AI

9月 092019
 

Editor's Note: This article was translated and edited by SAS USA and was originally written by Makoto Unemi. The original text is here.

SAS previously provided SAS Scripting Wrapper for Analytics Transfer (SWAT), a package for using SAS Viya functions from various general-purpose programming languages ​​such as Python.

In addition to SWAT, SAS launched Deep Learning Python (DLPy), a higher-level API package for Python, making it possible to use SAS Viya functions more efficiently from Python. In this article I outline more about what DLPy is and how it's implementation.

About DLPy

DLPy is a high-level package for the Python API created for deep learning and image action set after Viya3.3. DLPy provides an API similar to Keras to improve the efficiency of deep learning and image processing coding. With just a little rewriting of the existing Keras code, it is possible to execute the processing on SAS Viya.

For example, below is an example of a Convolutional Neural Network (CNN) layer definition; you can see that it is very similar to Keras.

The layers supported by DLPy are: InputLayer, Conv2d, Pooling, Dense, Recurrent, BN, Res, Proj, and OutputLayer. The following is an example of learning.

DLPy functions

Introducing DLPy's functions (partial excerpts), taking as an example the learning of multiple dolphins and giraffe images using CNN and applying test images to the model.

Implementation of major deep learning networks

DLPy offers the following pre-built deep learning models: VGG11/13/16/19, ResNet34/50/101/152, wide_resnet, and dense_net.

The following models also offer pre-trained weights using ImageNet data (these weights can be used for unique tasks by transfer learning): VGG16, VGG19, ResNet50, ResNet101, and ResNet152. The following is an example of transferring ResNet50 pre-trained weights.

CNN judgment basis information

Using the heat_map_analysis() method, you can output a colorful heat map and check where you focused on the image.

In addition, the get_feature_maps() method is used to get the feature map of each layer of CNN, and feature_maps.display() method is used to specify and display the obtained feature map layer and check can also do.

The following is the output result of layer 1 feature map.

The following is the output result of layer 18 feature map.

Deep learning & image processing related task support function

resize() method: Resize image data

as_patches() method: Image data expansion (generates a patch from the original image)

two_way_split() method: Data split (learning, testing)

plot_network() method: draws the structure of the defined deep learning layer (network) as a graphical diagram

plot_training_history() method: Iterative learning history display

predict() method: Display prediction (scoring) results

plot_predict_res() method: Display classification results

And of course, you can use DLPy to get data from a SAS Viya in-memory session, pass it to your local client, and convert it to common data formats like numpy arrays and Pandas DataFrames. The converted data can be smoothly supplied to models of other open source packages such as scikit-learn.

Regarding image classification using DLPy, videos are also available in the Deep Learning with Python (DLPy) Demo Series section of the DLPy product page.

SAS Viya: Package for Python API for deep learning and image processing: DLPy was published on SAS Users.

9月 032019
 

In part one of this blog series, we introduced hybrid marketing as a method that combines both direct and digital marketing capabilities while absorbing insights from machine learning. In part two, we will share perspectives on: How SAS Customer Intelligence 360 completes analytic's last mile. How campaign management processes can easily [...]

SAS Customer Intelligence 360: Hybrid marketing and analytic's last mile [Part 2] was published on Customer Intelligence Blog.

8月 262019
 

The marketing industry has never had greater access to data than it does today. However, data alone does not drive your marketing organization. Decisions do. And with all the recent hype regarding the potential of AI, a successful cross-channel campaign is propelled by a personalized, data-driven approach injected with machine [...]

SAS Customer Intelligence 360: Hybrid marketing and analytic's last mile [Part 1] was published on Customer Intelligence Blog.

8月 202019
 

‘Quality‘ means many things to many people. It’s subjective and depends on the industry and product being made, but the fundamental objective is to provide the best product to the right standard associated to fit, form and function. And cost and required profit margin must also be taken into account. [...]

How analytics can help meet the quality standard in manufacturing was published on SAS Voices by Tim Clark

8月 202019
 

‘Quality‘ means many things to many people. It’s subjective and depends on the industry and product being made, but the fundamental objective is to provide the best product to the right standard associated to fit, form and function. And cost and required profit margin must also be taken into account. [...]

How analytics can help meet the quality standard in manufacturing was published on SAS Voices by Tim Clark

8月 192019
 

In parts one and two of this blog series, we introduced the automation of AI (i.e., artificial intelligence) and natural language explanations applied to segmentation and marketing. Following this, we began marching down the path of practitioner-oriented examples, making the case for why we need it and where it applies. [...]

SAS Customer Intelligence 360: Automated AI and segmentation [Part 3] was published on Customer Intelligence Blog.

8月 192019
 

As you will have read in my last blog, businesses are demanding better outcomes, and through IoT initiatives big data is only getting bigger. This presents a clear opportunity for organisations to start thinking seriously about how to leverage analytics with their other investments. Demands on supply chains have also [...]

Can the artificial intelligence of things make the supply chain intelligent? was published on SAS Voices by Tim Clark

8月 122019
 

In part one of this blog series, we introduced the automation of AI (i.e., artificial intelligence) as a multifaceted and evolving topic for marketing and segmentation. After a discussion on maximizing the potential of a brand's first-party data, a machine learning method incorporating natural language explanations was provided in the context [...]

SAS Customer Intelligence 360: Automated AI and segmentation [Part 2] was published on Customer Intelligence Blog.

8月 052019
 

Marketers and brands have used segmentation as a technique to deliver customer personalization for communications, content, products, and services since the introduction of  customer relationship management (i.e., CRM) and database marketing. Within the context of segmentation, there are a variety of applications, ranging from consumer demographics, psychographics, geography, digital behavioral [...]

SAS Customer Intelligence 360: Automated AI and segmentation [Part 1] was published on Customer Intelligence Blog.

5月 292019
 

Interestingly enough, paperclips have their own day of honor. On May 29th, we celebrate #NationalPaperclipDay! That well-known piece of curved wire deserves attention for keeping our papers together and helping us stay organized. Do you remember who else deserved the same attention? Clippit – the infamous Microsoft Office assistant, popularly known as ‘Clippy’.

We saw the last of Clippy in 2004 before it was removed completely from Office 2007 after constant negative criticism. By then, most users considered it useless and decided to turn it off completely, despite the fact that it was supposed to help them perform certain tasks faster. Clippy was a conversational agent, like a chatbot, launched a decade before Apple’s Siri. The cartoonish paperclip-with-eyes resting on a yellow loose-leaf paper bed would pop up to offer assistance every time you opened a Microsoft Office program. Today, people seem to love AI. But a decade ago, why did everyone hate Clippy?

Why was Clippy such a failure?

One of the problems with Clippy was the terrible user experience. Clippy stopped to ask users if they needed help but in doing so would suspend their operations altogether. This was great for first timers, getting to know Clippy, goofing around and training themselves to use the agent. However, later on, mandatory conversations became frustrating. Second, Clippy was designed as a male agent. Clippy was born in a meeting room full of male employees working at Microsoft. The facial features resemble those of a male cartoon or at least that is what women in the testing focus group observed. In a farewell note for Clippy, the company addressed Clippy as ‘he’ affirming his gender. Some women even said that Clippy’s gaze created discomfort while they tried to do their tasks.

Rather than using Natural Language Processing (NLP), Clippy’s actions were invoked by Bayesian algorithms that estimated the probability of a user wanting help based on specific actions. Clippy’s answers were rule based and templated from Microsoft’s knowledge base. Clippy seemed like the agent you would use when you were stuck on a problem, but the fact that he kept appearing on the screen in random situations added negativity to users’ actions. In total, Clippy violated design principles, social norms and process workflows.

What lessons did Clippy teach us?

We can learn from Clippy. The renewed interest in conversational agents in the tech industry often overlooks the aspects that affect efficient communication, resulting in failures. It is important to learn lessons from the past and design interfaces and algorithms that tackle the needs of humans rather than hyping the capabilities of artificial intelligence.

At SAS, we are working to deliver a natural language interaction (NLI) service that converts keyed or spoken natural language text into application-specific, executable code; and using apps like Q –genderless AI voice for virtual assistants. We’re developing different ways to incorporate chatbots into business dashboards or analytics platforms. These capabilities have the potential to expand the audience for analytics results and attract new and less technical users.

“Chatbots are a key technology that could allow people to consume analytics without realizing that’s what they’re doing,” says Oliver Schabenberger, SAS Executive Vice President, Chief Operating Officer and Chief Technology Officer. “Chatbots create a humanlike interaction that makes results accessible to all.”

The use of chatbots is exponentially growing and all kinds of organizations are starting to see the exciting possibilities combining chatbots with AI analytics. Clippy was a trailblazer of sorts but has come to represent what to avoid when designing AI. At SAS we are focused on augmenting the human experience and that it is the customer who needs to be at the center, not the technology.

Interested in seeing what SAS is doing with Natural Language Processing? Check out SAS Visual Text Analytics and try it for free. We also have a brand new SAS Book hot off the press focusing on information extraction models for unstructured text / language data: SAS® Text Analytics for Business Applications: Concept Rules for Information Extraction Models. We even have a free chapter for you to take a peek!

Other Resources:

What can we learn from Clippy about AI? was published on SAS Users.