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37% of Artificial Intelligence Technologies are Adopted by High Tech Industry

#artificialintelligence

Hyderabad, November 23, 2020 –– Analytics Insight conducted a survey "The Global Artificial Intelligence Trends 2020" to understand the global adoption of Artificial Intelligence (AI) amongst enterprises and recognize the business perceptions of AI across sectors. Analytics Insight reached out to 2,200 professionals online located in different geographic regions across a wide range of industries to explore different views toward AI and its current implications among enterprises. Receiving 256 responses for the survey, Analytics Insight articulated a detailed report, which can be indicative of the market as a whole. Out of the 256 respondents, 48.5% were working at small-scale companies with the company size of fewer than 100 employees. About 29.8% of the respondents were employed at companies which had total employees ranging from 100-1000, while 21.7% of respondents had a company size of over 1000 employees.


Review: Kinetica analyzes billions of rows in real time

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In 2009, the future founders of Kinetica came up empty when trying to find an existing database that could give the United States Army Intelligence and Security Command (INSCOM) at Fort Belvoir (Virginia) the ability to track millions of different signals in real time to evaluate national security threats. So they built a new database from the ground up, centered on massive parallelization combining the power of the GPU and CPU to explore and visualize data in space and time. By 2014 they were attracting other customers, and in 2016 they incorporated as Kinetica. The current version of this database is the heart of Kinetica 7, now expanded in scope to be the Kinetica Active Analytics Platform. The platform combines historical and streaming data analytics, location intelligence, and machine learning in a high-performance, cloud-ready package.


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.


Applications of Differential Privacy to European Privacy Law (GDPR) and Machine Learning

#artificialintelligence

Differential privacy is a data anonymization technique that's used by major technology companies such as Apple and Google. The goal of differential privacy is simple: allow data analysts to build accurate models without sacrificing the privacy of the individual data points. But what does "sacrificing the privacy of the data points" mean? Well, let's think about an example. Suppose I have a dataset that contains information (age, gender, treatment, marriage status, other medical conditions, etc.) about every person who was treated for breast cancer at Hospital X.


2021 Trends in Big Data: The Interoperability Challenge - insideBIGDATA

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The dictates of big data--its inner manipulations and trends--have defined the very form of the data ecosystem since the inception of these technologies nearly a decade ago. It's become entrenched in the most meaningful dimensions of data management, implicit to all but its most mundane practices, and indistinguishable from almost any type of data leveraged for competitive advantage. As such, current momentum in the big data space isn't centered on devising new expressions of its capabilities, but rather on converging them to actualize the long sought, rarely realized, time honored IT ideal of what Cambridge Semantics CTO Sean Martin termed "interoperability. And, the more the data starts to support that, the more interesting that gets, too." The grand vision of interoperability involves the capacity to readily interchange enterprise systems and resources as needed to maximize business productivity without technological restrictions.


How to build the right data architecture for customer analytics

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Retail is one of the most obvious places you'll have seen this. Since the onset of the pandemic, traditional bricks-and-mortar brands have rushed to optimise their digital offerings, boost home delivery and launch'click and collect' services as consumers have flocked online. In July, a landmark McKinsey report concluded that in just three months, we'd seen 10 years' worth of e-commerce growth. It's a trend that isn't likely to reverse. The digital customer experience is rapidly shifting from being a competitive differentiator to the key to a company's survival in a new, digital-first economy.


Make a Compelling Case for Your Digital Supply Chain Transformation

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Supply chain disruption during the global pandemic offers chief supply chain officers (CSCOs) a prime opportunity to get leadership backing for supply chain digital transformation. Gartner research shows that the pandemic has only increased the urgency for digitalization. Many CEOs expect to accelerate digital initiatives, and the vast majority of CSCOs intend to dramatically speed up their supply chain digital maturity progress over the next five years. CSCOs must build a sound business case to support their digital vision's business value "Supply chain leaders should take advantage of this once-in-a-lifetime chance to raise the profile of the supply chain function and develop supply chain capabilities to support business priorities," says Pierfrancesco Manenti, VP Analyst, Gartner. "But CSCOs must build a sound business case to support their digital vision's business value and priority alignment."


Federated Learning for Privacy-Preserving AI

Communications of the ACM

There has been remarkable success of machine learning (ML) technologies in empowering practical artificial intelligence (AI) applications, such as automatic speech recognition and computer vision. However, we are facing two major challenges in adopting AI today. One is that data in most industries exist in the form of isolated islands. The other is the ever-increasing demand for privacy-preserving AI. Conventional AI approaches based on centralized data collection cannot meet these challenges.


Democratization of machine learning and artificial intelligence with Google Cloud

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In the first blog in this series, we discussed how data availability, data access, and insight access have evolved over time, and what Google Cloud is doing today to help customers democratize the production of insights across organizational personas. In this blog we'll discuss why artificial intelligence (AI) and machine learning (ML) are critical to generating insights in today's world of big data, as well as what Google Cloud is doing to expand access to this powerful method of analysis. A report by McKinsey highlights the stakes at play: by 2030, companies that fully absorb AI could double their cash flow, while companies that don't could see a 20% decline. ML and AI have traditionally been seen as the domain of experts and specialists with PhDs, so it's no surprise that many business leaders frame their ML goals around HR challenges: creating new departments, hiring new employees, developing retaining programs for the existing workforce, and so on. But this isn't the way it has to be.


Council Post: How No-Code Solutions Aid Text Mining In Big Data Analytics

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The quantity of digital text data has grown exponentially in recent years and will continue to grow. From social media posts to customer transactions, surveys, reviews, chats, emails and more, businesses face the challenge of monitoring various sources and extracting relevant data. The rise of unstructured data on the internet is an opportunity for both small and large enterprises. Along with data from new sources, businesses have found ways to generate new insights from unstructured data, leading to new technologies and opportunities for research. With the rapid evolution of big data analytics and with unstructured content making up an estimated 80% of organizations' data, financial enterprises have given significant attention to text mining.