to further extend the AI’s capabilities. It has teamed up with researchers from NASA and Clark University to pursue the effort.
As part of the initiative, IBM hopes to optimize the model for time-series segmentation and similarity research. Those are two popular data analysis methods that are used for not only geospatial research but also a range of other tasks. Time-series segmentation can, for example, be used to study the cause of stock price fluctuations.
IBM eventually plans to make a commercial version of the model available through itsWatsonx product suite. Introduced in May, the suite includes an array of software tools designed to help companies build advanced AI models and deploy them in production. There are also prepackaged neural networks optimized for various use cases.
Watsonx is powered by Red Hat OpenShift AI, anotherrecently launchedcomponent of IBM’s machine learning portfolio. It’s a version of the OpenShift application development and deployment platform that is specifically optimized for AI workloads. The offering eases tasks such as monitoring the performance of machine learning models running in production.
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Thu, 03 Aug 2023 07:42:00 -0500en-UStext/htmlhttps://siliconangle.com/2023/08/03/ibm-nasa-open-source-foundation-ai-model-analyzing-satellite-data/Killexams : IBM and NASA deploy open-source geospatial AI foundation model on Hugging Face
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There are a lot of different open source models available on Hugging Face — and today at least one more is being added to that number.
IBM and NASA today jointly announced the availability of the watsonx.ai geospatial foundation model on Hugging Face. The development of the model was first disclosed in February as an attempt to unlock the value of massive volumes of satellite imagery to help advance climate science and Strengthen life here on Earth. The open mode was trained on NASA’s Harmonized Landsat Sentinel-2 satellite data (HLS) with additional fine tuning using labeled data for several specific use cases including burn scar and flood mapping.
The geospatial foundation model benefits from enterprise technologies that IBM has been developing for its watsonx.ai effort and the company is hopeful that the innovations pioneered in the new model will help both scientific and business use cases.
“With foundation models, we have this opportunity to be able to do a lot of pre-training and then easily adapt and accelerate productivity and deployment,” Sriram Raghavan, VP for IBM Research AI told VentureBeat.
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Data labeling at scale is hard, foundation models solve that problem
A primary challenge that IBM’s enterprise users have faced with AI in the past is that training used to require very large sets of labeled data. Foundation models change that paradigm.
With a foundation model, the AI is pre-trained on a large dataset of unlabeled data. Fine tuning for a specific use case can then be executed using some labeled data to get a very customized model. Not only is the model customized, IBM and NASA found that using the foundation model approach enabled faster training and better accuracy than working with a model entirely built with labeled data.
For example, Raghavan said that for the use case of flood model prediction, the new foundation model was able to Strengthen prediction 15% over a state of the art with one half the amount of labeled data.
“You are now talking about basically half the work that an SME [Subject Matter Expert] has to do,” said Raghavan. “So, you use the base model that was trained in an unsupervised fashion, then an SME said, ‘I’m going to teach you how to do flood [prediction]’ and they use half the amount of labeled data that they had to use for other techniques.”
For the burn scar use case, which is increasingly important in an era where wildfires rage over wide areas of land, IBM recognized an even greater benefit. Raghavan said that the IBM model was able to train a model with 75% less labeled data than the current state-of-the-art model, providing what he referred to as ‘double digit’ improvements in performance.
Why Hugging Face matters for an open geospatial foundation model
As to why IBM and NASA are making the model available on Hugging Face, there are numerous reasons, Raghavan said.
For one, Hugging Face has become the leading community for open AI models, he said. It’s a recognition that IBM made earlier this year when it first announced the watsonx.ai approach to building foundation models. As part of the initial announcement, IBM partnered with Hugging Face to bring access to open AI models to IBM’s enterprise users.
By making the geospatial foundation model available on Hugging Face, IBM and NASA are hoping that the model will be used, and that there will be some lessons learned that help to Strengthen it over time.
Raghavan said that by making the model compatible with Hugging Face’ APIs, developers can make use of a wide range of existing tooling to benefit from and use the model.
“The purpose was to reduce the effort it takes for the audience, and the audience here is really scientists who are going to work on top of the satellite data,” he said. “Today Hugging Face APIs dominate the ecosystem in terms of familiarity.”
How enterprise users will benefit (eventually)
While the core audience for the geospatial foundation model is scientists, Raghavan expects that there will be learnings that will help enterprise use cases of AI as well.
In terms of direct impact, IBM has an environment intelligence suite that uses various models today to help organizations with sustainability efforts. Raghavan said that the new model will, in time, be integrated with that platform.
There is also potential for what Raghavan referred to as ‘meta learning’ where lessons learned will impact other areas of IBM’s AI development efforts.
“We believe that we’re in the journey of understanding what is the developer experience around foundation models,” he said. “By exposing a new class of users now with scientists who are going to be doing fine tuning on these models, we will start to understand what we have to offer to make that process better and better, and I believe some of those learnings we will take back.”
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Thu, 03 Aug 2023 01:44:00 -0500Sean Michael Kerneren-UStext/htmlhttps://venturebeat.com/ai/ibm-and-nasa-deploy-open-source-geospatial-ai-foundation-model-on-hugging-face/Killexams : IBM’s Brain-Inspired Chip Embraces Energy-Efficient AI
IBM has unveiled a “brain-like” chip with the potential to transform the energy consumption patterns of artificial intelligence systems. This development promises to redefine how energy is consumed in the AI sector.
IBM’s Energy-Efficient Vision
The current demands of AI operations on power and resources have been a growing concern. With large warehouses filled with computers, the carbon footprint is escalating.
IBM’s prototype chip seeks to address this.
Scientist Thanos Vasilopoulos from IBM’s Zurich lab highlighted the chip’s efficiency, stating, “the human brain is able to achieve remarkable performance while consuming little power.” This is what IBM aims to replicate with their prototype.
Reducing Carbon Emissions
Emissions from large warehouses filled with AI computers have sparked concerns. Vasilopoulos suggests this new chip means “large and more complex workloads could be executed in low power or battery-constrained environments”, such as cars and mobile phones.
He added, “cloud providers will be able to use these chips to reduce energy costs and their carbon footprint.”
Digital to Analogue: The Shift
Current standard chips operate on a digital foundation, storing information in binary – 0s and 1s. This new prototype from IBM, however, leans on memristors, which operate in an analogue manner, accommodating a spectrum of numbers.
This shift in technology could be likened to the transition from a basic light switch to a more intricate dimmer switch, as detailed by Professor Ferrante Neri from the University of Surrey.
He emphasised the chip’s ability to “remember” its electric history, much like synapses in human brains. Such capabilities push this technology closer to emulating human brain functions.
Navigating the Roadblocks and CHallenges
Though the chip presents a very inviting glimpse into a more energy-efficient future, Neri spoke on the challenges like material costs and complexities in manufacturing.
His feeling was one of guarded hopefulness: positive about the technology’s potential but very much aware of possible challenges.
Blending with Existing Systems
The way new technologies are first integrated is often a concern. However, IBM’s chip is constructed with adaptability in mind.
Considering that many of today’s smartphones use AI chips, highlighted by features such as the iPhone’s “neural engine”, IBM’s new technology might lead to extended battery durations and fresh applications for these devices.
A Shift for Data Centres?
If this prototype does become mainstream, it might just alter the face of data centres around the world. The considerable energy and water demands of contemporary data centres could see a substantial decrease.
While the potential is clear, experts like Professor James Davenport from the University of Bath have their reservations.
He recognises the chip’s potential but reminds us that it’s merely “a possible first step” in a much longer journey.
Regardless, IBM’s existing contribution could be a huge step towards a more sustainable technological future.
Mon, 14 Aug 2023 00:31:00 -0500en-GBtext/htmlhttps://techround.co.uk/news/ibms-brain-inspired-chip-embraces-energy-efficient-ai/Killexams : IBM unveils watsonx generative AI capabilities
IBM announced watsonx Code Assistant for Z, a new generative AI-assisted product that will help enable faster translation of COBOL to Java on IBM Z and enhances developer productivity on the platform. This product will be generally available in Q4 2023, and is being designed to help accelerate COBOL application modernization.
Watsonx Code Assistant for Z is a new addition to the watsonx Code Assistant product family, along with IBM watsonx Code Assistant for Red Hat Ansible Lightspeed, scheduled for release later this year. These solutions will be powered by IBM’s watsonx.ai code model, which will have knowledge of 115 coding languages1 having learned from 1.5 trillion tokens.2 At 20 billion parameters, it is on target to become one of the largest generative AI foundation models for code automation.3 The watsonx Code Assistant product portfolio will extend over time to address other programming languages, to Strengthen time to value for modernization and address growing skills challenges for developers.
Watsonx Code Assistant for Z is being designed to assist businesses in leveraging generative AI and automated tooling to accelerate their mainframe application modernization – all with the goal of preserving the performance, security and resiliency capabilities of IBM Z.
Source: IBM
The COBOL data processing language supports many vital business and operational processes at organizations globally. At scale, using watsonx Code Assistant for Z in comparison to other approaches could make it easier for developers to selectively and incrementally transform COBOL business services into well architected high-quality Java code – with estimated billions of lines of COBOL code as potential candidates for targeted modernization over time. Generative AI can help developers to more quickly assess, update, validate and test the right code, allowing them to more efficiently modernize large applications and focus on higher impact tasks.
IBM is designing these capabilities to provide tooling for each step of the modernization journey. The solution is expected to include IBM’s Application Discovery and Delivery Intelligence (ADDI) inventory and analysis tool. Following ADDI, key steps on the journey include refactoring business services in COBOL, transforming COBOL code to Java code with an optimized design, and validating the resulting outcome, including using automated testing capabilities.
Potential benefits for clients include:
* Accelerating code development and increasing developer productivity throughout the application modernization lifecycle
* Managing total cost, complexity, and risk of application modernization initiatives, including translation and optimization of code in-place on IBM Z
* Expanding access to a broader pool of IT skills and accelerating developer onboarding
* Achieving high quality, easy to maintain code through model customization and the application of best practices
“Our collaboration with IBM is an important element in our drive to leverage generative AI interfaces to challenge legacy approaches with material productivity gains, and reinvent our Capital Markets solutions,” said Roger Burkhardt, CTO, Capital Markets and AI, Broadridge Financial. “We have had excellent client response to our generative AI investments and we are intrigued by the opportunity to further our efforts by leveraging IBM watsonx Code Assistant for Z to address a broader range of platforms.”
AI-assisted mainframe application modernization is an imperative
According to new research from the IBM Institute for Business Value, organizations are 12x more likely to leverage existing mainframe assets rather than rebuild their application estates from scratch in the next two years. At the same time, however, the study shows that the number one challenge for those same organizations is a lack of resources and skills.
“By bringing generative AI capabilities through watsonx to new use cases, we plan to drive real progress for our clients,” said Kareem Yusuf, PhD, Senior Vice President, Product Management and Growth, IBM Software. “IBM is engineering watsonx Code Assistant for Z to take a targeted and optimized approach. It’s built to rapidly and accurately convert code optimized for IBM Z, accelerate time to market and broaden the skills pool. This can help enhance applications and add new capabilities while preserving the performance, resiliency, and security inherent in IBM Z.”
There are many application modernization approaches available today. Some options include rewriting all application code in Java, or migrating everything to public cloud, which may sacrifice capabilities that are core to the IBM Z value proposition while failing to deliver on expected cost reduction. Tools that convert COBOL applications to Java syntax can produce code that is hard to maintain and can be unrecognizable to a Java developer. Generative AI is promising, but current AI-assisted partial re-write technology lacks COBOL support and doesn’t optimize the resulting Java code for the given task.
The resulting Java code from watsonx Code Assistant for Z will be object-oriented. IBM is designing this solution to be optimized to interoperate with the rest of the COBOL application, with CICS, IMS, DB2, and other z/OS runtimes. Java on Z is designed to be performance-optimized versus a compared x86 platform.4
Tue, 22 Aug 2023 04:06:00 -0500en-UStext/htmlhttps://www.ept.ca/2023/08/ibm-unveils-watsonx-generative-ai-capabilities/Killexams : IBM building AI on the ‘foundation of trust’
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Sat, 22 Jul 2023 16:39:00 -0500text/htmlhttps://www.dailytelegraph.com.au/news/national/ibm-building-ai-on-the-foundation-of-trust/video/e33b8023c66e3a8715c0c11f14d13ab5Killexams : IBM and NASA Open Source Largest Geospatial AI Foundation Model on Hugging FaceIBM and NASA Open Source Largest Geospatial AI Foundation Model on Hugging Face
PR Newswire
YORKTOWN HEIGHTS, N.Y., Aug. 3, 2023
Effort aims to widen access to NASA earth science data for geospatial intelligence and accelerate climate-related discoveries
YORKTOWN HEIGHTS, N.Y., Aug. 3, 2023 /PRNewswire/ -- IBM (NYSE: IBM) and open-source AI platform Hugging Face today announced that IBM's watsonx.ai geospatial foundation model – built from NASA's satellite data – will now be openly available on Hugging Face. It will be the largest geospatial foundation model on Hugging Face and the first-ever open-source AI foundation model built in collaboration with NASA.
Access to the latest data remains a significant challenge in climate science where environmental conditions change almost daily. And, despite growing amounts of data — estimates from NASA suggest that by 2024, scientists will have 250,000 terabytes of data from new missions — scientists and researchers still face obstacles in analyzing these large datasets. As part of a Space Act Agreement with NASA, IBM set out earlier this year to build an AI foundation model for geospatial data. And now, by making a geospatial foundation model available via Hugging Face — a recognized leader in open-source and a well-known repository for all transformer models — efforts can advance to democratize access and application of AI to generate new innovations in climate and Earth science.
"The essential role of open-source technologies to accelerate critical areas of discovery such as climate change has never been clearer," said Sriram Raghavan, Vice President, IBM Research AI. "By combining IBM's foundation model efforts aimed at creating flexible, reusable AI systems with NASA's repository of Earth-satellite data, and making it available on the leading open-source AI platform, Hugging Face, we can leverage the power of collaboration to implement faster and more impactful solutions that will Strengthen our planet."
"AI remains a science-driven field, and science can only progress through information sharing and collaboration," said Jeff Boudier, head of product and growth at Hugging Face. "This is why open-source AI and the open release of models and datasets are so fundamental to the continued progress of AI, and making sure the technology will benefit as many people as possible."
"We believe that foundation models have the potential to change the way observational data is analyzed and help us to better understand our planet," said Kevin Murphy, Chief Science Data Officer, NASA. "And by open sourcing such models and making them available to the world, we hope to multiply their impact."
The model – trained jointly by IBM and NASA on Harmonized Landsat Sentinel-2 satellite data (HLS) over one year across the continental United States and fine-tuned on labeled data for flood and burn scar mapping — has demonstrated to date a 15 percent improvement over state-of-the-art techniques using half as much labeled data. With additional fine tuning, the base model can be redeployed for tasks like tracking deforestation, predicting crop yields, or detecting and monitoring greenhouse gasses. IBM and NASA researchers are also working with Clark University to adapt the model for applications such as time-series segmentation and similarity research.
The news follows IBM's announcement earlier this year to collaborate with NASA to build an AI model that could speed up the analysis of satellite images and boost scientific discovery. It's also part of NASA's decade-long Open-Source Science Initiative to build a more accessible, inclusive, and collaborative scientific community. NASA, along with the White House and other federal agencies, has declared 2023 a Year of Open Science to celebrate the benefits and successes created through the open sharing of data, information, and knowledge.
The model leverages IBM foundation model technology and is part of IBM's larger effort to create and train AI models that can be used for different tasks and apply information from one situation to another. In June, IBM announced the availability of watsonx, an AI and data platform that allows enterprises to scale and accelerate impact of the most advanced AI with trusted data. A commercial version of the geospatial model, which is part of IBM watsonx, will be available through the IBM Environmental Intelligence Suite (EIS) later this year.
For more information about this collaboration, visit the IBM Research Blog
Statements regarding IBM's future direction and intent are subject to change or withdrawal without notice, and represent goals and objectives only.
About IBM IBM is a leading provider of global hybrid cloud and AI, and consulting expertise. We help clients in more than 175 countries capitalize on insights from their data, streamline business processes, reduce costs, and gain the competitive edge in their industries. More than 4,000 government and corporate entities in critical infrastructure areas such as financial services, telecommunications and healthcare rely on IBM's hybrid cloud platform and Red Hat OpenShift to affect their digital transformations quickly, efficiently, and securely. IBM's breakthrough innovations in AI, quantum computing, industry-specific cloud solutions and consulting deliver open and flexible options to our clients. All of this is backed by IBM's legendary commitment to trust, transparency, responsibility, inclusivity, and service.
Wed, 02 Aug 2023 18:05:00 -0500entext/htmlhttps://www.morningstar.com/news/pr-newswire/20230803ny73968/ibm-and-nasa-open-source-largest-geospatial-ai-foundation-model-on-hugging-faceKillexams : IBM and NASA Open Source Largest Geospatial AI Foundation Model on Hugging Face
Effort aims to widen access to NASA earth science data for geospatial intelligence and accelerate climate-related discoveries
YORKTOWN HEIGHTS, N.Y., Aug. 3, 2023 /PRNewswire/ -- IBM (NYSE: IBM) and open-source AI platform Hugging Face today announced that IBM's watsonx.ai geospatial foundation model – built from NASA's satellite data – will now be openly available on Hugging Face. It will be the largest geospatial foundation model on Hugging Face and the first-ever open-source AI foundation model built in collaboration with NASA.
Access to the latest data remains a significant challenge in climate science where environmental conditions change almost daily. And, despite growing amounts of data — estimates from NASA suggest that by 2024, scientists will have 250,000 terabytes of data from new missions — scientists and researchers still face obstacles in analyzing these large datasets. As part of a Space Act Agreement with NASA, IBM set out earlier this year to build an AI foundation model for geospatial data. And now, by making a geospatial foundation model available via Hugging Face — a recognized leader in open-source and a well-known repository for all transformer models — efforts can advance to democratize access and application of AI to generate new innovations in climate and Earth science.
"The essential role of open-source technologies to accelerate critical areas of discovery such as climate change has never been clearer," said Sriram Raghavan, Vice President, IBM Research AI. "By combining IBM's foundation model efforts aimed at creating flexible, reusable AI systems with NASA's repository of Earth-satellite data, and making it available on the leading open-source AI platform, Hugging Face, we can leverage the power of collaboration to implement faster and more impactful solutions that will Strengthen our planet."
"AI remains a science-driven field, and science can only progress through information sharing and collaboration," said Jeff Boudier, head of product and growth at Hugging Face. "This is why open-source AI and the open release of models and datasets are so fundamental to the continued progress of AI, and making sure the technology will benefit as many people as possible."
"We believe that foundation models have the potential to change the way observational data is analyzed and help us to better understand our planet," said Kevin Murphy, Chief Science Data Officer, NASA. "And by open sourcing such models and making them available to the world, we hope to multiply their impact."
The model – trained jointly by IBM and NASA on Harmonized Landsat Sentinel-2 satellite data (HLS) over one year across the continental United States and fine-tuned on labeled data for flood and burn scar mapping — has demonstrated to date a 15 percent improvement over state-of-the-art techniques using half as much labeled data. With additional fine tuning, the base model can be redeployed for tasks like tracking deforestation, predicting crop yields, or detecting and monitoring greenhouse gasses. IBM and NASA researchers are also working with Clark University to adapt the model for applications such as time-series segmentation and similarity research.
The news follows IBM's announcement earlier this year to collaborate with NASA to build an AI model that could speed up the analysis of satellite images and boost scientific discovery. It's also part of NASA's decade-long Open-Source Science Initiative to build a more accessible, inclusive, and collaborative scientific community. NASA, along with the White House and other federal agencies, has declared 2023 a Year of Open Science to celebrate the benefits and successes created through the open sharing of data, information, and knowledge.
The model leverages IBM foundation model technology and is part of IBM's larger effort to create and train AI models that can be used for different tasks and apply information from one situation to another. In June, IBM announced the availability of watsonx, an AI and data platform that allows enterprises to scale and accelerate impact of the most advanced AI with trusted data. A commercial version of the geospatial model, which is part of IBM watsonx, will be available through the IBM Environmental Intelligence Suite (EIS) later this year.
For more information about this collaboration, visit the IBM Research Blog
Statements regarding IBM's future direction and intent are subject to change or withdrawal without notice, and represent goals and objectives only.
About IBM IBM is a leading provider of global hybrid cloud and AI, and consulting expertise. We help clients in more than 175 countries capitalize on insights from their data, streamline business processes, reduce costs, and gain the competitive edge in their industries. More than 4,000 government and corporate entities in critical infrastructure areas such as financial services, telecommunications and healthcare rely on IBM's hybrid cloud platform and Red Hat OpenShift to affect their digital transformations quickly, efficiently, and securely. IBM's breakthrough innovations in AI, quantum computing, industry-specific cloud solutions and consulting deliver open and flexible options to our clients. All of this is backed by IBM's legendary commitment to trust, transparency, responsibility, inclusivity, and service.
