New Features & Updates: Free Images, Quicker List Building, and Faster Delivery

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We’ve added new features, made some enhancements and bug fixes. Check out the highlights of the December 9th, 2015 release:

Editor

Finding the right, high-quality image for your email is important, and also challenging. A nice visual can help you communicate your message and get your reader to engage more. The quality of the image can affect how subscribers perceive your email – whether it is professional or not. This is why in our latest release, we’ve integrated Pixabay free, high-quality image search into the VerticalResponse email editor. Now you can:

  • Browse by category and search over 530,000 royalty free images
  • Automatically insert images into your email without copying and pasting, or downloading and uploading

Contacts

List growth is a major component of successful email marketing. Our new quick ‘add contact’ widget allows you to type or paste contacts in seconds. You can enter names and email addresses up to three at a time, or cut and paste (up to 50 email addresses at once). This is great if you used to send your newsletter from your own email client but have since “upgraded” to sending from VerticalResponse.

Signup Forms

Signup forms on the web are another great way to grow your email list. The best practice is to clearly explain why the visitor should join your list and then ask permission to collect whatever data is necessary (email address, name, location, etc.). With our new update, you can choose, right up front, if you want to publish your signup web page on Verticalresponse.com, or if you would rather indicate the data you want to collect during signup and generate the HTML code that can then be embedded on your own website. The choice is now yours.

Billing

New credit card? No problem! It happens, your credit card expires, and you get a new one. Or perhaps as a marketing consultant you want to charge VerticalResponse account fees to a client’s credit card. Now you can log in and update your billing info: add, update, and delete cards from your account.

Chrome Browser

Google recently updated the Chrome web browser, and some customers had difficulty with the email editor. We’ve resolved these issues by improving compatibility with the Chrome web browser. Creating emails is now back to smooth sailing.

Delivery

Customers with large audiences (greater than 50,000 contacts) can now get campaigns out faster – twice as fast in fact! Contact us to learn more about our high-volume pricing plans!

All product updates can be found here.

© 2015, Linzi Breckenridge. All rights reserved.

The post New Features & Updates: Free Images, Quicker List Building, and Faster Delivery appeared first on Vertical Response Blog.


Vertical Response Blog

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Facebook Updates Progress on Artificial Intelligence

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ImageRecognition

Facebook offered an update on its advances in artificial intelligence, touching on object detection, natural language understanding, predictive learning and planning.

Chief technology officer Mike Schroepfer detailed the social network’s advances in posts in its Newsroom and its engineering blog.

Object detection

Schroepfer said the Facebook AI Research team will present a new paper at AI conference NIPS 2015 next month detailing its state-of-the-art object-detection system, which segments images 30 percent quicker than previous industry benchmarks, using 10 times less training data.

He also shared the image below, writing:

How many zebras do you see in the photo? Hard to tell, right? Imagine how hard this is for a machine, which doesn’t even see the stripes—it sees only pixels. Our researchers have been working to train systems to recognize patterns in the pixels so they can be as good as or better than humans at distinguishing objects in a photo from one another—known in the field as “segmentation”—and then identifying each object.

ObjectDetectionZebras

Natural language understanding

Schroepfer wrote about the combination of the Memory Networks (MemNets) system Facebook introduced last year with image-recognition technology, which the social network refers to as VQA, or visual Q&A. He added:

MemNets add a type of short-term memory to the convolutional neural networks that power our deep-learning systems, allowing those systems to understand language more like a human would. Earlier this year, I showed you this demo of MemNets at work, reading and then answering questions about a short synopsis of The Lord of the Rings. Now we’ve scaled this system from being able to read and answer questions on tens of lines of text to being able to perform the same task on data sets exceeding 100,000 questions, an order of magnitude larger than previous benchmarks.

Predictive learning

The Facebook AI Research team developed a system that can “watch” a series of visual tests and predict the outcome, and Schroepfer wrote that the system is now making the correct predictions 90 percent of the time, which is better than the performance of most humans.

Planning

Schroepfer described how the Facebook AI Research team is using classic board game Go in its efforts:

Another area of longer-term research is teaching our systems to plan. One of the things we’ve built to help do this is an AI player for the board game Go. Using games to train machines is a pretty common approach in AI research. In the last couple of decades, AI systems have become stronger than humans at games like checkers, chess and even Jeopardy. But despite close to five decades of work on AI Go players, the best humans are still better than the best AI players. This is due in part to the number of different variations in Go. After the first two moves in a chess game, for example, there are 400 possible next moves. In Go, there are close to 130,000.

We’ve been working on our Go player for only a few months, but it’s already on par with the other AI-powered systems that have been published, and it’s already as good as a very strong human player. We’ve achieved this by combining the traditional search-based approach—modeling out each possible move as the game progresses—with a pattern-matching system built by our computer vision team. The best human Go players often take advantage of their ability to recognize patterns on the board as the game evolves, and with this approach our AI player is able to mimic that ability—with very strong early results.

Finally, Schroepfer offered a look at how M, the virtual digital assistant being tested for the social network’s Messenger applications, is incorporating AI technology:

This is a huge technology challenge—it’s so hard that, starting out, M is a human-trained system: Human operators evaluate the AI’s suggested responses, and then they produce responses while the AI observes and learns from them.

We’d ultimately like to scale this service to billions of people around the world, but for that to be possible, the AI will need to be able to handle the majority of requests itself, with no human assistance. And to do that, we need to build all the different capabilities described above—language, vision, prediction and planning—into M so that it can understand the context behind each request and plan ahead at every step of the way. This is a really big challenge, and we’re just getting started. But the early results are promising. For example, we recently deployed our new MemNets system into M, and it has accelerated M’s learning: When someone asks M for help ordering flowers, M now knows that the first two questions to ask are, “What’s your budget?” and “Where are you sending them?”

One last point here: Some of you may look at this and say, “So what? A human could do all of those things.” And you’re right, of course—but most of us don’t have dedicated personal assistants. And that’s the “superpower” offered by a service like M: We could give every one of the billions of people in the world their own digital assistants so they can focus less on day-to-day tasks and more on the things that really matter to them.

Readers: What are your thoughts on Facebook’s AI advancements?

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