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Six barriers to digital transformation; CIO strategies to conquer them

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That led to an aggressive pace of change over the past few years, said Merim Becirovic, Accenture's managing director of core infrastructure and business operations. Consider, for instance, this measure of success: Three years ago, Accenture had only 10% of its infrastructure and compute needs in the cloud, but now it has 90% in the cloud. Such gains didn't come without challenges, Becirovic said. Accenture leaders discovered a number of potential barriers to digital transformation, ranging from new skill requirements to security to just how fast the organization can keep changing. Accenture is far from alone in its quest for transformation.


Thriving Innovation by Strategic Integration of Hyperautomation in Workforce

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Hyperautomation implies the use of advanced technologies such as artificial intelligence, and robotic process automation to automate redundant tasks. The COVID 19 pandemic has dispensed organizations to adopt disruptive technology as swiftly as possible. Before COVID 19 outbreak staggered organizational workflow, adoption of disruptive technologies was taking place in a very slow pace. For example, a report by Deloitte states that only 56% of organizations have started their automation journey, with 72% still planning to reap the benefits of this new edge technology. As organizations work remotely, a strategic approach to implement intelligent automation would aid crunching up the investment that they make for redundant tasks.


Dataiku vs. Alteryx vs. Sagemaker vs. Datarobot vs. Databricks

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Code is only a small component of any machine learning solution. The goal of managed machine learning services is to centralize these components into a single packaged solution. But not all managed machine learning services are fully comparable. Tools like AWS Sagemaker help you manage the complexity inherent in any machine learning solution, but still expect you to have engineers on your team who can build and understand the code. These tools focus more on the compute layer.


Fetch.AI launches blockchain-based AI smart-city infrastructure in Munich

ZDNet

Blockchain can be used to decentralize federated learning algorithms so that the benefits of collective machine learning are shared across the multiple owners of data. And, in Munich, it is helping commuters efficiently find a parking space. Cambridge, UK-based artificial intelligence lab Fetch.ai is a building a decentralized machine learning network for smart infrastructures. In partnership with Munich, Germany-based enterprise blockchain solutions provider Datarella, it has announced the implementation of its smart city infrastructure trials In Munich, Germany. The smart city zoning trial in Munich, called M-Zone will launch in the Connex Buildings and will use multi-agent blockchain-based AI services to optimise parking resources in commercial real estate properties in the city center to reduce the city's carbon footprint.


Edge computing is here: what's next? - Embedded.com

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Across a range of industries, and specifically in the industrial automation vertical, there is broad agreement that the deployment of modern computing resources with cloud native models of software lifecycle management will become ever more pervasive. Placing virtualized computing resources nearer to where multiple streams of data are created is well established. It is the path to address system latency, privacy, cost and resiliency challenges that a pure cloud computing approach cannot address. This paradigm shift was initiated at Cisco Systems around 2010, under the label "fog computing" and progressively morphed into what is now known as "edge computing". The requirements of mission critical industrial systems That said, the full potential of this transformation in both computing and data analytics is far from being realized.


AI & Machine Learning: An Enterprise Guide - InformationWeek

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Artificial intelligence is no longer a buzz phrase -- it's doing real work for real companies. Even in the early stages of implementation, AI is providing enterprise organizations with benefits: Efficiency in operations, cybersecurity protections, digital innovation, and stronger customer relationships. Next up for AI in the enterprise is the ability to scale with more apps serving more departments. However, the race to implement AI and machine learning also raises citizen privacy concerns. There have been revelations about the potential for algorithmic bias reflected in data sources.


The Deep Learning Tool We Wish We Had In Grad School

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Machine learning PhD students are in a unique position: they often need to run large-scale experiments to conduct state-of-the-art research but they don't have the support of the platform teams that industrial ML engineers can rely on. As former PhD students ourselves, we recount our hands-on experience with these challenges and explain how open-source tools like Determined would have made grad school a lot less painful. When we started graduate school as PhD students at Carnegie Mellon University (CMU), we thought the challenge laid in having novel ideas, testing hypotheses, and presenting research. Instead, the most difficult part was building out the tooling and infrastructure needed to run deep learning experiments. While industry labs like Google Brain and FAIR have teams of engineers to provide this kind of support, independent researchers and graduate students are left to manage on their own.


Productization of AI: 5 Notable Barriers

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Artificial Intelligence has the potential to be become as embedded into everything that we do, just like the Internet. It is scaling rapidly and solving many problems and in future will change the very way we lead lives or conduct business. Most executives consider AI as a disruptive technology which will make or break their business, employees think of it as a job destroyer, consultants position it as a solution to everything and media delivers AI as the hype of the millennium. While there is an element of truth and myth in each, the observed reality is that productization of AI on the ground is extremely hard, rudimentary use cases have been addressed and barriers to go mainstream are several. Outside the Silicon Valley, even the most aggressive use cases of AI i.e., retail, banking, telecom etc. are still in their early stages.


Wireless charging on the moon

ZDNet

A company called WiBotic, which makes advanced wireless charging and fleet energy management solutions for technologies like drones and industrial robots, announced a major partnership to develop wireless charging solutions for robots on the moon. WiBotic will join in the $5.8 million contract with space robotics company Astrobotic, Bosch, and the University of Washington as part of NASA's'Tipping Point' program. "We're thrilled to have been selected by Astrobotic and NASA to deliver wireless charging capabilities to the next generation of lunar vehicles," says Ben Waters, CEO and co-founder, WiBotic. "While WiBotic specializes in wireless charging for military, industrial and commercial robots in all sorts of punishing environments here on Earth – from large warehouses to dusty deserts and corrosive saltwater – this is our first chance to take our technology into space. We're excited to work closely with NASA and be part of the next chapter of space exploration."


Confidential computing: the final frontier of data security

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Data threats never rest, nor should the protection of your sensitive information. That's the driving principle behind confidential computing, which seeks to plug a potentially crippling hole in data security. Confidential computing provides a secure platform for multiple parties to combine, analyze and learn from sensitive data without exposing their data or machine learning algorithms to the other party. This technique goes by several names -- multiparty computing, federated learning and privacy-preserving analytics, among them -- and confidential computing can enable this type of collaboration while preserving privacy and regulatory compliance. Data exists in three states: in transit when it is moving through the network; at rest when stored; and in use as it's being processed.