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After achieving the elite AI and Machine Learning in Microsoft Azure specialization, Protiviti has launched new Microsoft Artificial Intelligence (AI) Center of Excellence and AI Solutions to help clients adopt responsible AI

MENLO PARK, Calif., Aug. 9, 2023 /PRNewswire/ -- Global consulting firm Protiviti has achieved the Artificial Intelligence (AI) and Machine Learning (ML) in Microsoft Azure specialization. Microsoft solution specializations recognize partners for deep expertise in the designated capability areas and illustrate the partner has met demanding requirements to demonstrate solution competency, all validated by a third-party audit. With the AI and ML in Microsoft Azure specialization, Protiviti has shown proven capabilities enabling customer adoption of Al and implementing Azure-based solutions for Al-powered applications and machine learning.

As part of this Microsoft specialization achievement, Protiviti has launched a Microsoft AI Center of Excellence (MSAI COE) and new Microsoft-certified AI solutions. The MSAI COE is focused on researching and developing AI solutions that drive impactful change to businesses, and how they operate and serve their customers. It also brings together a high concentration of Microsoft MVPs, world-class data scientists, award-winning AI solutions, and Protiviti's risk heritage focusing on delivering trusted and secure AI solutions. Protiviti's MSAI COE will operate and deliver solutions that embed Microsoft's responsible AI principles: fairness, inclusiveness, reliability and safety, transparency, security and privacy, and accountability.

Within Protiviti's suite of 50+ AI solutions and accelerators, the company has developed Microsoft-certified offerings to help clients get started with generative AI, including: the Generative AI Roadmap and Generative AI Proof of Concept. The Generative AI Accelerated Roadmap is a four-week assessment to define objectives and develop a strategic plan for deploying Microsoft Azure-based Open AI-LLM applications across business functions. Generative AI Proof of Concept provides a six-week solution utilizing a proven methodology and a knowledgeable team to accelerate ideation to functional prototype.

"Protiviti is proud to collaborate with Microsoft to be a leading provider of AI enabled capabilities," said Christine Livingston, global leader of Protiviti's AI services. "Together with Microsoft, we look forward to helping organizations utilize AI solutions responsibly to drive impactful transformation in their businesses."

Microsoft's partnership with OpenAI and the acceleration of large language models have unlocked new ability to deliver transformational AI-enabled capabilities and experiences, while maintaining the security and privacy of organizational data. Protiviti's MSAI COE will enhance its 50+ proprietary models with the latest advancements in Microsoft and OpenAI technologies.

"We're excited to be a leader for Microsoft-enabled AI solutions and look forward to driving business value to our clients," said Tom Andreesen, Protiviti's Microsoft global alliance leader. "The MSAI COE will focus on leveraging the latest Microsoft AI capabilities and driving research and resulting solutions that consider the broad risk considerations in today's business environment and is well aligned with Protiviti's rich risk management and governance history."

To learn more about Microsoft AI, Protiviti and Microsoft will host a webinar on August 17 to discuss "Are You Ready for (Responsible) AI? How to Embrace the Future of Productivity and Limitless Innovation with Microsoft AI", register here.

About Protiviti

Protiviti (www.protiviti.com) is a global consulting firm that delivers deep expertise, objective insights, a tailored approach and unparalleled collaboration to help leaders confidently face the future. Protiviti and its independent and locally owned Member Firms provide clients with consulting and managed solutions in finance, technology, operations, data, digital, legal, governance, risk and internal audit through its network of more than 85 offices in over 25 countries.

Named to the 2022 Fortune 100 Best Companies to Work For list, Protiviti has served more than 80 percent of Fortune 100 and nearly 80 percent of Fortune 500 companies. The firm also works with smaller, growing companies, including those looking to go public, as well as with government agencies. Protiviti is a wholly owned subsidiary of Robert Half Inc. (NYSE: RHI). Founded in 1948, Robert Half is a member of the S&P 500 index.

SOURCE Protiviti

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In the dynamic landscape of modern technology, where innovation is the norm and data flows like an ever-expanding river, machine learning stands as the beacon of transformation. At its core, machine learning empowers computers to unravel intricate patterns, glean insights from vast datasets, and ultimately make intelligent decisions. Among the arsenal of tools that machine learning provides, two techniques shine particularly brightly: classification and regression. These twin pillars of predictive modeling are the architects of smart decision-making, enabling machines to not only comprehend the nuances of diverse data but also predict future outcomes with uncanny accuracy.

In the vast and intricate tapestry of machine learning, one captivating technique takes center stage, orchestrating a symphony of data like a virtuoso conductor leading a mesmerizing musical performance. This technique, known as classification, is the enchanting art of imbuing machines with the ability to unravel patterns, categorize information, and make decisions that resonate with unerring accuracy.

Imagine a realm where computers, akin to perceptive artists, possess the dexterity to discern the intricate strokes of a digital canvas. Classification empowers them to sort, classify, and label data points with an intuitive finesse that mirrors the human minds capacity to differentiate between shapes, colors, and textures. Its as though the machine is not merely analyzing numbers and variables, but is gracefully dancing with the essence of the data itself.

From identifying fraudulent transactions in the financial world to diagnosing diseases from medical images, classification stretches its imaginative wings across a diverse array of applications. Its the ingenious algorithm behind email filters that deftly segregate spam from legitimate messages, just as a seasoned sommelier distinguishes the subtle notes of fine wine.

As we delve deeper into the captivating realm of classification, we unravel the threads of a narrative where machines don the mantle of interpreters, translating the language of data into a harmonious melody of understanding.

In the realm of machine learning, where algorithms unfold like pages of a captivating novel, there exists a technique that resembles the crystal ball of ancient fortune-tellers, offering glimpses into the mysteries of the future. This technique, known as regression, is a masterful blend of art and science that empowers machines to peer into historical data, decipher intricate relationships, and forecast forthcoming outcomes with a precision that borders on the magical.

Imagine a virtual oracle that harnesses the echoes of the past, infusing them with the wisdom of algorithms to unveil a world yet to unfold. Regression is the compass guiding us through the terrain of continuous variables, enabling us to navigate complexities ranging from economic predictions to ecological phenomena. Its as though machines have been bestowed with the power to unveil a tantalizing glimpse of the future, just as a seasoned astronomer deciphers the movements of celestial bodies to predict cosmic events.

Consider a scenario where industries pivot and strategize based on insights that transcend time. A business, for instance, could employ regression to illuminate the path ahead by analyzing historical sales data, market trends, and external influences. Armed with this predictive prowess, the business might effortlessly adapt to changing conditions, pre-emptively altering its course to thrive in an ever-shifting landscape.

In our exploration of the captivating universe of regression, we embark on a voyage that stretches beyond numbers and equations. Its a journey where algorithms wield the brushstrokes of insight, painting a canvas of future possibilities and enabling us to chart our course with a blend of scientific acumen and imaginative foresight.

In the intricate labyrinth of machine learning, where algorithms weave a tapestry of intelligence, there exists a remarkable crossroads where two distinct techniques converge, creating a symphony of insight and foresight. This juncture, where classification and regression intersect, is a realm of boundless potential a place where machines harmoniously blend the art of categorization with the science of prediction, transcending boundaries and unlocking new dimensions of decision-making mastery.

Imagine a realm where the analytical mind meets the visionary eye, where data is not just dissected but also projected forward in time. Here, classification the ability to discern patterns and categorize information joins hands with regression the technique of forecasting continuous outcomes to form a dynamic partnership. Its akin to a skilled conductor leading an orchestra, seamlessly blending diverse instruments into a crescendo of harmonious melodies.

This fusion finds its resonance in fields as diverse as healthcare and finance. For instance, envision a scenario where a medical professional seeks to chart a patients trajectory. Classification steps in to diagnose and categorize the ailment based on symptoms, medical history, and tests. But the tale doesnt conclude there; regression then steps onto the stage, forecasting the patients recovery time and potential outcomes. Its an intricate pas de deux between understanding the present and predicting the future, resulting in a comprehensive narrative that guides medical decisions with a profound blend of accuracy and foresight.

As we venture into this captivating convergence of minds, we embark on a voyage that unveils the synergy of classification and regression. This harmonious merger offers a glimpse into the future where data, once disparate, becomes a unified source of wisdom, empowering us to make decisions that are not only informed by the past but also shaped by a visionary outlook. Together, classification and regression dance to a rhythm of discovery, illuminating the path toward a horizon where intelligence is a tapestry woven with threads of both understanding and prophecy.

In the grand tapestry of technological evolution, where the threads of innovation weave a complex narrative, we find ourselves at a crossroads where intelligence and possibility converge. The journey through the landscapes of classification and regression in machine learning has unveiled a panorama of potential, where data transforms into discernment, and predictions blossom into strategic prowess.

As we step back to contemplate the significance of this expedition, we are met with a resounding revelation: the fusion of classification and regression holds the key to empowering the future. It is not merely a journey through algorithms and equations; it is a voyage into a world where decisions, once rooted in uncertainty, are now fortified by the pillars of insight and foresight.

Classification, with its meticulous dance of patterns, teaches machines to decipher the language of data, categorizing and labeling with a precision that mirrors human intuition. Regression, the art of gazing into the crystal ball of historical information, equips us with the ability to predict and optimize, transforming the future from an enigma into a realm of calculated possibilities.

This synergy isnt limited to a single domain it extends its tendrils into every facet of human endeavor. From medicine to finance, agriculture to energy, the marriage of classification and regression reshapes industries, informs strategies, and guides choices that resonate with efficacy and ingenuity.

In a world propelled by intelligent algorithms, we embark on a future where decisions are not just made, but strategically crafted. The capabilities forged by classification and regression offer a tapestry of insights, inviting us to step into a landscape where innovation and informed choices are the cornerstones of progress.

So, as we stand on the precipice of this transformative era, let us embrace the elegance of these techniques, for they are the conduits through which the realm of machines transcends into one of partners, collaborators, and co-creators. The journey has been exhilarating, but the path ahead is even more luminous an ascent towards a horizon where intelligence is not just a tool, but a beacon guiding humanity toward a future of limitless potential.

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The field of autonomous driving research is at a pivotal stage, with groundbreaking advancements in artificial intelligence (AI) and machine learning taking it into uncharted territories. Researchers have made significant progress in overcoming challenges related to safety, decision-making, and environmental adaptability.

Collaborations between academia, industry, and regulatory bodies are shaping the future of transportation. These partnerships promise a revolutionary era of self-driving vehicles that will redefine commuting and how we interact with our environment.

Deploying cutting-edge technologies on devices like autonomous vehicles is no easy task. Most state-of-the-art technology, such as Chat-GPT, is built on top of the Transformers architecture in Machine Learning. However, slow inference times with machine learning transformers have been a challenge in the field of computer vision (CV) and natural language processing (NLP).

Transformers, especially large-scale models like BERT, GPT-3, and their variants, have achieved remarkable performance in various NLP tasks. Their computational complexity and memory requirements, however, limit their real-world applicability in time-sensitive or interactive applications.

Addressing the challenge of slow inference time is crucial for making transformer-based models more practical and accessible for real-world applications, including autonomous vehicles and social robots.

Recent research, led by Mr. Apoorv Singh, a Machine Learning Scientist at an autonomous vehicle company, has enabled the deployment of computation-hungry transformer models in real-time on autonomous cars. The research focuses on scaling down the inference time of Transformers-based computer vision models while maintaining detection performance.

These advancements give confidence that autonomous vehicles are rapidly progressing and will soon become a reality, bringing forth a transformative era of self-driving transportation. The future for autonomous vehicles seems brighter than ever, promising to revolutionize transportation and reshape our interaction with the world.

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