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Dr. William Casebeer is a true leader in the artificial intelligence and machine learning industry. His contributions will certainly impact how AI/ML develops in the coming years.

Dr. William Casebeer will be the moderator of POCs 10th Annual Defense R&D Summit. Dont miss the opportunity to meet him and the other industry leaders. Reserve your seat here.

Dr. William D. Casebeer, Ph.D., MA, leads the Open Innovation Center at Riverside Research as a Director. As the Director, he is in charge of AI and machine learning.

Dr. William Casebeer led a group of scientists and engineers from different fields at Riverside Research who worked on machine learning and artificial intelligence. Some of their main goals are to make progress in neuromorphic computing, adversarial AI, human-machine teaming, and object and activity recognition. These advances will give the Department of Defense and Intelligence Community new tools.

Dr. William Casebeer has broad academic achievements. Besides his bachelors degree, he holds a masters and Ph.D. title. Lets take a look at Dr. Casebeers educational attainments:

Potomac Officers Club invited prominent leaders and experts to speak at the 10th Annual Defense R&D Summit. Check out other speakers, including Capt. Jesse Black, Aditi Kumar, Hon. Heidi Shyu, and many more. Engage and learn more about the developments in defense technology with insightful discussions.

We invite you to join the summit on Wednesday, January 31, 2024, from 7:00 a.m. to 4:00 p.m. at the Hilton Alexandria Mark Center. We hope to see you there!

Before becoming an executive at Riverside Research, Dr. William Casebeer served the government for decades. Lets take a look at his career and leadership timeline.

The mission of the nonprofit organization Riverside Research is to promote national security. The organization can create research-driven solutions and yield faster results with its nonprofit framework and collaborative innovation strategy.

Riverside Researchs core competencies include radar systems, Artificial Intelligence and Machine Learning, and system engineering and integration.

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Dr. William Casebeer, Director Of Artificial Intelligence And Machine Learning, Riverside Research - Executive Gov

Oracle Open Source has introduced the Stable Diffusion in Java (SD4J) project, a modified port of the Stable Diffusion C# implementation with support for negative text inputs. Stable diffusion is a deep learning text-to-image model based on diffusion. SD4J can be used, via the GUI or programmatically in Java applications, to generate images. SD4J runs on top of the ONNX Runtime, a cross platform inference and training machine learning accelerator, allowing faster customer experience and reduced model training time.

Git Large File Storage, a Git extension for versioning large files, should be installed first, for example with the following command on Linux:

Afterwards, the SD4J project can be cloned locally with the following command:

SD4J uses models, the compatible pre-built ONNX models from Hugging Face, that will be used for the examples in this news story:

The README contains more information on using other models, such as those not in ONNX format.

ONNXRuntime-Extensions is a library which extends the capabilities of the ONNX models and the interference with the ONNX Runtime:

After cloning the project, the following command can be executed inside the onnxruntime-extensions directory to compile the ONNXRuntime-Extensions for your platform:

The following error might be displayed if CMake isn't installed:

Install at least version 3.25 of CMake to resolve the error, for example with the following command on Linux:

When the build is successful, the resulting library (libortextensions.[dylib,so] or ortextensions.dll) can be found inside the following directory:

The resulting library should be copied to the root directory of the SD4J project.

After these preparations, the GUI can be started by executing the Maven command, containing the model path, inside the sd4j directory:

The SD4J GUI is shown after the Maven command executed successfully:

The images in this news story are created with guidance scale 10, seed 42, inference steps 50 and image scheduler Euler Ancestral, unless stated otherwise.

First, the GUI is used to create an image of a sports car on the road, with the following image text:

This results in a red sports car on a road:

When generating images of sports cars, most of them are red. In order to create images with sports cars that aren't red, the image negative text may be used to specify what the image shouldn't contain. For example, by using the value red for image negative text, a white car is generated in this example:

The guidance scale indicates whether the resulting image should be closely related to the text prompt. A higher number indicates that they should be closely related. Conversely, a lower number may be used if more creativity in the image is desired. For stable diffusion, most models use a default guidance scale value between 7 and 7.5.

A clear picture of a house on a hill surrounded by trees is generated using the image text: Professional photograph of house on a hill, surrounded by trees, while it rains, high resolution, high quality and guidance scale 10:

Using the same image text with guidance scale 1allows more creativity and the house is now a bit hidden between the trees and the hill is less visible:

The seed is a random number used to generate noise. The generated images stay the same when using the same seed, prompt and other parameters.

Stable diffusion starts with an image of random noise. With each inference step, the noise is reduced and steered towards the prompt. Higher is not always better as it might introduce unwanted details. The Hugging Face website in general recommends 50 inference steps.

Creating an image of a tree in a park with inference 10 results in a relatively noisy tree image:

Increasing the inference steps to 50 results in a clearer image of a tree:

While increasing the inference steps further to 200 results in an image clearly displaying multiple trees and some other elements, for example in red:

The image scheduler takes a model's output to return a denoised version, while the batch size specifies the amount of generated images.

Working manually via the GUI allows generating images, however the project also provides the SD4J Java class to access SD4J programmatically.

Faster image generation is possible after enabling the CUDA integration for NVIDIA GPUs by changing the exec-maven-plugin in the pom.xml from CPU to CUDA.

More information can be found in the SD4J README and the Hugging Face documentation provides additional information about the different concepts.

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Stable Diffusion in Java (SD4J) Enables Generating Images with Deep Learning - InfoQ.com

India has emerged as a global technology and services hub, driven by both Indian and global IT companies who are at the forefront of cutting-edge technology innovation. Due to Indias enormous talent pool, supportive corporate and legislative climate, and developing infrastructure India was already home to capability centers of 1,300+ global organizations (GCCs) in 2020, directly employing 1.3+ million people, generating approximately US$33.8 billion in revenue.

As of 2023, the number of GCCs in India has now reached 1,580, and it is anticipated to surpass 1,900 by 2025 and 2,400 by 2030. India is deemed as the global GCC capital with over fifty percent stakes in the global GCC market.

GCCs in India are primarily driven by engineering and R&D services, which account for 56% of total revenue. They have evolved as the epicenter of innovation, even transforming the parent companies which were their origins. With a large pool of highly skilled IT talent, GCCs in India can easily find suitable talent with desired skills and align them with the objectives of the company.

Due to their focus on innovation, GCCs in India play a significant role in driving innovation and digital transformation in the manufacturing industry. With the emergence of Artificial Intelligence and Machine Learning, we are now entering a new era in manufacturing, one that has been dubbed the fourth industrial revolution, or Industry 4.0, or the second machine age.

The reason for AIs massive impact in manufacturing is due to its ability to increase productivity, decrease expenses, enhance quality, and decrease downtime in manufacturing. Emerging AI technologies, such as Deep Learning Neural Networks, are demonstrating immense potential in data analysis, aiding decision-making, and offering additional advantages including precise demand forecasting, elevated operational efficiency, supply chain optimization, tailored product offerings, and material waste reduction. AI for manufacturing is expected to grow from $1.1 billion in 2020 to $16.7 billion by 2026, an astonishing CAGR of 57 percent.

A key building block for GCC-driven manufacturing in India is the countrys rich talent pool in the AI/ML domain. India already produces 16% of global AI talent, placing it among the top three contributors in the world. The countrys technology workforce grew up in an internet/cloud-first world, and its ability to assemble solutions from combinations of legacy, cloud, and SaaS components is world-class.

Furthermore, to help this growth, India-born CSPs and Hyperscalers have rapidly built the Cloud GPU infrastructure and Machine Learning platforms needed for AI innovation. This is a crucial piece, as AI and ML technologies rely heavily on advanced Cloud GPUs and Cloud GPU Clusters, which provide the platform needed for training AI algorithms. GCCs are already leveraging this infrastructure, in addition to the incredible talent pool, in order to drive rapid innovation and build on the promise of Industry 4.0.

Additionally, AI in manufacturing in India is poised to be deeply influenced by the Indian governments keenness to be a key participant in the conversation on AI adoption and regulation at an international level. In the Union Budget of 2023-24, the finance minister called for Making AI in India and Making AI work for India. The budget also announced the setting up of three Centres of Excellence for research on AI in premier educational institutions. Already, in 2022, the revenue generated through AI in India stood at USD 12 billion in 2022, a number that is expected to grow rapidly over the next decade.

This collaborative effort between GCCs, government policies, and innovative IT companies is driving Indias transition into a global manufacturing powerhouse in an AI and ML-driven era. This collective endeavor not only highlights technological advancement but also presents a holistic vision encompassing policy support, talent nurturing, and global collaboration, positioning India firmly on the global tech stage.

Contributed by Kesava Reddy, Chief Revenue Officer,E2E Networks Ltd

The rest is here:
The Impact of Artificial Intelligence and Machine Learning in GCC-Driven Manufacturing - TechiExpert.com

As we look towards the future, the convergence of DevOps, MLOps, and AIOps is set to redefine the landscape of software development and IT operations. These methodologies, while distinct, are increasingly intertwined, signaling a new era of efficiency, automation, and intelligence in technology operations.

DevOps has already proven its value in improving collaboration between development and operations teams, enhancing software quality, and accelerating delivery times. Its future lies in further integration with emerging technologies and practices. The growing adoption of cloud computing and the increasing complexity of IT environments demand more sophisticated DevOps strategies. The integration of AI and machine learning within DevOps processes, often referred to as AIOps, is poised to automate and optimize many aspects of software development and deployment.

As machine learning becomes more prevalent in various industries, MLOps is set to play a critical role in ensuring the effective deployment and management of these models. The future of MLOps involves closer integration with DevOps practices, enabling a more streamlined pipeline for machine learning models from development to production. The focus will be on creating more robust, scalable, and compliant machine learning workflows, addressing challenges such as model drift, data quality, and regulatory compliance.

AIOps is rapidly becoming a cornerstone of modern IT operations, offering unprecedented capabilities in automating and optimizing IT processes. The future of AIOps lies in its ability to handle increasingly complex and dynamic IT environments, using AI to predict and prevent issues before they impact business operations. The integration of AIOps with DevOps and MLOps will create a more cohesive and intelligent IT ecosystem, capable of responding quickly to changes and delivering greater value.

More:
DevOps, MLOps, and AIOps: Navigating the Intersection of Development and Operations - Medium

To further empower businesses and tech professionals in the new era of work, Clapself, a leading AI-powered talent platform announced the launch of its AI Professionals service. Available immediately, this groundbreaking offering by Clapself provides businesses across industries swift access to exceptional AI talent.

Artificial Intelligence is fast transforming industries. As per a PwC report, 70% of businesses are involved with AI in some way. Securing specialized talent is critical for businesses to thrive. Acknowledging this growing demand, Clapself proudly introduces its AI Talent servicesan exclusive gateway to a pool of top-tier, pre-vetted AI tech professionals, saidBryan Verduzco, Co-founder and Chief Growth Officer of Clapself.

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Key Features of Clapself AI Talent Service:

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According to MarketsandMarkets, the global Artificial Intelligence market is expected to grow at a CAGR of 36.8% to reachUSD 1.3T by 2030 fromUSD 150.2Bin 2023. As more organizations are recognizing the transformative potential of AI/ML to improve operational efficiency, enhance customer experiences, and drive innovation, the demand for AI skills such as Machine Learning, Natural Language Processing, Computer Vision, and Predictive Analytics has reached unprecedented levels.

Clapself uses AI to connect customers with the top talent, for meeting project specific needs, saidRamna Sharma, Founder and President of Clapself.

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Dave Sanders, Chief Mentor at Clapself acknowledged the increased demand for the unique AI skillset. Clapself is well poised to lead the effort to identify and connect with some of the leading experts in this field, providing their clients a competitive advantage to access these limited resources,

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

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Clapself, an AI-Powered Talent Platform, Unveils Its Latest Offering: The AI Professionals Service - AiThority