How Automation Is Changing Data Science

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Data science can provide a high return on investment across multiple industries and use cases. Whether predicting new target customers, measuring product demand or detecting high product failures - the use cases are nearly as infinite as the problems that face modern businesses. Although data science undoubtedly has significant potential to impact business decision-making, leaders across multiple industries have struggled with getting value from data science projects. In fact, according to research by the Gartner Group, nearly 85 percent of big data projects fail. Even more telling, a 2019 survey by Dimensional Research found that 96% of companies struggle with AI and Machine Learning.


QM/ML: Datasets

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Water dimer (O-O distances 4.5 Å, geometries sampled from a 300 K MD using the AMOEBA forcefield), interaction energies and forces with counterpoise correction, using MP2 / AVDZ, AVTZ, AVQZ (10k, 10k, 1k configurations, respectively).


A Quick Guide to Reinforcement Learning

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General Electric is the 31st largest company in the world by revenue and one of the largest and most diverse manufacturers on the planet, making everything from large industrial equipment to home appliances. It has over 500 factories around the world and has only begun transforming them into smart facilities. The goal of GE's'Brilliant Manufacturing Suite' is to link design, engineering, manufacturing, supply chain, distribution and services into one globally scalable, intelligent system. It is powered by Predix, their industrial internet of things platform. In the manufacturing space, Predix can use sensors to automatically capture every step of the process and monitor each piece of complex equipment.


Google bought my friend's face for $5 ZDNet

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My engineer friend George is taking a break to work on himself. Personally, I've always liked him the way he is, but he insists that he could and should be better. Did I mention he's an engineer? So he spends his time taking esoteric self-help classes and sitting around New York, watching his fellow humans help themselves to the joys of summer life. Occasionally, people come up to him and chat.


Advisors' new toolkit: Biometrics, risk algorithms and machine learning

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Personal relationships have always been the lifeblood of wealth management, but the pressure to intensify personalization has increased dramatically, according to Capgemini's most recent World Wealth Report. In fact, there is a "measurable correlation linking high-net-worth clients' personal connection to their firm and advisor and the financial performance of firms," according to the report. Despite a dip in investment performance last year, 88% of wealthy clients in the U.S. and Canada with investable assets of $1 million or more said they still had faith in their advisors, Capgemini found. "Personal connections are still the differentiating factor," says Chirag Thakral, an analyst with Capgemini. What do wealth managers need to do to strengthen their ties to clients in the digital age?


Is Machine Learning the Future of Cloud-Native Security?

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Cloud-native architectures help businesses reduce application development time and increase agility, at a lower cost. Although flexibility and portability are key drivers for adoption, a cloud-native structure brings with it a new challenge: managing security and performance at scale. They have a dissolved perimeter, meaning that once a traditional perimeter is breached, lateral movement of attacks (such as malware or ransomware) often goes undetected across data centers and/or cloud environments. Sorting through interconnected data from thousands of services across millions of short-lived containers to understand a specific security or compliance violation in time is akin to finding a needle in a haystack. Developers are failing to bake security in early, opting instead to add it on at the end, and ultimately, they are increasing the chance of potential exposures in the infrastructure.


Intel Debuts Pohoiki Beach, Its 8M Neuron Neuromorphic Development System

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Neuromorphic computing has received less fanfare of late than quantum computing whose mystery has captured public attention and which seems to have generated more efforts (academic, government, and commercial) but whose payoff also seems more distant. Intel's introduction this week of Pohoiki Beach – an 8-million-neuron, neuromorphic system using 64 Loihi research chips – brings some (needed) attention back to neuromorphic technology. The newest system will be available to Intel's roughly 60 neuromorphic ecosystem partners and represents a significant scaling up of its development platform with more to come; Intel reportedly plans to introduce a 768-chip, 100-million-neuron system (Pohoiki Springs) near the end of 2019. "Researchers can now efficiently scale up novel neural-inspired algorithms – such as sparse coding, simultaneous localization and mapping (SLAM), and path planning – that can learn and adapt based on data inputs. Pohoiki Beach represents a major milestone in Intel's neuromorphic research, laying the foundation for Intel Labs to scale the architecture to 100 million neurons later this year," according to the official announcement.


Announcing ML.NET 1.2 and Model Builder updates (Machine Learning for .NET) .NET Blog

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We are excited to announce ML.NET 1.2 and updates to Model Builder and the CLI. ML.NET is an open-source and cross-platform machine learning framework for .NET developers. ML.NET also includes Model Builder (a simple UI tool for Visual Studio) and the ML.NET CLI (Command-line interface) to make it super easy to build custom Machine Learning (ML) models using Automated Machine Learning (AutoML). Using ML.NET, developers can leverage their existing tools and skill-sets to develop and infuse custom ML into their applications by creating custom machine learning models for common scenarios like Sentiment Analysis, Price Prediction, Image Classification and more! ML.NET 1.2 is a backwards compatible release with no breaking changes so please update to get the latest changes.


The Most Underused Asset At Work: Being Human

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I was moderating a panel on leadership for a client of mine and received the bios of the three very accomplished executive panelists. All three bios were simply a list of credentials-- impressive credentials, but that was it. Nothing that gave the audience any understanding of their thoughts on leadership or success. This robotic resume in prose form is all too common, and it erases our most valuable asset: our humanity. Especially in our digital world, being yourself--your unique, human self--gives you a distinctive competitive edge.


Smart Roads: The UK will use AI to determine the condition of roads

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The UK is planning to harness AI to help determine the condition of roads and where investment should be prioritised. British drivers are well-accustomed to poor road conditions, especially potholes and the long delays in getting them fixed (one ingenious man has even come up with an innovative way of getting the council to fix them faster...) To be fair to councils, keeping all the roads in top condition is expensive. Factors like minimising disruption along busy routes, and planning diversions, must also be considered. Fortunately, AI is beginning to help automate this automotive dilemma. The Department for Transport (DfT) has awarded £2m in funding to a project using AI to examine the condition of roads, forming part of a wider £350 million funding package.