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NIPS 2017 -- Day 2 Highlights – Insight Data

@machinelearnbot

We are back with some highlights from the second day of NIPS. A lot of fascinating research was showcased today, and we are excited to share some of our favorites with you. If you missed them, feel free to check our Day 1 and Day 3 Highlights! One of the most memorable sessions of the first two days was today's invited talk by Kate Crawford, about bias in Machine Learning. We recommend taking a look at the feature image of this post, representing modern Machine Learning datasets as an attempt at creating a taxonomy of the world.


What goes into the right storage for AI? - IBM IT Infrastructure Blog

#artificialintelligence

Artificial intelligence (AI), machine learning and cognitive analytics are having a tremendous impact in areas ranging from medical diagnostics to self-driving cars. AI systems are highly dependent on enormous volumes of data--both at rest in repositories and in motion in real time--to learn from experience, make connections and arrive at critical business decisions. Usage of AI is also expected to expand significantly in the not-so-distant future. As a result, having the right storage to support the massive amounts of data required for AI workloads is an important consideration for an increasing number of organizations. Availability: When a business leader uses AI for critical tasks such as understanding how best to run their manufacturing process or to optimize their supply chain, they cannot afford to risk any loss of availability in the supporting storage system.


Meet Your New Boss: An Algorithm

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Companies say the new tools make them more efficient and give employees more opportunities to do new kinds of work. But the software also is starting to take on management tasks that humans have long handled, such as scheduling and shepherding strategic projects. Researchers say the shift could lead to narrower roles for some managers and displace others. When Shell wanted help evaluating digital business models in the car-maintenance sector, executives plugged the project into an algorithm that scanned for available Shell staffers with the right expertise--and assigned the job with a click. Shell uses machine-learning software designed by Boston-based Catalant Inc. to match workers and projects.


Meet Your New Boss: An Algorithm

Wall Street Journal

Companies say the new tools make them more efficient and give employees more opportunities to do new kinds of work. But the software also is starting to take on management tasks that humans have long handled, such as scheduling and shepherding strategic projects. Researchers say the shift could lead to narrower roles for some managers and displace others. When Shell wanted help evaluating digital business models in the car-maintenance sector, executives plugged the project into an algorithm that scanned for available Shell staffers with the right expertise--and assigned the job with a click. Shell uses machine-learning software designed by Boston-based Catalant Inc. to match workers and projects.


New machines for The Old Lady

#artificialintelligence

Rapid advances in analytical modelling and information processing capabilities, particularly in machine learning (ML) and artificial intelligence (AI), combined with ever more granular data are currently transforming many aspects of everyday life and work. In this blog post we give a brief overview of basic concepts of ML and potential applications at central banks based on our research. We demonstrate how an artificial neural network (NN) can be used for inflation forecasting which lies at the heart of modern central banking. We show how its structure can help to understand model reactions. The NN generally outperforms more conventional models.


4 Steps to Machine Learning with Pentaho

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The power of Pentaho Data Integration (PDI) for data access, blending and governance has been demonstrated and documented numerous times. However, perhaps less well known is how PDI as a platform, with all its data munging[1] power, is ideally suited to orchestrate and automate up to three stages of the CRISP-DM[2] life-cycle for the data science practitioner: generic data preparation/feature engineering, predictive modeling, and model deployment. By "generic data preparation" we are referring to the process of connecting to (potentially) multiple heterogeneous data sources and then joining, blending, cleaning, filtering, deriving and denormalizing data so that it ready for consumption by machine learning (ML) algorithms. Further ML-specific data transformations, such as supervised discretization, one-hot encoding etc. can then be applied as needed in an ML tool. For the data scientist, PDI can be used to remove the repetitive drudgery involved with manually performing similar data preparation processes repetitively, from one dataset to the next.


Should You Use A.I. in Your Marketing? (Part One) By Laura Patterson – Hospitality Net

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In the words of author Malcolm Gladwell, A.I. is reaching a tipping point, "that magic moment when an idea, trend, or social behavior crosses a threshold, tips, and spreads like wildfire." According to data from CB Insights, the number of companies and the amount of funding funneling into A.I.-based companies continues to rise. International Data Corporation (IDC) forecasts a 54% growth in Marketing spend on AI software over the next four years, from $360 million in 2016 to more than $2 billion in 2020. Why? Efficiency and transparency are the promise of A.I. – where a machine takes over a repetitive but vital task. A.I. is being used by Marketing both behind the scenes and on center stage.


How Telstra is applying machine learning to marketing mix modelling

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Understanding the impact and optimal combination of marketing and media tactics for success is an increasingly complicated task for marketing functions. It's even more difficult if you want to interpret the data in a quick enough timeframe to optimise, pivot or swap out marketing programs while they're still in-market. But this is exactly what Telstra is becoming able to do thanks to a new marketing mix modelling approach based on machine learning, rolled out in June. Telstra director of research, insights and analytics, Liz Moore, told CMO the ASX-listed telco built an econometric approach to marketing mix modelling about five years ago based on the need to better understand the ROI of marketing at a strategic level. While it satisfied one organisational need – namely, giving the CFO a view of marketing ROI – it was an expensive exercise, taking 4-5 months to gather more than 300 data sources, plus months to build the modelling.


Machine Learning And The Future of Finance

#artificialintelligence

Artificial intelligence has conquered games and image recognition, but will it master investing? The short answer is yes, but how soon and how complete? Machine learning methods have had impressive recent successes. These include defeating humans at chess, Jeopardy, poker and Go, as well as providing superior image and speech recognition. Developers strive to create tools that automate decision making and that can mimic or exceed human performance for specific tasks.


Artificial Intelligence: Fusing Technology and Human Judgment?

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We usually think of the term "technology" in very modern, even futuristic contexts. Yet the word has a long history, deriving from the Greek tekhnologia, meaning "science of craft" or "systematic treatment" of actions. These traits have been with us since humans first discovered tools. In fact, the investment-analyst profession emerged from ad hoc investment approaches, using systematic processes to analyze and evaluate the health and value of companies. Increasingly, those processes are being undertaken by what we usually mean when we say "technology": computer hardware and software.