GE working on robot that it says can save $200B of power via @infomgmt

Researchers are looking into how so-called machine learning can be integrated into businesses from healthcare to computing, and now energy.

Wow - this is a great real-life use with a potentially game changing result for the world.

WEBINAR: Embedded AI, Machine Learning, and Analytics - 10 Oct 2017

Overview

Title: Embedded AI, Machine Learning, and Analytics

Date: Tuesday, October 10, 2017

Time: 09:00 AM Pacific Daylight Time

Duration: 1 hour

Summary

Embedded AI, Machine Learning, and Analytics

New forms of systems of intelligence are emerging through embedded artificial intelligence, machine learning, and analytics. These data-driven systems of intelligence are enabling digital disruption and new business models.

Many companies don’t know what steps to take to become digital, where to begin their journey to digital, or how to be sure they won’t waste money on innovation they can’t implement throughout their company to drive better business results. There is a massive opportunity to help companies take and complete this digital journey, not just to innovate, but to become scaled digital businesses.  In this Data Science Central webinar Join David Judge, Vice President, Chief Evangelist Leonardo at SAP, Bill Vorhies, Data Scientist, Editorial Director of Data Science Central, and Guilherme Rabello, Commercial and Market Intelligence Manager of InovaInCor, the Innovation department of the Heart Institute (InCor) in São Paulo as they discuss how new technologies are driving digital disruption and the need for innovation.

Speakers:

David Judge, Vice President, Chief Evangelist Leonardo -- SAP
Guilherme Rabello, Commercial and Market Intelligence Manager of InovaInCor -- Innovation department of the Heart Institute (InCor) in São Paulo

Hosted by: 

Bill Vorhies, Editorial Director -- Data Science Central

Register here

3 key steps to vet cloud services for GDPR compliance by Robert Cruz via @infomgmt

Organisations must ensure that their service providers satisfy all the mandates of the General Data Protection Regulation or risk noncompliance themselves.

These steps are really useful if you need a little help and guidance of what to do or where to start.

How Long Do We Have to Wait for the Internet of Things 4.0? by @fmarotob via @Datafloq

IoT 1.0 and IoT 2.0 provide the foundational layer, IoT 3.0 is the bridge from things to humans and IoT 4.0 will be the social IoT.

This is great for many reasons - it helps understand what each of these are actually are but it also gives you an insight into their development and where it is going to be going in the future.

What Skills Do I Need to Become a Data Scientist? by @ronald_vanloon via @Datafloq

What are the technical and non-technical skills that are critical for success in data science?

Great article by Ronald van Loon which may give you hints as to the next areas you need to focus on.

How Conversational AI Will Change Customer Service by @VanRijmenam via @Datafloq

By 2020, approximately 20.4 billion devices are estimated to be connected to the internet. These IoT devices are getting smarter, connecting to intelligent applications, such as Amazon's Alexa or Apple's Siri, and helping consumers make transactions and complete tasks. However, they are also sparking conversational AI, and it stands to change customer service. Explore conversational AI, its benefits and challenges, and how it will help change customer service.

I love the fact that there are examples with real companies in this to help you have a context for it. I think this helps companies, customers and evn us as the consumer as it all feeds into the likes of Alexa and Siri.

How Blockchains can revolutionise the supply chain

These applications create an immutable single version of truth, allow near real-time sense and respond features, and require no central owner.

Great opinion and great example of a use for Blockchain.

Machine Learning Translation and the Google Translate Algorithm by Daniil Korbut via @statsbotco

Great article explaining the basic principles of machine translation engines using the algorithm in Google Translate to demonstrate it.

I love this - it's really clear and well described.

Adoption of, satisfaction with, big data on the rise by David Weldon via @infomgmt

A growing number of organisations are investing in large-scale information deployments, and more firms are reporting success with those efforts, says a new study.

This is great and might finally marks a turning point where organisations are planning and monitoring big data efforts properly.

WEBINAR: Are you a true data master? - 28 September 2017

Sept. 28, 2017 | 2 PM ET/11 AM PT
Hosted by Information Management

Whether you’re delivering applications, analytics, reporting, or audit prep, you need to have complete command of all your data at all times—regardless of where it came from and where it presently resides.But becoming a true “data boss” can be extremely difficult, especially as business units create and acquire more types of data from a broader range of sources.

So how can you ensure that you and your colleagues always have the right data—as well as accurate, up-to-date metadata insight—at your command? And how can you leverage your data mastery to quickly and consistently deliver maximum value to the business?

Attend this highly informative webinar to learn:

• 3 common - and costly - mistakes CDOs make
• Why GDPR is the ideal opportunity for data mastery
• How “data bosses” get substantially greater business value from data than those who rely on data science/analytics alone

Ian Rowlands Vice President, Product Marketing ASG Technologies (Presenter)	Duffie Brunson Senior Data Executive KPMG Advisory (Presenter)	Julia Bardmesser Global Head of Data Integration Deutsche Bank (Presenter)	Lenny Leibmann Contributing Editor SourceMedia (Moderator)

Sponsored By:

Register here

WEBINAR: Human-in-the-Loop Deep Learning - 28 September 2017

Overview

Title: Human-in-the-Loop Deep Learning

Date: Thursday, September 28, 2017

Time: 09:00 AM Pacific Daylight Time

Duration: 1 hour

Summary

Human-in-the-Loop Deep Learning

AI systems need to continually learn from new data to perform well in real-world scenarios. However, it is non-trivial to decide what new data needs to be labelled for training, and what is the best workflow and user interface for providing human feedback. This critical component of Machine Learning, called Active Learning, is often absent from Machine Learning courses. This Data Science Central webinar will extend TensorFlow's Deep Learning functionality with several Active Learning strategies, and apply these to the well-known ImageNet Computer Vision data set. At the end of this webinar you should be comfortable with combining your data annotation and Machine Learning strategies to continually improve your training data at scale.

Speaker: Robert Munro, VP of Machine Learning  -- CrowdFlower 

Hosted by: Bill Vorhies, Editorial Director -- Data Science Central

 

Register here

4 Essential Tools any Data Scientist can use to improve their productivity by Faizan Shaikh via @AnalyticsVidhya

This article contains Faizan's stack of the tools he uses in the python ecosystem on Windows.

Very useful especially if you work using Python.

WEBINAR: Deep Learning on Apache® Spark™- Best Practices - 27 Sept 2017

Overview

Title: Deep Learning on Apache® Spark™- Best Practices

Date: Wednesday, September 27, 2017

Time: 09:00 AM Pacific Daylight Time

Duration: 1 hour

Summary

Deep Learning on Apache® Spark™- Best Practices

The combination of Deep Learning with Apache Spark has the potential for tremendous impact in many sectors of the industry. This webinar, based on the experience gained in assisting customers with the Databricks Unified Analytics Platform, will present some best practices for building deep learning pipelines with Spark.

Rather than comparing deep learning systems or specific optimisations, this webinar will focus on issues that are common to deep learning frameworks when running on a Spark cluster, including:

• Optimising cluster setup
• Configuring the cluster
• Ingesting data
• Monitoring long-running jobs 

Speaker: Tim Hunter, Software Engineer -- Databricks Inc.

Hosted by: Bill Vorhies, Editorial Director -- Data Science Central

Register here

My Neural Network isn't working! What should I do? by/via @anorangeduck

11 things you probably screwed up and how to fix them.

This is a vital list of things to check and you should bookmark it so you can refer to it in the future.

An Introduction to different Types of Convolutions in Deep Learning by @gopietz via @Medium

Behind the “C” in “CNN”. If you’re not super-familiar with how the internals of image classifiers work, this is a useful intro.

I love this and the great diagrams are really slick and help understanding a great deal.

An inside look at Alphabet's most ambitious AI project by Alexis C. Madrigal via @TheAtlantic

"In a corner of Alphabet's campus, there is a team working on a piece of software that may be the key to self-driving cars. No journalist has ever seen it in action until now. They call it Carcraft, after the popular game World of Warcraft." The Atlantic takes an in-depth look at how Waymo is training self-driving cars.

Interesting.  Worth reading and thinking about the way the future may be going to happen. I can't wait as long as they make sure it is safe.

How can R Users Learn Python for Data Science? by @Manish_Saraswt via @HackerEarth

Python is a supremely powerful and a multi-purpose programming language. It has grown phenomenally in the last few years. It is used for web development, game development, and now data analysis / machine learning. Data analysis and machine learning is a relatively new branch in python. For a beginner in data science, learning python for data analysis can be really painful.

This blog entry is really interesting and is perfect if you know R but want an easy intro to Python as it gives you the relevant translation.

20 Data Analytics Careers That Aren't Data Scientists by @metabrown312 via @Forbes

Here are twenty careers that use data and analytics skills but are not a Data Scientist.

Great article. So it could be that you might aspire to being a Data Scientist, but these careers may be more in your reach - definitely worth considering even if they are a bit off the wall.

Please note this is a 3 page article and you will need to switch off your ad blocker to access it.

Python overtakes R, becomes the leader in Data Science, Machine Learning platforms by Gregory Piatetsky via @kdnuggets

"Python did not quite 'swallow' R, but the results, based on 954 voters, show that in 2017 the Python ecosystem overtook R as the leading platform for analytics, data science, and machine learning."

Wow - maybe I need to focus on Python more than R now (even though I much prefer R).

Train a Machine to Turn Documents into Keywords, via Document Classification by @zeryx1211 via @algorithmia

Brilliant blog entry with examples showing how an algorithm can classify documents online.

I love this.  I'm sure many of us are good enough at one programming language at least who can implement something like this.

277 Data Science Key Terms, Explained by Matthew Mayo via @kdnuggets

This is a collection of 277 data science key terms, explained with a no-nonsense, concise approach. Read on to find terminology related to Big Data, machine learning, natural language processing, descriptive statistics, and much more.

This links to lots of articles grouping the terms by their general classification for example deep learning or predictive analytics.

SLIDESHOW: 14 top platforms for data integration by David Weldon via @infomgmt

Informatica, Talend and Oracle are among the leaders in this space, according to Gartner’s Magic Quadrant.

Interesting list and a few surprises for me too.

Building a data science team for the enterprise by Madison Moore via @sdtimes

Data scientists are no magicians, but they are in high demand.

Researchers and analysts in this space recognise the diversity and explosion of Big Data, but the only way enterprises are going to be able to prepare for the future of Big Data is with a data science team capable of working with dirty data, complex problems, and open-source languages, experts in the field say.

Nice look at this increasingly common problem.

Machine Learning: Are You Ready? A 7-Part Checklist by Kimberly Nevala via @datanami

Machine learning is all the rage. But while the topic is top of mind in boardrooms and the media alike, it is not always clear how machine learning is best applied.

Great article by Kimberly which gives you a few high level pointers that can be expanded upon in order to start a plan to audit and implement machine learning in your organisation.

Gaining business insights from social media analytics by Ari Lightman via @infomgmt

The challenge for many organisations is providing the fabric to integrate this information into the overall data strategy

I really like the phases and the way they describe a process to develop more.. 

WEBINAR: Streaming analytics and the immediacy of modern business - 14 September 2017

Sept. 14, 2017 | 1 PM ET/10 AM PT
Hosted by Information Management

Instant gratification? The power of streaming analytics can shorten time-to-value almost down to zero. That’s because streaming analytics enables companies to see what’s happening right now, across vast amounts of data, and in a visually compelling way. The potential benefits are vast, and can range from seizing short-lived opportunities for growth, to mitigating problematic situations, and even opening up whole new avenues of revenue. The critical step is to connect insights to a business process. Find out how you can do all of that on this episode of IM Live! Host Eric Kavanagh will interview Mark Madsen of Third Nature, and a special guest from Zoomdata.

Mark Madsen President Third Nature, Inc. (Speaker)	Eric Kavanagh CEO Bloor Group (Moderator)

Sponsor Content From:

Register here

From Lambda to Kappa: A Guide on Real-Time Big Data Architectures by Michael Verrilli via @DZone

This is a discussion of real-time big data architectures as there are options now.

A great article and a huge help in understanding the architectures. I'm really looking forward to his follow up articles where he goes into them in more detail.

Do We Need a Speedometer for Artificial Intelligence? by Tom Simonite via @WIRED

AI observers are trying to develop a more exact picture of how, and how fast, the technology is advancing. By measuring progress - or the lack of it - in different areas, they hope to pierce the fog of hype about AI.

Great article with some good points and diagrams.  I think the output of this measurement will help in many areas - pioneers will know which areas still need work, and adopters will be able to see which areas are already available,

The Power of Artificial Intelligence to Revolutionise the Oil & Gas Industry by @yosonu via @Datafoq

A detailed overview of how oil and gas is being revolutionised by artificial intelligence.

I love this - it is a great piece with lots of detail, plus it's a real example of AI use.

Here's Why Blockchain Matters for Your Online Reputation by @VanRijmenam via @Datafloq

Just as word-of-mouth can make or break your business, your online reputation can either help or hurt your business.

Good thought piece from Mark.  He has it completely right - especially when you see the effect on reputation of data breaches as an example.