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Tealium adds machine learning capability to Audience Stream – Which-50

tealium adds machine learning capability to audience stream – which-50

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Melina Gouveia 2020-07-12Tealium had added machine learning to Audience stream, its Customer Data Platform (CDP). Called Tealium Predict ML the company says it is designed to enable marketers to orchestrate real-time customer data, for instance by identifying buyers that are most likely to make a purchase or a segment most likely to churn.The company said in a press statement that the software was designed to pair with its AudienceStream CDP to continuously anticipate customer behaviours using machine learning to power more effective audience segmentation for better customer engagement, conversion rates, and lifetime value.By creating custom-tailored predictions in a matter of clicks, the company says marketers gain insights into the likelihood of customer behaviours as well as full visibility into the data used to generate them. According to Tealium, it is with these insights, brands can effectively and efficiently engage and delight the right customers while improving business outcomes.Tealium’s believes its expansion into prediction and decisioning features will help it martech offer stand out among CDP providers, particularly in an environment amidst budget reductions and heightened emphasis on customer retention, where marketers are being asked to deliver greater results with fewer resources. Tealium says it …

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Why is Data Science, AI and ML key to Lead Digital Transformation?

why is data science, ai and ml key to lead digital transformation?

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Data science is shifting towards a new paradigm where machines can be taught to learn from data to derive conclusive intelligent insights. Artificial Intelligence is a disruptive technology that collates the intelligence displayed by machines mimicking human intelligence. AI is a broad term for smart machines programmed to undertake cognitive human tasks that require judgment-based decision making.
With all the hype and excitement surrounding Artificial Intelligence, businesses are already churning data in massive quantities over call logs, emails, transactions and daily operations. Machine learning (ML) is a dynamic application of artificial intelligence (AI) that empowers the machines to learn and improve the model accuracy levels. Machine Learning is categorised into deep learning, reinforcement learning based on the capability of machine learning algorithms to relearn from experience. Machine learning deploys several theories and techniques from Data Science, which includes, classification, categorization, clustering, trend analysis, anomaly detection, visualization and decision making.
Modern enterprises seeking to apply for AI advance ahead to digitally transform their business and operational models by leveraging on the following roadmap-
• Step 1: Brainstorm the right use cases
• Step 2: Set up analytics capability
• Step 3: Deploy machine learning algorithms
Enterprises need data science know-how to connect data pipelines to analytics and …

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Covid-19, Jobs, Data and Machine Learning | The Union Journal

covid-19, jobs, data and machine learning | the union journal

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Americans worry machine learning and artificial intelligence are a threat to their livelihoods. But the global pandemic demonstrates these data technologies are essential to improving lives.– Advertisement –A recent survey captured the job-loss anxiety. Thirty-seven per cent of workers aged 18 to 24 say new technology will eliminate their job by 2025. This fear climbs to 45 percent for workers of most ages in advertising and marketing. It’s 42 per cent in business support and logistics.Studies of job loss from automation attended to more dramatic conclusions. An Oxford study predicted that “about 47 percent of total US employment is at risk.” The McKinsey Global Institute found 51 percent of work in the U.S. may be automated, accounting for $2.7 trillion in wages.AI and data concerns– Advertisement –These fears are valid. AI will threaten jobs and alter career paths. It is like the multitude of other digital innovations that have emerged in the forty years since the arrival of pcs. Professions like legal work and accounting have largely been sheltered from disruption in the past. But now an AI-driven “virtual lawyer” application runs on Amazon’s Alexa and is seen as “a real threat” to junior solicitors.The breakdown of societal systems also destroys jobs. …

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The “Learning” System: AI & Virtual Care

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Machine learning is critical to effective and scalable virtual care; allowing clinicians to simultaneously improve outcomes and reduce the cost of care.
With the proliferation of sensors and wearables in the home setting, a new host of data is now available for clinicians. And yet, no human can feasibly and economically make sense of this “deluge” of data. Enter machine learning, which according to Nature, is already showing significant promise augmenting clinicians’ ability to treat Type II Diabetes, analyze skin lesions, and electrocardiograms. According to Accenture, machine learning will save $150 Billion a year in healthcare costs by 2026. 
And yet, today’s care model, as is perpetuated by telehealth providers, struggles to adapt and learn from each patient interaction as any learned knowledge that can benefit a population, is effectively lost when clinicians press “end call” after each session.
Machine learning will unlock clinicians’ ability to deliver personalized care at population scale. 
Effectively “bridging” a capacity gap, machine learning is critical to understanding the “deluge” of data coming from multivariate sensors in the patient’s home.
Enabling clinicians to scale personalized care to thousands of patients, machine learning will not only allow clinicians to practice “top of license” it will also …

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Disrupting Quantum Computing With AI and Machine Learning

disrupting quantum computing with ai and machine learning

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Quantum Computing is approaching a period of commercialization that may change our reality. Early adopters of quantum’s remarkable capacity to take care of specific kinds of issues may accomplish achievements that empower new business models. Visionary enterprises are now lining up with the developing quantum computing ecosystem to become “quantum ready.” These ground breaking enterprises are exploring use cases and related algorithms that address complex business issues.
Artificial Intelligence (AI) and Machine Learning (ML) based analytics solutions require aggregating and analysing data to train them to copy real-world observed behaviours. For industrial solutions, it begins with a historian that totals time-series and event data. This data is taken care of into an analytics engine that models the data utilizing proprietary algorithms, combining data science and field operator expertise.
The bits of insights are introduced to the end-user, who takes explicit activities and records those activities. The application currently ‘learns’ in light of real-time activities. After some time, we begin seeing a convergence, which guarantees accurate decision-making at the operator level.
When quantum PCs can take care of some business issues that traditional PCs can’t, often called quantum advantage, shows up close within reach. Absolutely when a quantum advantage …

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Viral Post Highlights ‘Toxicity Problems’ in the Machine Learning Community

viral post highlights ‘toxicity problems’ in the machine learning community

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A Reddit post identifying eight “toxicity problems” in the machine learning (ML) community recently went viral, receiving some 3,300 upvotes and nearly 600 comments in a week.The post highlights perceived peer-review problems, the reproducibility crisis, and ethics and diversity issues. It arguing that the peer-review process is “broken” and that there is a “worshiping problem” and “a cut-throat publish-or-perish mentality” in the paper publishing process and beyond.Over 60 percent of published theoretical computer science and machine learning papers are on arXiv, according to a 2017 study. Indeed, 56 percent of papers published in 2017 appeared on arXiv (along with the authors’ names and institutions) before or during peer review. The Reddit post says this can negatively affect the double-blind peer review process, as reviewers could be more inclined to accept papers whose authors are from renowned institutions.Earlier this year, Synced also looked at some possible ways to improve the paper review process in the ML community. Since 1998, the volume of AI papers in peer-reviewed journals has grown by more than 300 percent, according to the AI Index 2019 Report. At the same time, major AI conferences like NeurIPS, AAAI, and CVPR are setting new paper submission records every year. All this has led to complaints …

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New machine learning framework enables efficiencies in quantum information processing

new machine learning framework enables efficiencies in quantum information processing

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In a robust tomography scheme with machine learning, noisy tomography measurements are fed to the convolutional neural network, which makes predictions of intermediate t-matrices as the outputs. At the end, the predicted matrices are inverted to reconstruct the pure density matrices for the given noisy measurements. Credit: U.S. Army image

A new machine learning framework could pave the way for small, mobile quantum networks.

Researchers from the U.S. Army Combat Capabilities Development Command’s Army Research Laboratory and Tulane University combined machine learning with quantum information science, or QIS, using photon measurements to reconstruct the quantum state of an unknown system.
QIS is a rapidly advancing field that exploits the unique properties of microscopic quantum systems, such as single particles of light or individual atoms, to achieve powerful applications in communication, computing and sensing, which are either impossible or less efficient under conventional means.
“We wanted to apply machine learning to problems in QIS, as machine learning systems are capable of making predictions based on example data sets without explicit programming for the given task,” said Dr. Brian Kirby, a scientist at the Army’s corporate research laboratory. “Machine learning has excelled in recent years in fields such as computer …

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Online machine learning can warn of impending crisis – Hélène Rey – Central Banking

online machine learning can warn of impending crisis – hélène rey – central banking

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Online machine learning holds promise as a means of detecting impending crises early enough to take action, economist Hélène Rey says.

“Macro-prudential authorities in some cases have to take action to increase buffers two years before the problem arises,” Rey says in a recent interview with Central Banking. “So, you need a framework for early warning indicators.”

The online approach has been applied more widely in the machine learning literature, where the “true” model of the underlying data

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WellAI Data Scientists to Present Latest Research on Machine Learning in Healthcare and Finance

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The Online Event is Jointly Organized with the Society of Quantitative AnalystsNEW YORK, July 9, 2020 /PRNewswire/ — WellAI data scientists Daniel Satchkov and Sergei Polevikov will present their most recent research entitled “Reading 25 Million Studies in Seconds: Implications for Fighting COVID-19 and Managing a Portfolio” at a free webinar on August 25, 2020. The webinar will take place from 12pm to 1pm EST, and is jointly organized by the Society of Quantitative Analysts (SQA) and WellAI.  Discussion will be partly based on a study “Artificial Intelligence-powered search tools and resources in the fight against COVID-19” published in the Journal of the International Federation of Clinical Chemistry and Laboratory Medicine in June 2020, and is currently available through the PubMed database of the National Institutes of Health (NIH).        Sergei Polevikov, CEO of WellAI and a board director at SQA, explained: “We wanted to share our unique experience as we believe our work is relevant to both medical researchers and finance professionals.  WellAI data scientists had built a free COVID-19 analytical tool for medical researchers around the world in early April 2020, to help fight the pandemic.  As some of us had also had previous experience as data scientists in the finance industry, we found some interesting …

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