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Summary of the news regarding Data and Analytics - April 2017

mikeshaw747

I am going to periodically post on what's happening in the news.

I'll choose a topic each time - "what's happening in digitization", "what's happening in data and analytics", "what's happening in IoT" or "what's happening in digitization of healthcare", and so on.

For no special reason, apart from that the blogs I track are full of it, I thought I'd start with data and analytics. And, the hot topic in data and analytics is machine learning. 


What is AI, machine learning and deep learning?
A really nice article on AI, machine learning and deep learning and the current issues in setting up a machine learning system (its “training” period).

What counts as artificially intelligent? AI and deep learning, explained | The Verge


Examples of every day “intelligent” apps that now include machine learning
Five examples of every day apps that use machine learning.

Gartner talks about a new type of app - intelligent apps - that include machine learning. Netflix and Yelp, for example. Again, see the first article in this blog post - my personal experience of recommendation engines is that they are a help, but have a ways to go.

5 ways deep learning improves your daily life | VentureBeat | by Mariya Yao


Understanding the limits of deep learning
A really nice desciption of the limits of deep learning - the “silly” mistakes it can make and how it can be fooled.

Understanding the limits of deep learning | VentureBeat | by Mariya Yao 

neutal network.png


Google's Deepmind applying deep learning to energy consumption

Deepmind has developed algorithms that can anticipate energy demand and supply.

They're already being used in Google's energy hungry data centres but now the company is talking to the National Grid, which owns and operates energy infrastructure across the UK, about how AI could be used to help balance energy supply and demand across the nation.

Deepmind (Google’s Deep Learning acquisition) is applying deep learning to energy consumption


Using AI to improve project management predictions

The success and costs of complex projects depend on a lot of different variables. Machine learning can be used to better predict the outcome of such projects. 

Using AI to improve project management


Using machine learning to link disease diagnostics to your DNA

Genome sequencing is the practice of decoding a person's DNA, a process that creates colossal amounts of data. Every single person's genome is comprised of 3.2 billion letters of DNA and contains around 20,000 individual genes.

helping humans.jpg

In the UK, the National Health Service and SOPHiA artificial intelligence plans to sequence 100,000 genomes (at the start of March 2017 it had completed 20,000).  

This sequencing will then have machine learning applied to it to try to find causal links between DNA patterns and susceptability to illnessses.

AI from Sophia Genetics has helped doctors diagnose 100,000 people | WIRED UK


This software does in seconds what took lawyers 360,000 hours
At JPMorgan, a learning machine is parsing financial deals that once kept legal teams busy for thousands of hours.

The program, called COIN, for Contract Intelligence, does the mind-numbing job of interpreting commercial-loan agreements that, until the project went online in June, consumed 360,000 hours of lawyers’ time annually. The software reviews documents in seconds, is less error-prone and never asks for vacation.

This software does in seconds what took lawyers 360,000 hours | 7wData


DeepMind is researching into passing machine learning from one situation to another

Google’s DeepMind is working on ways of transferring machine learning from one problem to another. Today, each machine learning solution has to be re-trained from scratch - no learning from one situation is passed on to another. This is not how humans works. If you have learnt how to play baseball, you can transfer some of that learning to playing cricket.

If learning could be passed between situations, this would be a major step forwards because training of a machine learning system is typically very laborious.

Why Google’s DeepMind next-gen machine learning will stay undercover | InfoWorld


Security needs machine learning because we don’t have enough cyber experts
One of the drivers for the use of machine learning in cyber security is the lack of enough security experts. Of course, machine learning systems can also adjust to new situations, so it’s not all about autonomously doing what security experts do, but it’s all part of the very strong drive in cyber security to use machine learning.

Smart machines v hackers: How cyber warfare is escalating - BBC News


Google shows off impressive recognition in videos
Google can autonomously tell you what it’s seeing in videos. But see the article at the top of this blog post on deep learning’s limitations. For example, the right digital noise on a video can completely fool recognition systems into thinking pandas are gorillas.

Google’s AI Can Now Identify What It’s Seeing In Videos And It’s Frighteningly Perceptive | The Huffington Post


How machine learning can be used in the world of work
A slideshare that describes the ways in which AI (machine learning, mainly) could be used in the world of work.

I know that each technology wave is predicted to “transform” either the workplace or our daily lives. But I think that this slideshare will go some way to persuading you that this time it really will - at least in some (quite a few) areas of work.

Massive Impact on the Workplace


How AI will change companies’ application of the 80:20 principal
Businesses rely on Pareto’s 80:20 principal. For example, 20% of a bank’s customer generate 80% of the profit. 20% of your time delivers 80% of your contribution to your organization (but which 20%? Hint : probably not meetings). Three are hundreds of examples (the other day, I realised that 80% of the painting required to the exterior of my windows lies in just 20% of those windows - the bottom 20%).

AI allows us to sift thru masses of data looking for those 80:20 (or even 90:10) situations, allowing businesses to really hone in on them.

AI Is Going to Change the 80/20 Rule


 

Mike Shaw
Director Strategic Marketing

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linkedin.gifMike Shaw

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About the Author

mikeshaw747

Mike has been with HPE for 30 years. Half of that time was in research and development, mainly as an architect. The other 15 years has been spent in product management, product marketing, and now, strategic marketing. .

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