Machine Archives - Business Intelligence Info

Tag Archives: Machine

Google Cloud launches machine images to simplify data science workflows

March 9, 2020 Big Data

Google today announced machine images, a new type of Compute Engine resource in Google Cloud that contains all the information required to create, back up, or restore a virtual machine. The company claims it’ll reduce the time network admins and data scientists spend managing their cloud environments by eliminating extra steps and streamlining operations.

The news is no doubt music to the ears of enterprises, which run 77% of their workloads in the cloud, according to Rightscale. Public cloud computing — a technology that’s intertwined with AI and machine learning — is anticipated to exceed $ 330 billion in market value this year, with organizations’ average yearly cloud budget exceeding $ 2.2 million.

In contrast to custom images that capture the contents of a single disk, machine images — which can be created whether the source instance is running or stopped — contain multiple disks as well as other data required to create a new instance. This includes instance properties like machine type, labels, volume mapping, and network tags; the data of all attached disks; instance metadata; and permissions, including the service account used to create the instance.

When machine images are created from an instance, the instance information and disk data are stored into a single resource, the location of which can be specified from the Google Cloud dashboard. When it comes time to restore the instance, all that’s required is providing the machine image and a new instance name.

“The new machine images make it easy to create new instances at the heart of your scalability, backup and disaster recovery strategy,” wrote Google Compute Engine product manager Ari Liberman. “Machine images use the same differential disk backup technology that powers incremental snapshots, giving you fast, reliable and cost-effective instance backups.”

To create a new machine image, try choosing the “Machine images” option from the left menu in the Compute Engine console. Then select “Create a machine image” from the menu. To create an instance from a machine image, either create it directly from the machine images page or from the instance creation page by selecting the “New VM instance from machine image” option from the left menu.

Machine images are currently in beta, Google says. For this reason, they’re currently not covered by service-level agreements or depreciation policy and might be subject to unspecified “backward-incompatible” changes.

The new Google Cloud machine images instance is not to be confused with Amazon Machine Image (AMI), a special type of virtual appliance that’s used to create a virtual machine within Amazon’s Elastic Compute Cloud service. AMIs are read-only filesystem images that include an operating system and any additional software required to deliver a service or a portion of it, like a template for the root volume for the instance and launch permissions that control which Amazon Web Services accounts can use the AMI to launch instances.

Let’s block ads! (Why?)

Big Data – VentureBeat

Lendio raises $55 million to surface small business loans using machine learning

March 2, 2020 Big Data

Lendio, a company that provides a platform that taps algorithms to match customers with lenders and secure loans, announced that it raised $ 55 million. CEO Brock Blake says the influx of funds will bolster Lendio’s marketplace while enabling it to introduce new bookkeeping and lender services functions.

Lendio claims to make it easier for small- and medium-sized business owners to secure loans. Using machine learning, it can streamline the whole process, which usually includes comparing lenders and juggling things like credit scores, collateral, and fees

“This … round is a significant milestone in Lendio’s mission to fuel the American dream as a financial matchmaker that brings high-tech, high-touch capital to the small business economy,” added Blake, who cofounded the South Jordon, Utah-based company in 2011 with Levi King and Trent Miskin. “With these funds, we are strongly positioned to grow our existing platform as a trusted loan facilitator that supports both lenders and borrowers, while building out a range of new integrated lending services that get the right loans into the right hands at the right time.”

On Lendio’s website, businesses fill out a fee- and obligation-free application that takes about 15 minutes and doesn’t impact their credit score. Then, they’re able to explore business line of credit, small business loan, short-term loan, business-term loan, merchant cash advance, business credit card, equipment financing, commercial mortgage, accounts receivable financing, startup loan, and business acquisition offers from over 75 partners including American Express, Chase, PayPal, LendingClub, Kabbage, Comcast Business, Staples, and Funding Circle.

The machine learning algorithms underpinning Lendio’s marketplace take into account factors like offer likelihood (i.e., whether the lender will give the applicant an offer), funding likelihood (whether the applicant is likely to accept an offer), and the applicant’s product market. According to Brock, they’re trained on over 140 dimensions about the borrowers and their business and more than 800,000 possible deals, enabling Lendio to find financing for more than 65% of applicants compared with the 20% who successfully obtain bank loans. “This helps capture the ideal balance between the lender’s funding target and what works well for the borrower, and deep integrations with lenders allow for programmatic and instant submissions,” he told VentureBeat via email.

Lendio says it’s funded $ 1.4 billion in capital across 73,000 loans, which the company asserts makes its marketplace the largest of its kind in the U.S. Over the past two years, year-over-year growth averaged 75%, and the number of loans facilitated grew to over 100,000 (totaling $ 2 billion),

Brock says the financing from this latest round will also be used to grow Lendio’s Sunrise product, a money management dashboard that helps customers visualize their cash flow and finances. It supports customized invoices and lets users link both bank accounts and credit cards, and it ships with templates for profit and loss, balance sheet, tax summary, customer statement, and accounts receivable reports. Business owners can invite accountants to access transactions and financial reports, who can add sales tax to invoices based on customers’ locations, and they benefit from backups across multiple data centers over encrypted connections.

Additionally, Lendio plans to enhance its lender services division, which provides banks, credit unions, and online lenders access to a custom, white-labeled online app that lets them accept loan applications online.

About $ 31 million of Lendio’s latest raise was equity, led by Mercato Partners’ Traverse Fund, while $ 24 million was a debt facility from Signature Bank. The company has raised over $ 100 million in venture capital to date, and it employs over 300 people according to LinkedIn data.

Let’s block ads! (Why?)

Big Data – VentureBeat

SQL Server Machine Learning 2019: Working with Security Changes

February 24, 2020 BI News and Info

I have a confession to make. Why, in my last article about shortest_path in SQL Server 2019, have I used Gephi in order to illustrate the relationships, instead of using a script in R for the same purpose and demonstrate Machine Learning Services as well?

The initial plan was to use an R script; however, the R script which works perfectly in SQL Server 2017 doesn’t work in SQL Server 2019.

This is the original R script I had planned to use:

EXEC Sp_execute_external_script

@language = N‘R’,

@script = N‘ require(igraph)

g <- graph.data.frame(graphdf)

V(g)$ label.cex <- 2

png(filename = “c:\R\plot1.png”, height = 1200, width = 1200, res = 100);

plot(g, vertex.label.family = “sans”, vertex.size = 40)

dev.off() ‘,

@input_data_1 = N‘select LikeMember.MemberName as LikeMember,

LikedMember.MemberName as LikedMember

from dbo.ForumMembers as LikeMember, dbo.ForumMembers as LikedMember,

Likes

where Match(LikeMember-(Likes)->LikedMember)’,

@input_data_1_name = N‘graphdf’

The purpose of the script was only to create an image file with the relationships stored in graph tables, but in SQL Server 2019, it results in an access denied message.

If you would like to reproduce this demonstration, you can use this script file to create the database in a SQL Server 2017. You must also install the R packages needed by the script. There are many ways to do it. My favourite is this:

Open windows explorer
Browse to the SQL Server R folder, usually is this one: C:\Program Files\Microsoft SQL Server\MSSQL15.MSSQLSERVER\R_SERVICES\bin
Execute the R.EXE application
Execute the instruction install.packages(“igraph”) , where “igraph” is the name of the package to be installed

When you try and run the above R script. It will work well in SQL Server 2017, and fail in SQL Server 2019 with the error message you can see in the image below:

a screenshot of a cell phone description automati 14 SQL Server Machine Learning 2019: Working with Security Changes

During PASS Summit, I was able to ask questions to the Microsoft employees in the Data Clinic, and they explained the differences between the two versions. The explanation hasn’t solved all the problems, but I was able to find a solution and create a workaround for this script.

While researching the workaround, I saw many examples of R scripts manipulating files and all these examples may fail in SQL Server 2019. Highlighting this problem, solution, and workaround in this article will be important to everyone using SQL Server Machine Learning and planning to migrate to SQL Server 2019.

First, I’ll review how Machine Learning in SQL Server works: it involves the execution of an external script, in R or Python, but has a flexible structure, you could create your own external language. The execution itself is started by a service called SQL Server Launchpad, isolating the execution from SQL Server itself. The image below (from What is the Machine Learning Server) illustrates how the execution architecture works, although this article will not go so deep into the architecture.

a screenshot of a cell phone description automati 15 SQL Server Machine Learning 2019: Working with Security Changes

Launchpad in SQL Server 2017

In SQL Server 2017 the Launchpad starts processes for each execution. The processes need an identity, so SQL Server dynamically creates users inside the group SQLRUserGroup. In the following image, you can see two groups, SQLRUserGroup, for SQL Server 2019 and SQLRUserGroupSQL2017 for SQL Server 2017, since I have both in the same machine.

The difference in the name is due to the instance name; SQL Server 2019 is the default instance in my machine. Due to that, its user group name has only the core name, while SQL Server 2017 is in an instance called SQL2017, so the instance name is attached to the core group name.

The processes have access to the machine resources, limited by the permissions given to the SQLRUserGroup. Since SQL Server 2017 creates many users, you shouldn’t grant permissions directly to the users, only to the group.

In order to be able to manipulate files in the file system, you only need to give the correct permissions to the group SQLRUserGroup. The image below shows the group and users for SQL Server 2017

a screenshot of a cell phone description automati 16 SQL Server Machine Learning 2019: Working with Security Changes

Launchpad in SQL Server 2019

In SQL Server 2019, the Launchpad was improved. Instead of using processes for each execution, it uses app containers. The app containers need only a single identity, so they use the same identity as the Launchpad service.

While the 2017 version, the SQLRUserGroup, had many users; in the 2019 version, it has only one, the Launchpad service account. The image below shows the group for SQL Server 2019.

a screenshot of a cell phone description automati 17 SQL Server Machine Learning 2019: Working with Security Changes

Nowadays, when talking about containers immediately, we think about Docker, but not in this case. The app containers I’m mentioning here are a sandbox set of APIs introduced in Windows 8. These allow any application to create app containers and start an execution inside an app container.

The app containers work as isolated virtual machines (but much more lightweight, of course). This means that you can’t access the file system of the host (your SQL Server), neither for reading or to save files. You save any files inside the app container, and they are destroyed after the app container is released. In fact, the container is redirected to save files in a temporary folder inside the host hard drive. This increases the security, no doubt about that, but any script dealing with the file system may need changes to work in SQL Server 2019.

These app containers are created by Launchpad using the windows Sandbox API. The Launchpad doesn’t offer any way to customize the app containers creation, which could solve the problem.

You can learn more about app containers with these two links:

https://techcommunity.microsoft.com/t5/Windows-Kernel-Internals/Windows-Sandbox/ba-p/301849
https://www.malwaretech.com/2015/09/advanced-desktop-application-sandboxing.html

Proving this Concept

In order to determine this concept, you can use a script in R to access the disk and list the files. You can execute the script below in both SQL Server versions, 2017 and 2019, and compare the results. In SQL Server 2017, the script will have access to the host disk. In SQL Server 2019, the script will have access only to a fake disk, which is, in fact, a temporary folder in the host disk and will be deleted after its use by the app container.

EXEC Sp_execute_external_script

@language = N‘R’,

@script = N‘data <- list.files()

data2 <- data.frame(data)’,

@output_data_1_name = N‘data2′

EXEC Sp_execute_external_script

@language = N‘R’,

@script = N‘data <- list.dirs(“..\”)

data2 <- data.frame(data)’,

@output_data_1_name = N‘data2′

EXEC Sp_execute_external_script

@language = N‘R’,

@script = N‘data <- list.dirs(“..\..”)

data2 <- data.frame(data)’,

@output_data_1_name = N‘data2′

The image below the script shows the result in SQL Server 2019.

Solutions

The app containers are created during the SQL Server Machine Learning services setup. They are objects inside the Windows local directory, and such as all the objects in the local directory, they have a unique SID to identify the app container.

You can define file system permissions directly for the SIDs. The problem is: How to identify the SID’s of the app containers since they are not listed as user or groups.

Analyse two possible solutions:

Easy and tempting: You can give permission to the object called All Application Containers. The R scripts will have the file system permission; however, any other app container eventually running on the same machine will have the file system permission as well.
Secure: You can identify the SIDs of the app containers installed by SQL Server and give permissions directly to them. In this way, only the app containers used by the SQL Server Machine Learning Services will receive these permissions

You can test both solutions using a straightforward script. First, create a folder called C:\testFolder and copy some files, any files, to the folder. Try to list the files in this folder using the script below.

EXEC Sp_execute_external_script

@language = N‘R’,

@script = N‘data <- list.files(“c:\testFolder”)

data2 <- data.frame(data)’,

@output_data_1_name = N‘data2′

All Application Containers

The All Application Container object has a fixed SID in the local directory, which is S-1-15-2-1 . You can use the application icacls to grant permission

Execute the R script above against the 2019 instance using SSMS. The script will return no result
Press Win+R and type CMD to open a command prompt as administrator
Type the following instruction and press ENTER

icacls c:\testFolder /grant *S-1-15-2-1:(OI)(CI)F /t

Using Windows Explorer, right-click the testFolder and select Properties in the context menu
In the Properties window, click the Security tab. You will be able to confirm the permission, like the image below.

Open SQL Server Configuration Manager
On the left side of SQL Server Configuration Manager, select SQL Server Services
In the right-side of SQL Server Configuration Manager, right-click the Launchpad service and select Restart item in the context menu
Execute the same R script again in SSMS. This time it will work and list the files in the folder.

a screenshot of a cell phone description automati 18 SQL Server Machine Learning 2019: Working with Security Changes

Delete the testFolder folder so that you can continue with the other solution below

Using the App Containers SID

The challenge you will face when giving permissions to the App Containers SID is to discover which are the App Containers SID. There is not a direct solution for that, although, after knowing where to look, it becomes easy. SQL Server installs firewall rules in Windows Firewall in order to forbid app containers from making external contact to the network. These firewall rules are created precisely to block the app containers SID from using the network, so you can identify these SIDs by analysing these firewall rules.

If you look at the firewall rules, you can see that the app containers are blocked.

word image 81 SQL Server Machine Learning 2019: Working with Security Changes

The firewall UI doesn’t provide the SIDs, however. You need to dig deeper using PowerShell to retrieve the SIDs from the firewall rules.

The PowerShell command is a combination of the cmdlet Get-NetFirewallRule, to retrieve the firewall rules and the cmdlet Get-NetFirewallApplicationFilter in order to retrieve, from each rule, the app container filter information.

The complete PowerShell command line is this:

(Get-NetFirewallRule | Where-Object { ($ _.Direction -eq “Outbound”) -and ($ _.DisplayName -like “*appcontainer*”)}) | %{ ($ _ | Get-NetFirewallApplicationFilter) | %{Write-Output $ _.Package } }

Analyse this command line in more detail:

Get-NetFirewallRule will retrieve all the firewall rules in Windows Firewall
Where-Object filters the firewall rules, retrieving only the outbound rules which contains ‘appcontainer’ in the name
The expression %{ } is a shortcut to foreach-object so that the instructions will be executed for each firewall rule
Get-NetFirewallApplicationFilter is executed for each firewall rule, having the firewall rule ($ _ ) as a parameter
The expression % { } is used again to run one line for each application filter found
The Write-Output shows the value of the Package property on each application filter

a black and blue text description automatically g 1 SQL Server Machine Learning 2019: Working with Security Changes

Having discovered the SIDs, you need to set the permission to the folder. The instruction will look like the one below for each SID.

ICACLS “C:\testFolder” /grant “*S-1-15-2-1853514363-3573294594-1452771951-225645021-3819702349-824866239-3302992986″:(OI)(CI)F /t

You could copy and paste all the sids returned to create a batch file to set the permissions, but PowerShell can do all of this you as well. The following script prompts you for a file path, the one where you want Machine Learning to have access, and sets the permissions:

Param (

[Parameter(

Mandatory)]

[string]

$ FolderName

)

Get-NetFirewallRule |

Where-Object {

$ _.Direction -eq “Outbound” -and

$ _.DisplayName -like “*appcontainer*”

} |

ForEach-Object {

$ _ |

Get-NetFirewallApplicationFilter |

ForEach-Object {

$ Acl = Get-Acl $ FolderName

$ Sec= New-Object System.Security.Principal.SecurityIdentifier($ _.Package)

$ AccessRule = New-Object System.Security.AccessControl.FileSystemAccessRule(

$ Sec,

“FullControl”,

(1 -bor 2),

“Allow”

)

$ Acl.SetAccessRule($ AccessRule)

$ Acl |

Set-Acl $ FolderName

} # end 2nd FE-O

} # end 1st FE-O

The script is using COM objects System.Security.Principal.SecurityIdentifier and System.Security.AccessControl.FileSystemAccessRule in order to set the access rule on the folder.

After running the script, take a look at the security properties of the folder, and the R script should now work from SQL Server 2019.

word image 82 SQL Server Machine Learning 2019: Working with Security Changes

Workaround with a FileTable

Although I have identified two solutions, it’s interesting to notice Microsoft is moving SQL Server Machine Learning towards a more secure environment, avoiding network and file system access. Considering this, you may like to use an alternative solution.

There are some options, all based on returning the image as an output parameter to SQL Server. Here is one of the options.

A filetable can map a disk folder as a table in SQL Server. This a very good solution when you need to deal with files. The script in R will see the table as a regular table. While reading or inserting into the table, the script will be reading and inserting from/to the disk.

It’s a very good solution for a permanent environment, however, it has some problems:

You can’t define the final location of each file using SQL Server configuration. The filetable will be exposed from the server as a sharing folder, not a disk folder, so there is a slight difference in relation to the initial planning.
Filetable is not supported in Azure PaaS cloud solutions, so you will be tied to IaaS solutions, which means creating and maintaining a virtual machine in Azure, or an on-premise environment.

The Filetable technology was created in SQL Server 2016, based on the filestream technology which was built on the previous version.

Enabling Filetable in SQL Server

A filetable solution needs some administrative care; for this reason, this solution can only be used after enabling it on a server configuration level. Follow these steps to enable this solution:

Open SQL Server Configuration Manager
On the left side of SQL Server Configuration Manager, select SQL Server Services
On the right side, double click the SQL Server service you are using
On the SQL Server properties window, select the FileStream tab

a screenshot of a cell phone description automati 19 SQL Server Machine Learning 2019: Working with Security Changes

In the filestream tab, check the box Enable filestream for Transact-SQL Access
In the filestream tab, check the box Enable filestream for I/O Access
The textbox Windows Share Name will be filled with the default value MSSQLSERVER. You can change it, but leave this default for now
In the filestream tab, check the box Allow remote clients to access filestream data.
Click the Ok button in the SQL Server Properties window
In the right-side of SQL Server Configuration Manager, right-click the SQL Server service and select Restart item in the context menu

a screenshot of a cell phone description automati 20 SQL Server Machine Learning 2019: Working with Security Changes

In SSMS, change the FileStream access level configuration using the following code:

EXEC sp_configure ‘filestream access level’, 2

RECONFIGURE

Value 1 enables filestream only for T-SQL access, which is not enough for filetable. Value 2 enables filestream for T-SQL and I/O access.

Preparing the Database to Support Filetable

In order to support filetable, you need to make some changes to the database. You need to create a filegroup to contain the filestream data, configure a special file, which will point to a folder and change some database settings.

Here is the T-SQL to execute for the database. Be sure to delete the c:\R folder before running this code:

ALTER DATABASE graphdemo ADD filegroup filestreamgroup

CONTAINS filestream

ALTER DATABASE graphdemo ADD FILE (NAME=fs, filename=‘c:\R’)

TO filegroup filestreamgroup

In SSMS, Object Explorer, right-click the GraphDemo database and select Properties in the context menu

In the database properties window, set the property Filestream Directory Name to RFiles. During further steps, you will better understand what this means.
In the database properties window, set the property Filestream Non-Transaction access to Full

In the database properties window, click the Ok button
Confirm the close of database connections clicking the Ok button in the dialog box that will appear

a screenshot of a cell phone description automati 21 SQL Server Machine Learning 2019: Working with Security Changes

Create the filetable using the following T-SQL:

CREATE TABLE images AS filetable WITH

(

filetable_directory = ‘Images’

);

The filetable you created, Images, is linked to a folder. Which folder? If you check the path c:\R, which was used to configure the database file, you will find some files and folders, but it’s not easy to understand them. So, what’s missing?

Follow these steps to see:

Click win+R
Type \localhost and click Ok You will find the share MSSQLSERVER, that’s the name you configured in SQL Server Configuration Manager. Now that you know how the name is used, you can choose a better name if needed.

Open the share MSSQLSERVER You will find a folder called RFiles. That’s the configuration made for the database. Each database configured to use filestream will have a share inside \Localhost\MSSQLSERVER

a screenshot of a cell phone description automati 22 SQL Server Machine Learning 2019: Working with Security Changes

Open the folder Rfiles You will find another folder called Images, the name used for the filetable. Each filetable in the database GraphDemo will have its own folder inside \localhost\MSSQLSERVER\RFILES

a screenshot of a cell phone description automati 23 SQL Server Machine Learning 2019: Working with Security Changes

Open the folder Images. The folder is empty.
In SSMS, execute the following query:

select * from Images

The filetable is empty.
Copy a file, any file, to the Images folder, \localhost\MSSQLServer\RFILES\Images
Execute the select again in SSMS:

select * from Images

This time the table has one row, exactly the row for the new file you copied. The filetable is linked with the folder. So, if you insert a record inside the filetable, the record will be a file available in the folder, in this way you can save to disk the image generated by the R script

New R Script for Filetable

After preparing the filetable environment, the new script will be this one:

DECLARE @img VARBINARY(max)

EXEC Sp_execute_external_script

@language = N‘R’,

@script = N‘ require(igraph)

require(hexView)

g <- graph.data.frame(graphdf)

V(g)$ label.cex <- 2 png(filename = “plot1.png”, height = 1200, width = 1200, res = 100);

plot(g, vertex.label.family = “sans”, vertex.size = 40)

dev.off()

img <- readRaw(“plot1.png”)

imageContent <- img$ fileRaw’,

@input_data_1 = N‘select LikeMember.MemberName as LikeMember,

LikedMember.MemberName as LikedMember

from dbo.ForumMembers as LikeMember, dbo.ForumMembers as LikedMember,

Likes

where Match(LikeMember-(Likes)->LikedMember)’,

@input_data_1_name = N‘graphdf’,

@params = N‘@imageContent varbinary(max) OUTPUT’

@imageContent = @img output;

INSERT INTO images (NAME, file_stream)

VALUES (‘test.png’, @img)

The script is very similar to the initial one; however, this time, it results in an output variable with the image.

Here are the differences between this script and the original:

A varbinary(max) variable which will be used as an output
A new R library, hexView, must be installed in order to do additional manipulations in the file (yes, you will still use a file)
After saving the plot to disk, it’s read back, reading the raw file content
The variable where you stored the raw image (imageContent) is defined as a parameter using the @params argument of the sp_execute_external_script
You need to link this script variable with a variable outside the script to get the content
Finally, you insert the variable into the filetable

The result of the execution of the script above will be a new file in the folder, you can check by accessing \localhost\MSSQLSERVER\RFILES\Images

a screenshot of a cell phone description automati 25 SQL Server Machine Learning 2019: Working with Security Changes

Conclusion

During my research on the web, I saw many examples of R scripts dealing with the file system. These scripts which are running nowadays in SQL Server 2017 will break in SQL Server 2019 unless one of the changes explained in this article is applied.

You can choose to fix the file system permission or start to avoid direct file system access from the R script. I believe Microsoft will provide a fix for this problem soon, what probably will be an easier way to give permissions to the app container SIDs

Let’s block ads! (Why?)

SQL – Simple Talk

AI Weekly: Machine learning could lead cybersecurity into uncharted territory

February 15, 2020 Big Data

Once a quarter, VentureBeat publishes a special issue to take an in-depth look at trends of great importance. This week, we launched issue two, examining AI and security. Across a spectrum of stories, the VentureBeat editorial team took a close look at some of the most important ways AI and security are colliding today. It’s a shift with high costs for individuals, businesses, cities, and critical infrastructure targets — data breaches alone are expected to cost more than $ 5 trillion by 2024 — and high stakes.

Throughout the stories, you may find a theme that AI does not appear to be used much in cyberattacks today. However, cybersecurity companies increasingly rely on AI to identify threats and sift through data to defend targets.

Security threats are evolving to include adversarial attacks against AI systems; more expensive ransomware targeting cities, hospitals, and public-facing institutions; misinformation and spear phishing attacks that can be spread by bots in social media; and deepfakes and synthetic media have the potential to become security vulnerabilities.

In the cover story, European correspondent Chris O’Brien dove into how the spread of AI in security can lead to less human agency in the decision-making process, with malware evolving to adapt and adjust to security firm defense tactics in real time. Should costs and consequences of security vulnerabilities increase, ceding autonomy to intelligent machines could begin to seem like the only right choice.

We also heard from security experts like McAfee CTO Steve Grobman, F-Secure’s Mikko Hypponen, and Malwarebytes Lab director Adam Kujawa, who talked about the difference between phishing and spear phishing, addressed an anticipated rise in personalized spear phishing attacks ahead, and spoke generally to the fears — unfounded and not — around AI in cybersecurity.

VentureBeat staff writer Paul Sawers took a look at how AI could be used to reduce the massive job shortage in the cybersecurity sector, while Jeremy Horwitz explored how cameras in cars and home security systems equipped with AI will impact the future of surveillance and privacy.

AI editor Seth Colaner examines how security and AI can seem heartless and inhuman but still relies heavily on people, who are still a critical factor in security, both as defenders and targets. Human susceptibility is still a big part of why organizations become soft targets, and education around how to properly guard against attacks can lead to better protection.

We don’t know yet the extent to which those carrying out attacks will come to rely on AI systems. And we don’t know yet if open source AI opened Pandora’s box, or to what extent AI might increase threat levels. One thing we do know is that cybercriminals don’t appear to need AI to be successful today.

I’ll leave it to you to read the special issue and draw your own conclusions, but one quote worth remembering comes from Shuman Ghosemajumder, formerly known as the “click fraud czar” at Google and now CTO at Shape Security, in Sawers’ article. “[Good actors and bad actors] are both automating as much as they can, building up DevOps infrastructure and utilizing AI techniques to try to outsmart the other,” he said. “It’s an endless cat-and-mouse game, and it’s only going to incorporate more AI approaches on both sides over time.”

For AI coverage, send news tips to Khari Johnson and Kyle Wiggers and AI editor Seth Colaner — and be sure to subscribe to the AI Weekly newsletter and bookmark our AI Channel.

Thanks for reading,

Khari Johnson

Senior AI Staff Writer

Let’s block ads! (Why?)

Big Data – VentureBeat

7 Questions on How to Use Machine Learning for Anomaly Detection

January 29, 2020 TIBCO Spotfire

Reading Time: 3 minutes

Asking questions is one of the best ways to learn. But sometimes you don’t know where to start or what to ask—especially on a topic like anomaly detection that you’re still becoming familiar with. In that case, it’s best to listen to the questions of others and let their line of thinking guide your learning. Below are some questions we received during our “Ask Me Anything: Anomaly Detection” webinar to help you get started.

What’s the difference between outliers and anomalies?

Outliers are observations that are distant from the mean or location of a distribution. However, they don’t necessarily represent abnormal behavior or behavior generated by a different process. On the other hand, anomalies are data patterns that are generated by different processes.

Are there any applications for anomaly detection in pharmaceuticals?

There are many applications of anomaly detection in the pharmaceutical life sciences space. Including, process monitoring and quality control using Statistical Process Control (SPC) or Quality Control (QC) and Multivariate SPC (MSPC) charts in pharmaceutical manufacturing. Timely anomaly detection is critical to avoid abnormal events and adhere to safety standards. Identifying anomalies in over-the-counter transactions can be used to fight prescription abuse in pharma retail data. And real-time detection of anomalies in multi-parameter clinical trial data helps ensure the success of clinical trials.

Are GANs also used for anomaly detection? If so, could you please provide an industry use case.

Generative Adversarial Networks (GANs) are novel unsupervised learning methods that are very efficient in identifying anomalies. Since GANs are iterative by design and the adversarial training is optimized to leverage reconstructed samples to reduce residual loss, they work well in semi-structured and unstructured data. They are specifically very useful for medical image analysis (helping radiologists find hard-to-identify tumors), facial recognition, text to image translation, etc.

Does correlation data affect anomaly detection? In what ways and how could we diminish the effects? Is it preferable to clean and remove correlations before starting anomaly detection?

As mentioned in the webinar, we do not think that correlations would affect anomaly detection, but we have many techniques available to help determine how to treat correlated variables. One suggestion would be to reduce the number of dimensions with a technique like Principle Component Analysis (PCA).

What is a suggested algorithm that would be appropriate for anomaly detection related to identifying unusual activities in network activities or data?

As mentioned in the webinar, there are many methods and algorithms that work well for various applications and use cases of anomaly detection. Some of them are Recurrent Neural Network (RNN), Generative Adversarial Network (GAN), Isolation forests, Deep Autoencoders, etc. If you are specifically interested in Network/Graph analytics, the two main methods used for identifying anomalies in network graphs are the Direct Neighbour Outlier Detection Algorithm (DNODA) and Community Neighbour Algorithm (CNA).

In my current work “novelty” is the main thing we struggle to detect. Quality control charts work well for known patterns, but automated identification of new patterns is harder. I’m hoping to get some ideas for tools to help in that area.

For univariate quality control charts, Western Electric rules are used to detect a handful of common patterns. Classic multivariate methods such as Partial Least Squares (PLS) will capture patterns involving more than one variable that would not be detected by univariate methods. Autoencoder is the most comprehensive tool that will cover the widest range of different patterns. It can capture patterns that are multivariate, cyclical, nonlinear, and have interactions. You train the autoencoder using a set of ‘normal’ data’; any patterns present in new data that were not present in the training set will be flagged. We have a template for Spotfire on the TIBCO Community.

Would reducing the dimensions by doing PCA affect the anomalies in a dataset? Would it lead to the disappearance of the anomalies? If so, how could it be prevented?

Doing PCA will capture some percentage of the variance in the original dataset. So the way we use PCA to do anomaly detection is to compute the ‘distance’ from the original point to the point represented in the lower-dimensional space. The larger the distance (i.e. the more that was ‘lost’ when mapping the observation to the lower-dimensional space), the more we regard it as an anomaly.

Chances are you had a similar question to the ones above, but didn’t know how to ask it. In fact, if any of the questions above spark another one in your mind, contact us and we can answer it for you. And watch the full on-demand “Ask Me Anything: Anomaly Detection and Machine Learning” webinar for more on the basics of anomaly detection, common use cases, and some key techniques to keep in mind as you get started.

Let’s block ads! (Why?)

The TIBCO Blog

Spares Pooling: Machine Learning To The Rescue

December 28, 2019 SAP

Spare parts. Every original equipment manufacturer (OEM) has to maintain an inventory of them. Machines break, pieces of equipment fail, and customers need spares to make repairs without delay.

In today’s on-demand global market, spares replenishment must work like magic: prompt, cost-effective, and seamless. Otherwise, prolonged downtime due to equipment failure can have serious direct and indirect impacts, for example, dysfunctional surgery suites in hospitals, flight cancellations, production delays, unfulfilled orders, or poor quality leading to product recalls.

Ensuring the right parts are on hand when needed

Having a sufficient and efficient supply of spare parts is a vexing inventory-management scenario. Forecasting parts-failures at the points of demand and planning for replenishment are essential, as parts are very expensive and costly to keep in stock. Meanwhile, warehouse management teams must routinely juggle multiple, complex scenarios with contradictory insights, forecast failures, and inventory based on complicated predictive analytics.

Demand and consumption forecasting is done at the stockkeeping unit (SKU) level by taking into account the way they handle failures in KPIs. Doing so can minimize the costs of downtime. The key is to apply a risk-ranking system estimates the consequences of a failure to the business, the probability that a failure will occur, and the risk the business is willing to accept.

In terms of a repair strategy, a lot depends on the OEM’s policy on parts replaceability versis repairability. Availability of onsite skills within the maintenance team and time to repair are crucial factors in these decisions. Sometimes, it is more cost-effective to replace the unit and to repair the old unit at one location. But stocking whole units is far more expensive than individual SKUs.

Calculating the efficient safety stock levels at the SKU level must take into account the predictability of demand, delivery lead times, delivery accuracy, obsolescence, transportation costs, and more. The strategy must include reorder points (ROP) and reorder quantities (ROQ), as well as cost-based optimization of order quantities and order cycles at the supplier or product level. Combining automated routine orders with order suggestions for specific important items as well as automatic exception management must also be considered.

Pooling dramatically changes the stocking strategy

Most OEMs have multiple warehouses with millions of dollars’ worth of inventory. If they were to stock at least one piece of each part in all these warehouses, the cost would be prohibitive. This is where parts pooling comes in.

Pooling builds on data that proves aggregation reduces variability and uncertainty. This implies that demand variability will decrease by aggregating spares across locations.

For example, low demand from one warehouse location offsets the demand at another warehouse location. This decline in variability allows a reduction in safety stock and reduces average inventory. When executed well, it is an excellent approach to reduce inventory at individual warehouses without impacting customers.

Shared and pooled warehousing have the potential to transform spares management of small and midsize businesses in competitive markets. This is quite valuable as business networking strategies and collaboration in logistics continue to gain momentum.

Easier said than done

Location-base parts pooling requires adding many highly complicated variables to the current set of variables. In this case, predictive modeling is no longer sufficient. It is time to move to artificial intelligence (AI) and machine learning models that take into account multiple variables and blend them to enable better decision-making in two critical areas:

Warehouse location and product flow: Decisions on whether to have many warehouses close to customers or to have more centralized locations can balance the advantages of aggregated demand and lead times. Proximity to customers and other factors may make maintaining more warehouses more advantageous.

Transportation: Managing parts pooling across locations means accounting for the geographical area along with logistics like availability and reliability of the ground transportation network, flights, or water corridors.

Within months of implementing warehouse pooling optimized through machine learning, most OEMs’ customer KPIs improve while simultaneously minimizing the capital invested in spares.

AI and machine learning models merge cost, operations, and criticality analysis for the relevant scenarios while factoring location criteria at a granular level to automate actions and enable better decisions. Optimization enable OEMs to reduce inventory without impacting customers by using a pooling model.

To reduce risk, OEMs can start by implementing an ML model with a few fixed locations and fewer optimization criteria, like the cost of parts, shipping costs, lead times, and the probability of failures. As the system matures, they can improve this model through a dynamic pooling model capable of handling more factors.

Lenient spares inventory management is a strict no-no as the costs of non-availability can be disastrous. Machine learning-enabled systems are set to transform the way warehouses operate. But before OEMs take the plunge, they must make sure they have the data and talent they need.

See some real-world examples of how machine learning is establishing itself as a mainstream technology tool for companies, enabling them to improve productivity, planning, and ultimately, profits, in the study “Machine Learning In The Real World.”

Let’s block ads! (Why?)

Digitalist Magazine

The Benefits of Doing Machine Learning and Analytics in the Cloud

December 17, 2019 TIBCO Spotfire

Reading Time: 4 mins 30 sec.

Analytics with data science has been one of the last enterprise systems to move to the cloud, but the situation has changed fundamentally in just the last year or two.

Suddenly, there is a proliferation of cloud-based databases and open-source machine learning development frameworks like SageMaker and TensorFlow—all of them now being heavily promoted by the major cloud vendors (Amazon, Microsoft, Google, and more).

The cloud is quickly becoming everyone’s preferred way of doing machine learning and analytics. If you know your way around all the available components, it can be easy to build even the most sophisticated machine learning models for everything from image recognition to fraud detection in the cloud.

What to use—when and how

There’s a ton of rich functionality available in the cloud that you can spin up right now. Over the last few years, there’s been a real shift from heavyweight on-premises installations of data science and predictive analytics to the more lightweight approach that the cloud offers.

The breadth of capabilities that the cloud providers have created combined with the ease of use of a data science platform like TIBCO’s, organizations can spin up environments very quickly without a great deal of IT overhead. This combination of scalability and flexibility is the central value of the cloud when it comes to doing analytics. In fact, with TIBCO^® Data Science, you can create solutions across all of these various cloud environments without needing to learn the nuances of each.

Here’s a helpful chart of the technologies available for artificial intelligence (AI) and machine learning in the cloud:

The Benefits of Doing Machine Learning and Analytics in the Cloud

Proof it’s as easy as it sounds

It really is that easy. The proof is in our customers’ success stories. Below are a couple of case studies that show how easy it can be to build applications based on sophisticated AI and machine learning, using the cloud.

Tipping Point Community fights poverty with data:

As a non-profit organization looking to better understand the drivers behind poverty, Tipping Point started a project to explore correlations between parking citations, late fees, and low-income individuals. Using TIBCO Data Science’s collaborative interface for business users and deploying machine learning models in the cloud to discover insights, Tipping Point found a disproportionate impact on low-income drivers. We’re proud to say these data-driven recommendations that Tipping Point made to the San Francisco Office of Financial Justice led to a change in policy to make the system fairer.

Leidos unlocks big data potential for healthcare analytics:

Leidos partnered with TIBCO Data Science, an enterprise-class cloud platform that leverages Amazon Web Services (AWS), to allow users to create machine learning workflows. By using the cloud, Leidos opened up collaboration across teams and was able to perform quicker analyses. It was able to analyze healthcare data to determine the cause of disease outbreaks like HIV and Zika, consolidate data around emerging healthcare policies, and explore human factors affecting space exploration for NASA.

Across these and many other examples, performing analytics and machine learning in the cloud gives organizations the ability to uncover hidden patterns, anticipate outcomes, and react quickly to real-world events. Data science teams can spin up new systems in the cloud in a matter of hours, performing advanced analytics in a low-code, visual data science environment like TIBCO’s to find the answers to their toughest problems, fast.

Connect teams & scale algorithms with cloud-enabled tech

The TIBCO Data Science platform provides an interactive interface for teams to collaborate on projects in the cloud. Teams scattered around the globe, across different departments, in different roles, can connect through the web-based interface to solve difficult data science problems together. Furthermore, the algorithms TIBCO provides through a simple drag and drop interface are not only easy to use, without requiring a lot of code, but they’re also readily scalable and immensely powerful. But if coding is your thing, you can also use the embedded Jupyter Notebooks that are built into the platform.

Watch this webinar for more on available cloud technologies and the many benefits of doing machine learning and analytics in the cloud.

Get started right now. Go to the AWS marketplace and sign up for a preview of the TIBCO Data Science platform, connect to open sources of data, and start building machine learning models today.

Let’s block ads! (Why?)

The TIBCO Blog

AI Weekly: NeurIPS proves machine learning at scale is hard

December 13, 2019 Big Data

The world’s largest AI research conference is underway in Vancouver, Canada. Researchers are presenting more than 1,400 papers at the Neural Information Processing Systems (NeurIPS) conference, ranging from work that organizers believe has had the greatest impact over the past decade to Yoshua Bengio’s continued march toward consciousness for deep learning.

But even as the conference showed theoretical research and neuroscience-related papers on the rise alongside categories like algorithms and deep learning, the mushrooming of the event itself — and the associated growing pains — was a constant theme, and it speaks to the growth of the AI field in general.

Organizers said that at the start of the conference Sunday, they expected about 400 people to show up for registration. Instead, 4,200 queued up in the conference center registration line. All told, NeurIPS 2019 welcomed 13,000 attendees, up 40% from the prior year. In all, the conference has quadrupled in size in the past five years.

Organizers started to use a lottery system this year, after 9,000 tickets to 2018 NeurIPS sold out in 12 minutes.

Researchers submitted a record 6,600 papers for consideration.

With industry, governments, and academia investing billions, machine learning is red hot right now. The AI Index 2019 report released Wednesday found that private investment, research, and the number of PhD candidates in AI are all growing at healthy rates.

But that growth can come at a cost. As intellectually driven as NeurIPS can be, crowds can get in the way of the intellectual exchanges and scientific critique the event is intended to spawn.

Organizers invited all conference attendees to share feedback in a town hall Thursday, and one of the scientists’ main concerns was how to deal with the event’s rapid growth.

Poster sessions are the most valuable part of the conference, according to many who spoke at the town hall, since they allow you to speak directly with research authors and gain exposure to a broad range of work. At one point, the poster sessions was filled to capacity, making those dialogs difficult.

This year’s organizers are considering a lot of potential ways forward: NeurIPS regional meetups were held in more 35 countries this year, and organizers considered splitting the global conference into regional events.

“The idea is instead of having one big meeting, we could have a bunch of satellite meetings,” Sejnowski said. “I think the idea is we’ll have a bigger audience and greater participation if we had more than one location.”

Should the conference break into regional events, Seejnowski said organizers could randomly dispatch speakers to regional locations and livestream their talks for consumption around the world.

NeurIPS livestreamed major presentations this year and gave researchers ways to critique and question posters online, but a regional approach naturally limits in-person interactions and could also dilute the kind of valuable interactions the global event is intended to produce.

NeurIPS 2020 is back in Vancouver and 2021 will move to Sydney, in a larger space than the Vancouver Convention Center. Organizers may aim for larger venues in the future, like convention centers in places like New Orleans and San Diego, which have capacity of 30,000. Doing so could allow doing away with the lottery system.

Among other changes under consideration for next year: a policy to exclude certain vendors from the industry expo. No rules currently exist to exclude specific vendors. The issue came up following a question from the audience about why the National Security Agency (NSA), a U.S. intelligence organization, had a booth at a conference held in Canada. Next year, researchers may also be asked to share the carbon footprint of training their AI model.

Toward a more open and welcoming NeurIPS

Like last year, attendees from parts of Asia and Africa encountered issues with Canadian immigration officials that kept them from reaching Vancouver. Diversity and inclusion chair Katherine Heller said that as of Monday, 158 initially denied visas were approved, 14 were in progress, and five were denied. Despite the change, some poster sessions displayed signs that said the presenter received a visa too late to attend.

Since the conference will take place in Vancouver again next year, organizers hope to improve communication with the Canadian government in 2020.

But organizers seem to be trying hard to make NeurIPS a more accepting and welcoming place. A child care program was full to capacity for the kids of about 75 researchers. That program is expected to expand next year.

UC Berkeley professor Celeste Kidd gave the opening keynote address, and touched on how tech can drive false facts and misunderstandings of the #metoo movement.

{Dis}ability in AI had its first meeting, and workshops highlighted the work of diverse machine learning practitioners as well as AI that can positively impact the lives of people of African or Latin descent, women, and followers of Islam and Judaism.

At the Queer in AI workshop, people got to choose whether they wanted to be photographed or not, and you could hear presentations about research that seeks to remove gender identity from AI systems.

Some workshops even opened with acknowledgment that the convention took place on the land of the native Coast Salish people.

Machine learning at scale

What you hear about at NeurIPS are the advances in stochastic gradient descent, covariants, and all manner of technical discoveries or steps forward, but there are also workshops on disaster response, privacy, machine learning for the developing world, climate change, and using computer vision to battle cancer.

It’s an insane rush of theoretical, complex approaches to improve the performance of machine intelligence, but also a drive to solve real-world problems and contribute to society.

The other challenge in front of the community, it seems, is growth management. Perhaps regional events help, but interest in a unified international event does not appear to be subsiding.

AI is the top focus of computer science school PhD programs today, and other conferences like ICLR or CVPR are also experiencing growth.

In other words, growth in the AI field will likely continue to drive the growth of NeurIPS. The organizers of the biggest machine learning research conference of the year have to deal with the challenges that growth brings, but that’s a nice problem to have. While they’re making progress toward solving some of those challenges, it seems clear that the importance of physical access to the conference will not subside anytime soon and will likely continue to grow in importance.

For AI coverage, send news tips to Khari Johnson and Kyle Wiggers and AI editor Seth Colaner — and be sure to subscribe to the AI Weekly newsletter and bookmark our AI Channel.

Thanks for reading,

Khari Johnson

Senior AI Staff Writer

Let’s block ads! (Why?)

Big Data – VentureBeat

Rensselaer focuses IBM’s AiMOS supercomputer on machine learning

December 6, 2019 Big Data

Sophisticated machine learning applications require not only enormous amounts of training data, but powerful computer hardware on which to train. An analysis conducted by San Francisco research firm OpenAI found that since 2012, the amount of compute used in the largest training runs has been increasing exponentially with a 3.4-month doubling time, and that it’s grown by more than 300,000 times over that same time period.

The trend spurred the development of supercomputers like the U.S. Department of Energy’s Sierra and Summit, which leverage dedicated accelerator chips to speed up AI computation. Now, IBM’s Hardware Center, in collaboration with New York State, SUNY Polytechnic Institute, and other members of IBM’s AI Hardware Center, has delivered a new machine for the Department of Computer Science at Rensselaer Polytechnic Institute (RPI) that’s optimized for state-of-the-art machine learning workloads.

It’s dubbed Artificial Intelligence Multiprocessing Optimized System, or AiMOS (in honor of Rensselaer cofounder Amos Eaton), and it will principally tackle projects in biology, chemistry, the humanities, and related domains underway at the new IBM Research AI Hardware Center on the SUNY campus in Albany. It’s built using the same IBM Power Systems technology as the aforementioned Sierra and Summit, including IBM Power9 processors and Nvidia Tesla V100 graphics cards. And at eight petaflops on the High-Performance Linpack benchmark, a yardstick of supercomputing performance, AiMOS is one of the most powerful computers to debut on the Top500 ranking on supercomputers.

In fact, IBM claims AiMOS is currently the most powerful supercomputer housed at a private university, the 24th-most powerful supercomputer in the world, and the third-most energy efficient.

“As the home of one of the top high-performance computing systems in the U.S. and in the world, Rensselaer is excited to accelerate our ongoing research in AI, deep learning, and in fields across a broad intellectual front,” said Rensselaer president Shirley Ann Jackson. “The creation of new paradigms requires forward-thinking collaborators, and we look forward to working with IBM and the state of New York to address global challenges in ways that were previously impossible.”

AiMOS will provide modeling, simulation, and computation to advance the development of computing chips and systems optimized for AI algorithms, according to IBM executive vice president John E. Kelly III, with the goal of helping to make AI systems 1,000 times more efficient within the next decade. “[AI] … will help us solve some of our most pressing problems, from healthcare to security to climate change. In order to realize AI’s full potential, special purpose computing hardware is emerging as the next big opportunity.”

Of course, as speedy as AiMOS may be, it’s behind Intel’s Frontera, which can achieve peak performance of 38.7 quadrillion floating point operations per second. Frontera is the fastest computer designed for academic workloads like modeling and simulation, big data, and machine learning, a distinction it earned for the second time in November by nabbing fifth place in the Top500 list.

And AiMOS will certainly be outgunned by AMD’s Frontier, a machine with more than 1.5 exaflops of theoretical performance that’s anticipated to be delivered to Oak Ridge National Laboratory in 2021. Intel for its part plans to deploy the “exaflop-class” Aurora cluster at the Argonne National Laboratory within the next two years as part of the Energy Department’s Exascale Computing Project, a grant program that seeks to accelerate exascale computing research in the U.S.

Currently, the U.S. hosts five of the 10 fastest computers in the world, with China’s best — the TaihuLight at the National Supercomputing Center in Wuxi (built on Sunway’s SW26010 processor architecture) and the Tianhe-2A in Guangzhou — ranking third and fourth, respectively, at roughly 125 peak petaflops and 100 peak petaflops. Cray’s Piz Daint sits in sixth, ahead of Trinity at Los Alamos National Laboratory, Fujitsu’s AI Bridge Clouding Infrastructure in Japan, and Lenovo’s SuperMUC-NG in Germany.

The race between China and the U.S. is fierce. In Top500 rankings, China two years ago surpassed the United States in total number of ranked supercomputers for the first time, with 202 to 143. That trend accelerated the following year; according to the Top500 fall 2018 report, the number of ranked U.S. supercomputers fell to 108 as China’s total climbed to 229.

China and the U.S. are followed in the largest number of ranked supercomputers by Japan, which has over 30 systems; the U.K., with over 20; France with nearly 20; Germany with over 15; and Ireland with just over 10.

Let’s block ads! (Why?)

Big Data – VentureBeat

A super-fast machine learning model for finding user search intent

November 30, 2019 Big Data

In April 2019, Benjamin Burkholder (who is awesome, by the way) published a Medium article showing off a script he wrote that uses SERP result features to infer a user’s search intent. The script uses the SerpAPI.com API for its data and labels search queries in the following way:

Informational — The person is looking for more information on a topic. This is indicated by whether an answer box or PAA (people also ask) boxes are present.
Navigational — The person is searching for a specific website. This is indicated by whether a knowledge graph is present or if site links are present.
Transactional — The person is aiming to purchase something. This is indicated by whether shopping ads are present.
Commercial Investigation — The person is aiming to make a purchase soon but is still researching. This is indicated by whether paid ads are present, an answer box is present, PAAs are present, or if there are ads present at the bottom of the SERP.

This is one of the coolest ways to estimate search intent, because it uses Google’s understanding of search intent (as expressed by the SERP features shown for that search).

The one problem with Burkholder’s approach is its reliance on the Serp API. If you have a large set of search queries you want to find intent for, you need to pass each query phrase through the API, which then actually does the search and returns the SERP feature results, which Burkholder’s script can then classify. So on a large set of search queries, this is time consuming and prohibitively expensive.

SerpAPI charges ~$ 0.01 per keyword, so analyzing 5,000 keywords will cost you $ 50. Running these results through Burkholder’s labeler script also takes 3 to 5 hours to get through these 5,000 keywords.

So I got to thinking: What if I adapted Burkholder’s approach so that, rather than use it to classify intent directly, I could use it to train a machine learning model that I would then use to classify intent? In other words, I’d incur one-time costs to produce my Burkholder-labeled training set, and, assuming it was accurate enough, I could then use that training set for all further classification, cost free.

With an accurate training set, anyone could label huge numbers of keywords super quickly, without spending a dime.

Finding a model

Hamlet Batista has written a few stellar posts about how to leverage Natural Language models like BERT for labeling intent.

In his posts, he uses an existing intent labeling model that returns categories from Kaggle’s Question Answering Dataset. While these labels can be useful, they are not really “intent categories” in line with what we typically think of for intent taxonomy categories and instead have labels such as Description, Entity, Human, Numeric, and Location.

He achieved excellent results by training a BERT encoder, getting near 90% accuracy in predicting labels for new/unlabeled search keywords.

The big question for me was, could I leverage the same tech (Uber’s Ludwig BERT encoder) to create an accurate model using the search intent labels I’d get from Burkholder’s code?

It turns out the answer is yes!

How to do it

Here’s how the process works:

1. Gather your list of keywords. If you’re planning on training your own model, I recommend doing so within a specific category/niche. Training on clothing-related keywords and then using that model to label finance related keywords will likely be significantly less accurate than training on clothing related keywords and then using that model to label other unlabeled clothing related keywords. That said, I did try using a model labeled on one category/niche to label another, and the results still seemed quite good to me.

2. Run Burkholder’s script over your list of keywords from Step 1. This will require signing up for SerpAPI.com and buying credits. I recommend getting labels for at least 10,000 search queries with this script to use for training. The more training data, the more accurate your model will likely be.

3. Use the labeled data from the previous step as your training data for the BERT model. Batista’s code to do this is very straightforward, and this article will guide you through the process. I was able to get about ~72% accuracy using about 10,000 labels of training data.

4. Use your model from Step 3 to label unlabeled search data, and then take a look at your results!

The results

I ran through this process using a huge list (13,000 keywords) of clothing/fashion-related search terms from SEMrush as my training data. My resulting model gets just about 80% accuracy.

It seems likely that training the model with more data will continue to improve its accuracy up to a point. If any of you attempt it and improve on 80% accuracy, I would love to hear about it. I think with 20,000+ labeled searches, we could see up to maybe 85-90% accuracy.

This means when you ask this model to predict the intent of unlabeled search queries, 8 times out of 10 it will give you the same label as what would have been returned by Burkholder’s Serp API rules-based classifier. It can also do this for free, in large volumes and incredibly fast.

So something that would have taken a few thousand dollars and days of scraping can now be done for free in just minutes.

In my case I used keywords from a related domain (makeup) instead of clothing keywords, and overall I think it did a pretty good job. Labeling 5,000 search queries took under two minutes with the BERT model. Here’s what my results looked like:

The implications

For SEO tools to be useful, they need to be scalable. Keyword research, content strategy, PPC strategy, and SEO strategy usually rely on being able to do analysis across entire niches/themes/topics/websites.

In many industries, the keyword longtails can extend into the millions. So a faster, more affordable approach to Burkholder’s solution can make a lot of difference.

I forsee AI and machine learning tools being used more and more in our industry, enabling SEOs, paid search specialists, and content marketers to gain superpowers that haven’t been possible before these new AI breakthroughs.

Happy analyzing!

Kristin Tynski is a founder and the SVP of Creative at Fractl, a boutique growth agency based in Delray Beach, FL.

Let’s block ads! (Why?)

Big Data – VentureBeat

Tag Archives: Machine

Lendio raises $55 million to surface small business loans using machine learning

SQL Server Machine Learning 2019: Working with Security Changes

Launchpad in SQL Server 2017

Launchpad in SQL Server 2019

Proving this Concept

Solutions

All Application Containers

Using the App Containers SID

Workaround with a FileTable

Enabling Filetable in SQL Server

Preparing the Database to Support Filetable

New R Script for Filetable

Conclusion

AI Weekly: Machine learning could lead cybersecurity into uncharted territory

7 Questions on How to Use Machine Learning for Anomaly Detection

What’s the difference between outliers and anomalies?

Are there any applications for anomaly detection in pharmaceuticals?

Are GANs also used for anomaly detection? If so, could you please provide an industry use case.

Does correlation data affect anomaly detection? In what ways and how could we diminish the effects? Is it preferable to clean and remove correlations before starting anomaly detection?

What is a suggested algorithm that would be appropriate for anomaly detection related to identifying unusual activities in network activities or data?

In my current work “novelty” is the main thing we struggle to detect. Quality control charts work well for known patterns, but automated identification of new patterns is harder. I’m hoping to get some ideas for tools to help in that area.

Would reducing the dimensions by doing PCA affect the anomalies in a dataset? Would it lead to the disappearance of the anomalies? If so, how could it be prevented?

Spares Pooling: Machine Learning To The Rescue

Ensuring the right parts are on hand when needed

Pooling dramatically changes the stocking strategy

Easier said than done

AI Weekly: NeurIPS proves machine learning at scale is hard

Toward a more open and welcoming NeurIPS

Machine learning at scale

Rensselaer focuses IBM’s AiMOS supercomputer on machine learning

A super-fast machine learning model for finding user search intent

Finding a model

How to do it

The results

The implications

Recent Posts

Categories

Archives