More organizations putting AI, blockchain and virtual reality to use by Bob Violino via @infomgmt

One in four firms now make regular use of artificial intelligence and many are interested in the potential of other emerging technologies, says Seth Robinson.

I never realised that so many companies are into some of these technologies.  Great things could be coming (maybe).

Don't Underestimate Your Data Engineer by @IE_James via @iegroup

The flood of data-related roles unleashed in recent years has the potential to cause untold confusion, particularly for senior executives inexperienced in the field.

Nice to have an article about an often overlooked role which is just as vital as a data scientist.

How to Collect and Transform Data into Value for Your Business by @ryankhgb via @SmartDataCo

The Age of Data is here and it’s more important than ever to take data into account when managing your business. There’s more data available around the world every year and more customers are sharing information about themselves with businesses.

Some important points in this useful article that at the very least will give you some pointers on what you need to think about.

Unstructured content: An untapped fuel source for AI and machine learning by Alex Welsh via @sdtimes

Advancements in AI (Artificial Intelligence) and machine learning now make it possible and affordable to sift through and find meaning in vast amounts of unstructured data obtained from video and audio files, emails, logs, social media posts and even notifications from Internet of Things (IoT) devices.

Nice to know that there is hope and something useful for unstructured data.

The 10 most important breakthroughs in Artificial Intelligence by James O'Malley via @techradar

“Artificial Intelligence” is currently the hottest buzzword in tech. And with good reason - after decades of research and development, the last few years have seen a number of techniques that have previously been the preserve of science fiction slowly transform into science fact.

Great reminder of what has already been achieved.  It's only when you stop and think about it that you realise just how far we have already come.

The Google Brain Team - Looking Back on 2017 by Jeff Dean via @googleresearch

Jeff Dean from the Google Brain Team highlights his team's accomplishments for 2017. This is an amazing assortment of projects that have wide-ranging impact. There are two parts to this post and both are high-level with lots of screenshots, videos, and links.

Part 1

Part 2

Wow - what a huge list of things that they have achieved - I can't wait to see what they do next.

The road to AI leads through information architecture by Rob Thomas via @VentureBeat

Enterprise AI is about solving sophisticated business problems in highly dynamic environments. This requires an understanding of well-defined use cases and starting points, as well as an acknowledgement that, per MIT professor Erik Brynjolfsson, “the bottleneck now is in management, implementation, and business imagination.”

This is a great article and I completely agree with Rob.  Definitely one to read and take notice of.

WEBINAR: Matei Zaharia’s Predictions for 2018: Big Data and AI Highlights - 31 Jan 2018

Overview

Title: Matei Zaharia’s Predictions for 2018: Big Data and AI Highlights

Date: Wednesday, January 31, 2018

Time: 09:00 AM Pacific Standard Time

Duration: 1 hour

Summary

Matei Zaharia’s Predictions for 2018: Big Data and AI Highlights

Over the past few years, AI and big data have powered numerous technologies that have changed the way we live, from autonomous cars to conversational systems to personalization. As a result, the excitement around these technologies has spiked. But how can we separate the hype from reality, and which advances will make an impact in practice next?

In this DSC webinar, Databricks co-founder and Stanford computer science professor Matei Zaharia, who started the Apache Spark project in 2009, will share his perspective on which big data and AI trends will come to fruition in 2018. He will discuss how centering organizations around high-quality data will be the main driver to AI, which AI applications are seeing broad success in practice, and how new technologies including deep learning, data marketplaces and cloud computing will affect the computing landscape.

Join this webinar to learn about:

• The current state of big data and AI
• Some of the new innovations taking place in research
• Key challenges that companies face in getting value from data and AI
• Matei’s predictions for 2018 for how companies and the technology industry will overcome these challenges

Speaker: Matei Zaharia, Co-founder and Chief Technologist -- Databricks

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

  

Register here

The hidden data organisations don’t realise is vulnerable to hackers by Nick Belov via @infomgmt

Long gone are the days of the small cyberattacks carried out by college kids in their garages. Today, organised criminals and professional hackers are developing frequent, debilitating attacks targeted at companies. Businesses now need to accept that a cyberattack is not an “if,” it’s a “when.”

I think this is definitely a time when organisations need to do thorough inventories of their data and make sure that they have everything documented and protected. The one time you don't protect something is the time hackers get through and maybe that is the may to something else.

How to choose a data science vendor by Nick Ismail via @InformationAge

Choosing the right data vendor is a matter of defining your own business needs and finding the most suitable provider.

These are really useful pointers and definitely worth a quick read.

Top 10 TED Talks for Data Scientists and Machine Learning Engineers by Ilan Reinstein via @kdnuggets

A comprehensive and diverse compilation of TED talks to understand the big picture of AI and Machine Learning.

Some great talks here - I particularly enjoyed #8 and #10.  Worth the time investment to watch them.

Quantum Machine Learning: An Overview by Reena Shaw via @kdnuggets

Quantum Machine Learning (Quantum ML) is the interdisciplinary area combining Quantum Physics and Machine Learning(ML). It is a symbiotic association- leveraging the power of Quantum Computing to produce quantum versions of ML algorithms, and applying classical ML algorithms to analyse quantum systems. Read this article for an introduction to Quantum ML.

This sounds great and very powerful although you definitely need the right skillset to benefit from this.

** Please note this is a two page article

Five Challenges of Analysing Internet of Things (IoT) Data by @billfranksga via @iianalytics

The analysis of Internet of Things (IoT) data is quickly becoming a mainstream activity. I’ve written about the Analytics of Things (AoT) before (some examples here, here, and here). For this blog, I’m going to focus on a few unique challenges that you’ll most likely encounter as you move to take IoT data into the AoT realm.

This is a great article by Bill and makes some great points.  On item I've seem overlooked is handling missing data  There will always be times when data is not available whether that be due to breakdown or the process not running. You need to decide how to handle that and what you will do instead. I like default values so that the data is not missing, It makes the analysis more meaningful and from a pure physical viewpoint your analysis will run faster.

Four Age-Old Business Problems Machine Learning Will Soon Solve by Rama Sekhar via @observer

The hype surrounding machine learning has been accelerating and expanding for years. Supporters talk about the potential of this Technology to improve every process and eliminate any issue.

Interesting ideas.  I think it is a case of having a problem and then the chances are you will find a machine learning solution to the problem - as long as you have the skill and knowledge to develop the solution.

Building new organisational models to achieve true digital transformation by @LOMBARDI_GLORIA via MARGINALIA

Every organisation should be developing their digital workplace. It’s not about one, single solution, but about understanding all the many and varied tools and digital experiences staff need and have when working.

Interesting to understand how digital transformation can change the way the organisation is structured and operates.

GDPR could drive sweeping changes in how organisations manage information by Bob Violino via @infomgmt

A new study finds that nearly three in four organisations plan to incentivise employees to improve data hygiene and take accountability for compliance.

This generally points to a need for companies to get their act together and sort this out before they end up in serious trouble.

How organisations will use intelligent data capture in 2018 by Kayla Matthews via @infomgmt

Systems extract key details from documents and then perform actions based on the contents. In most cases the technology becomes smarter with use, and firms can capitalise on that.

This has been around for a while in a very simple form but as technology develops instead of loading the data it can be interpreted and actions can be taken. Just imagine the cost savings possible from this.

Google’s voice-generating AI is now indistinguishable from humans by @davegershgorn via @qz

In this paper, Google researchers explain a text-to-speech system called Tacotron 2, which claims near-human accuracy at imitating audio of a person speaking from text.

This is a really exciting development and so worth keeping an eye on.

AI and big data converge to improve your airport customer experience by @joemckendrick via @ZDNet

What if they built a cognitive system that knew when you were coming through the gate and made sure the airport was ready to provide you a smoother experience?

For anyone who has travelled this sounds like heaven - the big question is how long will it take for something like this to be developed and implemented.

WEBINAR: Proactive Compliance: Applying virtualised Graphs to address the challenge of GDPR - 16 Jan 2018

Web Seminar  Proactive Compliance: Applying virtualized Graphs to address the challenge of GDPR

Jan. 16, 2018 | 2 PM ET/11 AM PT
Hosted by Information Management

Data governance requirements such as the relatively new General Data Protection

 Regulation (GDPR) for enterprises doing business with Europe are driving a need 

to better understand customer data assets and where they reside within the organization. 

Businesses are collecting mountains of personal data about their customers that, when 

organized effectively, offers the potential to reduce regulatory and compliance risk and expenses.

Many organizations store information in data warehouses, MDM hubs or more recently, data lakes. 

However, with such systems collecting hefty streams of data on a daily basis, wading through and 

determining what information is relevant for compliance initiatives such as GDPR is a daunting task.

A key approach is to develop an agile single view solution, understanding relevant data 

assets and their quality and suitability for purpose. Clearly, the ability to collaborate on 

whiteboard style models with maps of existing data assets to these models, and an 

ability to profile directly against these models to evaluate their relevance is key. A 

complete solution based around Graph provides a natural way to model these requirements 

and understand the Enterprise Metadata Graph.

In this webcast you will learn how to:

• Adopt a proven approach that develops an agile single view & enterprise metadata management strategy around Graph databases.
• Deliver a model that is far quicker to implement & more agile than prior IT capabilities
• Enable governance of key data assets such as customer data with an eye towards key business drivers such as GDPR.

Aaron Wallace Principal Product Manager Pitney Bowes (Presenter)	Aaron Zornes Chief Research Officer The MDM Institute (Moderator)

Sponsored By:

Register here

The Difference between Data Scientists, Data Engineers, Statisticians, and Software Engineers by @ronald_vanloon via @Datafloq

What is the difference between the different big data jobs, as it can be confusing and complicated to find out.

Interesting definitions.  I have noticed several things - first companies do not work to the same definition so a data scientist in one is a data engineer in another, and the second is that many people do hybrid roles that comprise of parts of each of these roles. Either way it is confusing to compare and contrast across organisations.

19 Code "smells" that are most common by Ekaterina Novoseltseva via @Apium_hub

Code Smells are signals that your code should be refactored in order to improve extendability, readability, and supportability.

Who knew there were so many ways to tune and improve your code. Definitely something that could be incorporated into coding standards and reviews to make sure that it is as good as it can be.

Optimisation for Deep Learning Highlights in 2017 by/via @seb_ruder

Sebastian Ruder describes the 2017 developments in optimisation for deep learning he finds the most promising.

This is a great article and well worth a read and bookmark so you can digest it over time.

A Startup Uses Quantum Computing to Boost Machine Learning by Will Knight via @techreview

A company in California just proved that an exotic and potentially game-changing kind of computer can be used to perform a common form of machine learning.

I think that quantum computing is an interesting area for expansion in the hardware market and could give some really big advantages to the through rate of computations for ML, AI and anything else.

3 Key Processes You Need to Implement AI by @gideonrubin via @B2Community

Google Executive Chairman Eric Schmidt has suggested machine learning would be the one commonality for every big startup over the next five years.

I like the three processes and they definitely need to be done carefully and for the testing one repeatedly. I also agree that data is alive and that the cleaning needs to be easy to repeat and you also may need to processes on the source to ensure you don't have to clean going forward.  Please also remember that your processes need to be repeatable in order to be able to do this more than once.

10 Surprising Ways Machine Learning is Being Used Today by Roxanna “Evan” Ramzipoor via @InformationWeek

As machine learning technologies evolve developers in a range of fields are finding innovative ways to solve challenges.

Some interesting applications for ML that make the point very clearly that there are amazing possibilities in the use of this technology.  Just think what we have to look forward to in the future.

Demystifying artificial intelligence in learning by @v_vansac via @born2invest

Artificial intelligence is evidently evolving and along with it are learning institutions that benefit from AI.

The possibilities are almost endless - it could be used by formal institutions that are part of the standard education system, but it can also be used by corporate training departments, external training companies and MOOCs.

WEBINAR: Enhancing Anti-Money Laundering (AML) Programs with Automated Machine Learning - 11 January 2018

Enabling the AI-Driven Enterprise

Thursday, January 11, 2018  6:00 PM. 45 minutes with Q&A

Compliance organisations within banks and other financial institutions are turning to machine learning for improving their AML compliance programs.  Today, the systems that aim to detect potentially suspicious activity are commonly rule-based, and suffer from ultra-high false positive rates.  Automated machine learning provides a solution to address this challenge.

In this webinar, Justin Dickerson, General Manager of Global Finance for DataRobot, and Dan Yelle, a Customer-Facing Data Scientist for DataRobot will show how automated machine learning can be used to reduce false positive rates, thereby improving the efficiency of AML transaction monitoring and reducing costs.

You’ll discover how Automated Machine Learning provides:

• The ability to develop and refresh AML predictive models at any time
• The ability to deploy models with a click of a button
• The ability to operationalize AML models by following a process that is user-centric

Speakers

Dan Yelle

Customer-Facing Data Scientist, DataRobot

Justin Dickerson, PhD

General Manager of Global Finance, DataRobot

Register here

As NoSQL thrives, so does data modelling by Pascal Desmarets via @infomgmt

But a new approach is needed to support agile development.

Here are a couple of useful articles on Agile Data Modelling:

Agile/Evolutionary Data Modelling: From Domain Modelling to Physical Modelling by @scottwambler

and

Fundamentals of Data Modelling in Agile Environments by Jelani Harper

Predictions 2018: The IoT will drive app building innovations by Guy Churchward via @infomgmt

As data from connected devices surges, so will demand for faster application development cycles.

Interesting predictions for the new year from Guy.

Blockchain Is Changing the Way We Protect and Track Our Identities by @BrianDEvans via @Inc

Although we are very much still in the early stages, blockchain technology is already beginning to show signs of its truly massive potential.

Interesting thoughts from Brian. I'd certainly never thought about how blockchain might affect where my identity data is and how it is protected. Definitely something to watch.

In the rush to big data, we forgot about search by @acoliver via @infoworld

In the cloud era, we need to look at search to be the glue that lets us find the data and analyse it together, no matter where it lives.

This is an important area that we all miss and it needs to be given a better focus as I'm sure we lose part of the benefit of putting the data or a system in the cloud because of the inadequate searching. If you think back to relational database design and then you think about searches then sometimes you even add indexes especially for common searches as part of tuning so we already know it needs attention.