BigData News Monday, February 12
BigData News TLDR / Table of Contents
- Beat the challenges of predictive analytics in big data systems
- Doing predictive analytics in big data environments creates a unique set of challenges, but also presents opportunities to those who are ready to handle them.
- predictive analytics, data, , ,
- The Data Science Behind AI
- Summary: For those of you traditional data scientist who are interested in AI but still haven’t given it a deep dive, here’s a high level overview of the data…
- data science, Neural Nets, Convolutional Neural Nets, Recurrent Neural Nets, data science technologies
- Future-proofing AI: Embrace machine learning now because healthcare adoption is picking up speed
- Hospitals are proving the merits of machine learning and cognitive computing, but deep learning and data dependence are key.
- AI, artificial intelligence, AI systems, healthcare, art AI systems
- Siemens partners with BRIDG for digital twin technology
- Cloud TPU machine learning accelerators now available in beta
- Google Cloud Platform, Cloud TPU machine, Cloud Platform Blog, Big Data,
Tweeted At: Mon Feb 12 16:00:33 +0000 2018
Author: David Loshin
- And while access to massive data volumes and new types of data can significantly enhance the ability to develop good predictive models, analytics managers and their teams need to consider the fundamental aspects of what makes data big and how the challenges of managing it affect predictive analytics in big…
- But implementing predictive models requires a number of steps, including the following: – – Next, let’s look at things in the context of the famous 3Vs of big data — volume, variety and velocity — and contemplate some specific challenges that must be addressed to effectively implement predictive analytics in…
- Be smart about your analytics strategy Design your strategy for predictive analytics in big data systems to address these challenges so you can successfully manage — or finesse — the critical points in the process.
- In other cases, the goal might be to ramp up the big data system’s compute resources to enable the analytics algorithms to handle a much larger training set — and to eliminate the need to filter out any records.
- Alternatively, your organization might be able to get what it needs from predictive analytics in big data applications from less complex models that don’t require processing reinforcements.
Tweeted At: Mon Feb 12 16:01:15 +0000 2018
Author: William Vorhies
- Summary: For those of you traditional data scientist who are interested in AI but still havent given it a deep dive, heres a high level overview of the data science technologies that combine into what the popular press calls artificial intelligence (AI).
- Artificial Neural Nets (ANNs), the highest summary level have been around since the 80s and have always been part of the standard data science machine learning tool kit for solving standard classification and regression problems.
- There are at least 27 different types of ANNs but the most important are the Convolutional Neural Nets (CNNs) and the Recurrent Neural Nets (RNNs) without which image and natural language processing would not be possible.
- Convolutional Neural Nets (CNNs): CNNs are at the heart of all types of image and video recognition, facial recognition, image tagging (think Facebook) and recognizing a stop sign from a pedestrian in our self-driving cars.
- Generative Adversarial Neural Nets (GANNs): CNNs and RNNs both suffer from the same problem of requiring huge and burdensome amounts of data in order to train, either to recognize that stop sign (image) or to learn the instructions necessary to answer your question about how to open that account (speech…
Tweeted At: Sat Feb 10 20:01:00 +0000 2018
Publish Date: 2017-12-03T08:40:22+00:00
Author: Bill Siwicki
- AI in healthcare, like in other industries, began as a way to help these organizations manage their vast amounts of data and simplify daily tasks, but were starting to see the emergence of truly innovative uses of AI in healthcare from finding complex patterns in medical imaging to genomic sequencing…
- The most common uses for AI in healthcare today are for search, classification and reasoning, in that order, said Ajay Royyuru, vice president, healthcare and life sciences research, at IBM Watson Health.
- One goal at DeepMind Health is to make a practical difference to patients, nurses and doctors and support the NHS and other healthcare systems, said Dominic King, MD, clinical lead at DeepMind Health and an academic surgeon in the UK National Health Service.
- So when systems are set up for accessing healthcare data in a compliant way, one will increasingly see the application of artificial intelligence and machine learning.
- At DeepMind Health, the organizations current research for the healthcare applications of tomorrow combines two AI approaches: deep learning and reinforcement learning.
Tweeted At: Mon Feb 12 16:00:09 +0000 2018
Publish Date: 2018-01-23T11:00:00+00:00
Author: Matthew Richardson
- BRIDG and Siemens will work together to develop digital twin technology a method of connecting the physical digital world.
- The partnership and digital twin project will require multiple new positions at BRIDG, spokeswoman Gloria LeQuang told Orlando Business Journal.
- The digital twin project is a perfect example of the type of catalyst the BRIDG public/private partnership creates, one that drives many direct and indirect positions across the high-tech and support services industries.
- The partnership will involve workers from Siemens coming to BRIDG, however, LeQuang made it clear that Siemens will not have a physical location within the tech campus.
- Siemens is one of many partnerships BRIDG has landed, which includes partners such as Melbourne-based Harris Corp. (NYSE: HRS), New York Polytechnic Institute, Argonne National Laboratory in Lemont, Ill., and more.
Tweeted At: Mon Feb 12 14:04:57 +0000 2018
Author: John Barrus
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