A Pessimistic View Of The State Of Deep Learning

William Vorhies provides us a negative (and necessary) look at the current state of Deep Learning solutions:

Reinforcement Learning (RL) is arguably the hottest research area in AI today because it appears RL can be adapted to any problem that has a well-defined reward function.  That encompasses game play, robotics, self-driving cars, and frankly pretty much else in machine learning.

Within RL, the hottest research area is Deep RL which means using a deep neural net as the ‘agent’ in the training.  Deep RL is seen as the form of RL with the most potential to generalize over the largest number of cases and perhaps the closest we’ve yet come to AGI (artificial general intelligence).

Importantly, Deep RL is also the technique used to win at Alpha Go which brought it huge attention.

The problem is, according to Alex Irpan, a researcher on the Google Brain Robotics team that about 70% of the time they just don’t work.

Alex has written a very comprehensive article critiquing the current state of Deep RL, the field with which he engages on a day-to-day basis.  He lays out a whole series of problems and we’ve elected to focus on the three that most clearly illustrate the current state of the problem with notes from his work.

Vorhies is not unduly negative and is optimistic in the medium to long term, but he is right in noting that there is a lot of work yet to do in this field.

Object Detection Methods In Convolutional Neural Networks

Xindian Long explains some of the techniques which convolutional neural networks use to discern objects in images:

Can we do object detection in a smart way by only looking at some of the windows? The answer is yes. There are two approaches to find this subset of windows, which lead to two different categories of object detection algorithms.

  1. The first algorithm category is to do region proposal first. This means regions highly likely to contain an object are selected either with traditional computer vision techniques (like selective search), or by using a deep learning-based region proposal network (RPN). Once you have gathered the small set of candidate windows, you can formulate a set number of regression models and classification models to solve the object detection problem. This category includes algorithms like Faster R-CNN[1], R_FCN[2] and FPN-FRCN[3]. Algorithms in this category are usually called two-stage methods. They are generally more accurate, but slower than the single-stage method we introduce below.
  2. The second algorithm category only looks for objects at fixed locations with fixed sizes. These locations and sizes are strategically selected so that most scenarios are covered. These algorithms usually separate the original images into fixed size grid regions. For each region, these algorithms try to predict a fixed number of objects of certain, pre-determined shapes and sizes. Algorithms belonging to this category are called single-stage methods. Examples of such methods include YOLO[4], SSD[5] and RetinaNet[6]. Algorithms in this category usually run faster but are less accurate. This type of algorithm is often utilized for applications requiring real-time detection.

We’ll discuss two common object detection methods below in more detail.

This is a high-level explanation with no code, but it does a good job of describing at that level what is going on.

Deploying SQL Server To Minikube

Chris Adkin continues a series on Kubernetes with a look at Minikube:

Once the cluster is up, lets deploy a stand alone SQL Server instance to it, availability groups will be covered at a later date. To do this several different types of object will need to be created:

  • Storage objects; persistent volume claims, these will lead to the automatic creation of a persistent volumes.

  • A secret to hold the password for the sa account.

  • A deployment, this embodies the SQL Server instance.

  • A service, this provides the means of accessing the instance via an ip address and port from outside the cluster.

Click through for a demo.

Running R Scripts In Power BI’s Query Editor

Kevin Feasel

2018-11-20

Power BI, R

Brad Lewellyn walks us through the process of executing an R script against a table in Power Query:

If you aren’t able to open the R Script Editor, check out our previous post, Getting Started with R Scripts.  While it’s possible to develop and test code using the built-in R Script Editor, it’s not great.  Unfortunately, there doesn’t seem to be a way to develop this script using an external IDE like RStudio.  So, we typically export files to csv for development in RStudio.  This is obviously not optimal and should be done with caution when data is extremely large or sensitive in some way.  Fortunately, the write.csv() function is pretty easy to use.  You can read more about it here.

It’s not a perfect experience, but Brad does show us how to get it done.

Inactive Relationships In Power BI

Reza Rad explains the value of inactive relationships and shows how you can implement this in Power BI:

As you can see this new type of relationship is different. It is dashed line, compared to the active, which was a solid line. This is an inactive relationship. You can only have one active relationship between two tables. Any other relationships will become inactive.

An inactive relationship doesn’t pass filtering. It doesn’t do anything by itself. I still see many people creating inactive relationships in their model thinking that just the inactive relationship by itself will do some filtering. It doesn’t. If I use the FullDateAlternateKey from the DimDate table to slice and dice the SalesAmount from the FactInternetSales table, which field I’m filtering based on? The field that is related through an Active relationship of course. Here is a result for that (which is apparently same as what you have seen in the previous example because the inactive relationship doesn’t do anything. It is just the active relationship that passes the filter);

Read the whole thing.

Minor Differences Between Plan Cache And Query Store Plans

Grant Fritchey shows us some minor differences between what the Query Store shows for a particular execution plan versus what exists in the plan cache:

As you can see, while the structure of the plans are identical, not everything is. The Compile values are different (although sometimes, they’ll be the same, that one is kind of luck of the draw to a degree) because they were compiled at different times with varying load on the system, so certainly that will be reflected. However, the other differences are also interesting. Which of the plans was retrieved from cache for example and, more importantly, the statement for the plans. The one on the left is the plan from the Query Store. It was not retrieved from cache and, the statement is for the query, not the stored procedure. Meanwhile, the plan on the right is from cache and, it’s based on the plan handle from the stored procedure, so it reflects that in the Statement value.

Click through for the full set of differences as well as Grant’s explanation.

Working With Temporal Line Charts In Power BI

Marco Russo shows off a few things you can do with Power BI to make displaying temporal data in line charts better:

The first line is related to the week ending on February 2nd, so Sales Amount includes only 2 days (February 1st and 2nd) excluding the amount of other 5 days in the same week (January 27th to 31st). The same happens in the last week, which includes June 29th and 30th but does not include sales for the remaining 5 days in the same week (July 1st to 5th). This also explains why the report includes a week ending in July 2008 even though the Month slicer only includes dates up to June 2018.

We can create a measure that removes incomplete weeks from the calculation, as shown in the following code. A similar technique could be used for incomplete months and quarters.

There are some interesting techniques that Marco shows off, including hiding incomplete weeks.

Configuring Snapshot Replication

Nisarg Upadhyay shows us how to configure snapshot replication:

On the next screen, configure the SQL Agent security. To configure the Agent security, click the Security Settings button. The Snapshot Agent Security dialog box opens. In the dialog box, provide the account under which the subscriber connects to the publisher. Moreover, provide the account information under which the SQL Server agent job will be executed. For this demo, SQL Server jobs are executed under the SQL server agent service account, hence select the Run under the SQL Server Agent service account option. Subscribers will be connected to the publisher using SQL login, hence select the Using the following SQL Server login option and provide SQL login and password. In this demo, connect using the sa login. Click OK to close the dialog box and Click Next.

Snapshot replication is the easiest to get right, but most of the setup is the same for transactional or merge replication.

Operating Management Studio With Multiple Active Directory Accounts

Kenneth Fisher shows how to use different Active Directory credentials when using SQL Server Management Studio:

To help promote the seperation of duties one of the things my company has done is to divide our permissions into two accounts. We have one account that is for our daily tasks. Reading email, searching the internet, basic structure changes in a database etc. The other account is our admin account. It’s for remoting to servers, security tasks, really anything that requires sysadmin. I’m not going to argue the advisability of this because honestly, I’m kind of on the fence. That said, I do have to deal with it and there are a few tips in case you have to deal with it as well.

And if you’re not on the domain as well, runas /netonly /user:[domain\username] ssms.exe will do the job.

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